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Probabilities out.":"输入上下文，输出概率。","Decision examples":"决策示例","Illustrative example · Not a live model response":"演示数据 · 非模型实时输出",Copied:qp,Copy:Up,"API example code":"API 示例代码","API reference":"API 文档","Explore WALD":"探索 WALD","Tool calls. Clarifying questions. Request routing. See the decision points and explore the API examples.":"工具调用、澄清问题、请求分流。查看决策场景与 API 示例。","By the numbers":"用成绩说话","Compare the models":"查看模型对比","More decision":"更少参数，","per parameter":"更强决策","From a pocket-sized decision engine to a larger reasoning system. A model family built to read the situation, weigh the options, and make every parameter count.":"从轻量决策引擎，到更大的推理系统。覆盖多种尺寸的模型家族：读懂情境、权衡选项，让每个参数都用在关键判断上。","Model size":"模型大小","A compact research checkpoint. The 2B model exceeds a 20B reference on JevBench, showing how decision-focused training can change the size equation.":"紧凑的研究检查点。2B 模型在 JevBench 上超过 20B 对照模型，展示专注决策的训练如何改变参数与能力的关系。",vs:_p,"Part of the expanded WALD family. Comparable checkpoint scores and release details for this size will be added when verified.":"WALD 扩展家族的一部分。这一尺寸的可比检查点成绩与发布信息，将在验证后补充。","Explore all benchmarks":"查看完整测试对比","Homepage benchmark":"主页基准测试","Benchmark columns, descending scores on a 0–100 scale":"基准柱状图：0–100 刻度，按分数降序",pts:Hp,"WALD 4B shown as a reference. ":"WALD 4B 作为参照展示。 ","Self-scored snapshot. Reference protocols differ; see full results and conditions.":"自行评测快照。对照模型协议有差异，完整结果页列明测试条件。","Comparable results pending.":"可比成绩待补充。","See the scene,":"看见场景，","Choose the action":"选准动作",questions:Gp,"pp over the unadapted graft":"个百分点，相对未做图像适配的嫁接模型","Self-scored, one pass. Gain 95% CI":"自行评测，一遍读。增益的 95% 置信区间","View training & vision evidence":"查看训练与视觉证据","LoRA decision training":"LoRA 决策训练","Full-parameter decision training":"全参数决策训练","Base + LoRA only":"底座 + LoRA","Full decision training":"全量决策训练","Full training + LoRA refinement":"全量训练 + LoRA 精修","Train the foundation":"重塑决策基础","Our flagship Qwen recipe updates all language-model weights on decision examples. Supervision targets the option distribution, so choosing becomes a learned capability in the backbone.":"旗舰 Qwen 配方在决策样本上更新全部语言模型权重，以选项分布为监督目标，让权衡与选择成为骨干模型学到的能力。","Refine the edge":"精修关键判断","LoRA builds on that foundation: harder decisions, short-thought distillation, visual evidence and domain adaptation. KL replay anchors refinement to the parent’s behavior.":"LoRA 在这一基础上继续增强：困难决策、短思考蒸馏、视觉证据与领域适配。KL 回放将精修锚定在父模型的行为上。","Make confidence useful":"让置信度可用","Calibration reinforcement learning uses proper scoring rewards with KL guardrails. The aim is a probability your workflow can use to act, think again or hand off.":"校准强化学习采用恰当评分规则作为奖励，并以 KL 护栏约束更新。让概率可以被工作流用来行动、再想一步，或交接。","Built into":"决策能力，","the weights":"训进权重里","A decision model deserves its own training recipe. Full-parameter learning establishes the foundation; targeted LoRA and calibration sharpen what comes next.":"决策模型，需要为决策设计的训练路线。全参数学习建立基础，定向 LoRA 与校准继续打磨后续判断。","What does the foundation buy?":"这层基础，带来了什么？","percentage points":"个百分点","The earlier c6 control favored LoRA. Updating every weight alone is not enough: the data, objective and refinement recipe matter.":"早期 c6 对照中，LoRA 更好。仅仅更新全部权重还不够：数据、目标与精修配方同样关键。","Our full-training-plus-refinement recipe scored 87.0%, compared with 79.2% for an earlier base-plus-LoRA recipe. Different data and training budgets; this is a recipe comparison, not an isolated full-training ablation.":"全量训练加精修的配方得到 87.0%，早期底座加 LoRA 配方为 79.2%。两者数据与训练预算不同，这是配方对比，不能单独归因于全量训练。","Training comparison":"训练对比","Recipe progression":"配方演进","Same-data control":"同数据对照","Historical 4B research runs, one pass, self-scored.":"历史 4B 研究实验，一遍读，自行评测。"," Same 35M c6 tokens and data order; update method and learning rate differ.":" 同样的 3,500 万 c6 token 与数据顺序；更新方式和学习率不同。","Inspect the experiment evidence":"查看实验依据","Choose your model,":"选择模型，","Small models,":"小模型，","Compare the evidence":"对比实测","Bigger moves":"大表现",Benchmark:Wp,"PUBLIC / 231":"公开 / 231 题","SAMPLE / 6,948":"抽样 / 6,948 请求","INTERNAL / 656":"内部 / 656 题","columns, descending scores on a 0–100 scale":"柱状图：0–100 刻度，按分数降序","No comparable read":"暂无可比成绩","Columns use a 0–100 scale. Whiskers and brackets show 95% bootstrap intervals.":"柱状图采用 0–100 刻度；须线与括号内数��表示 95% bootstrap 区间。","Same question set. Read modes shown beside each model.":"同一题集；每个模型旁标注对应读法。","JevBench public 231 · self-scored with the research harness. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. External models use their recorded native readouts. These are observed scores, not a verified leaderboard or a significance claim.":"JevBench 公开 231 题 · 研究 harness 自测。WALD 0.8B / 4B / 9B 使用 medium，2B 使用一遍读取；外部模型使用记录中的原生读法。这里展示实测分数，不代表官方榜单或显著性结论。","Decision Index 0.2.1 · author-run 6,948-request sample, separate from the 150,317-request full suite. WALD uses one pass; raw-model prompting and calibration differ. Parameter labels are size classes; MoE total and active sizes are shown separately.":"Decision Index 0.2.1 · 作者运行的 6,948 请求抽样，与 150,317 请求完整测试分开。WALD 使用一遍读取，原始模型的提示词与校准方式不同。参数标注为模型尺寸档位；MoE 区分总参数与活跃参数。","Transfer v4 · internal development bank, 656 clean questions. This is not an independent external test. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. Results are not claims of universal superiority.":"Transfer v4 · 内部开发题库，656 道干净题，不是独立外部测试。WALD 0.8B / 4B / 9B 使用 medium，2B 使用一遍读取。结果不代表在所有任务上都更优。","Sources & read conditions":"来源与测试条件","Read conditions table":"测试条件表","Aggregates checked against source reports":"汇总数据已核对源报告",Model:Qp,"Read condition":"读法",Source:Xp,"Download scores, intervals and source hashes":"下载成绩、区间与来源校验值","No comparable score":"暂无可比成绩","Every choice,":"每次选择，","A clearer next move":"更清晰的下一步","Put WALD at the decision points in your workflow. Route a tool call, ask a better question, or send a request to the right team.":"把 WALD 放在工作流的决策点：选择工具、判断何时澄清，或把请求交给合适的团队。","Switch the model size. Switch the benchmark. Compare observed scores with Laya, Jev and larger base models, with the evidence in view.":"切换模型大小与测试集，对比 Laya、Jev 及更大的基础模型。每个实测成绩，都能查看对应证据。","Skip to content":"跳到正文","Main navigation":"主导航",Home:Yp,"Use Cases":"应用场景",Docs:Jp,Playground:Vp,"Build with WALD":"开始使用","Back to top":"回到顶部","Font licenses":"字体许可","Your agent,":"你的智能体，","Its next instinct":"它的下一步直觉","Download command":"下载命令","Explore the source":"查看源码","Enough confidence? Make the call. Need another look? Take a short thinking step. WALD lets you choose how much thought goes into the next move.":"有把握，直接决定；没把握，再看一眼，先想一小段。WALD 让你决定，下一步需要多少思考。","Explore thinking efforts":"了解思考档位","Thinking effort":"思考档位","THINK GATE":"思考门槛",always:Kp,"Illustrative decision flow":"决策流程演示",LOOK:Zp,"Think a bit":"想一小段","Direct read":"直接读取",DECIDE:Ip,Probabilities:$p,"Illustrative policy · No live inference":"策略演示 · 非实时推理","Call the right tool":"调用合适的工具","Give WALD the current state and the available tools. Use the option probabilities to choose the next call.":"提供当前状态与可用工具，用每个选项的概率辅助选择下一次调用。","Know when to ask":"判断何时追问","Include asking the user as an option when a request is missing key details. Your workflow chooses when to clarify.":"请求缺少关键信息时，把向用户澄清也列为选项，由你的工作流决定何时追问。","Find the right team":"交给合适的团队","Define your teams as choices. Turn an incoming request into probabilities your routing system can use.":"把团队定义为选项，将收到的请求转为分流系统可使用的概率。","Decision patterns":"决策场景","You define the options and control the actions. The examples below use illustrative probabilities.":"你定义选项，也掌控行动。下方示例的概率为演示数据。","See how the model family compares.":"看看系列模型的测试成绩。","Explore benchmarks":"查看测试对比","Give WALD the context and the options. Get a probability for each — and a clearer basis for your next move.":"提供上下文与备选动作。WALD 为每个选项返回概率，让下一步决策更清晰。","See how it decides":"看看它如何决策","Copy unavailable. Select and copy the command or code above.":"无法自动复制，请选中上方命令或代码手动复制。","Copied to clipboard.":"已复制到剪贴板。",Language:Fp,"WALD home":"WALD 主页","API docs":"API 文档","One pass. No generated thought.":"一遍直出，不生成思考。","Think when the top initial probability is below 0.5.":"初次最高概率低于 0.5 时，先想一小段。","Think when the top initial probability is below 0.7.":"初次最高概率低于 0.7 时，先想一小段。","Think before every eligible decision.":"每道符合条件的问题，都先思考。","Tool routing":"工具路由",Clarification:Pp,"Request routing":"请求分流","What will the weather be in Lisbon tomorrow afternoon?":"明天下午里斯本的天气如何？","Book me a table for Friday.":"帮我预订周五的餐桌。","Refund request for order 1182; the parcel arrived damaged.":"订单 1182 申请退款；包裹送达时已损坏。","Which tool should the agent call next?":"智能体下一步应该调用哪个工具？","What should the agent do next?":"智能体下一步应该做什么？","Which team should handle this request?":"哪个团队应该处理此请求？","Forecast for a city and time":"城市与时间对应的天气预报","General web search":"通用网络搜索","Read calendar events":"读取日历事件","Answer directly":"直接回答","Ask for restaurant, time and party size":"询问餐厅、时间与人数","Make a reservation":"进行预订","Search for restaurants":"搜索餐厅","Take no action":"不采取行动","Refunds and returns":"退款与退货","Delivery tracking":"物流追踪","New purchases":"新购买","Other requests":"其他请求","index points":"指数分","{count}× the total parameters":"对照总参数为 {count} 倍","Private candidate":"私有候选","Research checkpoint":"研究检查点","Public v1.1":"公开 v1.1",": comparable results on these cohorts pending.":"：这组题集的可比成绩待测。",Undisclosed:ev,Encoder:av,"95% CI":"95% 置信区间","Public 231":"公开 231 题","Internal · 656 clean questions":"内部 · 656 道干净题","0.2.1 · 6,948-request sample":"0.2.1 · 6,948 请求抽样","Selected base-model comparisons, with Jev and Laya for context. See Benchmark for all measured models and read conditions.":"这里展示选定的基础模型对照，并保留 Jev 与 Laya 作为参照。全部实测模型与读法见基准测试页。","Open alternatives to Jev,":"Jev 的开放替代方案，","with vision.":"支持视觉。","Get the 4B":"获取 4B","Download WALD 4B v2.1":"下载 WALD 4B v2.1","Public v2":"公开 v2","Public v2.1":"公开 v2.1",Public:tv,"* Measured on WALD 4B v2.0.":"* 为 WALD 4B v2.0 的实测结果。","Text and images in. Probabilities out. On your hardware.":"输入文字与图像，输出决策概率。在你的硬件上运行。","Measured WALD speed":"WALD 实测速度","26B-A4B one-pass median":"26B-A4B 一遍读取中位延迟","4B v2.1 Auto 0.7 median":"4B v2.1 Auto 0.7 中位延迟","Speed evidence":"速度测量依据","A compact v2 candidate for tight memory budgets. The selected 0.8B checkpoint is measured in one pass.":"适合紧凑显存预算的 v2 候选。选定的 0.8B 检查点采用一遍读取评测。","The public 4B v2.1 flagship. Auto 0.7 scores 60.05 on the complete Decision Index 0.3 public suite and 67.75% on JevBench-XL TEST.":"公开的 4B v2.1 旗舰。Auto 0.7 在完整 Decision Index 0.3 公开套件上取得 60.05，在 JevBench-XL TEST 上取得 67.75%。","Our latest completed 9B first-stage research checkpoint, measured in one pass. Weights remain private.":"最新已完成的 9B 第一阶段研究检查点，采用一遍读取评测。权重仍为私有。","A Gemma-based decision model with 26B total and 4B active parameters, released as Wald-26B-A4B. One pass scores 62.30 on the complete Decision Index 0.3 public suite, with a 53 ms median.":"基于 Gemma 的决策模型，总参数 26B、激活参数 4B，已公开为 Wald-26B-A4B。一遍读取在完整 Decision Index 0.3 公开套件上取得 62.30，中位延迟 53 ms。","WALD 4B v2 reads images through its base model’s frozen vision tower. Its visual capability transfers from decision training without vision fine-tuning.":"WALD 4B v2 通过底座冻结的视觉塔读取图像。决策训练带来的能力直接迁移至视觉，无需视觉微调。","pp over the raw vision base":"个百分点，高于原始视觉底座","Self-scored, zero-shot, one pass. Gain 95% CI":"自行评测、零样本、一遍读取。增益 95% 置信区间","WALD 4B v2 defaults to Auto 0.7: think once when the top initial probability is below 0.7, with a 512-token cap. Use none for deterministic one-pass decisions.":"WALD 4B v2 默认使用 Auto 0.7：初始最高概率低于 0.7 时思考一次，最多 512 token。使用 none 可获得确定性的一遍决策。","Five model sizes. Four benchmark views. Compare WALD with Jev and other measured models.":"五种模型尺寸、四种基准视图。将 WALD 与 Jev 及其他实测模型比较。","From 0.8B to 26B. Compare the latest completed checkpoints on matching test sets, with their read conditions shown.":"从 0.8B 到 26B。在相同测试集上比较最新完成的检查点，并查看读取条件。","XL TEST · 8,177 questions":"XL TEST · 8,177 道题","0.3 · 140,620 public requests":"0.3 · 140,620 个公开请求","Held-out TEST only, not the pooled 10,100 TEST + DEV score. Modes differ and are recorded per model; missing measurements stay empty.":"仅使用留出的 TEST，不是 TEST + DEV 合计 10,100 的分数。各模型的模式单独记录；缺失测量留空。","Author-run DI 0.2.1 sample with 95% bootstrap intervals. Native readouts and thinking modes differ. These scores cannot be compared with DI 0.3.":"作者自测 DI 0.2.1 抽样，附 95% bootstrap 区间。原生读取和思考模式不同，不能与 DI 0.3 分数比较。","Complete DI 0.3 public suite, official kit. WALD rows are author-run, with the mode stated per model; Jev is the board's public column. Not the organizer's Full score. S1 projections and DI 0.2.1 samples are excluded; no confidence interval is available.":"完整 DI 0.3 公开套件，官方工具包。WALD 为作者自测，读取模式按模型标注；Jev 为排行榜公开栏。不是主办方的 Full 分数。不纳入 S1 投影及 DI 0.2.1 抽样；无可用置信区间。","WALD 4B v2 uses full-parameter decision training from Qwen3.5-4B chat. Supervision targets option probabilities while the vision tower stays frozen.":"WALD 4B v2 从 Qwen3.5-4B chat 进行全参数决策训练，以选项概率为监督目标，并保持视觉塔冻结。","Full-parameter continuation adds harder decisions and short native thoughts. State-KL anchors behavior to a frozen reference; earlier research also explored LoRA refinement.":"全参数续训加入更难的决策和短原生思考。State-KL 将行为锚定至冻结参照；早期研究也探索了 LoRA 精修。","The public v2 server uses a frozen temperature table fitted on our development data. Earlier research explored calibration reinforcement learning; the release uses the recorded calibration table.":"公开 v2 服务使用在自有开发数据上拟合并冻结的温度表。早期研究探索过校准强化学习；本次发布使用记录在案的校准表。","Full-parameter decision training, native thinking and calibrated probabilities. The public v2 recipe and historical research comparisons are shown separately.":"全参数决策训练、原生思考和校准概率。公开 v2 配方与历史研究对照分开展示。","Public set, self-scored. Research harness; modes and native external servers differ and are recorded per model. Not a verified leaderboard ranking.":"公开题集，自行评测。研究评测工具、模式和原生外部服务存在差异，并按模型记录。不是经核验的榜单排名。","Local speed.":"本地速度。","Open weights.":"开放权重。","Faster at the median":"中位响应更快","Timing details & hosted references":"耗时详情与托管参考",Median:nv,"Client time":"客户端时间","Server time":"服务端时间","Separate public-set read":"单独公开题集测量","Faster median decisions than OpenAI Decisions in our matched 200-request test. Run locally, keep vision, and choose when to think.":"在相同的 200 个请求测试中，中位决策速度快于 OpenAI Decisions。本地运行，保留视觉能力，自主选择何时思考。","Same 200 DI 0.3 requests, serial and warm. WALD native reader on one RTX PRO 6000; OpenAI client time includes the network.":"相同的 200 个 DI 0.3 请求，预热后串行执行。WALD 原生评测读取器使用一张 RTX PRO 6000；OpenAI 客户端时间包含网络往返。","Median decision time":"决策耗时中位数","Lower is faster":"越低越快","Same 200 requests":"相同的 200 个请求","Separate recorded runs":"独立测量的参考运行","Decisions in milliseconds.":"毫秒级决策。","Published benchmark":"已发布的基准结果","Jev uses the published JevBench v1.4.2.2 timing: 242 standard and judge decisions, serial, with the network included. It is a separate benchmark from our DI sample.":"Jev 使用已发布的 JevBench v1.4.2.2 耗时：242 个标准及评判决策，串行执行，包含网络时间。它与我们的 DI 样本是不同的基准。","Qwen 27B is shown once using its paren one-pass receipt on an RTX PRO 6000, with client time through an SSH tunnel. The separate 231-request receipt does not record sample IDs or concurrency; no matched speedup ratio is asserted.":"Qwen 27B 只展示一行，采用 RTX PRO 6000 上的 paren 单次读取记录，客户端时间包含 SSH 隧道。独立的 231 请求记录未注明样本 ID 或并发数；不计算配对加速比。","Original Qwen3.5-4B Base":"原始 Qwen3.5-4B Base","More models":"更多模型","Vision transfer":"视觉迁移","measured model sizes":"已测模型尺寸","Base scores included":"含底座分数","vs original base":"相对原始底座","Original base":"原始底座",Difference:iv,"One pass":"一遍读取",Combined:lv,"The original 4B base scored 67.5%. LoRA reached 79.2%, full training 83.5%, and full training plus refinement 87.0%. Historical recipes use different data and budgets; this is a recipe comparison.":"原始 4B 底座为 67.5%。LoRA 达到 79.2%，全参数训练达到 83.5%，全参数训练加精修达到 87.0%。历史配方的数据和预算不同，此处比较的是整体配方。","Both recipes improved over the 67.5% base. With the same 35M tokens, LoRA scored 83.5% and full training 82.3%. Update methods and learning rates differed.":"两种配方都超过了 67.5% 的底座。同样使用 3,500 万 token，LoRA 为 83.5%，全参数训练为 82.3%。更新方法和学习率不同。","Original backbones and trained checkpoints, on each row's benchmark. 2B and 9B use one pass; 26B uses Auto 0.7. These are separate recorded runs, with different training recipes.":"各行按所列基准比较原始底座和训练后的检查点。