Preregistered placebo-controlled decomposition shows external executable counterexamples drive self-repair gains in small code models more than re-exposure or self-critique.
Tian et al., ``Debugbench: Evaluating debugging capability of large language models,''
6 Pith papers cite this work. Polarity classification is still indexing.
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Healer uses LLMs to dynamically generate and execute runtime error-handling code, with GPT-4 recovering from 72.8% of errors across four datasets.
Acoda uses a genetic algorithm to optimize eight obfuscation methods that reduce LLM code analysis success rates to as low as 30% while preserving original semantics.
ChatHLS presents a multi-agent LLM system for HLS error debugging via adaptive case expansion and QoR-aware directive optimization, reporting 32.6% better debugging than Gemini-3-pro plus speedups on kernels and accelerators.
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
Retriever-side choices, particularly the retrieval algorithm, exert more influence on RAG performance than generator selection across code generation, summarization, and repair tasks.
citing papers explorer
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Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models
Preregistered placebo-controlled decomposition shows external executable counterexamples drive self-repair gains in small code models more than re-exposure or self-critique.
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Towards Agentic Runtime Healing
Healer uses LLMs to dynamically generate and execute runtime error-handling code, with GPT-4 recovering from 72.8% of errors across four datasets.
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Acoda: Adversarial Code Obfuscation for Defending against LLM-based Analysis
Acoda uses a genetic algorithm to optimize eight obfuscation methods that reduce LLM code analysis success rates to as low as 30% while preserving original semantics.
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ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis
ChatHLS presents a multi-agent LLM system for HLS error debugging via adaptive case expansion and QoR-aware directive optimization, reporting 32.6% better debugging than Gemini-3-pro plus speedups on kernels and accelerators.
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Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
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Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks
Retriever-side choices, particularly the retrieval algorithm, exert more influence on RAG performance than generator selection across code generation, summarization, and repair tasks.