Evo-L2S uses multi-objective evolutionary model merging to produce reasoning models that cut generated chain-of-thought length by over 50% while preserving or improving accuracy on math benchmarks.
From system 1 to system 2: A survey of reasoning large language models.IEEE Trans
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VPG-EA applies variational posterior guidance and efficiency-aware distillation to compress LLM reasoning chains while preserving performance.
WebUncertainty improves web agent performance on benchmarks by adaptively selecting planning modes based on task uncertainty and using confidence-induced action uncertainty in MCTS to quantify aleatoric and epistemic uncertainty for better decisions.
citing papers explorer
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Multi-objective Evolutionary Merging Enables Efficient Reasoning Models
Evo-L2S uses multi-objective evolutionary model merging to produce reasoning models that cut generated chain-of-thought length by over 50% while preserving or improving accuracy on math benchmarks.
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Efficient LLM Reasoning via Variational Posterior Guidance with Efficiency Awareness
VPG-EA applies variational posterior guidance and efficiency-aware distillation to compress LLM reasoning chains while preserving performance.
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WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent
WebUncertainty improves web agent performance on benchmarks by adaptively selecting planning modes based on task uncertainty and using confidence-induced action uncertainty in MCTS to quantify aleatoric and epistemic uncertainty for better decisions.