2B 和 9B 一遍读取；26B 使用 Auto 0.7。它们是分别记录的读数，训练配方不同。","Text decision training transferred to vision: 80.23% for the original Qwen3.5-4B chat model, 83.05% for WALD 4B v2. Same native-chat one-pass readout, frozen vision tower, no image fine-tuning.":"文字决策训练迁移到了视觉：原始 Qwen3.5-4B chat 为 80.23%，WALD 4B v2 为 83.05%。两者都用原生 chat 一遍读取；视觉塔冻结，未做图像微调。"},ov="已複製",cv="複製",rv="對比",uv="分",dv="題",fv="基準測試",hv="模型",mv="來源",pv="主頁",vv="文件",bv="工作臺",gv="始終",yv="觀察",Av="決定",xv="返回機率",Sv="語言",Bv="澄清意圖",Dv="未公開",Tv="編碼器",jv="公開",Lv="中位數",Ev="差值",zv="合計",wv={"Put the next decision into practice.":"把下一次決策用起來。","Explore use cases":"探索應用場景","A clear next move":"讓下一步更清晰","Context in. Probabilities out.":"輸入上下文，輸出機率。","Decision examples":"決策範例","Illustrative example · Not a live model response":"演示資料 · 非模型即時輸出",Copied:ov,Copy:cv,"API example code":"API 範例程式碼","API reference":"API 文件","Explore WALD":"探索 WALD","Tool calls. Clarifying questions. Request routing. See the decision points and explore the API examples.":"工具呼叫、澄清問題、請求分流。檢視決策場景與 API 範例。","By the numbers":"用成績說話","Compare the models":"檢視模型對比","More decision":"更少參數，","per parameter":"更強決策","From a pocket-sized decision engine to a larger reasoning system. A model family built to read the situation, weigh the options, and make every parameter count.":"從輕量決策引擎，到更大的推理系統。覆蓋多種尺寸的模型家族：讀懂情境、權衡選項，讓每個參數都用在關鍵判斷上。","Model size":"模型大小","A compact research checkpoint. The 2B model exceeds a 20B reference on JevBench, showing how decision-focused training can change the size equation.":"緊湊的研究檢查點。2B 模型在 JevBench 上超過 20B 對照模型，展示專注決策的訓練如何改變參數與能力的關係。",vs:rv,"Part of the expanded WALD family. Comparable checkpoint scores and release details for this size will be added when verified.":"WALD 擴展家族的一部分。這一尺寸的可比檢查點成績與發布資訊，將在驗證後補充。","Explore all benchmarks":"檢視完整測試對比","Homepage benchmark":"主頁基準測試","Benchmark columns, descending scores on a 0–100 scale":"基準柱狀圖：0–100 刻度，按分數降序",pts:uv,"WALD 4B shown as a reference. ":"WALD 4B 作為參照展示。 ","Self-scored snapshot. Reference protocols differ; see full results and conditions.":"自行評測快照。對照模型協議有差異，完整結果頁列明測試條件。","Comparable results pending.":"可比成績待補充。","See the scene,":"看見場景，","Choose the action":"選準動作",questions:dv,"pp over the unadapted graft":"個百分點，相對未做影像適配的嫁接模型","Self-scored, one pass. Gain 95% CI":"自行評測，一遍讀。增益的 95% 信賴區間","View training & vision evidence":"檢視訓練與視覺證據","LoRA decision training":"LoRA 決策訓練","Full-parameter decision training":"全參數決策訓練","Base + LoRA only":"底座 + LoRA","Full decision training":"全量決策訓練","Full training + LoRA refinement":"全量訓練 + LoRA 精修","Train the foundation":"重塑決策基礎","Our flagship Qwen recipe updates all language-model weights on decision examples. Supervision targets the option distribution, so choosing becomes a learned capability in the backbone.":"旗艦 Qwen 配方在決策樣本上更新全部語言模型權重，以選項分佈為監督目標，讓權衡與選擇成為骨幹模型學到的能力。","Refine the edge":"精修關鍵判斷","LoRA builds on that foundation: harder decisions, short-thought distillation, visual evidence and domain adaptation. KL replay anchors refinement to the parent’s behavior.":"LoRA 在這一基礎上繼續增強：困難決策、短思考蒸餾、視覺證據與領域適配。KL 回放將精修錨定在父模型的行為上。","Make confidence useful":"讓信心程度可用","Calibration reinforcement learning uses proper scoring rewards with KL guardrails. The aim is a probability your workflow can use to act, think again or hand off.":"校準強化學習採用恰當評分規則作為獎勵，並以 KL 護欄約束更新。讓機率可以被工作流程用來行動、再想一步，或交接。","Built into":"決策能力，","the weights":"訓進權重裡","A decision model deserves its own training recipe. Full-parameter learning establishes the foundation; targeted LoRA and calibration sharpen what comes next.":"決策模型，需要為決策設計的訓練路線。全參數學習建立基礎，定向 LoRA 與校準繼續打磨後續判斷。","What does the foundation buy?":"這層基礎，帶來了什麼？","percentage points":"個百分點","The earlier c6 control favored LoRA. Updating every weight alone is not enough: the data, objective and refinement recipe matter.":"早期 c6 對照中，LoRA 更好。僅僅更新全部權重還不夠：資料、目標與精修配方同樣關鍵。","Our full-training-plus-refinement recipe scored 87.0%, compared with 79.2% for an earlier base-plus-LoRA recipe. Different data and training budgets; this is a recipe comparison, not an isolated full-training ablation.":"全量訓練加精修的配方得到 87.0%，早期底座加 LoRA 配方為 79.2%。兩者資料與訓練預算不同，這是配方對比，不能單獨歸因於全量訓練。","Training comparison":"訓練對比","Recipe progression":"配方演進","Same-data control":"同資料對照","Historical 4B research runs, one pass, self-scored.":"歷史 4B 研究實驗，一遍讀，自行評測。"," Same 35M c6 tokens and data order; update method and learning rate differ.":" 同樣的 3,500 萬 c6 token 與資料順序；更新方式和學習率不同。","Inspect the experiment evidence":"檢視實驗依據","Choose your model,":"選擇模型，","Small models,":"小模型，","Compare the evidence":"對比實測","Bigger moves":"大表現",Benchmark:fv,"PUBLIC / 231":"公開 / 231 題","SAMPLE / 6,948":"抽樣 / 6,948 請求","INTERNAL / 656":"內部 / 656 題","columns, descending scores on a 0–100 scale":"柱狀圖：0–100 刻度，按分數降序","No comparable read":"暫無可比成績","Columns use a 0–100 scale. Whiskers and brackets show 95% bootstrap intervals.":"柱狀圖採用 0–100 刻度；須線與括號內數字表示 95% bootstrap 區間。","Same question set. Read modes shown beside each model.":"同一題集；每個模型旁標註對應讀法。","JevBench public 231 · self-scored with the research harness. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. External models use their recorded native readouts. These are observed scores, not a verified leaderboard or a significance claim.":"JevBench 公開 231 題 · 研究 harness 自測。WALD 0.8B / 4B / 9B 使用 medium，2B 使用一遍讀取；外部模型使用記錄中的原生讀法。這裡展示實測分數，不代表官方榜單或顯著性結論。","Decision Index 0.2.1 · author-run 6,948-request sample, separate from the 150,317-request full suite. WALD uses one pass; raw-model prompting and calibration differ. Parameter labels are size classes; MoE total and active sizes are shown separately.":"Decision Index 0.2.1 · 作者執行的 6,948 請求抽樣，與 150,317 請求完整測試分開。WALD 使用一遍讀取，原始模型的提示詞與校準方式不同。參數標註為模型尺寸等級；MoE 區分總參數與活躍參數。","Transfer v4 · internal development bank, 656 clean questions. This is not an independent external test. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. Results are not claims of universal superiority.":"Transfer v4 · 內部開發題庫，656 道乾淨題，不是獨立外部測試。WALD 0.8B / 4B / 9B 使用 medium，2B 使用一遍讀取。結果不代表在所有任務上都更優。","Sources & read conditions":"來源與測試條件","Read conditions table":"測試條件表","Aggregates checked against source reports":"彙總資料已核對源報告",Model:hv,"Read condition":"讀法",Source:mv,"Download scores, intervals and source hashes":"下載成績、區間與來源校驗值","No comparable score":"暫無可比成績","Every choice,":"每次選擇，","A clearer next move":"更清晰的下一步","Put WALD at the decision points in your workflow. Route a tool call, ask a better question, or send a request to the right team.":"把 WALD 放在工作流程的決策點：選擇工具、判斷何時澄清，或把請求交給合適的團隊。","Switch the model size. Switch the benchmark. Compare observed scores with Laya, Jev and larger base models, with the evidence in view.":"切換模型大小與測試集，對比 Laya、Jev 及更大的基礎模型。每個實測成績，都能檢視對應證據。","Skip to content":"跳到正文","Main navigation":"主導航",Home:pv,"Use Cases":"應用場景",Docs:vv,Playground:bv,"Build with WALD":"開始使用","Back to top":"回到頂部","Font licenses":"字型授權","Your agent,":"你的智能體，","Its next instinct":"它的下一步直覺","Download command":"下載指令","Explore the source":"���視原始碼","Enough confidence? Make the call. Need another look? Take a short thinking step. WALD lets you choose how much thought goes into the next move.":"有把握，直接決定；沒把握，再看一眼，先想一小段。WALD 讓你決定，下一步需要多少思考。","Explore thinking efforts":"瞭解思考等級","Thinking effort":"思考等級","THINK GATE":"思考門檻",always:gv,"Illustrative decision flow":"決策流程演示",LOOK:yv,"Think a bit":"想一小段","Direct read":"直接讀取",DECIDE:Av,Probabilities:xv,"Illustrative policy · No live inference":"策略演示 · 非即時推理","Call the right tool":"呼叫合適的工具","Give WALD the current state and the available tools. Use the option probabilities to choose the next call.":"提供當前狀態與可用工具，用每個選項的機率輔助選擇下一次呼叫。","Know when to ask":"判斷何時追問","Include asking the user as an option when a request is missing key details. Your workflow chooses when to clarify.":"請求缺少關鍵資訊時，把向使用者澄清也列為選項，由你的工作流程決定何時追問。","Find the right team":"交給合適的團隊","Define your teams as choices. Turn an incoming request into probabilities your routing system can use.":"把團隊定義為選項，將收到的請求轉為分流系統可使用的機率。","Decision patterns":"決策場景","You define the options and control the actions. The examples below use illustrative probabilities.":"你定義選項，也掌控行動。下方範例的機率為演示資料。","See how the model family compares.":"看看系列模型的測試成績。","Explore benchmarks":"檢視測試對比","Give WALD the context and the options. Get a probability for each — and a clearer basis for your next move.":"提供上下文與備選動作。WALD 為每個選項返回機率，讓下一步決策更清晰。","See how it decides":"看看它如何決策","Copy unavailable. Select and copy the command or code above.":"無法自動複製，請選中上方命令或程式碼手動複製。","Copied to clipboard.":"已複製到剪貼簿。",Language:Sv,"WALD home":"WALD 主頁","API docs":"API 文件","One pass. No generated thought.":"一遍直出，不生成思考。","Think when the top initial probability is below 0.5.":"初次最高機率低於 0.5 時，先想一小段。","Think when the top initial probability is below 0.7.":"初次最高機率低於 0.7 時，先想一小段。","Think before every eligible decision.":"每道符合條件的問題，都先思考。","Tool routing":"工具路由",Clarification:Bv,"Request routing":"請求分流","What will the weather be in Lisbon tomorrow afternoon?":"明天下午里斯本的天氣如何？","Book me a table for Friday.":"幫我預訂週五的餐桌。","Refund request for order 1182; the parcel arrived damaged.":"訂單 1182 申請退款；包裹送達時已損壞。","Which tool should the agent call next?":"智能體下一步應該呼叫哪個工具？","What should the agent do next?":"智能體下一步應該做什麼？","Which team should handle this request?":"哪個團隊應該處理此請求？","Forecast for a city and time":"城市與時間對應的天氣預報","General web search":"通用網路搜尋","Read calendar events":"讀取日曆事件","Answer directly":"直接回答","Ask for restaurant, time and party size":"詢問餐廳、時間與人數","Make a reservation":"進行預訂","Search for restaurants":"搜尋餐廳","Take no action":"不採取行動","Refunds and returns":"退款與退貨","Delivery tracking":"物流追蹤","New purchases":"新購買","Other requests":"其他請求","index points":"指數分","{count}× the total parameters":"對照總參數為 {count} 倍","Private candidate":"私有候選","Research checkpoint":"研究檢查點","Public v1.1":"公開 v1.1",": comparable results on these cohorts pending.":"：這組題集的可比成績待測。",Undisclosed:Dv,Encoder:Tv,"95% CI":"95% 信賴區間","Public 231":"公開 231 題","Internal · 656 clean questions":"內部 · 656 道乾淨題","0.2.1 · 6,948-request sample":"0.2.1 · 6,948 請求抽樣","Selected base-model comparisons, with Jev and Laya for context. See Benchmark for all measured models and read conditions.":"這裡展示選定的基礎模型對照，並保留 Jev 與 Laya 作為參照。全部實測模型與讀法見基準測試頁。","Open alternatives to Jev,":"Jev 的開放替代方案，","with vision.":"支援視覺。","Get the 4B":"取得 4B","Download WALD 4B v2.1":"下載 WALD 4B v2.1","Public v2":"公開 v2","Public v2.1":"公開 v2.1",Public:jv,"* Measured on WALD 4B v2.0.":"* 為 WALD 4B v2.0 的實測結果。","Text and images in. Probabilities out. On your hardware.":"輸入文字與圖像，輸出決策機率。在你的硬體上執行。","Measured WALD speed":"WALD 實測速度","26B-A4B one-pass median":"26B-A4B 一遍讀取中位延遲","4B v2.1 Auto 0.7 median":"4B v2.1 Auto 0.7 中位延遲","Speed evidence":"速度量測依據","A compact v2 candidate for tight memory budgets. The selected 0.8B checkpoint is measured in one pass.":"適合緊湊顯存預算的 v2 候選。選定的 0.8B 檢查點採用一遍讀取評測。","The public 4B v2.1 flagship. Auto 0.7 scores 60.05 on the complete Decision Index 0.3 public suite and 67.75% on JevBench-XL TEST.":"公開的 4B v2.1 旗艦。Auto 0.7 在完整 Decision Index 0.3 公開套件上取得 60.05，在 JevBench-XL TEST 上取得 67.75%。","Our latest completed 9B first-stage research checkpoint, measured in one pass. Weights remain private.":"最新已完成的 9B 第一階段研究檢查點，採用一遍讀取評測。權重仍為私有。","A Gemma-based decision model with 26B total and 4B active parameters, released as Wald-26B-A4B. One pass scores 62.30 on the complete Decision Index 0.3 public suite, with a 53 ms median.":"基於 Gemma 的決策模型，總參數 26B、啟用參數 4B，已公開為 Wald-26B-A4B。一遍讀取在完整 Decision Index 0.3 公開套件上取得 62.30，中位延遲 53 ms。","WALD 4B v2 reads images through its base model’s frozen vision tower. Its visual capability transfers from decision training without vision fine-tuning.":"WALD 4B v2 透過底座凍結的視覺塔讀取圖像。決策訓練帶來的能力直接遷移至視覺，無需視覺微調。","pp over the raw vision base":"個百分點，高於原始視覺底座","Self-scored, zero-shot, one pass. Gain 95% CI":"自行評測、零樣本、一遍讀取。增益 95% 信賴區間","WALD 4B v2 defaults to Auto 0.7: think once when the top initial probability is below 0.7, with a 512-token cap. Use none for deterministic one-pass decisions.":"WALD 4B v2 預設使用 Auto 0.7：初始最高機率低於 0.7 時思考一次，最多 512 token。使用 none 可取得確定性的一遍決策。","Five model sizes. Four benchmark views. Compare WALD with Jev and other measured models.":"五種模型尺寸、四種基準視圖。將 WALD 與 Jev 及其他實測模型比較。","From 0.8B to 26B. Compare the latest completed checkpoints on matching test sets, with their read conditions shown.":"從 0.8B 到 26B。在相同測試集上比較最新完成的檢查點，並查看讀取條件。","XL TEST · 8,177 questions":"XL TEST · 8,177 道題","0.3 · 140,620 public requests":"0.3 · 140,620 個公開請求","Held-out TEST only, not the pooled 10,100 TEST + DEV score. Modes differ and are recorded per model; missing measurements stay empty.":"僅使用留出的 TEST，不是 TEST + DEV 合計 10,100 的分數。各模型的模式單獨記錄；缺失量測留空。","Author-run DI 0.2.1 sample with 95% bootstrap intervals. Native readouts and thinking modes differ. These scores cannot be compared with DI 0.3.":"作者自測 DI 0.2.1 抽樣，附 95% bootstrap 區間。原生讀取和思考模式不同，不能與 DI 0.3 分數比較。","Complete DI 0.3 public suite, official kit. WALD rows are author-run, with the mode stated per model; Jev is the board's public column. Not the organizer's Full score. S1 projections and DI 0.2.1 samples are excluded; no confidence interval is available.":"完整 DI 0.3 公開套件，官方工具包。WALD 為作者自測，讀取模式依模型標註；Jev 為排行榜公開欄。不是主辦方的 Full 分數。不納入 S1 投影及 DI 0.2.1 抽樣；無可用信賴區間。","WALD 4B v2 uses full-parameter decision training from Qwen3.5-4B chat. Supervision targets option probabilities while the vision tower stays frozen.":"WALD 4B v2 從 Qwen3.5-4B chat 進行全參數決策訓練，以選項機率為監督目標，並保持視覺塔凍結。","Full-parameter continuation adds harder decisions and short native thoughts. State-KL anchors behavior to a frozen reference; earlier research also explored LoRA refinement.":"全參數續訓加入更難的決策和短原生思考。State-KL 將行為錨定至凍結參照；早期研究也探索了 LoRA 精修。","The public v2 server uses a frozen temperature table fitted on our development data. Earlier research explored calibration reinforcement learning; the release uses the recorded calibration table.":"公開 v2 服務使用在自有開發資料上擬合並凍結的溫度表。早期研究探索過校準強化學習；本次發布使用記錄在案的校準表。","Full-parameter decision training, native thinking and calibrated probabilities. The public v2 recipe and historical research comparisons are shown separately.":"全參數決策訓練、原生思考和校準機率。公開 v2 配方與歷史研究對照分開展示。","Public set, self-scored. Research harness; modes and native external servers differ and are recorded per model. Not a verified leaderboard ranking.":"公開題集，自行評測。研究評測工具、模式和原生外部服務存在差異，並按模型記錄。不是經核驗的榜單排名。","Local speed.":"本機速度。","Open weights.":"開放權重。","Faster at the median":"中位回應更快","Timing details & hosted references":"耗時詳情與託管參考",Median:Lv,"Client time":"用戶端時間","Server time":"伺服器時間","Separate public-set read":"獨立公開題集測量","Faster median decisions than OpenAI Decisions in our matched 200-request test. Run locally, keep vision, and choose when to think.":"在相同的 200 個請求測試中，中位決策速度快於 OpenAI Decisions。本機執行，保留視覺能力，自主選擇何時思考。","Same 200 DI 0.3 requests, serial and warm. WALD native reader on one RTX PRO 6000; OpenAI client time includes the network.":"相同的 200 個 DI 0.3 請求，預熱後循序執行。WALD 原生評測讀取器使用一張 RTX PRO 6000；OpenAI 用戶端時間包含網路往返。","Median decision time":"決策耗時中位數","Lower is faster":"越低越快","Same 200 requests":"相同的 200 個請求","Separate recorded runs":"獨立量測的參考執行","Decisions in milliseconds.":"毫秒級決策。","Published benchmark":"已發布的基準結果","Jev uses the published JevBench v1.4.2.2 timing: 242 standard and judge decisions, serial, with the network included. It is a separate benchmark from our DI sample.":"Jev 使用已發布的 JevBench v1.4.2.2 耗時：242 個標準及評判決策，循序執行，包含網路時間。它與我們的 DI 樣本是不同的基準。","Qwen 27B is shown once using its paren one-pass receipt on an RTX PRO 6000, with client time through an SSH tunnel. The separate 231-request receipt does not record sample IDs or concurrency; no matched speedup ratio is asserted.":"Qwen 27B 只展示一行，採用 RTX PRO 6000 上的 paren 單次讀取記錄，用戶端時間包含 SSH 通道。獨立的 231 請求記錄未註明樣本 ID 或並行數；不計算配對加速比。","Original Qwen3.5-4B Base":"原始 Qwen3.5-4B Base","More models":"更多模型","Vision transfer":"視覺遷移","measured model sizes":"已測模型尺寸","Base scores included":"含底座分數","vs original base":"相對原始底座","Original base":"原始底座",Difference:Ev,"One pass":"一遍讀取",Combined:zv,"The original 4B base scored 67.5%. LoRA reached 79.2%, full training 83.5%, and full training plus refinement 87.0%. Historical recipes use different data and budgets; this is a recipe comparison.":"原始 4B 底座為 67.5%。