Another 240papers do not have category tags yet and are not included in filtered results.
Proposes RATED, which reranks agent tool retrieval using execution feedback
Proposes SAFE-QE, which filters unsupported hallucinated words in LLM query expansion
AbstractSAFE-QE suppresses expansion hallucinations with reproducibility and coverage selection; the authors say ranking quality shows no visible cost.
Auditing training labels for label-only backdoors, comparing spaces and statistics, and judging by retraining
The authors ask: can a defender without trusted data decide which training labels to keep, and how much clean data this destroys, and what the final model accuracy is.
Proposes the Cancerous backdoor, which progressively corrupts reasoning using invisible stage tokens.
Cancerous Backdoor Attack changes LLM backdoors from one-time activation to multi-stage reasoning corruption that accumulates progressively with triggers.
Proposes Task Bank self-replay to mitigate held-out forgetting in continual instruction tuning
The authors study how language models, when learning new tasks, can use their own knowledge for broader self-preservation: given an incomplete list and forgetting that extends beyond the list, can the model generate replay and help choose targets.
Proposes the ISE framework to evaluate the epistemic humility of LLM agents under knowledge conflicts
What the authors want to test is not whether the agent's final answer is correct, but whether, when a knowledge conflict arises, it recognizes the gap in its trajectory, performs bounded tool follow-up, and writes the unresolved uncertainty to the user.
Analyzing why optimizing teaching adaptation metrics collapses tutoring choices and diverges from expert judgment
After turning teaching adaptation metrics into a fine-tuning objective, the metrics rise while scores from trained educators decline.
Proposes LGWM, using an action-conditioned world model to verify GUI agent screen transitions.
Measures citation fragility in AI search, demonstrating that ordinary public posts can enter citations and answers.
AbstractThe authors claim the paper quantifies vulnerabilities in AI search caused by citation preferences and low posting thresholds; tests show public web posts can penetrate AI citations, and they call for auditing the citation supply chain.
提出义务识别基准 ObligationBench，并训练护栏识别 Agent 未履行的安全义务
作者认为编码智能体的安全不能只靠拦截禁止动作，还要找出终止时仍未做的安全关键动作，并将其称为义务。
评测编码 Agent 中相似技能共同安装对已装技能核心功能的替代
作者对共同安装、做同一件事的良性 skill 做实证研究，用核心功能是否还在，而不是任务过不过，来看冲突。。
提出 LTBD，用可学习分隔符隔离可信指令与不可信数据
LTBD 是一种不改基座权重的提示注入防御：只学习用来标出可信指令与不可信数据边界的四个 token 嵌入。
构建 Workerville 基准，测量组织条件如何改变 Agent 的安全行为
Workerville 用组织行为测量智能体安全如何随监督、同伴和记忆条件变化。。要处理的缺口是：持续运行的 LLM 智能体同时接收用户指令、同伴消息和长期记忆，但既有安全评测多把这些通道拆开，没有统一的定量测量。
提出 SSCBench，评测工具调用 Agent 故障注入测试的证据有效性
SSCBench 研究工具使用智能体的故障注入结果，在什么证据条件下可以被解释。
提出 RL-ARC，用推理置信度在强化学习中校准推理模型
RL-ARC 是校准感知的强化学习框架，用推理过程的口头置信度调节答案置信度。
重测并扩展对 AI Agent 的提示注入，并用 Agent Card 欺骗操纵 A2A 选择
作者在自建的 LangChain、AutoGen 和 A2A 系统上重测提示注入，并改 Agent Card 看选择是否偏移。摘要称试验主要凸显单智能体弱点，同时把攻击扩到多智能体。
用时钟 harness 和预算塑形 GRPO 改善 Agent 的按时返回
作者研究小规模 LLM 智能体在显式 wall-clock 预算下能否按时返回，并把更多时间换成更好的解。
测量工具调用 Agent 对显式报错与静默损坏的检测和恢复
作者在 BFCL 多轮可执行环境上注入一次工具层故障，把注意到、改计划、恢复和重复分开记分，而不是只看任务有没有做完。
提出热启动 Bandit 的部署前有界离线注入攻击
比较连续后训练阶段如何改变语言模型的说服力
作者用同一人类协议，把 LLM 说服力拆成阴谋数据上的失调 SFT、说服数据上的 SFT，以及其后的 IPO。