BLUE trains a lightweight gate on frozen VLA hidden states to selectively activate language generation only when beneficial, achieving SOTA results with 2.54x inference speedup on driving benchmarks.
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introduce Neural Chain-of-Thought Search (NCoTS), a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. By quantitatively characterizing the solution space, we reveal the existence of sparse superior reasoning paths that are simultaneously more accurate and concise than standard outputs. Our method actively navigates towards these paths by evaluating candidate reasoning operators using a dual-factor heuristic that optimizes for both correctness and computational cost. Consequently, NCoTS achieves a Pareto improvement across diverse reasoning benchmarks, boosting accuracy by over 3.5% while reducing generation length by over 22%. Our code and data are available at https://github.com/MilkThink-Lab/Neural-CoT-Search.
years
2026 6verdicts
UNVERDICTED 6representative citing papers
LayerTracer analysis identifies deep LLM layers as stable task-critical regions, leading to a shallow-train deep-freeze strategy that outperforms full fine-tuning on C-Eval and CMMLU.
ATTNPO guides process-supervised RL with intrinsic attention signals to shorten reasoning traces while raising accuracy on nine benchmarks.
ReQueR trains a single RL-based query refiner with an adaptive curriculum to decompose raw queries into structured logic, delivering 1.7-7.2% absolute gains on reasoning tasks across diverse LLMs and generalizing to unseen models.
LayerTracer defines task particles as the first layer where target token probability rises sharply and vulnerable layers via maximum JS divergence after masking, showing task particles in deep layers and greater robustness in larger models.
ActorMind is a four-agent chain-of-thought framework that emulates human actors to produce spontaneous, emotion-infused speech responses for role-playing scenarios.
citing papers explorer
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BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving
BLUE trains a lightweight gate on frozen VLA hidden states to selectively activate language generation only when beneficial, achieving SOTA results with 2.54x inference speedup on driving benchmarks.
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Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training
LayerTracer analysis identifies deep LLM layers as stable task-critical regions, leading to a shallow-train deep-freeze strategy that outperforms full fine-tuning on C-Eval and CMMLU.
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ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning
ATTNPO guides process-supervised RL with intrinsic attention signals to shorten reasoning traces while raising accuracy on nine benchmarks.
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One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement
ReQueR trains a single RL-based query refiner with an adaptive curriculum to decompose raw queries into structured logic, delivering 1.7-7.2% absolute gains on reasoning tasks across diverse LLMs and generalizing to unseen models.
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LayerTracer: A Joint Task-Particle and Vulnerable-Layer Analysis framework for Arbitrary Large Language Model Architectures
LayerTracer defines task particles as the first layer where target token probability rises sharply and vulnerable layers via maximum JS divergence after masking, showing task particles in deep layers and greater robustness in larger models.
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ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing
ActorMind is a four-agent chain-of-thought framework that emulates human actors to produce spontaneous, emotion-infused speech responses for role-playing scenarios.