LoRA 達到 79.2%，全參數訓練達到 83.5%，全參數訓練加精修達到 87.0%。歷史配方的資料和預算不同，此處比較的是整體配方。","Both recipes improved over the 67.5% base. With the same 35M tokens, LoRA scored 83.5% and full training 82.3%. Update methods and learning rates differed.":"兩種配方都超過了 67.5% 的底座。同樣使用 3,500 萬 token，LoRA 為 83.5%，全參數訓練為 82.3%。更新方法和學習率不同。","Original backbones and trained checkpoints, on each row's benchmark. 2B and 9B use one pass; 26B uses Auto 0.7. These are separate recorded runs, with different training recipes.":"各列按所列基準比較原始底座和訓練後的檢查點。2B 和 9B 一遍讀取；26B 使用 Auto 0.7。它們是分別記錄的讀數，訓練配方不同。","Text decision training transferred to vision: 80.23% for the original Qwen3.5-4B chat model, 83.05% for WALD 4B v2. Same native-chat one-pass readout, frozen vision tower, no image fine-tuning.":"文字決策訓練遷移到了視覺：原始 Qwen3.5-4B chat 為 80.23%，WALD 4B v2 為 83.05%。兩者都用原生 chat 一遍讀取；視覺塔凍結，未做圖像微調。"},Mv="Copié",Rv="Copier",kv="face à",Nv="pts",Cv="questions",Ov="Benchmarks",qv="Modèle",Uv="Source",_v="Accueil",Hv="Documentation",Gv="Atelier",Wv="toujours",Qv="OBSERVER",Xv="DÉCIDER",Yv="Probabilités",Jv="Langue",Vv="Clarification",Kv="Non divulgué",Zv="Encodeur",Iv="Public",$v="Médiane",Fv="Écart",Pv="Total",eb={"Put the next decision into practice.":"Passez à la prochaine décision.","Explore use cases":"Explorer les cas d’usage","A clear next move":"La prochaine étape, plus claire","Context in. Probabilities out.":"Du contexte aux probabilités.","Decision examples":"Exemples de décision","Illustrative example · Not a live model response":"Exemple illustratif · Pas une réponse du modèle en direct",Copied:Mv,Copy:Rv,"API example code":"Code d’exemple de l’API","API reference":"Référence de l’API","Explore WALD":"Explorer WALD","Tool calls. Clarifying questions. Request routing. See the decision points and explore the API examples.":"Appels d’outils. Questions de clarification. Routage des requêtes. Découvrez les points de décision et les exemples d’API.","By the numbers":"Les chiffres à l’appui","Compare the models":"Comparer les modèles","More decision":"Plus de décision","per parameter":"par paramètre","From a pocket-sized decision engine to a larger reasoning system. A model family built to read the situation, weigh the options, and make every parameter count.":"D’un moteur de décision compact à un système de raisonnement plus grand. Une famille de modèles conçue pour comprendre la situation, peser les options et tirer parti de chaque paramètre.","Model size":"Taille du modèle","A compact research checkpoint. The 2B model exceeds a 20B reference on JevBench, showing how decision-focused training can change the size equation.":"Un checkpoint de recherche compact. Le modèle 2B dépasse une référence de 20B sur JevBench, montrant comment l’entraînement axé sur la décision change le rapport entre taille et capacité.",vs:kv,"Part of the expanded WALD family. Comparable checkpoint scores and release details for this size will be added when verified.":"Un membre de la famille WALD élargie. Les scores comparables et les détails de publication de cette taille seront ajoutés après vérification.","Explore all benchmarks":"Explorer tous les benchmarks","Homepage benchmark":"Benchmark de la page d’accueil","Benchmark columns, descending scores on a 0–100 scale":"Colonnes du benchmark, scores décroissants sur une échelle de 0 à 100",pts:Nv,"WALD 4B shown as a reference. ":"WALD 4B est présenté comme référence. ","Self-scored snapshot. Reference protocols differ; see full results and conditions.":"Résultats auto-évalués. Les protocoles des références diffèrent ; consultez les résultats complets et les conditions.","Comparable results pending.":"Résultats comparables en attente.","See the scene,":"Observez la scène,","Choose the action":"Choisissez l’action",questions:Cv,"pp over the unadapted graft":"points de pourcentage au-dessus du modèle greffé non adapté","Self-scored, one pass. Gain 95% CI":"Auto-évalué, une passe. IC à 95 % du gain","View training & vision evidence":"Voir les preuves d’entraînement et de vision","LoRA decision training":"Entraînement décisionnel LoRA","Full-parameter decision training":"Entraînement décisionnel de tous les paramètres","Base + LoRA only":"Base + LoRA uniquement","Full decision training":"Entraînement décisionnel complet","Full training + LoRA refinement":"Entraînement complet + affinage LoRA","Train the foundation":"Entraînez les fondations","Our flagship Qwen recipe updates all language-model weights on decision examples. Supervision targets the option distribution, so choosing becomes a learned capability in the backbone.":"Notre recette Qwen phare met à jour tous les poids du modèle linguistique sur des exemples de décision. La supervision vise la distribution des options : choisir devient une capacité apprise par le modèle de base.","Refine the edge":"Affinez les décisions","LoRA builds on that foundation: harder decisions, short-thought distillation, visual evidence and domain adaptation. KL replay anchors refinement to the parent’s behavior.":"LoRA prolonge cette base : décisions difficiles, distillation de pensées courtes, indices visuels et adaptation au domaine. La répétition KL ancre l’affinage dans le comportement du modèle parent.","Make confidence useful":"Rendez la confiance utile","Calibration reinforcement learning uses proper scoring rewards with KL guardrails. The aim is a probability your workflow can use to act, think again or hand off.":"L’apprentissage par renforcement de la calibration utilise des récompenses issues de règles de score propres, avec des garde-fous KL. L’objectif est une probabilité exploitable pour agir, réfléchir à nouveau ou passer le relais.","Built into":"Intégré","the weights":"aux poids","A decision model deserves its own training recipe. Full-parameter learning establishes the foundation; targeted LoRA and calibration sharpen what comes next.":"Un modèle de décision mérite sa propre recette d’entraînement. L’apprentissage de tous les paramètres établit la base ; LoRA ciblé et calibration affinent la suite.","What does the foundation buy?":"Qu’apporte cette base ?","percentage points":"points de pourcentage","The earlier c6 control favored LoRA. Updating every weight alone is not enough: the data, objective and refinement recipe matter.":"Le contrôle c6 antérieur favorisait LoRA. Mettre à jour tous les poids ne suffit pas : les données, l’objectif et la recette d’affinage comptent.","Our full-training-plus-refinement recipe scored 87.0%, compared with 79.2% for an earlier base-plus-LoRA recipe. Different data and training budgets; this is a recipe comparison, not an isolated full-training ablation.":"Notre recette d’entraînement complet puis d’affinage a obtenu 87.0 %, contre 79.2 % pour une recette antérieure base + LoRA. Les données et budgets diffèrent : il s’agit d’une comparaison de recettes, pas d’une ablation isolant l’entraînement complet.","Training comparison":"Comparaison d’entraînement","Recipe progression":"Évolution de la recette","Same-data control":"Contrôle à données identiques","Historical 4B research runs, one pass, self-scored.":"Expériences historiques de recherche 4B, une passe, auto-évaluées."," Same 35M c6 tokens and data order; update method and learning rate differ.":" Mêmes 35M tokens c6 et même ordre des données ; méthode de mise à jour et taux d’apprentissage différents.","Inspect the experiment evidence":"Examiner les preuves expérimentales","Choose your model,":"Choisissez votre modèle,","Small models,":"Petits modèles,","Compare the evidence":"Comparez les preuves","Bigger moves":"Grandes décisions",Benchmark:Ov,"PUBLIC / 231":"PUBLIC / 231","SAMPLE / 6,948":"ÉCHANTILLON / 6,948","INTERNAL / 656":"INTERNE / 656","columns, descending scores on a 0–100 scale":"colonnes, scores décroissants sur une échelle de 0 à 100","No comparable read":"Aucune évaluation comparable","Columns use a 0–100 scale. Whiskers and brackets show 95% bootstrap intervals.":"Les colonnes utilisent une échelle de 0 à 100. Les barres et crochets indiquent les intervalles bootstrap à 95 %.","Same question set. Read modes shown beside each model.":"Même jeu de questions. Le mode d’évaluation figure à côté de chaque modèle.","JevBench public 231 · self-scored with the research harness. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. External models use their recorded native readouts. These are observed scores, not a verified leaderboard or a significance claim.":"JevBench public 231 · auto-évalué avec l’outil de recherche. WALD 0.8B / 4B / 9B utilisent medium ; 2B utilise une passe. Les modèles externes utilisent leurs modes natifs enregistrés. Ce sont des scores observés, pas un classement vérifié ni une affirmation de significativité.","Decision Index 0.2.1 · author-run 6,948-request sample, separate from the 150,317-request full suite. WALD uses one pass; raw-model prompting and calibration differ. Parameter labels are size classes; MoE total and active sizes are shown separately.":"Decision Index 0.2.1 · échantillon de 6,948 requêtes évalué par les auteurs, distinct de la suite complète de 150,317 requêtes. WALD utilise une passe ; les prompts et la calibration des modèles bruts diffèrent. Les paramètres indiquent des classes de taille ; les tailles totales et actives des MoE sont séparées.","Transfer v4 · internal development bank, 656 clean questions. This is not an independent external test. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. Results are not claims of universal superiority.":"Transfer v4 · banque interne de développement, 656 questions nettoyées. Ce n’est pas un test externe indépendant. WALD 0.8B / 4B / 9B utilisent medium ; 2B utilise une passe. Les résultats n’affirment pas une supériorité universelle.","Sources & read conditions":"Sources et conditions d’évaluation","Read conditions table":"Tableau des conditions d’évaluation","Aggregates checked against source reports":"Agrégats vérifiés dans les rapports sources",Model:qv,"Read condition":"Condition d’évaluation",Source:Uv,"Download scores, intervals and source hashes":"Télécharger les scores, intervalles et empreintes des sources","No comparable score":"Aucun score comparable","Every choice,":"Chaque choix,","A clearer next move":"La prochaine étape, plus claire","Put WALD at the decision points in your workflow. Route a tool call, ask a better question, or send a request to the right team.":"Placez WALD aux points de décision de votre workflow. Routez un appel d’outil, posez une meilleure question ou envoyez une requête à la bonne équipe.","Switch the model size. Switch the benchmark. Compare observed scores with Laya, Jev and larger base models, with the evidence in view.":"Changez la taille du modèle et le benchmark. Comparez les scores observés à Laya, Jev et aux modèles de base plus grands, preuves à l’appui.","Skip to content":"Aller au contenu","Main navigation":"Navigation principale",Home:_v,"Use Cases":"Cas d’usage",Docs:Hv,Playground:Gv,"Build with WALD":"Créer avec WALD","Back to top":"Retour en haut","Font licenses":"Licences des polices","Your agent,":"Votre agent,","Its next instinct":"Son prochain instinct","Download command":"Commande de téléchargement","Explore the source":"Explorer le code source","Enough confidence? Make the call. Need another look? Take a short thinking step. WALD lets you choose how much thought goes into the next move.":"Assez de confiance ? Décidez. Besoin d’un autre regard ? Prenez un court temps de réflexion. WALD vous laisse choisir la réflexion consacrée à la prochaine étape.","Explore thinking efforts":"Explorer les niveaux de réflexion","Thinking effort":"Niveau de réflexion","THINK GATE":"SEUIL DE RÉFLEXION",always:Wv,"Illustrative decision flow":"Démonstration du processus de décision",LOOK:Qv,"Think a bit":"Réfléchir un peu","Direct read":"Lecture directe",DECIDE:Xv,Probabilities:Yv,"Illustrative policy · No live inference":"Politique illustrée · Pas d’inférence en direct","Call the right tool":"Appelez le bon outil","Give WALD the current state and the available tools. Use the option probabilities to choose the next call.":"Donnez à WALD l’état actuel et les outils disponibles. Utilisez les probabilités des options pour choisir le prochain appel.","Know when to ask":"Sachez quand demander","Include asking the user as an option when a request is missing key details. Your workflow chooses when to clarify.":"Incluez la clarification auprès de l’utilisateur parmi les options si des détails essentiels manquent. Votre workflow choisit quand demander.","Find the right team":"Trouvez la bonne équipe","Define your teams as choices. Turn an incoming request into probabilities your routing system can use.":"Définissez vos équipes comme options. Transformez une requête entrante en probabilités exploitables par votre système de routage.","Decision patterns":"Schémas de décision","You define the options and control the actions. The examples below use illustrative probabilities.":"Vous définissez les options et contrôlez les actions. Les exemples ci-dessous utilisent des probabilités illustratives.","See how the model family compares.":"Découvrez la comparaison des modèles.","Explore benchmarks":"Explorer les benchmarks","Give WALD the context and the options. Get a probability for each — and a clearer basis for your next move.":"Donnez à WALD le contexte et les options. Recevez une probabilité pour chacune et une base plus claire pour la prochaine étape.","See how it decides":"Voir comment il décide","Copy unavailable. Select and copy the command or code above.":"Copie indisponible. Sélectionnez et copiez la commande ou le code ci-dessus.","Copied to clipboard.":"Copié dans le presse-papiers.",Language:Jv,"WALD home":"Accueil WALD","API docs":"Documentation de l’API","One pass. No generated thought.":"Une passe. Aucune pensée générée.","Think when the top initial probability is below 0.5.":"Réfléchir si la probabilité initiale la plus élevée est inférieure à 0.5.","Think when the top initial probability is below 0.7.":"Réfléchir si la probabilité initiale la plus élevée est inférieure à 0.7.","Think before every eligible decision.":"Réfléchir avant chaque décision admissible.","Tool routing":"Routage des outils",Clarification:Vv,"Request routing":"Routage des requêtes","What will the weather be in Lisbon tomorrow afternoon?":"Quel temps fera-t-il à Lisbonne demain après-midi ?","Book me a table for Friday.":"Réservez-moi une table pour vendredi.","Refund request for order 1182; the parcel arrived damaged.":"Demande de remboursement pour la commande 1182 ; le colis est arrivé endommagé.","Which tool should the agent call next?":"Quel outil l’agent doit-il appeler ensuite ?","What should the agent do next?":"Que doit faire l’agent ensuite ?","Which team should handle this request?":"Quelle équipe doit traiter cette requête ?","Forecast for a city and time":"Prévisions pour une ville et un horaire","General web search":"Recherche web générale","Read calendar events":"Lire les événements du calendrier","Answer directly":"Répondre directement","Ask for restaurant, time and party size":"Demander le restaurant, l’horaire et le nombre de personnes","Make a reservation":"Effectuer une réservation","Search for restaurants":"Rechercher des restaurants","Take no action":"Ne rien faire","Refunds and returns":"Remboursements et retours","Delivery tracking":"Suivi de livraison","New purchases":"Nouveaux achats","Other requests":"Autres requêtes","index points":"points d’indice","{count}× the total parameters":"{count}× le nombre total de paramètres","Private candidate":"Candidat privé","Research checkpoint":"Checkpoint de recherche","Public v1.1":"v1.1 publique",": comparable results on these cohorts pending.":" : résultats comparables sur ces cohortes en attente.",Undisclosed:Kv,Encoder:Zv,"95% CI":"IC à 95 %","Public 231":"public 231","Internal · 656 clean questions":"Interne · 656 questions nettoyées","0.2.1 · 6,948-request sample":"0.2.1 · échantillon de 6,948 requêtes","Selected base-model comparisons, with Jev and Laya for context. See Benchmark for all measured models and read conditions.":"Comparaisons avec des modèles de base sélectionnés, avec Jev et Laya comme repères. Tous les modèles évalués et leurs conditions figurent sur la page Benchmark.","Open alternatives to Jev,":"Des alternatives ouvertes à Jev,","with vision.":"avec la vision.","Get the 4B":"Obtenir le 4B","Download WALD 4B v2.1":"Télécharger WALD 4B v2.1","Public v2":"v2 public","Public v2.1":"v2.1 public",Public:Iv,"* Measured on WALD 4B v2.0.":"* Mesuré sur WALD 4B v2.0.","Text and images in. Probabilities out. On your hardware.":"Texte et images en entrée. Probabilités en sortie. Sur votre matériel.","Measured WALD speed":"Vitesse mesurée de WALD","26B-A4B one-pass median":"Médiane 26B-A4B en un passage","4B v2.1 Auto 0.7 median":"Médiane 4B v2.1 Auto 0.7","Speed evidence":"Mesures de vitesse","A compact v2 candidate for tight memory budgets. The selected 0.8B checkpoint is measured in one pass.":"Un candidat v2 compact pour une mémoire limitée. Le checkpoint 0.8B retenu est évalué en un passage.","The public 4B v2.1 flagship. Auto 0.7 scores 60.05 on the complete Decision Index 0.3 public suite and 67.75% on JevBench-XL TEST.":"Le modèle phare public 4B v2.1. Auto 0.7 obtient 60.05 sur la suite publique complète Decision Index 0.3 et 67.75 % sur JevBench-XL TEST.","Our latest completed 9B first-stage research checkpoint, measured in one pass. Weights remain private.":"Notre dernier checkpoint de recherche 9B de première étape achevé, évalué en un passage. Les poids restent privés.","A Gemma-based decision model with 26B total and 4B active parameters, released as Wald-26B-A4B. One pass scores 62.30 on the complete Decision Index 0.3 public suite, with a 53 ms median.":"Un modèle de décision basé sur Gemma : 26B paramètres au total, 4B actifs, publié sous le nom Wald-26B-A4B. En un passage, il obtient 62.30 sur la suite publique complète Decision Index 0.3, avec une médiane de 53 ms.","WALD 4B v2 reads images through its base model’s frozen vision tower. Its visual capability transfers from decision training without vision fine-tuning.":"WALD 4B v2 lit les images avec la tour visuelle gelée du modèle de base. L’entraînement aux décisions se transfère à la vision sans ajustement visuel.","pp over the raw vision base":"points au-dessus de la base visuelle brute","Self-scored, zero-shot, one pass. Gain 95% CI":"Auto-évalué, sans adaptation visuelle, en un passage. IC à 95% du gain","WALD 4B v2 defaults to Auto 0.7: think once when the top initial probability is below 0.7, with a 512-token cap. Use none for deterministic one-pass decisions.":"WALD 4B v2 utilise Auto 0.7 par défaut : une réflexion si la probabilité initiale maximale est inférieure à 0.7, plafonnée à 512 tokens. Utilisez none pour un passage déterministe.","Five model sizes. Four benchmark views. Compare WALD with Jev and other measured models.":"Cinq tailles, quatre vues de benchmarks. Comparez WALD à Jev et aux autres modèles mesurés.","From 0.8B to 26B. Compare the latest completed checkpoints on matching test sets, with their read conditions shown.":"De 0.8B à 26B. Comparez les derniers checkpoints achevés sur les mêmes jeux de test, avec leurs conditions de lecture.","XL TEST · 8,177 questions":"XL TEST · 8 177 questions","0.3 · 140,620 public requests":"0.3 · 140 620 requêtes publiques","Held-out TEST only, not the pooled 10,100 TEST + DEV score. Modes differ and are recorded per model; missing measurements stay empty.":"TEST réservé uniquement, sans fusion avec DEV sur 10 100 questions. Les modes sont indiqués par modèle ; les mesures absentes restent vides.","Author-run DI 0.2.1 sample with 95% bootstrap intervals. Native readouts and thinking modes differ. These scores cannot be compared with DI 0.3.":"Échantillon DI 0.2.1 exécuté par les auteurs, avec intervalles bootstrap à 95%. Lectures natives et modes de réflexion différents. Non comparable à DI 0.3.","Complete DI 0.3 public suite, official kit. WALD rows are author-run, with the mode stated per model; Jev is the board's public column. Not the organizer's Full score. S1 projections and DI 0.2.1 samples are excluded; no confidence interval is available.":"Suite publique DI 0.3 complète, kit officiel. Les lignes WALD sont exécutées par les auteurs, avec le mode indiqué par modèle ; Jev provient de la colonne publique du classement. Ce n’est pas le score Full de l’organisateur. Projections S1 et échantillons DI 0.2.1 exclus ; pas d’intervalle disponible.","WALD 4B v2 uses full-parameter decision training from Qwen3.5-4B chat. Supervision targets option probabilities while the vision tower stays frozen.":"WALD 4B v2 entraîne tous les paramètres de décision à partir de Qwen3.5-4B chat. La supervision cible les probabilités des options, avec la tour visuelle gelée.","Full-parameter continuation adds harder decisions and short native thoughts. State-KL anchors behavior to a frozen reference; earlier research also explored LoRA refinement.":"La poursuite de l’entraînement complet ajoute des décisions difficiles et de courtes réflexions natives. State-KL ancre le comportement à une référence gelée ; la recherche antérieure a aussi exploré LoRA.","The public v2 server uses a frozen temperature table fitted on our development data. Earlier research explored calibration reinforcement learning; the release uses the recorded calibration table.":"Le serveur v2 public utilise une table de températures gelée, ajustée sur nos données de développement. La recherche antérieure a exploré l’apprentissage par renforcement pour la calibration ; cette version utilise la table documentée.","Full-parameter decision training, native thinking and calibrated probabilities. The public v2 recipe and historical research comparisons are shown separately.":"Entraînement complet aux décisions, réflexion native et probabilités calibrées. La recette v2 publique et les comparaisons historiques sont présentées séparément.","Public set, self-scored. Research harness; modes and native external servers differ and are recorded per model. Not a verified leaderboard ranking.":"Jeu public, auto-évalué. Outils de recherche, modes et serveurs natifs externes diffèrent et sont documentés par modèle. Ce n’est pas un classement vérifié.","Local speed.":"Vitesse locale.","Open weights.":"Poids ouverts.","Faster at the median":"Plus rapide en médiane","Timing details & hosted references":"Détails des temps et références hébergées",Median:$v,"Client time":"Temps client","Server time":"Temps serveur","Separate public-set read":"Mesure distincte sur le jeu public","Faster median decisions than OpenAI Decisions in our matched 200-request test. Run locally, keep vision, and choose when to think.":"Des décisions plus rapides en médiane qu’OpenAI Decisions sur notre test des mêmes 200 requêtes. Exécutez localement, gardez la vision et choisissez quand réfléchir.","Same 200 DI 0.3 requests, serial and warm. WALD native reader on one RTX PRO 6000; OpenAI client time includes the network.":"Mêmes 200 requêtes DI 0.3, en série et après préchauffage. Lecteur natif WALD sur une RTX PRO 6000 ; le temps client OpenAI inclut le réseau.","Median decision time":"Temps de décision médian","Lower is faster":"Plus bas = plus rapide","Same 200 requests":"Mêmes 200 requêtes","Separate recorded runs":"Mesures de référence distinctes","Decisions in milliseconds.":"Des décisions en millisecondes.","Published benchmark":"Résultats publiés","Jev uses the published JevBench v1.4.2.2 timing: 242 standard and judge decisions, serial, with the network included. It is a separate benchmark from our DI sample.":"Jev utilise les temps publiés de JevBench v1.4.2.2 : 242 décisions standard et de jugement, en série, réseau inclus. Ce benchmark est distinct de notre échantillon DI.","Qwen 27B is shown once using its paren one-pass receipt on an RTX PRO 6000, with client time through an SSH tunnel. The separate 231-request receipt does not record sample IDs or concurrency; no matched speedup ratio is asserted.":"Qwen 27B apparaît une seule fois, avec sa mesure paren en un passage sur RTX PRO 6000, tunnel SSH inclus dans le temps client. Le relevé distinct de 231 requêtes ne donne ni les identifiants ni la concurrence ; aucun rapport de vitesse apparié n’est affirmé.","Original Qwen3.5-4B Base":"Qwen3.5-4B Base original","More models":"Autres modèles","Vision transfer":"Transfert visuel","measured model sizes":"tailles de modèles mesurées","Base scores included":"Scores des modèles de base inclus","vs original base":"par rapport au modèle de base","Original base":"Modèle de base",Difference:Fv,"One pass":"Un passage",Combined:Pv,"The original 4B base scored 67.5%. LoRA reached 79.2%, full training 83.5%, and full training plus refinement 87.0%. Historical recipes use different data and budgets; this is a recipe comparison.":"Le modèle 4B original obtient 67,5 %. LoRA atteint 79,2 %, l’entraînement complet 83,5 %, et l’entraînement complet avec affinage 87,0 %. Ces recettes historiques utilisent des données et budgets différents ; nous comparons les recettes.","Both recipes improved over the 67.5% base. With the same 35M tokens, LoRA scored 83.5% and full training 82.3%. Update methods and learning rates differed.":"Les deux recettes dépassent le modèle de base à 67,5 %. Avec les mêmes 35 millions de tokens, LoRA atteint 83,5 % et l’entraînement complet 82,3 %. Les méthodes de mise à jour et les taux d’apprentissage diffèrent.","Original backbones and trained checkpoints, on each row's benchmark. 2B and 9B use one pass; 26B uses Auto 0.7. These are separate recorded runs, with different training recipes.":"Comparaison des modèles de base et des checkpoints entraînés sur le benchmark de chaque ligne. Les 2B et 9B utilisent un passage ; le 26B utilise Auto 0.7. Les mesures et recettes d’entraînement sont distinctes.","Text decision training transferred to vision: 80.23% for the original Qwen3.5-4B chat model, 83.05% for WALD 4B v2. Same native-chat one-pass readout, frozen vision tower, no image fine-tuning.":"L’entraînement aux décisions textuelles se transfère à la vision : 80,23 % pour Qwen3.5-4B chat original, 83,05 % pour WALD 4B v2. Même lecture native en un passage, encodeur visuel gelé, sans affinage sur images."},ab="Kopiert",tb="Kopieren",nb="gegenüber",ib="Pkt.",lb="Fragen",sb="Benchmarks",ob="Modell",cb="Quelle",rb="Startseite",ub="Dokumentation",db="Playground",fb="immer",hb="BETRACHTEN",mb="ENTSCHEIDEN",pb="Wahrscheinlichkeiten",vb="Sprache",bb="Klärung",gb="Nicht offengelegt",yb="Encoder",Ab="Öffentlich",xb="Median",Sb="Differenz",Bb="Gesamt",Db={"Put the next decision into practice.":"Setzen Sie die nächste Entscheidung um.","Explore use cases":"Anwendungsfälle entdecken","A clear next move":"Ein klarer nächster Schritt","Context in. Probabilities out.":"Kontext rein. Wahrscheinlichkeiten raus.","Decision examples":"Entscheidungsbeispiele","Illustrative example · Not a live model response":"Beispiel zur Veranschaulichung · Keine Live-Modellantwort",Copied:ab,Copy:tb,"API example code":"API-Beispielcode","API reference":"API-Referenz","Explore WALD":"WALD entdecken","Tool calls. Clarifying questions. Request routing. See the decision points and explore the API examples.":"Werkzeugaufrufe. Rückfragen. Anfrageverteilung. Entdecken Sie Entscheidungspunkte und API-Beispiele.","By the numbers":"Die Zahlen sprechen","Compare the models":"Modelle vergleichen","More decision":"Mehr Entscheidung","per parameter":"pro Parameter","From a pocket-sized decision engine to a larger reasoning system. A model family built to read the situation, weigh the options, and make every parameter count.":"Vom kompakten Entscheidungsmotor bis zum größeren Denksystem. Eine Modellfamilie, die Situationen versteht, Optionen abwägt und jeden Parameter wirksam nutzt.","Model size":"Modellgröße","A compact research checkpoint. The 2B model exceeds a 20B reference on JevBench, showing how decision-focused training can change the size equation.":"Ein kompakter Forschungs-Checkpoint. Das 2B-Modell übertrifft eine 20B-Referenz auf JevBench und zeigt, wie Entscheidungstraining das Verhältnis von Größe und Leistung verändert.",vs:nb,"Part of the expanded WALD family. Comparable checkpoint scores and release details for this size will be added when verified.":"Teil der erweiterten WALD-Familie. Vergleichbare Checkpoint-Ergebnisse und Veröffentlichungsdetails für diese Größe folgen nach Prüfung.","Explore all benchmarks":"Alle Benchmarks ansehen","Homepage benchmark":"Benchmark der Startseite","Benchmark columns, descending scores on a 0–100 scale":"Benchmark-Säulen, absteigende Werte auf einer Skala von 0–100",pts:ib,"WALD 4B shown as a reference. ":"WALD 4B wird als Referenz gezeigt. ","Self-scored snapshot. Reference protocols differ; see full results and conditions.":"Selbst bewertete Momentaufnahme. Referenzprotokolle unterscheiden sich; siehe vollständige Ergebnisse und Bedingungen.","Comparable results pending.":"Vergleichbare Ergebnisse stehen noch aus.","See the scene,":"Die Szene erkennen,","Choose the action":"Die Aktion wählen",questions:lb,"pp over the unadapted graft":"Prozentpunkte über dem unangepassten transplantierten Modell","Self-scored, one pass. Gain 95% CI":"Selbst bewertet, ein Durchlauf. 95%-KI des Zugewinns","View training & vision evidence":"Trainings- und Vision-Belege ansehen","LoRA decision training":"LoRA-Entscheidungstraining","Full-parameter decision training":"Entscheidungstraining aller Parameter","Base + LoRA only":"Basismodell + nur LoRA","Full decision training":"Vollständiges Entscheidungstraining","Full training + LoRA refinement":"Vollständiges Training + LoRA-Verfeinerung","Train the foundation":"Die Grundlage trainieren","Our flagship Qwen recipe updates all language-model weights on decision examples. Supervision targets the option distribution, so choosing becomes a learned capability in the backbone.":"Unser Qwen-Flaggschiff-Rezept aktualisiert alle Sprachmodellgewichte anhand von Entscheidungsbeispielen. Die Überwachung zielt auf die Verteilung der Optionen, sodass die Auswahl eine erlernte Fähigkeit des Grundmodells wird.","Refine the edge":"Die Entscheidungen verfeinern","LoRA builds on that foundation: harder decisions, short-thought distillation, visual evidence and domain adaptation. KL replay anchors refinement to the parent’s behavior.":"LoRA baut darauf auf: schwierigere Entscheidungen, Destillation kurzer Denkprozesse, visuelle Hinweise und Domänenanpassung. KL-Replay bindet die Verfeinerung an das Verhalten des Elternmodells.","Make confidence useful":"Konfidenz nutzbar machen","Calibration reinforcement learning uses proper scoring rewards with KL guardrails. The aim is a probability your workflow can use to act, think again or hand off.":"Kalibrierungs-RL nutzt Belohnungen aus properen Bewertungsregeln mit KL-Begrenzungen. Das Ziel sind Wahrscheinlichkeiten, mit denen Ihr Workflow handeln, erneut nachdenken oder übergeben kann.","Built into":"In den Gewichten","the weights":"verankert","A decision model deserves its own training recipe. Full-parameter learning establishes the foundation; targeted LoRA and calibration sharpen what comes next.":"Ein Entscheidungsmodell braucht ein eigenes Trainingsrezept. Lernen mit allen Parametern legt die Grundlage; gezieltes LoRA und Kalibrierung schärfen die nächsten Schritte.","What does the foundation buy?":"Was bringt die Grundlage?","percentage points":"Prozentpunkte","The earlier c6 control favored LoRA. Updating every weight alone is not enough: the data, objective and refinement recipe matter.":"Der frühere c6-Kontrollversuch fiel zugunsten von LoRA aus. Alle Gewichte zu aktualisieren reicht allein nicht: Daten, Ziel und Verfeinerungsrezept zählen.","Our full-training-plus-refinement recipe scored 87.0%, compared with 79.2% for an earlier base-plus-LoRA recipe. Different data and training budgets; this is a recipe comparison, not an isolated full-training ablation.":"Unser Rezept aus vollständigem Training und Verfeinerung erreichte 87.0 %, gegenüber 79.2 % für ein früheres Basismodell-plus-LoRA-Rezept. Daten und Trainingsbudgets unterscheiden sich; dies vergleicht Rezepte und isoliert nicht den Effekt vollständigen Trainings.","Training comparison":"Trainingsvergleich","Recipe progression":"Entwicklung des Rezepts","Same-data control":"Kontrolle mit gleichen Daten","Historical 4B research runs, one pass, self-scored.":"Historische 4B-Forschungsläufe, ein Durchlauf, selbst bewertet."," Same 35M c6 tokens and data order; update method and learning rate differ.":" Gleiche 35M c6-Tokens und Datenreihenfolge; Aktualisierungsmethode und Lernrate unterscheiden sich.","Inspect the experiment evidence":"Experimentbelege prüfen","Choose your model,":"Ihr Modell wählen,","Small models,":"Kleine Modelle,","Compare the evidence":"Die Belege vergleichen","Bigger moves":"Große Wirkung",Benchmark:sb,"PUBLIC / 231":"ÖFFENTLICH / 231","SAMPLE / 6,948":"STICHPROBE / 6,948","INTERNAL / 656":"INTERN / 656","columns, descending scores on a 0–100 scale":"Säulen, absteigende Werte auf einer Skala von 0–100","No comparable read":"Keine vergleichbare Auswertung","Columns use a 0–100 scale. Whiskers and brackets show 95% bootstrap intervals.":"Die Säulen nutzen eine Skala von 0–100. Fehlerbalken und Klammern zeigen 95%-Bootstrap-Intervalle.","Same question set. Read modes shown beside each model.":"Gleicher Fragensatz. Auswertungsmodi stehen neben jedem Modell.","JevBench public 231 · self-scored with the research harness. