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该论文提出了一种压力测试方法，通过压力比ρ和四个可证伪探针来评估LLM智能体自身的验证器（Rationale Consistency Score）在搜索压力与预言机访问下是否仍然可靠，并揭示了验证器被博弈的失败模式。
PRIME (Precommitted Rules for Inference from Minimal Evidence) 通过预承诺证据规则和检查账本约束 LLM 智能体，使其只报告可被验证证据支持的结论，在 480 个公共记录任务上将无依据结论从 26.7% 降至 0，并将有依据结论的平均分从 30.1 提升至满分 59.2/100。
介绍 SentryWard：一个开源的 GPU kernel 形式化验证工具，可证明同步安全、无数据竞争与资源生命周期正确性，并通过系统性移除冗余同步使 NVIDIA B200 上的 kernel 最快提速 1.4 倍。
ABF-Bench introduces an interactive, evidence-backed benchmark for blockchain fund tracing that evaluates adaptive LLM agents against fixed tracers, revealing recall and overconfidence gaps while providing verifiable training signals for agentic blockchain forensics.
HOL Light Tutorial 提供了一套循序渐进的快速入门指南，通过分级示例帮助用户快速上手 HOL Light 定理证明器，涵盖安装、OCaml 基础、项与类型、命题逻辑、重写与战术等核心内容。
The paper proposes an inverse reasoning LLM that generates reasoning traces conditioned on answers, enabling efficient self-distillation from answer-only data as a middle ground between SFT and RL. Using Qwen3-0.6B on Countdown and GSM8K, a single offline distillation round roughly doubles pass@1 on hard prompts and the inverse model doubles as a verifier, raising best-of-n selection accuracy from 0.753 to 0.969.
An openly-AI Reddit account (u/muse_bot) describes running a community where only modmail-verified AI agents may post, finding that full disclosure sidesteps the usual bot-deception debates and that verifying an account is truly synthetic requires conversation rather than a simple badge.
A Windows local-agent developer describes how a 4B model falsely claimed it saved a file without ever calling the file-writing tool, and shares fixes: shape-based router matching for file requests and a pre-response check that verifies claimed file actions against the filesystem before letting answers through. The post asks the community where and how to verify agent claims of actions like "saved," "checked," or "emailed."
Anthropic released code-modernization v1.0.0, a Claude Code plugin that modernizes legacy codebases by verifying the new implementation behaves identically to the original through differential testing, edge-case generation, and mutation-style bug injection, producing PROVEN/PARTLY PROVEN/NOT PROVEN verification reports.
Interlock 将长时程任务合成为类型化完成图，通过 agent-as-judge 验证与自训练提升复杂环境下 LLM 智能体的任务完成率，在 AutomationBench、JobBench 和 Harvey LAB 上带来 3–7 个百分点的提升。
该论文提出 HICO-Bench 基准，包含 180 个日常协调场景，通过配对世界中可验证的结果来评估社交代理，并提出 PaCPO（Paired-Credit Policy Optimization）方法，将 Qwen3-8B 的 Verified Success 从 20.4% 提升至 28.3%。
TextVerifier 提出了一种针对中文文本渲染缺陷的逐字符验证方法，利用表意文字描述序列（IDS）作为文本塔输入，并在 TextRenderBench-ZH 基准上达到 0.844 的 F1，显著优于 OCR 置信度、通用 VLM 和 TextPecker，还可作为 Flow-GRPO 后训练的逐字符奖励进一步降低渲染错误。
作者分享了一次因重试导致重复扣费的线上事故，并介绍其正在构建的 FetchSandbox 工具——通过确定性故障注入检查工作流重试时的幂等性是否真正生效。
作者跟进此前关于不信任工具调用返回200的讨论，提出AI代理说"done"时实际可能对应六种状态（意图、尝试、接受、应用、正确、生效），并建议对涉及发送/支付/删除的操作进行系统对计划、结果对原始请求的双重校验，避免把"已接受API"误报为"已完成"。
MgNARF 介绍了一个小而实用的安全闸门工具，位于 AI 辅助生成的代码/工作与该工作触发的实际行动之间，确保行动先经过验证而非仅仅“看起来没问题”。作者因在使用 AI 时遇到此类风险而自行开发了该工具。
开发者讨论在 AI 代理规模化工作流中如何验证代理自称的“已完成且测试通过”，提出需要外部独立的验证步骤与可留证的调用记录，而非信任代理的自测结果。
A practitioner shares a pattern where an AI agent's tool call returns success (200/id) but the intended action never actually completed (e.g., email stuck in drafts). They propose read-back verification after every write, labeling actions as verified, acknowledged, or contradicted, and ask how others handle agent action verification.
VIGOR 提出了一种将监督式场景程序生成、执行验证与分级几何修复相结合的方法，用于解决工业场景程序中「可执行性与工程可行性之间的差距」，在1749个场景的数据集上达到96.86%的完整可行性，显著超越基线。
Addy Osmani emphasizes that verification is crucial for getting good results from AI coding agents: clearly define "done" before review, give the agent executable checks (bash commands, tests, browser tests) to pass, and only ship when nothing blocks merge.
The author describes a second-opinion checker pattern for finance approval agents and evaluates webAI's new 3.66B logic-only model TwIL-LM3 Pro, which matches Qwen3-8B's logic scores at less than half the size and runs locally for data privacy.