FuzzingBrain‑Bench V1 is a new benchmark that tests large language models (LLMs) on their ability to discover software bugs in open‑source projects. Unlike prior benchmarks that focus on a single target vulnerability, this benchmark gives models a Docker‑based harness and asks them to generate inputs that trigger as many distinct crashes as possible. The first version contains 77 challenges from 43 projects (36 C, 32 C++, 9 Java/JVM) and evaluates Claude Haiku 4.5, Claude Sonnet 4.6, and Claude Opus 4.8, with Claude Opus 4.8 achieving the highest score by triggering crashes in 60 of 77 challenges.
By Ze Sheng, Aleksandar Kezic, Zhicheng Chen, Jeff Huang
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.
FaultLens is a technique for generating compact behavioral test suites for programs produced by automated generators. It learns probe orderings from earlier program generations, combining a fault‑driven greedy component with a mutation‑independent diversity component to cover a wide range of probe families, cases, templates, and temporal bins. In experiments on twenty generated operational policies across four environments, a 32‑probe hybrid suite learned from early generations covered 99.0% of dynamically killable faults in later generations while using only 1.2–2.0% of the exhaustive test domain.
By Zeming Liu, Hang Lyu, Jingtao Zhang
arXiv:2609.37371v1 Announce Type: cross
Abstract: Model adaptation is typically governed by a fixed recipe, even though different update programs can produce substantially different behavioral outcom...
By Rebecca Ramnauth, Brian Scassellati
arXiv:2606. 04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality.
By Guilin Zhang, Chuanyi Sun, Shahryar Sarkani, John M. Fossaceca
arXiv:2608. 09168v1 Announce Type: new Abstract: Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge.
By Liang He, Jingbo Wen, Hongyu Gu, Hao Li, Haoyu Wang, Yixiong Chen, Kangning Cui, Xilu Wang
arXiv:2606. 29646v1 Announce Type: cross Abstract: Sleeper agents are the canonical model organism of deception: models trained to behave normally but to emit an unsafe behaviour on a specific trigger.
By Mohammed Abu Baker, Lakshmi Babu-Saheer
The paper introduces Trace, a framework that transforms sparse-reward trajectories into executable walkthroughs by identifying progress anchors, propagating credit, and estimating action prerequisites. Trace compiles noisy trajectories into state‑conditioned, verifiable procedures that remove loops and detours, enabling reuse, intermediate‑state resumption, and programmatic verification. Experiments on J‑TTL, WebShop, and ScienceWorld with three open‑source LLMs show that Trace outperforms eight baselines, improving average AUC and Final‑$3$ by 30.0% and 40.5% while using fewer inference tokens.
By Kaijie Chen, Chenyu Fang, Liang Yan, Bo Li, Bo Zhang, Peng Ye
arXiv:2608. 14761v1 Announce Type: cross Abstract: At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evaluate before each regret update.
By Jiaxing Guo, Lei Ye
arXiv:2606. 17508v1 Announce Type: new Abstract: Training a model to predict the next step in a concurrent program is harder than it looks: two runs of the same program from the same trace prefix can produce different next events, both valid, because the scheduler is nondeterministic.
By Kaviru Hapuarachchi
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:2608. 07303v1 Announce Type: new Abstract: Comparisons between AutoML systems at short time budgets -- tens of seconds rather than hours -- are common in tool READMEs and workshop papers, and they are easy to get wrong.
By Guilin Zhang, Kai Zhao