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. External models use their recorded native readouts. These are observed scores, not a verified leaderboard or a significance claim.":"JevBench öffentlich 231 · selbst bewertet mit dem Forschungswerkzeug. WALD 0.8B / 4B / 9B nutzen medium; 2B nutzt einen Durchlauf. Externe Modelle verwenden ihre dokumentierten nativen Auswertungen. Dies sind beobachtete Werte, keine bestätigte Rangliste oder Aussage zur Signifikanz.","Decision Index 0.2.1 · author-run 6,948-request sample, separate from the 150,317-request full suite. WALD uses one pass; raw-model prompting and calibration differ. Parameter labels are size classes; MoE total and active sizes are shown separately.":"Decision Index 0.2.1 · von den Autoren ausgewertete Stichprobe mit 6,948 Anfragen, getrennt von der vollständigen Suite mit 150,317 Anfragen. WALD nutzt einen Durchlauf; Prompts und Kalibrierung der Rohmodelle unterscheiden sich. Parameterangaben sind Größenklassen; MoE-Gesamtgröße und aktive Größe werden getrennt gezeigt.","Transfer v4 · internal development bank, 656 clean questions. This is not an independent external test. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. Results are not claims of universal superiority.":"Transfer v4 · interner Entwicklungsbestand mit 656 bereinigten Fragen. Kein unabhängiger externer Test. WALD 0.8B / 4B / 9B nutzen medium; 2B nutzt einen Durchlauf. Die Ergebnisse belegen keine allgemeine Überlegenheit.","Sources & read conditions":"Quellen und Auswertungsbedingungen","Read conditions table":"Tabelle der Auswertungsbedingungen","Aggregates checked against source reports":"Aggregate anhand der Quellberichte geprüft",Model:ob,"Read condition":"Auswertungsbedingung",Source:cb,"Download scores, intervals and source hashes":"Werte, Intervalle und Quellen-Hashes herunterladen","No comparable score":"Kein vergleichbarer Wert","Every choice,":"Jede Wahl,","A clearer next move":"Ein klarerer nächster Schritt","Put WALD at the decision points in your workflow. Route a tool call, ask a better question, or send a request to the right team.":"Setzen Sie WALD an den Entscheidungspunkten Ihres Workflows ein. Wählen Sie ein Werkzeug, stellen Sie eine bessere Rückfrage oder leiten Sie eine Anfrage an das passende Team.","Switch the model size. Switch the benchmark. Compare observed scores with Laya, Jev and larger base models, with the evidence in view.":"Wechseln Sie Modellgröße und Benchmark. Vergleichen Sie beobachtete Werte mit Laya, Jev und größeren Basismodellen, mit den Belegen im Blick.","Skip to content":"Zum Inhalt springen","Main navigation":"Hauptnavigation",Home:rb,"Use Cases":"Anwendungsfälle",Docs:ub,Playground:db,"Build with WALD":"Mit WALD entwickeln","Back to top":"Zurück nach oben","Font licenses":"Schriftlizenzen","Your agent,":"Ihr Agent,","Its next instinct":"Sein nächster Instinkt","Download command":"Download-Befehl","Explore the source":"Quellcode ansehen","Enough confidence? Make the call. Need another look? Take a short thinking step. WALD lets you choose how much thought goes into the next move.":"Genug Sicherheit? Entscheiden. Noch ein Blick nötig? Kurz nachdenken. Mit WALD bestimmen Sie, wie viel Überlegung in den nächsten Schritt fließt.","Explore thinking efforts":"Denkaufwand entdecken","Thinking effort":"Denkaufwand","THINK GATE":"DENKSCHWELLE",always:fb,"Illustrative decision flow":"Veranschaulichung des Entscheidungsablaufs",LOOK:hb,"Think a bit":"Kurz nachdenken","Direct read":"Direkte Auswertung",DECIDE:mb,Probabilities:pb,"Illustrative policy · No live inference":"Veranschaulichte Strategie · Keine Live-Inferenz","Call the right tool":"Das passende Werkzeug aufrufen","Give WALD the current state and the available tools. Use the option probabilities to choose the next call.":"Geben Sie WALD den aktuellen Zustand und die verfügbaren Werkzeuge. Nutzen Sie die Optionswahrscheinlichkeiten für die Wahl des nächsten Aufrufs.","Know when to ask":"Wissen, wann eine Rückfrage nötig ist","Include asking the user as an option when a request is missing key details. Your workflow chooses when to clarify.":"Fehlen wichtige Angaben, nehmen Sie eine Rückfrage an den Nutzer als Option auf. Ihr Workflow bestimmt, wann geklärt werden soll.","Find the right team":"Das passende Team finden","Define your teams as choices. Turn an incoming request into probabilities your routing system can use.":"Definieren Sie Teams als Optionen. Wandeln Sie eine eingehende Anfrage in Wahrscheinlichkeiten um, die Ihr Verteilungssystem nutzen kann.","Decision patterns":"Entscheidungsmuster","You define the options and control the actions. The examples below use illustrative probabilities.":"Sie definieren die Optionen und steuern die Aktionen. Die folgenden Beispiele nutzen illustrative Wahrscheinlichkeiten.","See how the model family compares.":"Sehen Sie den Vergleich der Modellfamilie.","Explore benchmarks":"Benchmarks entdecken","Give WALD the context and the options. Get a probability for each — and a clearer basis for your next move.":"Geben Sie WALD Kontext und Optionen. Erhalten Sie für jede eine Wahrscheinlichkeit und eine klarere Grundlage für Ihren nächsten Schritt.","See how it decides":"Seine Entscheidungen ansehen","Copy unavailable. Select and copy the command or code above.":"Kopieren nicht verfügbar. Markieren und kopieren Sie den Befehl oder Code oben.","Copied to clipboard.":"In die Zwischenablage kopiert.",Language:vb,"WALD home":"WALD-Startseite","API docs":"API-Dokumentation","One pass. No generated thought.":"Ein Durchlauf. Kein generierter Denkprozess.","Think when the top initial probability is below 0.5.":"Nachdenken, wenn die höchste anfängliche Wahrscheinlichkeit unter 0.5 liegt.","Think when the top initial probability is below 0.7.":"Nachdenken, wenn die höchste anfängliche Wahrscheinlichkeit unter 0.7 liegt.","Think before every eligible decision.":"Vor jeder geeigneten Entscheidung nachdenken.","Tool routing":"Werkzeugauswahl",Clarification:bb,"Request routing":"Anfrageverteilung","What will the weather be in Lisbon tomorrow afternoon?":"Wie wird das Wetter morgen Nachmittag in Lissabon?","Book me a table for Friday.":"Reserviere mir einen Tisch für Freitag.","Refund request for order 1182; the parcel arrived damaged.":"Erstattungsanfrage für Bestellung 1182; das Paket kam beschädigt an.","Which tool should the agent call next?":"Welches Werkzeug soll der Agent als Nächstes aufrufen?","What should the agent do next?":"Was soll der Agent als Nächstes tun?","Which team should handle this request?":"Welches Team soll diese Anfrage bearbeiten?","Forecast for a city and time":"Vorhersage für Stadt und Zeitpunkt","General web search":"Allgemeine Websuche","Read calendar events":"Kalenderereignisse lesen","Answer directly":"Direkt antworten","Ask for restaurant, time and party size":"Nach Restaurant, Uhrzeit und Personenzahl fragen","Make a reservation":"Eine Reservierung vornehmen","Search for restaurants":"Restaurants suchen","Take no action":"Nichts unternehmen","Refunds and returns":"Erstattungen und Retouren","Delivery tracking":"Sendungsverfolgung","New purchases":"Neue Käufe","Other requests":"Andere Anfragen","index points":"Indexpunkte","{count}× the total parameters":"{count}× so viele Gesamtparameter","Private candidate":"Privater Kandidat","Research checkpoint":"Forschungs-Checkpoint","Public v1.1":"Öffentlich v1.1",": comparable results on these cohorts pending.":": vergleichbare Ergebnisse für diese Kohorten stehen noch aus.",Undisclosed:gb,Encoder:yb,"95% CI":"95%-KI","Public 231":"öffentlich 231","Internal · 656 clean questions":"Intern · 656 bereinigte Fragen","0.2.1 · 6,948-request sample":"0.2.1 · Stichprobe mit 6,948 Anfragen","Selected base-model comparisons, with Jev and Laya for context. See Benchmark for all measured models and read conditions.":"Vergleich mit ausgewählten Basismodellen sowie Jev und Laya zur Einordnung. Alle gemessenen Modelle und Testbedingungen stehen auf der Benchmark-Seite.","Open alternatives to Jev,":"Offene Alternativen zu Jev,","with vision.":"mit Bildverständnis.","Get the 4B":"4B herunterladen","Download WALD 4B v2.1":"WALD 4B v2.1 herunterladen","Public v2":"Öffentliche v2","Public v2.1":"Öffentliche v2.1",Public:Ab,"* Measured on WALD 4B v2.0.":"* Gemessen an WALD 4B v2.0.","Text and images in. Probabilities out. On your hardware.":"Text und Bilder rein. Wahrscheinlichkeiten raus. Auf deiner Hardware.","Measured WALD speed":"Gemessene Geschwindigkeit von WALD","26B-A4B one-pass median":"26B-A4B, Median, ein Durchlauf","4B v2.1 Auto 0.7 median":"4B v2.1, Median mit Auto 0.7","Speed evidence":"Geschwindigkeitsmessungen","A compact v2 candidate for tight memory budgets. The selected 0.8B checkpoint is measured in one pass.":"Ein kompakter v2-Kandidat für wenig Speicher. Der ausgewählte 0.8B-Checkpoint wurde in einem Durchlauf gemessen.","The public 4B v2.1 flagship. Auto 0.7 scores 60.05 on the complete Decision Index 0.3 public suite and 67.75% on JevBench-XL TEST.":"Das öffentliche 4B-v2.1-Flaggschiff. Auto 0.7 erreicht 60.05 auf der vollständigen öffentlichen Decision-Index-0.3-Suite und 67.75 % auf JevBench-XL TEST.","Our latest completed 9B first-stage research checkpoint, measured in one pass. Weights remain private.":"Unser neuester abgeschlossener 9B-Forschungscheckpoint der ersten Stufe, in einem Durchlauf gemessen. Die Gewichte sind privat.","A Gemma-based decision model with 26B total and 4B active parameters, released as Wald-26B-A4B. One pass scores 62.30 on the complete Decision Index 0.3 public suite, with a 53 ms median.":"Ein Gemma-basiertes Entscheidungsmodell mit 26B Gesamt- und 4B aktiven Parametern, veröffentlicht als Wald-26B-A4B. Ein Durchlauf erreicht 62.30 auf der vollständigen öffentlichen Decision-Index-0.3-Suite, bei 53 ms im Median.","WALD 4B v2 reads images through its base model’s frozen vision tower. Its visual capability transfers from decision training without vision fine-tuning.":"WALD 4B v2 liest Bilder mit dem eingefrorenen Bildencoder des Basismodells. Entscheidungstraining überträgt sich ohne visuelles Fine-Tuning auf Bilder.","pp over the raw vision base":"Prozentpunkte über der visuellen Rohbasis","Self-scored, zero-shot, one pass. Gain 95% CI":"Selbst gemessen, Zero-Shot, ein Durchlauf. 95%-KI des Gewinns","WALD 4B v2 defaults to Auto 0.7: think once when the top initial probability is below 0.7, with a 512-token cap. Use none for deterministic one-pass decisions.":"WALD 4B v2 nutzt standardmäßig Auto 0.7: einmal nachdenken, wenn die höchste Anfangswahrscheinlichkeit unter 0.7 liegt, höchstens 512 Token. none liefert deterministische Entscheidungen in einem Durchlauf.","Five model sizes. Four benchmark views. Compare WALD with Jev and other measured models.":"Fünf Modellgrößen, vier Benchmark-Ansichten. Vergleichen Sie WALD mit Jev und anderen gemessenen Modellen.","From 0.8B to 26B. Compare the latest completed checkpoints on matching test sets, with their read conditions shown.":"Von 0.8B bis 26B. Vergleichen Sie die neuesten abgeschlossenen Checkpoints auf denselben Testsätzen mit sichtbaren Lesebedingungen.","XL TEST · 8,177 questions":"XL TEST · 8.177 Fragen","0.3 · 140,620 public requests":"0.3 · 140.620 öffentliche Anfragen","Held-out TEST only, not the pooled 10,100 TEST + DEV score. Modes differ and are recorded per model; missing measurements stay empty.":"Nur zurückgehaltener TEST, nicht TEST + DEV mit 10.100 Fragen. Modi werden je Modell erfasst; fehlende Messungen bleiben leer.","Author-run DI 0.2.1 sample with 95% bootstrap intervals. Native readouts and thinking modes differ. These scores cannot be compared with DI 0.3.":"DI-0.2.1-Stichprobe der Autoren mit 95%-Bootstrap-Intervallen. Native Auslese- und Denkmodi unterscheiden sich. Nicht mit DI 0.3 vergleichbar.","Complete DI 0.3 public suite, official kit. WALD rows are author-run, with the mode stated per model; Jev is the board's public column. Not the organizer's Full score. S1 projections and DI 0.2.1 samples are excluded; no confidence interval is available.":"Vollständige öffentliche DI-0.3-Suite, offizielles Kit. WALD-Zeilen stammen von den Autoren, der Modus ist je Modell angegeben; Jev ist die öffentliche Spalte der Rangliste. Kein Full-Score des Veranstalters. S1-Projektionen und DI-0.2.1-Stichproben sind ausgeschlossen; kein Konfidenzintervall verfügbar.","WALD 4B v2 uses full-parameter decision training from Qwen3.5-4B chat. Supervision targets option probabilities while the vision tower stays frozen.":"WALD 4B v2 nutzt vollständiges Entscheidungstraining ab Qwen3.5-4B chat. Ziel sind Optionswahrscheinlichkeiten; der Bildencoder bleibt eingefroren.","Full-parameter continuation adds harder decisions and short native thoughts. State-KL anchors behavior to a frozen reference; earlier research also explored LoRA refinement.":"Vollständiges Weitertraining ergänzt schwierigere Entscheidungen und kurze native Gedanken. State-KL verankert das Verhalten an einer eingefrorenen Referenz; frühere Forschung untersuchte auch LoRA.","The public v2 server uses a frozen temperature table fitted on our development data. Earlier research explored calibration reinforcement learning; the release uses the recorded calibration table.":"Der öffentliche v2-Server nutzt eine auf unseren Entwicklungsdaten angepasste, eingefrorene Temperaturtabelle. Frühere Forschung untersuchte Kalibrierungs-RL; die Veröffentlichung verwendet die dokumentierte Tabelle.","Full-parameter decision training, native thinking and calibrated probabilities. The public v2 recipe and historical research comparisons are shown separately.":"Vollständiges Entscheidungstraining, natives Denken und kalibrierte Wahrscheinlichkeiten. Die öffentliche v2-Rezeptur und historische Forschungsvergleiche werden getrennt dargestellt.","Public set, self-scored. Research harness; modes and native external servers differ and are recorded per model. Not a verified leaderboard ranking.":"Öffentlicher Testsatz, selbst gemessen. Forschungswerkzeuge, Modi und native externe Server unterscheiden sich und sind je Modell dokumentiert. Keine verifizierte Rangliste.","Local speed.":"Lokale Geschwindigkeit.","Open weights.":"Offene Gewichte.","Faster at the median":"Schneller im Median","Timing details & hosted references":"Zeitdetails und gehostete