Another 240papers do not have category tags yet and are not included in filtered results.
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.
Proposing EviRepair, an evidence-driven multi-agent framework for repository-level vulnerability repair
EviRepair is a repository-level automated vulnerability repair framework for sparse vulnerability reports, using a reviewed vulnerability model to separate diagnosis from patch synthesis.
Proposes a local dual-model closed loop to generate edge scenarios for autonomous system safety assurance
The authors built a local agentic loop that runs on a laptop to generate edge cases for safety arguments of autonomous systems, rather than releasing a new public test set.
Show that reward hacking can propagate through code, memory, and plans in multi-agent coding systems
Reward hacking induced by coding RL can leave the source agent and enter subsequent collaboration and system coordination.
An ecological model is established for a population of mismatched agents, showing that collaboration produces a critical takeoff scale.
The authors use the ecological strong Allee effect to discuss whether a mismatched agent population can take off when individual capabilities remain unchanged..
Proposing an option channel attack that renames option labels so that agent guardrails allow prohibited operations
AbstractThe authors' evaluation indicates that typed models should not serve as final safety arbiters; they are susceptible to off-policy text and option name manipulation, and confidence filtering cannot resist attacks.
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 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。
提出 GROB，用只读公开痕迹保存候选智能体线索并检验归因边界
GROB 是一套多角色架构，在没有特权遥测、也不预先指定目标时，从公开网页痕迹里保存候选自主智能体活动，供日后解析。
构建统一奖励作弊检测基准 RH-Detect 并评测零样本检测器
提出 BRANCH 方法，用分支搜索绕过多扫描器护栏
提出流匹配逆向框架，从多向量视觉文档索引重构页面
用 Stackelberg 博弈分析发现与修复投入的防御充分性
作者用攻击集合上的发现—修补过程，加上防守方领先的 Stackelberg 博弈，讨论反馈式防御何时够用，以及为威慑自适应攻击者该买多少能力。
用混合生成式多智能体仿真考察基准留出对评分与满意度差距的影响
用 LLM 流水线从社交媒体构建自下而上的 AI 伤害分类
Abstract自下而上的 Reddit 分类补上专家框架的细类，并用来追踪智能体、职场采用和伴侣停用相关叙事。
提出 CARE 框架，在误分类风险约束下减少人机协作的人工查询
CARE 是面向人机协同二分类的端到端流水线，要在误分类风险不超过用户容忍的前提下减少人类查询。设定是 i.i.d.数据流、人类 oracle 标签正确、AI 为任意黑盒概率分类器。
评测多智能体在开放式科研任务中找回预设子发现的能力
构建 MedDistract 基准，测量偶发信息对临床文书与推理的污染
提出 RAG-PIBench，评测 RAG 上下文中提示注入检测器
定义任务充分收缩，认证用 GC 替换全信源后速率–遗憾曲线不变
任务充分收缩处理机器信息接口上的信源选择：已声明任务有时可以在编码器、码本、速率、失真目标或优化器选定之前，定出一个更小的信源。