arXiv:2608. 14711v1 Announce Type: new Abstract: AI coding agent benchmarks rank agents with the Chen et al.
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AgentAudit is an open, extensible framework that evaluates the full lifecycle of AI agents, assessing planning, tool selection, execution, memory, and reasoning across ten dimensions such as instruction integrity, security, and alignment. Unlike existing benchmarks that focus on single aspects, AgentAudit analyzes the entire execution trace to attribute failures to specific stages. The framework was tested on five large language models, revealing significant differences in trustworthiness even among models with similar task‑completion performance.
The paper investigates the reliability of software produced by agentic AI by comparing AI-generated versions of ten well-known Linux utilities to their human-written counterparts. Using fuzz testing (both black-box and coverage-guided AFL++), the authors find that AI-generated code is often as reliable or more reliable than the latest human versions, with fewer memory errors but a higher incidence of hangs. The study emphasizes that robust AI-generated software requires careful prompting, skilled human oversight, and that the AI workflow can serve as a cost-effective specification for sustainable code.
arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.
arXiv:2609.35889v1 Announce Type: cross Abstract: Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing...
arXiv:2608.29128v1 Announce Type: new Abstract: Tool-using agents are commonly evaluated by a single bit: whether an end-to-end workflow completed. This metric fails to distinguish failures that matt...
arXiv:2602. 16666v3 Announce Type: replace Abstract: AI agents are increasingly deployed to execute important tasks.