2 citations · 2 across the 14 of their papers we have counts for
14 papers
DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents
Junshuo Zhang, Chengrui Huang, Feng Guo +6
Large language model (LLM) agents that follow the sequential "reason-then-act" paradigm have achieved superior performance in many complex tasks.However, these methods suffer from…
HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation
Chengrui Huang, Junshuo Zhang, Zhiyuan Ma +7
Enabling large language models to scale and reliably use hundreds of tools is critical for real-world applications, yet challenging due to the inefficiency and error accumulation i…
FAVE: Flow-based Average Velocity Establishment for Sequential Recommendation
Ke Shi, Yao Zhang, Feng Guo +4
Generative recommendation has emerged as a transformative paradigm for capturing the dynamic evolution of user intents in sequential recommendation. While flow-based methods improv…
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning
Ruixiang Feng, Yuntao Wen, Silin Zhou +14
Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from ``overthinking'', producing excessively long reasoning traces tha…
TOOL4POI: A Tool-Augmented LLM Framework for Next POI Recommendation
Dongsheng Wang, Shen Gao, Chengrui Huang +3
Next Point-of-Interest (POI) recommendation is a fundamental task in location-based services. While recent advances leverage Large Language Model (LLM) for sequential modeling, exi…
CoSineVerifier: Tool-Augmented Answer Verification for Computation-Oriented Scientific Questions
Ruixiang Feng, Zhenwei An, Yuntao Wen +9
Answer verification methods are widely employed in language model training pipelines spanning data curation, evaluation, and reinforcement learning with verifiable rewards (RLVR).…
Beyond Superficial Forgetting: Thorough Unlearning through Knowledge Density Estimation and Block Re-insertion
Feng Guo, Yuntao Wen, Shen Gao +2
Machine unlearning, which selectively removes harmful knowledge from a pre-trained model without retraining from scratch, is crucial for addressing privacy, regulatory compliance,…
CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
Yifan Wang, Shen Gao, Jiabao Fang +3
Sequential Recommendation Systems (SRS) have become essential in many real-world applications. However, existing SRS methods often rely on collaborative filtering signals and fail…
Evolution without Large Models: Training Language Model with Task Principles
Minghang Zhu, Shen Gao, Zhengliang Shi +5
A common training approach for language models involves using a large-scale language model to expand a human-provided dataset, which is subsequently used for model training.This me…
Generative Next POI Recommendation with Semantic ID
Dongsheng Wang, Yuxi Huang, Shen Gao +3
Point-of-interest (POI) recommendation systems aim to predict the next destinations of user based on their preferences and historical check-ins. Existing generative POI recommendat…
CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis
Ruixiang Feng, Shen Gao, Xiuying Chen +2
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often exhibit a specific cultural biases, neglecting the values and linguistic…
TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation
Chengrui Huang, Shen Gao, Zhengliang Shi +2
Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in prefer…
What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
Chengrui Huang, Zhengliang Shi, Yuntao Wen +4
Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to en…
Simulating Financial Market via Large Language Model based Agents
Shen Gao, Yuntao Wen, Minghang Zhu +4
Most economic theories typically assume that financial market participants are fully rational individuals and use mathematical models to simulate human behavior in financial market…