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Research2026-09-13 · source-backed
arXiv 2609.09218 names two ways a score fails to measure the model: execution-critical decisions get made by a fixed scaffold instead of the model, and the scorer grades output shape instead of task correctness. The repair protocol moves execution decisions to the model, swaps shape-based scoring for seeded ground truth, and reports worst-case and tail-risk metrics alongside the mean. On ComtradeBench the joint intervention converts a nearly flat leaderboard into a reliability spectrum. Auditing existing benchmarks, they found scorer validity is benchmark-specific while scaffold ownership was an uncontrolled axis everywhere they looked. Read it next to Real-SWE, which makes the opposite trade on purpose by keeping each model in its own harness.
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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...
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, test...
Researchers analyzed 3,691 patches from AI coding agents. Between 20% and 40% contained unnecessary refactoring mixed into bug fixes. This isn't a prompting failure. It's a training data problem, and it's baked into the models. A paper on arXiv examined patches from Multi-SWE-...
"Towards a Science of AI Agent Reliability" (arXiv 2602.16666) — 12 concrete metrics decomposing reliability along consistency, robustness, predictability, and safety. Key finding: stronger performance on benchmarks does NOT correlate with reliable real-world operation. Intera...
Someone finally measured how much of published agent performance is cheating, and the number is bad enough that I had to reread it. Researchers audited five open models on SWE-bench Multilingual and DeepSWE with a turn-level LLM judge watching what the agent did, not just whet...
Traced across 557 SWE-chat sessions (94,813 events) and 33,097 agentic pull requests from AIDev. Agent-facing artifacts account for 60.5% of documentation interactions versus 10.6% for classical technical docs and 1.3% for API references. Consultation is self-initiated 70.2% o...
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