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Evaluation
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Meta 的 AI 数学论文开始标注模型参与
Meta 公布 6 篇与 Muse Spark 协作的数学论文。更值得关注的是贡献来源、第二组复核、并行结果和形式化验证怎样进入公开论文。
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Keep review evidence separate from compacted agent context
Independent Codex Guardian history shows why generation context and review evidence need separate records, with provenance through resume and rollback.
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主 Agent 压缩上下文后,审查证据为什么要独立保存
从 Codex Guardian 历史独立保存出发,解释生成上下文与审查证据为什么不能共用一份压缩摘要,以及 resume、rollback 应如何保留来源。
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Why old CLAUDE.md rules can hinder a model upgrade
Use prompt audits, instruction layers, and migration evals to retain real requirements, reassess model workarounds, and enforce hard limits in code.
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模型升级后,旧 CLAUDE.md 为什么可能拖后腿
拆解 Prompt Audit、指令分层和迁移 Eval:模型能力变化后,哪些规则应保留,哪些模型补丁要重新验收,哪些限制必须下沉到 Hook 或策略层。
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Agent markets: 85% of the loss comes before negotiation
Anthropic's book market experiment locates most efficiency loss in preference representation. A look at model choice, preference evals, and market rules.
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Anthropic Agent 市场:85% 的效率损失发生在谈判前
Anthropic 的 Agent 市场实验显示,主要效率损失发生在偏好表示和匹配阶段。本文拆解模型选择、偏好 Eval 与市场规则。
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GitHub Security Lab makes fuzzing an agent feedback loop
GitHub Security Lab Fuzzing Taskflow uses coverage gaps to guide model decisions while deterministic tools execute, measure, and persist results.
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GitHub Security Lab 把 Fuzzing 做成 Agent 反馈环
拆解 GitHub Security Lab 的 Fuzzing Taskflow,模型根据 coverage 缺口决定下一轮,确定性工具继续负责执行、测量与持久化。
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A Minecraft agent separates planning from fast decisions
This Minecraft agent uses Astra for slow planning, JEV for frequent action selection, and Mineflayer for execution, with logs and scripts to verify runs.
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Minecraft Agent 把规划和即时决策拆成两层
这个 Minecraft Agent 让 Astra 负责慢规划、JEV 负责高频动作选择、Mineflayer 负责确定性执行,并用日志和验证脚本保留运行证据。
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When to split a task across agents: check dependencies
How task dependencies shape multi-agent results, with SAIGE experiments, ablations, and OpenAI guidance for deciding when to delegate.
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一个任务什么时候该拆成多个 Agent?先看依赖关系
Rethinking Multi-Agent Collaboration 说明多 Agent 的收益取决于任务依赖结构。本文结合 SAIGE 主实验、消融实验和 OpenAI 多 Agent 工程建议,整理可执行的委派判断。
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How to score a coding agent that finishes 80% but fails
Android Bench 2.0 keeps pass rate as a hard gate and adds completion rate to diagnose progress and unfinished acceptance requirements.
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Coding Agent 做完 80% 还失败,Eval 怎么记
Android Bench 2.0 保留 Pass rate 作为硬门禁,再用 Completion rate 解释长任务已经完成多少,以及问题卡在哪一类验收。
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Anthropic opens internal processes to embedded evaluators
Anthropic and Accenture extend evaluation beyond model outputs to training, deployment, monitoring, and incident processes, with independence unresolved.
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Anthropic 让第三方 Eval 进入内部,模型评测开始看过程
Anthropic 与 Accenture 的 embedded evaluator 计划把评测范围从模型输出扩展到训练、部署、监控和事故流程,也暴露出独立性边界。
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skill-up: evaluate real stateful multi-turn sessions
Alibaba skill-up resumes real sessions turn by turn, letting skill evaluations test context continuity, intermediate state, and per-turn assertions.
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多轮 Skill Eval 不该把对话压成一包:skill-up 开始续接真实 Session
Alibaba skill-up 的 stateful 多轮模式逐轮恢复真实 Session,让 Agent Skill Eval 能检查上下文连续性、中间状态和逐轮断言。
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Foreman: an independent supervisor for coding agents
How Foreman separates a coding agent from supervision, using runtime evidence, Jev judgments, and deterministic policy to continue, verify, stop, or escalate.
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Foreman:给 Coding Agent 加一个独立监工
拆解 Foreman 如何把 Coding Agent 的执行循环与独立监督分开,用运行证据、Jev 判断和确定性 policy 决定继续、验证、停止或交给人工。
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Should an agent keep going or start a fresh attempt?
Use When Agents Slow Down, Reflexion, Hyperband, and agent evaluation to compare continued revision with independent attempts under reproducible budgets.
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Agent 该继续跑,还是重新开一轮?读懂预算分配、记忆与评测
结合 When Agents Slow Down、Reflexion、Hyperband 和 Agent 评测,说明怎样比较连续修改与独立尝试,并设计可复现的预算分配实验。
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Turn production agent failures into regression tests
Use Kitaru Replay, Evaluator, and Regression Suite to preserve production failures as repeatable agent tests, with tool policies and CI gates.
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Agent 线上出过的错,怎么自动变成以后每次改动都要重跑的测试?
结合 Kitaru 的 Replay、Evaluator 和 Regression Suite,说明怎样把线上失败固定成可重放的 Agent 回归测试,并用工具策略与评测结果接入 CI。
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AWS agents propose; deterministic code validates
How AWS separates agent proposals, rule checks, human approval, and execution, with code enforcing boundaries and idempotency.
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Agentic search missed an incident. Test the eval too
The Anthropic incident review shows why automated monitors and LLM graders need coverage, independence, and tests for their own failure modes.
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Agentic Search 漏掉第四起事故,Eval 也要被测试
从 Anthropic Agentic Search 事故复盘看自动监控和 LLM grader 的漏检问题,说明 Agent Eval 也需要覆盖率、独立性和失效模式测试。
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OpenAI agent workdays and shifting builder bottlenecks
OpenAI organization-level data suggests that more coding-agent use shifts bottlenecks to task decomposition, concurrency, harnesses, evaluation, and selection.
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OpenAI 研究组织跑到 3.1 Agent 工作日后,Builder 的瓶颈开始转移
OpenAI 的组织级数据说明 Coding Agent 使用规模上升后,瓶颈会转向任务拆分、并发调度、Harness、Eval 和结果选择。
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AI Engineering Harness: verifiable delivery for AI code
A multi-agent engineering workflow uses isolated worktrees, CI, cold-start review, evidence gates, and layered context to control delivery of AI-generated code.
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AI Engineering Harness:把 AI 代码放进可验证的工程闭环
介绍一个多 Agent 工程工作流,如何用独立 worktree、CI、冷启动审查、证据门和分层上下文控制 AI 生成代码的交付。
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Loop Engineering Harness: controlling agent iterations
Break down planning, execution, evaluation, state management, and stopping rules to keep multi-step agent tasks controlled and traceable.
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Loop Engineering Harness:怎样控制 Agent 的迭代循环
把 Harness 作为一种工程模式,拆解计划、执行、评估、状态管理和终止判断,说明 Agent 多轮任务怎样保持可控和可追溯。
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