arXiv Machine Learning
CAROL: Context-Aware Online Learning for Fuzzer Scheduling
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Aug 27
FuzzingBrain-Bench V1: Evaluating Open-Ended Bug Discovery by LLMs
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.
Hugging Face Trending Papers
Aug 27
FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs
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 AI
Aug 28
FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs
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.
arXiv AI
Sep 30
Compiling Learning Problems into Adaptation Programs for Language Models
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...
arXiv AI
Jun 4
EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms
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.
arXiv AI
Aug 11
From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents
arXiv:2608. 09168v1 Announce Type: new Abstract: Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge.