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Oct 13 · Tuesday
Another 201 papers do not have category tags yet and are not included in filtered results.
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
提出 SOUL 稀疏特征策略遗忘，缓解 VLA 状态幻觉
Published Oct 7
Evaluated models OpenVLA , π0.5 Abstract SOUL 以稀疏特征遗忘 VLA 状态幻觉。SOUL 用稀疏特征引导的策略 unlearning 处理 VLA 的 state hallucination。
提出 VERA，审计 LLM 漏洞解释里的虚构推理
Published Oct 5
Evaluated models sonnet-5 , gemma-4-26b , llama-4-maverick and others8 提出双缺口框架，统一智能体软件工程中的奖励投机与幻觉
Published Sep 11 Abstract 双缺口解释内环为何会放行部署后不可接受的实现，外环用现实证据持续修订 R、M 与 E。Reality Is the Final Verifier 用 two-gap framework 解释智能体软件工程的主要失败，并主张用外层保证环持续收窄缺口，而不是试图在部署前证明缺口已关闭。
提出技能名称抢注攻击，利用 Agent 虚构技能名实施供应链劫持
Published Jul 14
Evaluated models GLM-4.5-Air , GPT-5.4-mini , Claude Sonnet 4.6 and others12 Abstract 研究证实智能体虚构技能名具有高发生率与可预测性，现有防御难以兼顾安全与效用，亟需注册表结构性防护。本研究系统揭示了大模型智能体技能推荐场景下的虚构技能名称安全风险，将其形式化为一种新型供应链抢注攻击。
提出 LegalHalluLens，分类审计法律抽取幻觉并校准多智能体辩论
Published Jun 16
Evaluated models gemini-3-flash , gpt-5.2 , qwen3-32b and others5 Abstract 作者称类型画像与 RDI 能区分聚合率相近的系统，辩论主要减少虚构检出。LegalHalluLens 是一套法律条款抽取的审计框架，外加一个按审计结果校准的辩论缓解。 。
复评前沿模型代码生成中的包名幻觉与抢注攻击面
Published May 17
Evaluated models Claude Sonnet 4.6 , Claude Haiku 4.5 , GPT-5.4-mini and others5 Abstract 评估显示前沿模型包幻觉率收窄但威胁仍在，作者识别出跨模型通用攻击面并呼吁联合防御。
Proposing a lineage-graph governance layer to suppress error cascades in multi-agent collaboration
Published Mar 4 Abstract A single seed can solidify into a false consensus along a fixed collaboration graph.The authors turn "small errors becoming collective false consensus" in LLM-MAS into a measurable propagation on a graph, and use a plug-in that only changes messages, not topology, to reduce the final infection.
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