arXiv Machine Learning
Jul 1
Fork-Think with Confidence
arXiv:2606. 31484v1 Announce Type: new Abstract: Parallel thinking has enjoyed great success for boosting LLM performance on reasoning tasks without the need for any re-training.
Second Thought: Reasoning in Parallel as LLM Agents Act and Observe
arXiv:2608. 13667v1 Announce Type: new Abstract: LLM agents in the ReAct paradigm alternate between reasoning, acting, and observing, but deliberate reasoning is confined to the Thought phase: while the agent serializes an action and waits for the environment, its reasoning is frozen.
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arXiv AI
Jun 2
ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks
arXiv:2606. 01080v1 Announce Type: cross Abstract: Large language models often improve on difficult tasks by spending inference-time compute on a reasoning trace before producing the final answer.
arXiv AI
1d ago
ParaTempo: Efficient Parallel Reasoning via Temporal Confidence
arXiv:2608. 16425v1 Announce Type: new Abstract: Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count.
arXiv AI
Jun 3
Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
arXiv:2606. 03965v1 Announce Type: cross Abstract: Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control.
arXiv Machine Learning
Jul 2
Message Passing Enables Efficient Reasoning
arXiv:2607. 01077v1 Announce Type: cross Abstract: While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is a computational bottleneck.
arXiv AI
1d ago
Funnel of Thoughts: Efficient Test-Time Scaling via Early Voting and Rollout Pruning
arXiv:2608. 15065v1 Announce Type: new Abstract: Large Reasoning Models produce diverse, sometimes inconsistent answers across repeated queries on the same problem, so multi-sample inference is a prerequisite for reliable deployment.
arXiv AI
Jun 4
Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time
arXiv:2504. 12329v2 Announce Type: replace-cross Abstract: Recent advances leverage post-training to enhance model reasoning performance, which typically requires costly training pipelines and still suffers from inefficient, overly lengthy outputs.
arXiv AI
Jun 24
The Latent Bridge: A Continuous Slow-Fast Channel for Real-Time Game Agents
arXiv:2606. 24470v1 Announce Type: new Abstract: A real-time agent for general computer use - with games as the most demanding case - must act within tens of milliseconds while still planning over seconds.
arXiv Machine Learning
Jun 9
Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization
arXiv:2510. 13554v2 Announce Type: replace-cross Abstract: The reasoning pattern of Large language models (LLMs) remains opaque, and reinforcement learning (RL) typically applies uniform credit across an entire generation, blurring the distinction between pivotal and routine steps.
arXiv AI
Jun 2
On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
arXiv:2603. 12109v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a de facto paradigm for building LLM-based agents that act, interact, and reason over extended task horizons.
arXiv AI
Jul 16
Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution
arXiv:2601. 20379v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption.
arXiv Machine Learning
Jun 9
From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning
arXiv:2606. 09092v1 Announce Type: new Abstract: Theory of Mind (ToM) is a must-acquire skill for modern foundation model systems to operate effectively and safely in the real world.