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TENET is proposed, an agentic framework for repository-level code generation under the TDD paradigm that includes a test harness mechanism that selects a concise test suite to maximize diversity of the target usage scenarios and a reflection-based refinement workflow that iteratively analyzes failures and updates imple...
The first study of behavioral cost inefficiencies in coding agents is conducted, analyzing 1,200 trajectories from Claude Code and Mini-SWE-Agent across four configurations on SWE-bench Verified.
A TBC-TBA self-refine learning multi-agent framework that enables dynamic normative adaptation through iterative multi-agent feedback that integrates Think-Before-Chat (social feedback processing) and Think-Before-Act (norm-guided decision making) phases, allowing agents to progressively refine their normative understa...
This work introduces LexRubric, a rubric-based benchmark for evaluating open-ended Chinese legal tasks and evaluates 18 recent general and legal-domain LLMs on LexRubric, showing that different models exhibit distinct capability profiles, and that open-ended legal tasks remain challenging for current LLMs.
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