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State Key Laboratory for Novel Software Technology
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LLMs can bootstrap their code generation abilities without external supervision by leveraging semantic entropy to identify learnable tasks and behavioral consensus to filter noisy self-generated training signals.
LLMs' chain-of-thought explanations often fail to reflect the true drivers of their decisions, and this benchmark reveals that closed-source models are particularly opaque, with monitorability dropping by up to 30% under stress.