Referenzen",Median:xb,"Client time":"Client-Zeit","Server time":"Server-Zeit","Separate public-set read":"Separater öffentlicher Test","Faster median decisions than OpenAI Decisions in our matched 200-request test. Run locally, keep vision, and choose when to think.":"Im Test mit denselben 200 Anfragen entscheidet WALD im Median schneller als OpenAI Decisions. Lokal ausführen, Bildverständnis behalten und wählen, wann es nachdenkt.","Same 200 DI 0.3 requests, serial and warm. WALD native reader on one RTX PRO 6000; OpenAI client time includes the network.":"Dieselben 200 DI-0.3-Anfragen, seriell und aufgewärmt. Nativer WALD-Reader auf einer RTX PRO 6000; die OpenAI-Client-Zeit enthält das Netzwerk.","Median decision time":"Mediane Entscheidungszeit","Lower is faster":"Niedriger ist schneller","Same 200 requests":"Dieselben 200 Anfragen","Separate recorded runs":"Separat gemessene Referenzläufe","Decisions in milliseconds.":"Entscheidungen in Millisekunden.","Published benchmark":"Veröffentlichter Benchmark","Jev uses the published JevBench v1.4.2.2 timing: 242 standard and judge decisions, serial, with the network included. It is a separate benchmark from our DI sample.":"Jev nutzt die veröffentlichten JevBench-v1.4.2.2-Zeiten: 242 Standard- und Judge-Entscheidungen, seriell, einschließlich Netzwerk. Dieser Benchmark unterscheidet sich von unserer DI-Stichprobe.","Qwen 27B is shown once using its paren one-pass receipt on an RTX PRO 6000, with client time through an SSH tunnel. The separate 231-request receipt does not record sample IDs or concurrency; no matched speedup ratio is asserted.":"Qwen 27B erscheint einmal mit seinem paren-Ein-Pass-Beleg auf einer RTX PRO 6000; die Client-Zeit enthält den SSH-Tunnel. Der separate Beleg über 231 Anfragen nennt keine Stichproben-IDs oder Parallelität; kein gepaartes Geschwindigkeitsverhältnis wird behauptet.","Original Qwen3.5-4B Base":"Ursprüngliches Qwen3.5-4B Base","More models":"Weitere Modelle","Vision transfer":"Transfer auf Bildaufgaben","measured model sizes":"gemessene Modellgrößen","Base scores included":"Mit Ergebnissen der Ausgangsmodelle","vs original base":"gegenüber dem Ausgangsmodell","Original base":"Ausgangsmodell",Difference:Sb,"One pass":"Ein Durchlauf",Combined:Bb,"The original 4B base scored 67.5%. LoRA reached 79.2%, full training 83.5%, and full training plus refinement 87.0%. Historical recipes use different data and budgets; this is a recipe comparison.":"Das ursprüngliche 4B-Modell erreichte 67,5 %. LoRA erreichte 79,2 %, vollständiges Training 83,5 % und vollständiges Training mit Verfeinerung 87,0 %. Die historischen Verfahren nutzen unterschiedliche Daten und Budgets; verglichen werden die gesamten Verfahren.","Both recipes improved over the 67.5% base. With the same 35M tokens, LoRA scored 83.5% and full training 82.3%. Update methods and learning rates differed.":"Beide Verfahren übertrafen das Ausgangsmodell mit 67,5 %. Bei denselben 35 Millionen Tokens erreichte LoRA 83,5 % und vollständiges Training 82,3 %. Aktualisierungsmethode und Lernrate unterschieden sich.","Original backbones and trained checkpoints, on each row's benchmark. 2B and 9B use one pass; 26B uses Auto 0.7. These are separate recorded runs, with different training recipes.":"Ausgangsmodelle und trainierte Checkpoints auf dem jeweiligen Benchmark. 2B und 9B nutzen einen Durchlauf, 26B nutzt Auto 0.7. Es sind getrennte Messläufe mit unterschiedlichen Trainingsverfahren.","Text decision training transferred to vision: 80.23% for the original Qwen3.5-4B chat model, 83.05% for WALD 4B v2. Same native-chat one-pass readout, frozen vision tower, no image fine-tuning.":"Training für Textentscheidungen überträgt sich auf Bildaufgaben: 80,23 % für das ursprüngliche Qwen3.5-4B-Chatmodell, 83,05 % für WALD 4B v2. Gleiche native Chat-Auswertung in einem Durchlauf, eingefrorener Bildencoder, kein Bild-Finetuning."},Tb="Copiado",jb="Copiar",Lb="frente a",Eb="pts",zb="preguntas",wb="Benchmarks",Mb="Modelo",Rb="Fuente",kb="Inicio",Nb="Documentación",Cb="Entorno de pruebas",Ob="siempre",qb="OBSERVAR",Ub="DECIDIR",_b="Probabilidades",Hb="Idioma",Gb="Aclaración",Wb="No divulgado",Qb="Codificador",Xb="Público",Yb="Mediana",Jb="Diferencia",Vb="Total",Kb={"Put the next decision into practice.":"Lleva la próxima decisión a la práctica.","Explore use cases":"Explorar casos de uso","A clear next move":"Un próximo paso más claro","Context in. Probabilities out.":"Entra contexto. Salen probabilidades.","Decision examples":"Ejemplos de decisión","Illustrative example · Not a live model response":"Ejemplo ilustrativo · No es una respuesta del modelo en directo",Copied:Tb,Copy:jb,"API example code":"Código de ejemplo de la API","API reference":"Referencia de la API","Explore WALD":"Explorar WALD","Tool calls. Clarifying questions. Request routing. See the decision points and explore the API examples.":"Llamadas a herramientas. Preguntas aclaratorias. Enrutamiento de solicitudes. Explora los puntos de decisión y los ejemplos de la API.","By the numbers":"Los resultados en cifras","Compare the models":"Comparar los modelos","More decision":"Más decisión","per parameter":"por parámetro","From a pocket-sized decision engine to a larger reasoning system. A model family built to read the situation, weigh the options, and make every parameter count.":"Desde un motor de decisión de bolsillo hasta un sistema de razonamiento mayor. Una familia diseñada para comprender la situación, sopesar opciones y aprovechar cada parámetro.","Model size":"Tamaño del modelo","A compact research checkpoint. The 2B model exceeds a 20B reference on JevBench, showing how decision-focused training can change the size equation.":"Un checkpoint de investigación compacto. El modelo 2B supera a una referencia de 20B en JevBench y muestra cómo el entrenamiento centrado en decisiones cambia la relación entre tamaño y capacidad.",vs:Lb,"Part of the expanded WALD family. Comparable checkpoint scores and release details for this size will be added when verified.":"Parte de la familia WALD ampliada. Los resultados comparables y los detalles de publicación de este tamaño se añadirán tras su verificación.","Explore all benchmarks":"Explorar todos los benchmarks","Homepage benchmark":"Benchmark de la página de inicio","Benchmark columns, descending scores on a 0–100 scale":"Columnas del benchmark, puntuaciones descendentes en una escala de 0 a 100",pts:Eb,"WALD 4B shown as a reference. ":"WALD 4B se muestra como referencia. ","Self-scored snapshot. Reference protocols differ; see full results and conditions.":"Resultados de evaluación propia. Los protocolos de referencia difieren; consulta los resultados completos y las condiciones.","Comparable results pending.":"Resultados comparables pendientes.","See the scene,":"Observa la escena,","Choose the action":"Elige la acción",questions:zb,"pp over the unadapted graft":"puntos porcentuales sobre el modelo injertado sin adaptar","Self-scored, one pass. Gain 95% CI":"Evaluación propia, una pasada. IC del 95 % de la mejora","View training & vision evidence":"Ver evidencias de entrenamiento y visión","LoRA decision training":"Entrenamiento de decisiones con LoRA","Full-parameter decision training":"Entrenamiento de decisiones de todos los parámetros","Base + LoRA only":"Base + solo LoRA","Full decision training":"Entrenamiento completo de decisiones","Full training + LoRA refinement":"Entrenamiento completo + refinamiento LoRA","Train the foundation":"Entrena la base","Our flagship Qwen recipe updates all language-model weights on decision examples. Supervision targets the option distribution, so choosing becomes a learned capability in the backbone.":"Nuestra receta Qwen insignia actualiza todos los pesos del modelo lingüístico con ejemplos de decisión. La supervisión se centra en la distribución de opciones para que elegir sea una capacidad aprendida en la base del modelo.","Refine the edge":"Refina las decisiones","LoRA builds on that foundation: harder decisions, short-thought distillation, visual evidence and domain adaptation. KL replay anchors refinement to the parent’s behavior.":"LoRA amplía esa base: decisiones difíciles, destilación de razonamientos breves, evidencias visuales y adaptación al dominio. La repetición KL ancla el refinamiento al comportamiento del modelo padre.","Make confidence useful":"Haz útil la confianza","Calibration reinforcement learning uses proper scoring rewards with KL guardrails. The aim is a probability your workflow can use to act, think again or hand off.":"El aprendizaje por refuerzo para calibración usa recompensas con reglas de puntuación propias y límites KL. El objetivo es una probabilidad que permita actuar, volver a pensar o derivar la tarea.","Built into":"Integrado en","the weights":"los pesos","A decision model deserves its own training recipe. Full-parameter learning establishes the foundation; targeted LoRA and calibration sharpen what comes next.":"Un modelo de decisión merece su propia receta de entrenamiento. El aprendizaje de todos los parámetros establece la base; LoRA específico y la calibración afinan lo que sigue.","What does the foundation buy?":"¿Qué aporta la base?","percentage points":"puntos porcentuales","The earlier c6 control favored LoRA. Updating every weight alone is not enough: the data, objective and refinement recipe matter.":"El control c6 anterior favoreció a LoRA. Actualizar todos los pesos no basta: importan los datos, el objetivo y la receta de refinamiento.","Our full-training-plus-refinement recipe scored 87.0%, compared with 79.2% for an earlier base-plus-LoRA recipe. Different data and training budgets; this is a recipe comparison, not an isolated full-training ablation.":"Nuestra receta de entrenamiento completo y refinamiento obtuvo 87.0 %, frente a 79.2 % de una receta anterior de base más LoRA. Los datos y presupuestos difieren; es una comparación de recetas, no una ablación que aísle el entrenamiento completo.","Training comparison":"Comparación de entrenamiento","Recipe progression":"Evolución de la receta","Same-data control":"Control con los mismos datos","Historical 4B research runs, one pass, self-scored.":"Experimentos históricos de investigación 4B, una pasada, evaluación propia."," Same 35M c6 tokens and data order; update method and learning rate differ.":" Los mismos 35M tokens c6 y orden de datos; difieren el método de actualización y la tasa de aprendizaje.","Inspect the experiment evidence":"Examinar las evidencias del experimento","Choose your model,":"Elige tu modelo,","Small models,":"Modelos pequeños,","Compare the evidence":"Compara las evidencias","Bigger moves":"Grandes decisiones",Benchmark:wb,"PUBLIC / 231":"PÚBLICO / 231","SAMPLE / 6,948":"MUESTRA / 6,948","INTERNAL / 656":"INTERNO / 656","columns, descending scores on a 0–100 scale":"columnas, puntuaciones descendentes en una escala de 0 a 100","No comparable read":"Sin evaluación comparable","Columns use a 0–100 scale. Whiskers and brackets show 95% bootstrap intervals.":"Las columnas usan una escala de 0 a 100. Las barras y los corchetes muestran intervalos bootstrap del 95 %.","Same question set. Read modes shown beside each model.":"El mismo conjunto de preguntas. Los modos de evaluación se indican junto a cada modelo.","JevBench public 231 · self-scored with the research harness. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. External models use their recorded native readouts. These are observed scores, not a verified leaderboard or a significance claim.":"JevBench público 231 · evaluación propia con la herramienta de investigación. WALD 0.8B / 4B / 9B usan medium; 2B usa una pasada. Los modelos externos usan sus lecturas nativas registradas. Son puntuaciones observadas, no una clasificación verificada ni una afirmación de significación estadística.","Decision Index 0.2.1 · author-run 6,948-request sample, separate from the 150,317-request full suite. WALD uses one pass; raw-model prompting and calibration differ. Parameter labels are size classes; MoE total and active sizes are shown separately.":"Decision Index 0.2.1 · muestra de 6,948 solicitudes evaluada por los autores, separada del conjunto completo de 150,317. WALD usa una pasada; los prompts y la calibración de los modelos base difieren. Las etiquetas indican clases de tamaño; los parámetros totales y activos de MoE se muestran por separado.","Transfer v4 · internal development bank, 656 clean questions. This is not an independent external test. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. Results are not claims of universal superiority.":"Transfer v4 · banco interno de desarrollo, 656 preguntas depuradas. No es una prueba externa independiente. WALD 0.8B / 4B / 9B usan medium; 2B usa una pasada. Los resultados no afirman una superioridad universal.","Sources & read conditions":"Fuentes y condiciones de evaluación","Read conditions table":"Tabla de condiciones de evaluación","Aggregates checked against source reports":"Agregados contrastados con los informes originales",Model:Mb,"Read condition":"Condición de evaluación",Source:Rb,"Download scores, intervals and source hashes":"Descargar puntuaciones, intervalos y hashes de las fuentes","No comparable score":"Sin puntuación comparable","Every choice,":"Cada elección,","A clearer next move":"Un próximo paso más claro","Put WALD at the decision points in your workflow. Route a tool call, ask a better question, or send a request to the right team.":"Coloca WALD en los puntos de decisión de tu flujo de trabajo. Enruta una llamada a herramienta, formula una mejor pregunta o envía una solicitud al equipo adecuado.","Switch the model size. Switch the benchmark. Compare observed scores with Laya, Jev and larger base models, with the evidence in view.":"Cambia el tamaño y el benchmark. Compara puntuaciones observadas con Laya, Jev y modelos base mayores, con las evidencias a la vista.","Skip to content":"Saltar al contenido","Main navigation":"Navegación principal",Home:kb,"Use Cases":"Casos de uso",Docs:Nb,Playground:Cb,"Build with WALD":"Crear con WALD","Back to top":"Volver arriba","Font licenses":"Licencias de fuentes","Your agent,":"Tu agente,","Its next instinct":"Su próximo instinto","Download command":"Comando de descarga","Explore the source":"Explorar el código fuente","Enough confidence? Make the call. Need another look? Take a short thinking step. WALD lets you choose how much thought goes into the next move.":"¿Hay suficiente confianza? Decide. ¿Necesitas otra mirada? Dedica un breve paso a pensar. WALD permite elegir cuánto razonamiento aplicar al próximo paso.","Explore thinking efforts":"Explorar niveles de razonamiento","Thinking effort":"Nivel de razonamiento","THINK GATE":"UMBRAL DE RAZONAMIENTO",always:Ob,"Illustrative decision flow":"Flujo de decisión ilustrativo",LOOK:qb,"Think a bit":"Pensar un poco","Direct read":"Lectura directa",DECIDE:Ub,Probabilities:_b,"Illustrative policy · No live inference":"Política ilustrativa · Sin inferencia en directo","Call the right tool":"Llama a la herramienta adecuada","Give WALD the current state and the available tools. Use the option probabilities to choose the next call.":"Proporciona a WALD el estado actual y las herramientas disponibles. Usa las probabilidades de las opciones para elegir la próxima llamada.","Know when to ask":"Sabe cuándo preguntar","Include asking the user as an option when a request is missing key details. Your workflow chooses when to clarify.":"Incluye preguntar al usuario como opción cuando falten detalles clave. Tu flujo de trabajo decide cuándo aclarar.","Find the right team":"Encuentra el equipo adecuado","Define your teams as choices. Turn an incoming request into probabilities your routing system can use.":"Define tus equipos como opciones. Convierte una solicitud entrante en probabilidades que tu sistema de enrutamiento pueda usar.","Decision patterns":"Patrones de decisión","You define the options and control the actions. The examples below use illustrative