1 citations · 3 across the 7 of their papers we have counts for
9 papers
Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents
Yu Li, Zheng Zhang, Xin Liu +3
Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condition subsequent decisions. In…
From Outcome to Representation: Tracing Reasoning Mechanisms through Integrated Policy Gradient
Changming Li, Kaixing Zhang, Haoyun Xu +4
Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, yet the internal mechanisms driving these multi-step processes remain opaque. Existing mechanistic inter…
Grad2Reward: From Sparse Judgment to Dense Rewards for Improving Open-Ended LLM Reasoning
Zheng Zhang, Ao Lu, Yuanhao Zeng +5
Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant breakthroughs in complex LLM reasoning within verifiable domains, such as mathematics and programmin…
SkipKV: Selective Skipping of KV Generation and Storage for Efficient Inference with Large Reasoning Models
Jiayi Tian, Seyedarmin Azizi, Yequan Zhao +7
Large reasoning models (LRMs) often incur significant key-value (KV) cache overhead, due to their linear growth with the verbose chain-of-thought (CoT) reasoning. This incurs both…
Better LLM Reasoning via Dual-Play
Zhengxin Zhang, Chengyu Huang, Aochong Oliver Li +1
Large Language Models (LLMs) have achieved remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR), yet still rely heavily on external supervision (e.g.,…
Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning
Zheng Zhang, Ziwei Shan, Kaitao Song +2
Process Reward Models (PRMs) have emerged as a promising approach to enhance the reasoning capabilities of large language models (LLMs) by guiding their step-by-step reasoning towa…
Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
Zheng Zhang
Large Language Models (LLMs) display striking surface fluency yet systematically fail at tasks requiring symbolic reasoning, arithmetic accuracy, and logical consistency. This pape…
Learning to Select In-Context Demonstration Preferred by Large Language Model
Zheng Zhang, Shaocheng Lan, Lei Song +3
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks during inference using only a few demonstrations. However, ICL performance is highly dependent…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…