Feedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology i
arXiv:2605.26720v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong empirical gains as self-evolving agents for CUDA kernel generation, driven by
arXiv:2608.23632v1 Announce Type: new Abstract: Process supervision has improved mathematical reasoning, where intermediate steps are naturally expressed as chains of thought. In
arXiv:2606.30774v1 Announce Type: new Abstract: We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In
arXiv:2609.38639v1 Announce Type: new Abstract: LLM-guided evolutionary search can discover complex programs, but existing methods mostly only save candidate programs and fitness
arXiv:2609.02067v1 Announce Type: new Abstract: Scientific benchmarks are commonly built by domain experts who write tasks and cross-check one another's work, or who adapt
arXiv:2605.05657v1 Announce Type: new Abstract: Multi-agent LLM systems for code generation face a fundamental routing problem: the optimal orchestration topology depends on the
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