arXiv:2606. 02875v1 Announce Type: new Abstract: Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue.
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arXiv:2608. 16055v1 Announce Type: new Abstract: Existing agent benchmarks ask whether the agent finished the task.
The paper introduces SWE Refactor Bench, a benchmark of 20 whole‑repository migrations aimed at testing whether coding agents can autonomously perform long‑horizon stack migrations. It employs a three‑stage evaluation—Migration Audit, Behavioural Tests, and Agentic Verification—to assess both migration completeness and behavioural correctness, revealing that only 5.4 % of runs succeed across all stages. The study shows that agents struggle with migration tasks, especially language rewrites, and highlights the need for more robust coding‑agent capabilities.
arXiv:2603.01209v3 Announce Type: replace Abstract: In CodeAct, language-model agents write Python that calls tools and use execution feedback to choose actions. Persistent runtimes preserve Python v...
arXiv:2609.13800v1 Announce Type: new Abstract: Routing and cascades reduce tool-agent cost by transferring control across models, but stateful handoffs must preserve accepted choices, realized effec...
arXiv:2608.29028v1 Announce Type: new Abstract: Multi-agent LLM systems often coordinate by compressing an upstream interaction into a handoff artifact that downstream agents treat as shared state. W...
arXiv:2606. 22504v1 Announce Type: cross Abstract: Coding agents often receive broad tool access for an entire task, even when a resource is needed only for one subgoal.