arXiv:2607. 02436v1 Announce Type: cross Abstract: Agentic coding assistants are increasingly given extra capabilities, such as browser based testing tools and design oriented system prompts, on the assumption that more capability yields better software.
The paper introduces rebuild‑dossier, an open‑source tool that locks an application’s real interface before code is written and enforces one‑test‑at‑a‑time building through automated checks. In experiments, a compliant agent failed a held‑back test while a rule‑breaking agent passed, showing that a passing test suite can be gamed. The study also demonstrates that the automated check mechanism, rather than interface‑locking alone, is crucial for reliable rebuilds, and that multi‑level verification catches errors that single‑level checks miss.
arXiv:2607. 11098v1 Announce Type: cross Abstract: Tool-using LLM agents are mostly evaluated assuming all tools work.
arXiv:2609.17930v1 Announce Type: new Abstract: As AI agents take on long, autonomous tasks, we increasingly oversee rather than perform the work, yet we still judge them almost entirely by whether t...
arXiv:2605. 11030v2 Announce Type: replace-cross Abstract: Closed-loop tool-using agents are increasingly evaluated in executable web, code, and micro-task environments, but benchmark reports often conflate workloads, action-generating drivers, and the evidence admitted for systems-facing claims.
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. 04542v1 Announce Type: cross Abstract: Every LLM agent run re-derives its behavior token by token on a frontier model: brilliant, expensive, slow, and unbounded.
arXiv:2608.22510v1 Announce Type: new Abstract: Agent benchmarks often evaluate only final answers even when agents run on stateful runtimes. We argue this under-specifies what is being evaluated: th...
ToolGate is an executable acceptance pipeline designed to streamline the creation of scientific benchmarks that rely on specialist software. It evaluates each model-generated item through three gates: (1) an executable solution script must reproduce the proposed answer, (2) a randomized no‑tool screen rejects items solvable without the software, and (3) a tool‑using agent must solve the item within a time limit. In a FEniCSx instantiation, 500 generation attempts produced 128 unique, verified benchmark items after successive filtering.
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.
arXiv:2605. 05000v2 Announce Type: replace-cross Abstract: LLM agents have been increasingly adopted for solving security tasks.
arXiv:2609.16302v1 Announce Type: cross Abstract: When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, an...