probabilities.":"Tú defines las opciones y controlas las acciones. Los ejemplos siguientes usan probabilidades ilustrativas.","See how the model family compares.":"Mira cómo se compara la familia de modelos.","Explore benchmarks":"Explorar benchmarks","Give WALD the context and the options. Get a probability for each — and a clearer basis for your next move.":"Proporciona a WALD el contexto y las opciones. Obtén una probabilidad para cada una y una base más clara para tu próximo paso.","See how it decides":"Ver cómo decide","Copy unavailable. Select and copy the command or code above.":"Copia no disponible. Selecciona y copia el comando o código anterior.","Copied to clipboard.":"Copiado al portapapeles.",Language:Hb,"WALD home":"Inicio de WALD","API docs":"Documentación de la API","One pass. No generated thought.":"Una pasada. Sin razonamiento generado.","Think when the top initial probability is below 0.5.":"Pensar si la probabilidad inicial más alta es inferior a 0.5.","Think when the top initial probability is below 0.7.":"Pensar si la probabilidad inicial más alta es inferior a 0.7.","Think before every eligible decision.":"Pensar antes de cada decisión apta.","Tool routing":"Enrutamiento de herramientas",Clarification:Gb,"Request routing":"Enrutamiento de solicitudes","What will the weather be in Lisbon tomorrow afternoon?":"¿Qué tiempo hará en Lisboa mañana por la tarde?","Book me a table for Friday.":"Resérvame una mesa para el viernes.","Refund request for order 1182; the parcel arrived damaged.":"Solicitud de reembolso del pedido 1182; el paquete llegó dañado.","Which tool should the agent call next?":"¿Qué herramienta debe llamar el agente a continuación?","What should the agent do next?":"¿Qué debe hacer el agente a continuación?","Which team should handle this request?":"¿Qué equipo debe gestionar esta solicitud?","Forecast for a city and time":"Pronóstico para una ciudad y hora","General web search":"Búsqueda web general","Read calendar events":"Leer eventos del calendario","Answer directly":"Responder directamente","Ask for restaurant, time and party size":"Preguntar por restaurante, hora y número de personas","Make a reservation":"Hacer una reserva","Search for restaurants":"Buscar restaurantes","Take no action":"No actuar","Refunds and returns":"Reembolsos y devoluciones","Delivery tracking":"Seguimiento de entrega","New purchases":"Nuevas compras","Other requests":"Otras solicitudes","index points":"puntos del índice","{count}× the total parameters":"{count}× los parámetros totales","Private candidate":"Candidato privado","Research checkpoint":"Checkpoint de investigación","Public v1.1":"v1.1 pública",": comparable results on these cohorts pending.":": resultados comparables en estos conjuntos pendientes.",Undisclosed:Wb,Encoder:Qb,"95% CI":"IC del 95 %","Public 231":"público 231","Internal · 656 clean questions":"Interno · 656 preguntas depuradas","0.2.1 · 6,948-request sample":"0.2.1 · muestra de 6,948 solicitudes","Selected base-model comparisons, with Jev and Laya for context. See Benchmark for all measured models and read conditions.":"Comparaciones con modelos base seleccionados, con Jev y Laya como referencia. Consulta Benchmark para ver todos los modelos evaluados y sus condiciones.","Open alternatives to Jev,":"Alternativas abiertas a Jev,","with vision.":"con visión.","Get the 4B":"Obtener el 4B","Download WALD 4B v2.1":"Descargar WALD 4B v2.1","Public v2":"v2 público","Public v2.1":"v2.1 público",Public:Xb,"* Measured on WALD 4B v2.0.":"* Medido en WALD 4B v2.0.","Text and images in. Probabilities out. On your hardware.":"Texto e imágenes de entrada. Probabilidades de salida. En tu hardware.","Measured WALD speed":"Velocidad medida de WALD","26B-A4B one-pass median":"Mediana 26B-A4B en una pasada","4B v2.1 Auto 0.7 median":"Mediana 4B v2.1 con Auto 0.7","Speed evidence":"Mediciones de velocidad","A compact v2 candidate for tight memory budgets. The selected 0.8B checkpoint is measured in one pass.":"Un candidato v2 compacto para memoria limitada. El checkpoint 0.8B seleccionado se evalúa en una pasada.","The public 4B v2.1 flagship. Auto 0.7 scores 60.05 on the complete Decision Index 0.3 public suite and 67.75% on JevBench-XL TEST.":"El modelo insignia público 4B v2.1. Auto 0.7 alcanza 60.05 en la suite pública completa de Decision Index 0.3 y 67.75 % en JevBench-XL TEST.","Our latest completed 9B first-stage research checkpoint, measured in one pass. Weights remain private.":"Nuestro último checkpoint de investigación 9B de primera etapa completado, evaluado en una pasada. Los pesos siguen privados.","A Gemma-based decision model with 26B total and 4B active parameters, released as Wald-26B-A4B. One pass scores 62.30 on the complete Decision Index 0.3 public suite, with a 53 ms median.":"Un modelo de decisión basado en Gemma con 26B parámetros totales y 4B activos, publicado como Wald-26B-A4B. En una pasada alcanza 62.30 en la suite pública completa de Decision Index 0.3, con una mediana de 53 ms.","WALD 4B v2 reads images through its base model’s frozen vision tower. Its visual capability transfers from decision training without vision fine-tuning.":"WALD 4B v2 lee imágenes con la torre visual congelada del modelo base. El entrenamiento de decisiones se transfiere a la visión sin ajuste visual.","pp over the raw vision base":"puntos sobre la base visual original","Self-scored, zero-shot, one pass. Gain 95% CI":"Evaluación propia, zero-shot, una pasada. IC del 95% de la mejora","WALD 4B v2 defaults to Auto 0.7: think once when the top initial probability is below 0.7, with a 512-token cap. Use none for deterministic one-pass decisions.":"WALD 4B v2 usa Auto 0.7 por defecto: razona una vez si la probabilidad inicial máxima es inferior a 0.7, hasta 512 tokens. Usa none para decisiones deterministas en una pasada.","Five model sizes. Four benchmark views. Compare WALD with Jev and other measured models.":"Cinco tamaños y cuatro vistas de benchmarks. Compara WALD con Jev y otros modelos evaluados.","From 0.8B to 26B. Compare the latest completed checkpoints on matching test sets, with their read conditions shown.":"De 0.8B a 26B. Compara los últimos checkpoints completados en los mismos conjuntos de prueba, con sus condiciones de lectura.","XL TEST · 8,177 questions":"XL TEST · 8.177 preguntas","0.3 · 140,620 public requests":"0.3 · 140.620 solicitudes públicas","Held-out TEST only, not the pooled 10,100 TEST + DEV score. Modes differ and are recorded per model; missing measurements stay empty.":"Solo TEST reservado, no TEST + DEV agrupados con 10.100 preguntas. Cada modelo indica su modo; las mediciones ausentes quedan vacías.","Author-run DI 0.2.1 sample with 95% bootstrap intervals. Native readouts and thinking modes differ. These scores cannot be compared with DI 0.3.":"Muestra DI 0.2.1 ejecutada por los autores con intervalos bootstrap del 95%. Las lecturas y modos de razonamiento difieren. No es comparable con DI 0.3.","Complete DI 0.3 public suite, official kit. WALD rows are author-run, with the mode stated per model; Jev is the board's public column. Not the organizer's Full score. S1 projections and DI 0.2.1 samples are excluded; no confidence interval is available.":"Suite pública DI 0.3 completa, kit oficial. Las filas WALD las ejecutan los autores, con el modo indicado por modelo; Jev es la columna pública de la clasificación. No es el Full del organizador. Se excluyen proyecciones S1 y muestras DI 0.2.1; no hay intervalo disponible.","WALD 4B v2 uses full-parameter decision training from Qwen3.5-4B chat. Supervision targets option probabilities while the vision tower stays frozen.":"WALD 4B v2 entrena todos los parámetros de decisión desde Qwen3.5-4B chat. La supervisión apunta a probabilidades de opciones; la torre visual queda congelada.","Full-parameter continuation adds harder decisions and short native thoughts. State-KL anchors behavior to a frozen reference; earlier research also explored LoRA refinement.":"La continuación del entrenamiento completo añade decisiones difíciles y razonamiento nativo breve. State-KL ancla el comportamiento a una referencia congelada; la investigación anterior también exploró LoRA.","The public v2 server uses a frozen temperature table fitted on our development data. Earlier research explored calibration reinforcement learning; the release uses the recorded calibration table.":"El servidor v2 público usa una tabla de temperaturas congelada y ajustada con nuestros datos de desarrollo. La investigación anterior exploró RL para calibración; esta versión usa la tabla documentada.","Full-parameter decision training, native thinking and calibrated probabilities. The public v2 recipe and historical research comparisons are shown separately.":"Entrenamiento completo de decisiones, razonamiento nativo y probabilidades calibradas. La receta v2 pública y las comparaciones históricas se muestran por separado.","Public set, self-scored. Research harness; modes and native external servers differ and are recorded per model. Not a verified leaderboard ranking.":"Conjunto público, evaluación propia. Las herramientas, modos y servidores externos nativos difieren y se documentan por modelo. No es una clasificación verificada.","Local speed.":"Velocidad local.","Open weights.":"Pesos abiertos.","Faster at the median":"Más rápido en la mediana","Timing details & hosted references":"Detalles de tiempos y referencias alojadas",Median:Yb,"Client time":"Tiempo del cliente","Server time":"Tiempo del servidor","Separate public-set read":"Medición separada del conjunto público","Faster median decisions than OpenAI Decisions in our matched 200-request test. Run locally, keep vision, and choose when to think.":"Decisiones más rápidas en la mediana que OpenAI Decisions en nuestra prueba de las mismas 200 solicitudes. Ejecútalo localmente, conserva la visión y elige cuándo razonar.","Same 200 DI 0.3 requests, serial and warm. WALD native reader on one RTX PRO 6000; OpenAI client time includes the network.":"Las mismas 200 solicitudes DI 0.3, en serie y tras calentamiento. Lector nativo WALD en una RTX PRO 6000; el tiempo del cliente OpenAI incluye la red.","Median decision time":"Tiempo mediano de decisión","Lower is faster":"Menor es más rápido","Same 200 requests":"Las mismas 200 solicitudes","Separate recorded runs":"Mediciones de referencia separadas","Decisions in milliseconds.":"Decisiones en milisegundos.","Published benchmark":"Resultados publicados","Jev uses the published JevBench v1.4.2.2 timing: 242 standard and judge decisions, serial, with the network included. It is a separate benchmark from our DI sample.":"Jev usa los tiempos publicados de JevBench v1.4.2.2: 242 decisiones estándar y de evaluación, en serie, con la red incluida. Es un benchmark distinto de nuestra muestra DI.","Qwen 27B is shown once using its paren one-pass receipt on an RTX PRO 6000, with client time through an SSH tunnel. The separate 231-request receipt does not record sample IDs or concurrency; no matched speedup ratio is asserted.":"Qwen 27B aparece una vez usando su registro paren de un paso en RTX PRO 6000; el tiempo del cliente incluye el túnel SSH. El registro separado de 231 solicitudes no indica IDs ni concurrencia; no se afirma un ratio de aceleración emparejado.","Original Qwen3.5-4B Base":"Qwen3.5-4B Base original","More models":"Más modelos","Vision transfer":"Transferencia visual","measured model sizes":"tamaños de modelo medidos","Base scores included":"Incluye puntuaciones base","vs original base":"frente al modelo base original","Original base":"Modelo base original",Difference:Jb,"One pass":"Una pasada",Combined:Vb,"The original 4B base scored 67.5%. LoRA reached 79.2%, full training 83.5%, and full training plus refinement 87.0%. Historical recipes use different data and budgets; this is a recipe comparison.":"El modelo 4B original obtuvo un 67,5 %. LoRA alcanzó un 79,2 %, el entrenamiento completo un 83,5 % y el entrenamiento completo con refinamiento un 87,0 %. Estas recetas históricas usan datos y presupuestos distintos; se comparan las recetas completas.","Both recipes improved over the 67.5% base. With the same 35M tokens, LoRA scored 83.5% and full training 82.3%. Update methods and learning rates differed.":"Ambas recetas superaron el 67,5 % del modelo base. Con los mismos 35 millones de tokens, LoRA obtuvo un 83,5 % y el entrenamiento completo un 82,3 %. Los métodos de actualización y las tasas de aprendizaje fueron distintos.","Original backbones and trained checkpoints, on each row's benchmark. 2B and 9B use one pass; 26B uses Auto 0.7. These are separate recorded runs, with different training recipes.":"Modelos base y checkpoints entrenados en el benchmark de cada fila. 2B y 9B usan una pasada; 26B usa Auto 0.7. Son ejecuciones registradas por separado, con recetas de entrenamiento distintas.","Text decision training transferred to vision: 80.23% for the original Qwen3.5-4B chat model, 83.05% for WALD 4B v2. Same native-chat one-pass readout, frozen vision tower, no image fine-tuning.":"El entrenamiento de decisiones de texto se transfirió a visión: 80,23 % para Qwen3.5-4B chat original y 83,05 % para WALD 4B v2. Misma lectura nativa de chat en una pasada, torre visual congelada, sin ajuste con imágenes."},Zb="コピー済み",Ib="コピー",$b="比較対象：",Fb="点",Pb="問",e0="ベンチマーク",a0="モデル",t0="出典",n0="ホーム",i0="ドキュメント",l0="プレイグラウンド",s0="常に",o0="観察",c0="決定",r0="確率",u0="言語",d0="意図の確認",f0="非公開",h0="エンコーダー",m0="公開",p0="中央値",v0="差",b0="合計",g0={"Put the next decision into practice.":"次の判断を実践へ。","Explore use cases":"活用例を見る","A clear next move":"次の一手を明確に","Context in. Probabilities out.":"文脈を入力し、確率を出力。","Decision examples":"判断の例","Illustrative example · Not a live model response":"説明用の例 · モデルのリアルタイム出力ではありません",Copied:Zb,Copy:Ib,"API example code":"API のサンプルコード","API reference":"API リファレンス","Explore WALD":"WALD を知る","Tool calls. Clarifying questions. Request routing. See the decision points and explore the API examples.":"ツールの呼び出し、確認の質問、リクエストの振り分け。判断の場面と API の例をご覧ください。","By the numbers":"数字で見る実力","Compare the models":"モデルを比較","More decision":"パラメータあたりの","per parameter":"判断力を高める","From a pocket-sized decision engine to a larger reasoning system. A model family built to read the situation, weigh the options, and make every parameter count.":"小型の判断エンジンから、より大きな推論システムまで。状況を理解し、選択肢を比較し、すべてのパラメータを判断に活かすモデル群です。","Model size":"モデルサイズ","A compact research checkpoint. The 2B model exceeds a 20B reference on JevBench, showing how decision-focused training can change the size equation.":"コンパクトな研究用チェックポイント。2B モデルは JevBench で 20B の比較モデルを上回り、判断に特化した学習がサイズと能力の関係を変えることを示します。",vs:$b,"Part of the expanded WALD family. Comparable checkpoint scores and release details for this size will be added when verified.":"拡張 WALD ファミリーの一員。このサイズの比較可能なスコアと公開情報は、検証後に追加します。","Explore all benchmarks":"すべてのベンチマークを見る","Homepage benchmark":"ホームページのベンチマーク","Benchmark columns, descending scores on a 0–100 scale":"ベンチマークの棒グラフ：0〜100 の尺度でスコアの降順",pts:Fb,"WALD 4B shown as a reference. ":"WALD 4B を参考として表示しています。 ","Self-scored snapshot. Reference protocols differ; see full results and conditions.":"自己評価のスナップショット。比較モデルの評価手順は異なります。全結果と条件をご確認ください。","Comparable results pending.":"比較可能な結果は準備中です。","See the scene,":"場面を捉える、","Choose the action":"行動を選ぶ",questions:Pb,"pp over the unadapted graft":"ポイント向上（画像適応前の移植モデル比）","Self-scored, one pass. Gain 95% CI":"自己評価、1 パス。改善幅の 95% 信頼区間","View training & vision evidence":"学習と視覚評価の根拠を見る","LoRA decision training":"LoRA による判断学習","Full-parameter decision training":"全パラメータの判断学習","Base + LoRA only":"ベース + LoRA のみ","Full decision training":"全体の判断学習","Full training + LoRA refinement":"全体学習 + LoRA 調整","Train the foundation":"判断の基礎を学習","Our flagship Qwen recipe updates all language-model weights on decision examples. Supervision targets the option distribution, so choosing becomes a learned capability in the backbone.":"主力の Qwen 学習手法は、判断の例を使って言語モデルの全重みを更新します。