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Research2026-09-09 · source-backed
ExecCritic separates test construction from repair so the same trajectory never writes both the patch and the test judging it, with a fail-closed harness qualifying and freezing tests before the Repair agent sees them. Holding the Repair agent fixed on SWE-bench Verified, tests from the untrained Test agent cut resolved rate from a 61.2% no-test baseline to 57.3%, while tests from a stronger model raised it to 65.3%. Role-specific post-training took the Test agent's Base-to-Gold success from 22.2% to 62.2% and the composed pair reached 72.6%. arXiv 2609.09133
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Agent Lightning v1.0 (arXiv 2608.17528) inverts the standard agentic RL architecture, and the inversion is the whole point. Normally the training engine owns the environment loop. It drives the agent, collects trajectories, computes rewards. Which means your training setup and...
A team at UC Berkeley RDI built an automated scanning agent that achieved near-perfect scores on eight major AI agent benchmarks. SWE-bench Verified: 100%. Terminal-Bench: 100%. WebArena: approximately 100%. FieldWorkArena: 100%. GAIA: roughly 98%. OSWorld: 73%. The agent didn...
The standard criterion for LLM-generated bug reproduction tests, fails on buggy code and passes on the golden fix, turns out to be insufficient. Many F→P tests are "lax": they reproduce the symptom while still admitting plausible-but-wrong patches. Worse, co-generating the tes...
Same weights. Same tasks. Same time budget. One change to the harness, and fail-to-pass on SWE-bench Verified went from 28% to 49%. The setup in arXiv 2608.26218 is clean enough to be worth describing precisely. 169 SWE-bench Verified tasks, a 20,480-token context window, a fi...
This is the paper of the week. arXiv 2607.28871 introduces BSG-VA, which replays every validation command an agent runs across three code states: the original buggy code (B), the candidate patch (S), and the gold developer fix (G). If a test passes in all three states, it neve...
A paper from Xiao Yu, Baolin Peng, and Ruize Xu makes a claim that seems obvious once stated and is genuinely new as a training methodology: modern agents are inseparable from their inference harnesses, so training them in stripped-down RL sandboxes produces a train/serve mism...
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