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Chenxu Yang

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4 papers

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AAAI Conference 2026 Conference Paper

Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models

Chenxu Yang
Qingyi Si
Lanrui Wang
Zheng Lin

Grounding responses in external knowledge represents an effective strategy for mitigating hallucinations in Large Language Models (LLMs). However, current LLMs struggle to seamlessly integrate knowledge while simultaneously maintaining faithfulness (or fidelity) and expressiveness, capabilities that humans naturally possess. This limitation results in outputs that either lack support from external knowledge, thereby compromising faithfulness, or appear overly verbose and unnatural, thus sacrificing expressiveness. In this work, to break the trade-off between faithfulness and expressiveness, we propose Collaborative Decoding (CoDe), a novel approach that dynamically integrates output probabilities generated with and without external knowledge. This integration is guided by distribution divergence and model confidence, enabling the selective activation of relevant and reliable expressions from the model's internal parameters. Furthermore, we introduce a knowledge-aware reranking mechanism that prevents over-reliance on prior parametric knowledge while ensuring proper utilization of provided external information. Through comprehensive experiments, our plug-and-play CoDe framework demonstrates superior performance in enhancing faithfulness without compromising expressiveness across diverse LLMs and evaluation metrics, validating both its effectiveness and generalizability.

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AAAI Conference 2026 Conference Paper

Test-time Prompt Intervention

Chenxu Yang
Qingyi Si
Mz Dai
Dingyu Yao
Mingyu Zheng
Minghui Chen
Zheng Lin
Weiping Wang

Test-time compute has led to remarkable success in the large language model (LLM) community, particularly for complex tasks, where longer chains of thought (CoTs) are generated to enhance reasoning capabilities. However, growing evidence reveals that such reasoning models often produce CoTs plagued by excessive redundancy, including repetitive verification steps and unnecessary reasoning shifts. The root cause lies in post-training of them that overly rely on outcome reward paradigms, as the data of process reward paradigms, which regulate intermediate reasoning steps, is difficult to construct at scale. To address this, we propose PI, a novel framework for Test-time Prompt Intervention. PI provides an interface to dynamically guide and regulate reasoning paths during inference through timely (When module) and proper (How module) interventions and post-intervention sampling (Which module). This allows human problem-solving expertise and cognitive science principles to be seamlessly integrated into LLMs’ reasoning processes, enhancing controllability and interpretability. Extensive experiments across multiple models and datasets demonstrate that PI significantly shortens CoTs while reducing hallucination, yielding more concise and reliable reasoning.

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IJCAI Conference 2025 Conference Paper

Categorical Attention: Fine-grained Language-guided Noise Filtering Network for Occluded Person Re-Identification

Minghui Chen
Dayan Wu
Chenxu Yang
Qinghang Su
Zheng Lin

Person Re-Identification (ReID) aims to match individuals across different camera views, but occlusions in real-world scenarios, such as vehicles or crowds, hinder feature extraction and matching. Current occluded ReID methodologies typically leverage visual augmentation techniques in an attempt to mitigate the disruptive effects of occlusion-induced noise. However, relying solely on visual data fail to effectively filter out occlusion noise. In this paper, we introduce the Fine-grained Language-guided Noise Filtering Network (FLaN-Net) for occluded ReID. FLaN-Net innovatively employs categorical attention mechanism to generate adaptive tokens that capture the following three distinct types of visual information: comprehensive descriptions of individuals, detailed visible attributes, and characteristics of occluding objects. Subsequently, a cross-attention mechanism aligns these prompts with the image, guiding the model to focus on relevant regions. To generate robust and discriminative features for occluded pedestrians, we further introduce a dynamic weighting fusion module that integrates visual, textual, and cross-attention features based on their reliability. Experimental results demonstrate that FLaN-Net outperforms existing methods on occluded ReID benchmarks, offering a robust solution for challenging real-world conditions.

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NeurIPS Conference 2025 Conference Paper

S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models

Muzhi Dai
Chenxu Yang
Qingyi Si

As Test-Time Scaling emerges as an active research focus in the large language model community, advanced post-training methods increasingly emphasize extending chain-of-thought (CoT) generation length, thereby enhancing reasoning capabilities to approach Deepseek R1-like reasoning models. However, recent studies reveal that reasoning models (even Qwen3) consistently exhibit excessive thought redundancy in CoT generation. This overthinking issue arises from the inherent limitations of conventional outcome-reward reinforcement learning, which systematically overlooks the regulation of intermediate reasoning processes. This paper introduces Serial-Group Decaying-Reward Policy Optimization (S-GRPO), a novel reinforcement learning paradigm that enables models to implicitly evaluate the sufficiency of intermediate reasoning steps, thereby facilitating early exit in CoT generation. Unlike GRPO, which samples multiple possible reasoning paths in parallel (parallel group), S-GRPO only samples one reasoning path and serially selects multiple temporal positions from the path to exit thinking and directly generate answers (serial group). For correct answers within a serial group, rewards gradually decrease based on the exit positions along the reasoning path from front to back. This design encourages the model to produce more accurate and concise thoughts, while also incentivizing early thinking termination when appropriate. Empirical evaluations demonstrate that S-GRPO is compatible with state-of-the-art reasoning models, including Qwen3 and Deepseek-distill. Across diverse benchmarks such as GSM8K, AIME 2024, AMC 2023, MATH-500, and GPQA Diamond, S-GRPO achieves a substantial reduction in sequence length (40. 4%～61. 1%) while simultaneously improving accuracy (absolute 0. 72%～3. 92%).

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