CoT prompting in LLM4Code shows mixed robustness that depends on model family, task structure, and perturbations destabilizing structural anchors, leading to trajectory deformations like lengthening, branching, and simplification.
Towards bet- ter chain-of-thought prompting strategies: A survey
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TDA-RC embeds topological patterns from multi-round reasoning into CoT via persistent homology and a repair agent, yielding better accuracy-efficiency trade-offs than ToT or GoT on tested datasets.
CoT reasoning is a brittle mirage governed by distribution discrepancy between training and test data, demonstrated via controlled experiments in the new DataAlchemy environment.
TransAgent improves LLM code translation by up to 33.3% via multi-agent fine-grained execution alignment on a new benchmark of recent tasks.
CoT prompting improves LLM performance on control-flow deobfuscation of C benchmarks, yielding ~16% better CFG reconstruction and ~20.5% better semantic preservation for GPT5 versus zero-shot prompting.
citing papers explorer
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Structural Anchors and Reasoning Fragility:Understanding CoT Robustness in LLM4Code
CoT prompting in LLM4Code shows mixed robustness that depends on model family, task structure, and perturbations destabilizing structural anchors, leading to trajectory deformations like lengthening, branching, and simplification.
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TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models
TDA-RC embeds topological patterns from multi-round reasoning into CoT via persistent homology and a repair agent, yielding better accuracy-efficiency trade-offs than ToT or GoT on tested datasets.
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Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
CoT reasoning is a brittle mirage governed by distribution discrepancy between training and test data, demonstrated via controlled experiments in the new DataAlchemy environment.
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TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment
TransAgent improves LLM code translation by up to 33.3% via multi-agent fine-grained execution alignment on a new benchmark of recent tasks.
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Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks
CoT prompting improves LLM performance on control-flow deobfuscation of C benchmarks, yielding ~16% better CFG reconstruction and ~20.5% better semantic preservation for GPT5 versus zero-shot prompting.