選択肢の分布を教師信号とし、選ぶ能力をモデル本体に学習させます。","Refine the edge":"判断を磨く","LoRA builds on that foundation: harder decisions, short-thought distillation, visual evidence and domain adaptation. KL replay anchors refinement to the parent’s behavior.":"LoRA はその基礎を、難しい判断、短い思考の蒸留、視覚的根拠、ドメイン適応で強化します。KL リプレイで親モデルの振る舞いに調整を結び付けます。","Make confidence useful":"確信度を使えるものに","Calibration reinforcement learning uses proper scoring rewards with KL guardrails. The aim is a probability your workflow can use to act, think again or hand off.":"キャリブレーションの強化学習は、適正スコアリングルールによる報酬と KL 制約を使います。行動、再考、引き継ぎの判断に使える確率を目指します。","Built into":"判断力を","the weights":"重みに学習","A decision model deserves its own training recipe. Full-parameter learning establishes the foundation; targeted LoRA and calibration sharpen what comes next.":"判断モデルには、判断のための学習手法が必要です。全パラメータ学習で基礎を築き、対象を絞った LoRA とキャリブレーションで磨きます。","What does the foundation buy?":"基礎学習がもたらすものは？","percentage points":"ポイント","The earlier c6 control favored LoRA. Updating every weight alone is not enough: the data, objective and refinement recipe matter.":"以前の c6 対照実験では LoRA が優位でした。全重みの更新だけでは不十分で、データ、目的、調整方法も重要です。","Our full-training-plus-refinement recipe scored 87.0%, compared with 79.2% for an earlier base-plus-LoRA recipe. Different data and training budgets; this is a recipe comparison, not an isolated full-training ablation.":"全体学習と調整を組み合わせた手法�� 87.0%、以前のベース + LoRA 手法は 79.2% でした。データと学習予算が異なるため、手法全体の比較であり、全体学習だけの効果を分離した実験ではありません。","Training comparison":"学習の比較","Recipe progression":"学習手法の変遷","Same-data control":"同一データの対照実験","Historical 4B research runs, one pass, self-scored.":"過去の 4B 研究実験、1 パス、自己評価。"," Same 35M c6 tokens and data order; update method and learning rate differ.":" 同じ 3,500 万 c6 トークンとデータ順序。更新方法と学習率は異なります。","Inspect the experiment evidence":"実験の根拠を見る","Choose your model,":"モデルを選ぶ、","Small models,":"小さなモデル、","Compare the evidence":"実測結果を比較","Bigger moves":"大きな成果",Benchmark:e0,"PUBLIC / 231":"公開 / 231 問","SAMPLE / 6,948":"サンプル / 6,948 リクエスト","INTERNAL / 656":"内部 / 656 問","columns, descending scores on a 0–100 scale":"棒グラフ：0〜100 の尺度でスコアの降順","No comparable read":"比較可能な評価なし","Columns use a 0–100 scale. Whiskers and brackets show 95% bootstrap intervals.":"棒グラフは 0〜100 の尺度です。ひげ線と括弧は 95% ブートストラップ区間を示します。","Same question set. Read modes shown beside each model.":"同一の問題セット。各モデルの横に評価モードを表示。","JevBench public 231 · self-scored with the research harness. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. External models use their recorded native readouts. These are observed scores, not a verified leaderboard or a significance claim.":"JevBench 公開 231 問 · 研究用ツールによる自己評価。WALD 0.8B / 4B / 9B は medium、2B は 1 パスを使用。外部モデルは記録された標準の評価方法を使用しています。実測値であり、公式順位や統計的有意性を示すものではありません。","Decision Index 0.2.1 · author-run 6,948-request sample, separate from the 150,317-request full suite. WALD uses one pass; raw-model prompting and calibration differ. Parameter labels are size classes; MoE total and active sizes are shown separately.":"Decision Index 0.2.1 · 著者による 6,948 リクエストのサンプル評価。150,317 リクエストの全評価とは別です。WALD は 1 パスを使用し、元モデルのプロンプトとキャリブレーションは異なります。パラメータ表示はサイズ区分で、MoE の総数と活性数は別に表示します。","Transfer v4 · internal development bank, 656 clean questions. This is not an independent external test. WALD 0.8B / 4B / 9B use medium; 2B uses one pass. Results are not claims of universal superiority.":"Transfer v4 · 内部開発用の精査済み 656 問。独立した外部評価ではありません。WALD 0.8B / 4B / 9B は medium、2B は 1 パスを使用。すべてのタスクでの優位性を示すものではありません。","Sources & read conditions":"出典と評価条件","Read conditions table":"評価条件の表","Aggregates checked against source reports":"集計値を元のレポートと照合済み",Model:a0,"Read condition":"評価方法",Source:t0,"Download scores, intervals and source hashes":"スコア、区間、出典のハッシュをダウンロード","No comparable score":"比較可能なスコアなし","Every choice,":"すべての選択に、","A clearer next move":"より明確な次の一手を","Put WALD at the decision points in your workflow. Route a tool call, ask a better question, or send a request to the right team.":"ワークフローの判断地点に WALD を。ツールの選択、適切な確認の質問、担当チームへのリクエスト振り分けに使えます。","Switch the model size. Switch the benchmark. Compare observed scores with Laya, Jev and larger base models, with the evidence in view.":"モデルサイズとベンチマークを切り替えて、Laya、Jev、より大きなベースモデルと実測スコアを比較。根拠も確認できます。","Skip to content":"本文へ移動","Main navigation":"メインナビゲーション",Home:n0,"Use Cases":"活用例",Docs:i0,Playground:l0,"Build with WALD":"WALD を使う","Back to top":"ページの先頭へ","Font licenses":"フォントライセンス","Your agent,":"あなたのエージェントに、","Its next instinct":"次の一手の直感を","Download command":"ダウンロードコマンド","Explore the source":"ソースを見る","Enough confidence? Make the call. Need another look? Take a short thinking step. WALD lets you choose how much thought goes into the next move.":"確信があれば決定。もう一度見る必要があれば、少し考える。WALD では次の一手に使う思考量を選べます。","Explore thinking efforts":"思考レベルを見る","Thinking effort":"思考レベル","THINK GATE":"思考の閾値",always:s0,"Illustrative decision flow":"判断フローの例",LOOK:o0,"Think a bit":"少し考える","Direct read":"直接評価",DECIDE:c0,Probabilities:r0,"Illustrative policy · No live inference":"方針の説明 · リアルタイム推論なし","Call the right tool":"適切なツールを呼ぶ","Give WALD the current state and the available tools. Use the option probabilities to choose the next call.":"現在の状態と利用可能なツールを WALD に渡します。各選択肢の確率を使って次の呼び出しを選びます。","Know when to ask":"確認すべき時を判断","Include asking the user as an option when a request is missing key details. Your workflow chooses when to clarify.":"重要な情報が不足している場合、ユーザーへの確認も選択肢に含めます。ワークフローが確認のタイミングを判断します。","Find the right team":"適切なチームへ","Define your teams as choices. Turn an incoming request into probabilities your routing system can use.":"チームを選択肢として定義します。受信したリクエストを、振り分けシステムが使える確率に変換します。","Decision patterns":"判断のパターン","You define the options and control the actions. The examples below use illustrative probabilities.":"選択肢を定義し、行動を制御するのはあなたです。以下の確率は説明用のデータです。","See how the model family compares.":"モデル群の比較を見る。","Explore benchmarks":"ベンチマークを見る","Give WALD the context and the options. Get a probability for each — and a clearer basis for your next move.":"文脈と選択肢を WALD に渡すと、各選択肢の確率が返ります。次の一手をより明確な根拠で選べます。","See how it decides":"判断の仕組みを見る","Copy unavailable. Select and copy the command or code above.":"自動コピーできません。上のコマンドまたはコードを選択してコピーしてください。","Copied to clipboard.":"クリップボードにコピーしました。",Language:u0,"WALD home":"WALD ホーム","API docs":"API ドキュメント","One pass. No generated thought.":"1 パス。思考は生成しません。","Think when the top initial probability is below 0.5.":"初回の最大確率が 0.5 未満なら考えます。","Think when the top initial probability is below 0.7.":"初回の最大確率が 0.7 未満なら考えます。","Think before every eligible decision.":"対象となるすべての判断の前に考えます。","Tool routing":"ツールの選択",Clarification:d0,"Request routing":"リクエストの振り分け","What will the weather be in Lisbon tomorrow afternoon?":"明日の午後、リスボンの天気はどうなりますか？","Book me a table for Friday.":"金曜日の席を予約してください。","Refund request for order 1182; the parcel arrived damaged.":"注文 1182 の返金依頼。届いた荷物が破損していました。","Which tool should the agent call next?":"エージェントは次にどのツールを呼ぶべきですか？","What should the agent do next?":"エージェントは次に何をすべきですか？","Which team should handle this request?":"どのチームがこのリクエストを担当すべきですか？","Forecast for a city and time":"都市と時刻の天気予報","General web search":"一般的なウェブ検索","Read calendar events":"カレンダーの予定を読む","Answer directly":"直接回答する","Ask for restaurant, time and party size":"レストラン、時間、人数を確認する","Make a reservation":"予約する","Search for restaurants":"レストランを検索する","Take no action":"何もしない","Refunds and returns":"返金と返品","Delivery tracking":"配送追跡","New purchases":"新規購入","Other requests":"その他の依頼","index points":"指数ポイント","{count}× the total parameters":"比較モデルの総パラメータ数は {count} 倍","Private candidate":"非公開候補","Research checkpoint":"研究用チェックポイント","Public v1.1":"公開 v1.1",": comparable results on these cohorts pending.":"：これらの評価セットでの比較可能な結果は準備中です。",Undisclosed:f0,Encoder:h0,"95% CI":"95% 信頼区間","Public 231":"公開 231 問","Internal · 656 clean questions":"内部 · 精査済み 656 問","0.2.1 · 6,948-request sample":"0.2.1 · 6,948 リクエストのサンプル","Selected base-model comparisons, with Jev and Laya for context. See Benchmark for all measured models and read conditions.":"選定したベースモデルを比較し、Jev と Laya も参考として掲載しています。測定済みの全モデルと評価条件は Benchmark ページをご覧ください。","Open alternatives to Jev,":"Jev のオープンな代替モデル、","with vision.":"視覚にも対応。","Get the 4B":"4B を入手","Download WALD 4B v2.1":"WALD 4B v2.1 をダウンロード","Public v2":"公開 v2","Public v2.1":"公開 v2.1",Public:m0,"* Measured on WALD 4B v2.0.":"* WALD 4B v2.0 での測定値です。","Text and images in. Probabilities out. On your hardware.":"テキストと画像を入力。確率を出力。あなたのハードウェアで。","Measured WALD speed":"WALD の実測速度","26B-A4B one-pass median":"26B-A4B 1 回処理の中央値","4B v2.1 Auto 0.7 median":"4B v2.1 Auto 0.7 の中央値","Speed evidence":"速度の測定根拠","A compact v2 candidate for tight memory budgets. The selected 0.8B checkpoint is measured in one pass.":"メモリ制約向けの小型 v2 候補。選定した 0.8B チェックポイントは 1 回処理で評価しています。","The public 4B v2.1 flagship. Auto 0.7 scores 60.05 on the complete Decision Index 0.3 public suite and 67.75% on JevBench-XL TEST.":"公開フラッグシップ 4B v2.1。Auto 0.7 は Decision Index 0.3 の公開全セットで 60.05、JevBench-XL TEST で 67.75% を記録しました。","Our latest completed 9B first-stage research checkpoint, measured in one pass. Weights remain private.":"最新の完了済み 9B 第 1 段階研究チェックポイント。1 回処理で評価し、重みは非公開です。","A Gemma-based decision model with 26B total and 4B active parameters, released as Wald-26B-A4B. One pass scores 62.30 on the complete Decision Index 0.3 public suite, with a 53 ms median.":"Gemma ベースの決定モデル。総パラメータ 26B、アクティブ 4B で、Wald-26B-A4B として公開しています。1 回処理で Decision Index 0.3 の公開全セット 62.30、中央値 53 ms です。","WALD 4B v2 reads images through its base model’s frozen vision tower. Its visual capability transfers from decision training without vision fine-tuning.":"WALD 4B v2 はベースモデルの凍結済み視覚エンコーダで画像を読みます。視覚の追加学習なしで決定学習の能力が転移します。","pp over the raw vision base":"ポイント、元の視覚ベースモデルを上回る","Self-scored, zero-shot, one pass. Gain 95% CI":"自己評価、ゼロショット、1 回処理。改善の 95% 信頼区間","WALD 4B v2 defaults to Auto 0.7: think once when the top initial probability is below 0.7, with a 512-token cap. Use none for deterministic one-pass decisions.":"WALD 4B v2 の既定値は Auto 0.7。初期最大確率が 0.7 未満なら最大 512 トークンで一度思考します。none は決定論的な 1 回処理です。","Five model sizes. Four benchmark views. Compare WALD with Jev and other measured models.":"5 サイズ、4 つのベンチマーク表示。WALD を Jev や他の実測モデルと比較できます。","From 0.8B to 26B. Compare the latest completed checkpoints on matching test sets, with their read conditions shown.":"0.8B から 26B。最新の完了済みチェックポイントを同じテストセットで比較し、読み取り条件も確認できます。","XL TEST · 8,177 questions":"XL TEST · 8,177 問","0.3 · 140,620 public requests":"0.3 · 140,620 公開リクエスト","Held-out TEST only, not the pooled 10,100 TEST + DEV score. Modes differ and are recorded per model; missing measurements stay empty.":"ホールドアウト TEST のみ。TEST + DEV の 10,100 問の合算スコアではありません。モードはモデルごとに表示し、未測定は空欄です。","Author-run DI 0.2.1 sample with 95% bootstrap intervals. Native readouts and thinking modes differ. These scores cannot be compared with DI 0.3.":"作者による DI 0.2.1 標本評価と 95% ブートストラップ区間。ネイティブ読み取りと思考モードは異なります。DI 0.3 とは比較できません。","Complete DI 0.3 public suite, official kit. WALD rows are author-run, with the mode stated per model; Jev is the board's public column. Not the organizer's Full score. S1 projections and DI 0.2.1 samples are excluded; no confidence interval is available.":"DI 0.3 公開全セット、公式キット。WALD は作者による評価で、モードはモデルごとに記載。Jev はリーダーボードの公開列です。主催者の Full スコアではありません。S1 推計と DI 0.2.1 標本は含まず、信頼区間は未提供です。","WALD 4B v2 uses full-parameter decision training from Qwen3.5-4B chat. Supervision targets option probabilities while the vision tower stays frozen.":"WALD 4B v2 は Qwen3.5-4B chat から全パラメータ決定学習を行います。選択肢の確率を教師信号とし、視覚エンコーダは凍結します。","Full-parameter continuation adds harder decisions and short native thoughts. State-KL anchors behavior to a frozen reference; earlier research also explored LoRA refinement.":"全パラメータの追加学習で難しい決定と短いネイティブ思考を加えます。State-KL は凍結参照に挙動を固定し、過去の研究では LoRA も検討しました。","The public v2 server uses a frozen temperature table fitted on our development data. Earlier research explored calibration reinforcement learning; the release uses the recorded calibration table.":"公開 v2 サーバーは独自の開発データで調整した凍結温度テーブルを使います。過去には校正強化学習を検討しましたが、このリリースは記録済みの校正表を使います。","Full-parameter decision training, native thinking and calibrated probabilities. The public v2 recipe and historical research comparisons are shown separately.":"全パラメータ決定学習、ネイティブ思考、校正済み確率。公開 v2 の手法と過去の研究比較を分けて示します。","Public set, self-scored. Research harness; modes and native external servers differ and are recorded per model. Not a verified leaderboard ranking.":"公開セット、自己評価。研究評価ツール、モード、外部ネイティブサーバーは異なり、モデルごとに記録しています。検証済みの順位ではありません。","Local speed.":"ローカルの速さ。","Open weights.":"オープンウェイト。","Faster at the median":"中央値で高速","Timing details & hosted references":"計測詳細とホスト型の参考値",Median:p0,"Client time":"クライアント時間","Server time":"サーバー時間","Separate public-set read":"別途計測した公開セット","Faster median decisions than OpenAI Decisions in our matched 200-request test. Run locally, keep vision, and choose when to think.":"同じ200件のリクエストを使ったテストで、決定時間の中央値はOpenAI Decisionsより高速。ローカルで動かし、視覚機能を保ち、考えるタイミングを選べます。","Same 200 DI 0.3 requests, serial and warm. WALD native reader on one RTX PRO 6000; OpenAI client time includes the network.":"同じDI 0.3の200リクエストを、ウォームアップ後に直列実行。WALDはRTX PRO 6000一枚のネイティブ評価リーダー。OpenAIのクライアント時間は通信を含みます。","Median decision time":"決定時間の中央値","Lower is faster":"小さいほど高速","Same 200 requests":"同じ200リクエスト","Separate recorded runs":"別途計測した参考実行","Decisions in milliseconds.":"ミリ秒で決定。","Published benchmark":"公開ベンチマーク","Jev uses the published JevBench v1.4.2.2 timing: 242 standard and judge decisions, serial, with the network included. It is a separate benchmark from our DI sample.":"Jevは公開されたJevBench v1.4.2.2の時間を使用。標準・判定の242件を直列実行し、通信を含みます。DIサンプルとは別のベンチマークです。","Qwen 27B is shown once using its paren one-pass receipt on an RTX PRO 6000, with client time through an SSH tunnel. The separate 231-request receipt does not record sample IDs or concurrency; no matched speedup ratio is asserted.":"Qwen 27BはRTX PRO 6000上のparenワンパス記録を使い、1行だけ表示。クライアント時間にはSSHトンネルを含みます。別途計測した231件の記録にはサンプルIDや同時実行数がなく、対応比較の速度比は主張していません。","Original Qwen3.5-4B Base":"元の Qwen3.5-4B Base","More models":"その他のモデル","Vision transfer":"視覚への転移","measured model sizes":"評価済みのモデルサイズ","Base scores included":"ベースモデルのスコアを含む","vs original base":"元のベースモデルとの差","Original base":"元のベースモデル",Difference:v0,"One pass":"1 回の処理",Combined:b0,"The original 4B base scored 67.5%. LoRA reached 79.2%, full training 83.5%, and full training plus refinement 87.0%. Historical recipes use different data and budgets; this is a recipe comparison.":"元の 4B ベースモデルは 67.5%。LoRA は 79.2%、全パラメータ学習は 83.5%、全学習と追加調整は 87.0% でした。過去の学習手法はデータと予算が異なり、ここでは手法全体を比較しています。","Both recipes improved over the 67.5% base. With the same 35M tokens, LoRA scored 83.5% and full training 82.3%. Update methods and learning rates differed.":"どちらの手法もベースの 67.5% を上回りました。同じ 3,500 万トークンで、LoRA は 83.5%、全パラメータ学習は 82.3%。更新方法と学習率は異なります。","Original backbones and trained checkpoints, on each row's benchmark. 2B and 9B use one pass; 26B uses Auto 0.7. These are separate recorded runs, with different training recipes.":"各行のベンチマークで、元のベースモデルと学習済みチェックポイントを比較。2B と 9B は 1 回の処理、26B は Auto 0.7。学習手法が異なる別々の評価結果です。","Text decision training transferred to vision: 80.23% for the original Qwen3.5-4B chat model, 83.05% for WALD 4B v2. Same native-chat one-pass readout, frozen vision tower, no image fine-tuning.":"テキストの意思決定学習が視覚へ転移：元の Qwen3.5-4B chat は 80.23%、WALD 4B v2 は 83.05%。同じネイティブチャットの 1 回処理、視覚エン��ーダーは凍結し、画像による追加学習はしていません。"},jt=[{code:"en",label:"English",short:"EN"},{code:"zh-Hans",label:"简体中文",short:"简"},{code:"zh-Hant",label:"繁體中文",short:"繁"},{code:"fr",label:"Français",short:"FR"},{code:"de",label:"Deutsch",short:"DE"},{code:"es",label:"Español",short:"ES"},{code:"ja",label:"日本語",short:"日"}],y0={"zh-Hans":sv,"zh-Hant":wv,fr:eb,de:Db,es:Kb,ja:g0};function Uf(u){const j=u?.trim().replace(/_/g,"-").toLowerCase();if(j)return j==="zh"||j.startsWith("zh-")?j.includes("hant")?"zh-Hant":j.includes("hans")?"zh-Hans":/^zh-(tw|hk|mo)(-|$)/.test(j)?"zh-Hant":"zh-Hans":jt.find(({code:m})=>j===m||j.startsWith(`${m}-`))?.code}function A0(){try{const u=Uf(localStorage.getItem("wald.lang"));if(u)return u}catch{}if(typeof navigator<"u")for(const u of navigator.languages?.length?navigator.languages:[navigator.language]){const 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