LLM chain-of-thought crosses a commitment boundary early; subsequent steps are epiphenomenal, enabling early-exit that shortens traces 55% with negligible performance change.
Council of LLM s: Evaluating Capability of Large Language Models to Annotate Propaganda
5 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.
The paper introduces a three-source decomposition showing that answer flips in multi-agent LLM debate include 37% spontaneous instability and 29% harmful conformity, with even vacuous reasoning persuading 20-39% of resistant agents and interventions reducing harmful conformity by 13.6 points.
LLMs show low endorsement of persuasion-infused messages unless given partisan personas, which then increase polarized endorsements varying by technique and topic.
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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.