arXiv AI

When Correct Memory Goes Wrong: Fuzzing Persistent Memory Use in LLM Agents

arXiv AI

Jul 7

MemTrace: Tracing and Attributing Errors in Large Language Model Memory Systems

arXiv:2605. 28732v2 Announce Type: replace-cross Abstract: Memory is essential for enabling large language models to support long-horizon reasoning, yet existing memory systems remain unreliable and difficult to debug.

By Xinle Deng, Ruobin Zhong, Hujin Peng, Xiaoben Lu, Yanzhe Wu, Guang Li, Buqiang Xu, Yunzhi Yao, Jizhan Fang, Haoliang Cao, Junjie Guo, Yuan Yuan, Ziqing Ma, Yuanqiang Yu, Rui Hu, Baohua Dong, Hangcheng Zhu, Ningyu Zhang

llms rag benchmarks

arXiv Computation and Language

Sep 22

EvalMem: An Operation-Level Diagnostic Framework for Long-Term Memory Systems

arXiv:2609.22231v1 Announce Type: new Abstract: Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories...

By Zeyu Liu, Jian Zhong, Rongduo Han, Ziyang Wu, Shunye Tang, Chenghao He, Yaxuan Yang, Yihang Qiu, Ailing Wang, Xiao Liang, Guohuan Xie, Xiaokang Xue, Gongchen Li, Haining Zhang, Wei Wang

llms rag agents

arXiv AI

Jul 31

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

arXiv:2607. 27080v1 Announce Type: cross Abstract: Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist.

By Xuanze Chen, Xukang Xie, Wentao Fu, Jiajun Zhou, Shanqing Yu, Qi Xuan

llms agents benchmarks

arXiv Machine Learning

Jun 19

The Correctness Illusion in LLM-Generated GPU Kernels

arXiv:2606. 20128v1 Announce Type: cross Abstract: Benchmarks for LLM-generated GPU kernels (KernelBench, TritonBench, GEAK) score correctness through fixed-shape, small-sample allclose-style checks.

By Dipankar Sarkar

llms benchmarks

arXiv AI

Sep 7

Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents

arXiv:2609. 04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every serving API, a KV cache.

By Chao Yao, Yangbo Wei, Zhen Huang, Junhong Qian, Chenle Chen, Shaoqiang Lu, Chen Wu, Lei He

llms agents efficiency

arXiv AI

Sep 10

SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction

arXiv:2609.06229v1 Announce Type: cross Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical...

By Yuanxiang Shi, Jiayi Lin, Xuanyong Lin, Liangcai Su, Yeheng Duan, Wei Wang, Qi Han, Bing Zhao, Wei Hu, Xander Xu, Chenxiong Qian

llms agents benchmarks

arXiv AI

Aug 25

MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance

arXiv:2608.21867v1 Announce Type: new Abstract: LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a core capability for terminal, software-engineering...

By Haoyu Wang, Guangyuan Dong, He Liang, Zijing Zhang, Jiachen Luo, Chuang Liu, Chao Xue, Hao Tang

llms agents nlp benchmarks

arXiv AI

Jun 2

MemPro: Agentic Memory Systems as Evolvable Programs

arXiv:2606. 00619v1 Announce Type: cross Abstract: Long-horizon autonomous agents require memory systems to retain historical information, track evolving states, and reuse relevant knowledge beyond finite context windows.

By Qingshan Liu, Guoqing Wang, Wen Wu, Jingqi Huang, Xinqi Tao, Dejia Song, Jie Zhou, Liang He

agents

arXiv AI

Sep 23

SyzHarness: Patch-Based Kernel Bug Reproduction with LLM-Synthesized Fuzzing Harnesses

arXiv:2609.23889v1 Announce Type: cross Abstract: Automated kernel vulnerability reproduction is essential for bug triage, patch validation, and regression testing, but still lacks an effective and...

By Xingyu Li, Juefei Pu, Haonan Li, Arrdya Srivastav, Kareem Shehada, Srikanth V. Krishnamurthy, Zhiyun Qian

llms agents benchmarks

arXiv AI

Sep 16

The Immutable Past: Formalizing State Mutability and Conflict Resolution in Mutable RAG

arXiv:2609.16073v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) serves as the primary memory architecture for long-horizon autonomous agents. However, treating shared memory as...

By Hamed HaddadPajouh, Amir AmiriTabat

llms rag agents benchmarks

arXiv AI

Aug 12

From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents

arXiv:2608. 10502v1 Announce Type: new Abstract: Persistent memory lets language-model agents reuse information across sessions, but it also makes errors durable: a poisoned, stale, or misattributed record can alter reasoning, tool use, answers, and subsequent memory writes.

By Caili Yu, Yiqi Wang, Jiaqi Zhang, Yiqun Duan, Mingkai Zheng, Zhangkai Wu, Kaize Shi, Taotao Cai

llms agents benchmarks

arXiv AI

4d ago

Beyond Rule-Based Mutation Testing: Test-Aware Mutant Generation Using Large Language Models

arXiv:2609.35841v1 Announce Type: cross Abstract: Mutation testing evaluates test-suite adequacy by injecting synthetic faults into program code. However, traditional rule-based tools often generate...

By Nils Kiele, Zainab Saad, Zirui Wang, Steve Drew, Samira Ebrahimi Kahou

llms benchmarks

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