Papers with self-reflection mechanisms

2 papers
Self-Reflective Generation at Test Time (2026.acl-long)

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Challenge: Existing self-reflection mechanisms are reactive and inefficient for large language models . a fundamental tension persists between the ability to execute complex multi-step reasoning and the ability of the model to generate coherent traces.
Approach: They propose a test-time framework that reflects before generating at uncertain points . SRGen utilizes dynamic entropy thresholding to identify high-uncertainty tokens .
Outcome: The proposed framework can significantly strengthen large language models' reasoning process.
Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility (2026.findings-acl)

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Challenge: a recent development of code-LLMs has demonstrated remarkable performance across various software engineering applications.
Approach: They propose a round-trip code execution reasoning task to test round- trip consistency . they use zero-shot prompting, supervised fine-tuning on execution traces and self-reflection mechanisms to evaluate models .
Outcome: The proposed benchmarks show that LLMs struggle with round-trip consistency . the benchmarks lack the internal coherence required for trustworthy code reasoning .

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