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LoLBench is a multilingual benchmark of 100 tasks across 29 software systems in five domains that shows that both perception and implementation remain central challenges for coding agents in practical modular development on large software systems.
Evaluating twelve open and proprietary models, it is found that abstraction failure is lawful, and the central finding is a gap between recognition and cross-voice mapping: models identify a structure within one voice far better than they carry it across voices; every model pays this tax, and mapping strong enough to n...
Experimental results on CAIL2018 demonstrate that legal-oriented adaptation based on DRAGIN improves retrieval-augmented reasoning in this Chinese legal judgment prediction setting while maintaining low retrieval overhead.
It is found that defenses rarely improve downstream capability, but instead vary in how they trade safety gains against usability and efficiency as well as performance impact, over-refusal on benign inputs, and inference cost.
TraceProbe is presented, a trajectory-diagnostic framework that recovers what resolve rate hides and adds auditable diagnostic context to outcomes by localizing inspection targets, suggesting failure hypotheses, and prioritizing runs for review.
SWE-RPG is introduced, a repository-level benchmark that combines executable patch evaluation with validated ground-truth references (GTs) for Requirement Clarification and Implementation Planning, and suggests implicit-requirement recovery as a key candidate direction for improving coding agents.
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