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LLMs can edit code 30% faster and cheaper without sacrificing accuracy, simply by learning to choose between generating full code and structure-aware diffs.
LLMs signal their internal certainty during answer decoding through predictable attention patterns on their own reasoning traces.
Achieving superior 3D scene edits hinges on a dual-path consistency mechanism that effectively integrates structural and semantic cues across multiple views.
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.
