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Research2026-08-21 · source-backed
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% of the time and failure-driven only 7.5%. arXiv Reading docs was associated with less immediate testing (adjusted OR 0.39), and no explicit doc-based validation sequence was observed at all. Everyone optimizing their API reference for agent consumption is optimizing the 1.3%.
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A June 16 position paper argues today's benchmarks predate AI agents: they conflate multiple system components into single scores, penalize valid alternative solutions, and lack the granular feedback needed to iterate on agent systems. Read the current wave of open-weight SWE-...
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...
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SWE-Touch mines task-critical regions from repair trajectories, builds plausible Counter-Edits that conflict with task completion, and injects them with contextual user messages when the agent reaches that code. Across nine models on SWE-bench Verified, average resolve rate dr...
Coding agents ace correctness benchmarks and flail at repository-level performance work, because bottlenecks hide behind abstraction layers and the agent stops at the first passing patch. PerfAgent wraps an off-the-shelf agent with a profiler-guided, verifier-in-the-loop workf...
While the capability stories pile up, here's the counterweight. As SWE-bench Verified scores cluster near saturation on July leaderboards, an enhanced analysis (SWE-Bench+, on the AIware 2026 benchmark track) found 60.83% of commonly resolved issues contain solution leakage ri...
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