FaultLens is a method for creating compact behavioral test suites for generated operational programs, balancing thoroughness with cost. It executes a rich probe domain once, stores fault‑probe kill relations, and learns probe orderings from earlier program generations using a fault‑driven greedy component and a mutation‑independent diversity component. In evaluations across multiple environments and program generations, a 32‑probe hybrid suite achieved 99.0% coverage of dynamically killable faults while using only 1.2‑2.0% of the exhaustive domain, and improved macro coverage when a fault family was withheld from training.
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
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
arXiv:2607. 08124v1 Announce Type: cross Abstract: The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures.
By Jun Nie, Yonggang Zhang, Jun Song, Qianshu Cai, Dahai Yu, Yike Guo, Xinmei Tian, Bo Han
End-to-end task-success is the dominant way to evaluate LLM agents, but one aggregate number tells you that an agent regressed, not where. We present layer-isolated evaluation: a deployed ordering agent is decomposed into a fixed taxonomy of layers (ontology, intent, routing, decomposition, escalation, safety, memory, and cross-cutting envelope/defense), each exercised by its own assertion slice in a deterministic, no-LLM "pure" mode.
arXiv:2607. 19843v1 Announce Type: cross Abstract: Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained.
By Yuhao Tan, Zhibang Yang, Fangkai Yang, Yuan Yao, Yu Kang, Lu Wang, Pu Zhao, Xin Zhang, Xiaoxing Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
arXiv:2607. 22880v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have driven growing interest in using LLMs to automate test generation.
By Junda Zhao, Shurui Zhou, Eldan Cohen
arXiv:2606. 11686v1 Announce Type: cross Abstract: End-to-end task-success is the dominant way to evaluate LLM agents, but one aggregate number tells you that an agent regressed, not where.
By Sawyer Zhang, Alexander Wang, Sophie Lei
arXiv:2606. 10241v1 Announce Type: new Abstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history.
By Yohei Nakajima
arXiv:2605. 17450v2 Announce Type: replace-cross Abstract: As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifacts rather than repair guidance.
By Simiao Liu, Fang Liu, Peiding Wang, Taichuan Li, Yinghao Zhu, Xiaoli Lian, Li Zhang
Chronicle introduces a method called cut‑point replay to make regression testing of large language model (LLM) agents reproducible. It records an agent’s run at non‑deterministic boundaries as immutable envelopes and then replays selected boundaries while executing the rest live, enabling continuous‑integration tests that detect faulty code changes. Benchmarks show minimal overhead, perfect bit‑stability, and effective detection of unsafe actions in a mutation study.
By Tisha Chawla, Susheem Koul
arXiv:2608. 02712v1 Announce Type: cross Abstract: Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art systems raise correctness through pipelines that couple LLMs with agentic reinforcement learning and evolutionary search.
By Yansong Sun, Shenxiu Wu, Siyuan Chen, Runlin Hou, Junhao Qiu, Junming Cao, Shudi Shao, Zhichao Lu, Qingfu Zhang