ACC compiles agent trajectories from search, software engineering, and database tasks into long-context QA examples to train LLMs for direct long-range dependency resolution without tool use at inference.
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M2A uses null-space model merging to combine mathematical and agentic reasoning in LLMs, raising SWE-Bench Verified performance from 44.0% to 51.2% on Qwen3-8B without retraining.
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ACC: Compiling Agent Trajectories for Long-Context Training
ACC compiles agent trajectories from search, software engineering, and database tasks into long-context QA examples to train LLMs for direct long-range dependency resolution without tool use at inference.
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M2A: Synergizing Mathematical and Agentic Reasoning in Large Language Models
M2A uses null-space model merging to combine mathematical and agentic reasoning in LLMs, raising SWE-Bench Verified performance from 44.0% to 51.2% on Qwen3-8B without retraining.