Reasoning

We propose PRISM-Δ, a differential subspace steering method for prompt highlighting that matches or exceeds the best existing method on 19 of 20 configurations with relative gains up to +10.6%, while halving the fluency cost.

Probing hidden states during reasoning reveals that LLMs already know the answer before finishing thinking. We detect overthinking via ‘jumps’ and intervene during inference to improve reasoning.

Abstract: We present PromptCD, a test-time method for controlling LLM behavior without additional training. The approach creates paired positive and negative guiding prompts for a target behavior and contrasts model responses at the token-probability level for LLMs and through visual attention patterns for VLMs.

Abstract: Reinforcement learning (RL) has shown great promise in enhancing LLM reasoning, but current approaches mainly focus on single domains with verifiable rewards. We propose RGR-GRPO, a rubric-driven RL framework for multi-domain reasoning that uses rubrics to provide fine-grained reward signals and offline guidance.

The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding …

Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in …

We present the first comprehensive analysis of how the order of graph descriptions impacts LLM performance, evaluating four graph description orders across six graph problems using six mainstream LLMs.

Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical …

We present the first comprehensive analysis of the impacts of CoT prompting on Reasoning LLMs, finding that one-shot CoT consistently enhances performance and reduces excessive reflections by approximately 90%.

作者提出CoT-Influx方法，一种对CoT的示例和内容进行优化从而提高LLMs推理能力的方法，其核心思想是通过剪枝最大化有效信息的输入。

本文提出了一种新的方法，全面评估大模型多个候选答案的可信度，以减轻大模型对于错误答案的过度自信。

3月最新用于游戏的大模型Agent综述

在过去CoT有两种范式，一种是Zero-shot，在问题末尾添加"Let’s think step by step"。另一种Manual CoT(Few-shot CoT)，每个例子由问题和推理链组成。第二种方法表现是否好取决于CoT写的好不好，不过这需要人手工来写。本文通过提出Auto-CoT这一方法使得Few-shot CoT可以自动生成，解放双手！

思维链(Chain of thought - CoT)在过去的实践中已经证明对提升大模型的推理能力有显著帮助。然而，目前还没有一项工作解释思维链长度与推理能力之间的关系。本文围绕这一核心问题，围绕CoT做了系统实验，并给出许多有意思和反直觉的结论。

提出了一种基于大模型的图引导的面向多步推理问题的推理方式。本文的主要贡献有两点：提出上述推理方式，提出允许变量定义的用于知识三元组提取的上下文学习方法

LLM ReAct范式，在大语言模型中结合推理和动作