LLM chain-of-thought crosses a commitment boundary early; subsequent steps are epiphenomenal, enabling early-exit that shortens traces 55% with negligible performance change.
Teaching NLP in the AI Era: Experiences from the U niversity of L atvia
3 Pith papers cite this work. Polarity classification is still indexing.
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ACTS models LLM reasoning control as an MDP solved by a controller agent initialized on synthetic multi-budget trajectories and refined with budget-conditioned RL, achieving token savings while matching full-reasoning accuracy.
SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.
citing papers explorer
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Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
LLM chain-of-thought crosses a commitment boundary early; subsequent steps are epiphenomenal, enabling early-exit that shortens traces 55% with negligible performance change.
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Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
ACTS models LLM reasoning control as an MDP solved by a controller agent initialized on synthetic multi-budget trajectories and refined with budget-conditioned RL, achieving token savings while matching full-reasoning accuracy.
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When Languages Disagree: Self-Evolving Multilingual LLM Judges
SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.