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Research2026-09-01 · source-backed
IssueLoc-Bench evaluated five explorer models under an identical read-only interface on 499 SWE-bench Verified tasks plus 500 from 153 other repositories, measuring file-finding separately from patching (arXiv 2608.29675). Lower-cost explorers retained 78-94% of reference Hit@3 and 73-92% of F1 while cutting mean agent time 41-88% and tokens 84-95%. Which metric you optimize depends on the handoff: ranking matters if the next stage re-reads candidates, F1 and exact match matter if localization is a hard file gate.
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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...
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...
arXiv 2608.05144 runs Manager, Planner, Engineer and Reviewer roles over persistent project state with *fixed* model weights, self-evolving through runtime state and control policies rather than training. 76.8% on AARRI-Bench, and mature waves use 21% fewer solve-input tokens...
CodeGrep measures a 30B OpenHands agent averaging 23 rounds and 631K tokens per resolved SWE-Bench Verified issue, much of it grep, glob and view_file. A 14B retrieval agent trained end-to-end with GRPO raises resolve rate to 27.0% from 25.8% while cutting 15% of rounds and 19...
Real GitHub issues carry error dialogs, rendered UI states, and logs, yet repo-level issue localization is still evaluated text-only. MM-IssueLoc is a controlled benchmark that isolates localization from patch synthesis to measure whether visual input helps, hurts, or gets ign...
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