5 citations · 12 across the 21 of their papers we have counts for
22 papers
Search-G1: Co-Evolving Representation-Based Rewards for Selective Grounded Search
Ruoxi Cheng, Haoxuan Ma, Hongyi Zhang +7
Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. Yet terminal correctness assigns equal cre…
Training Documents Reranker with Search Rubrics for Deep Research Agent
Wenhan Liu, Yu Lu, Qiaolin Xia +8
Retrieval systems help deep research agents generate high-quality answers by providing relevant documents. However, existing retrievers typically select documents through relevance…
MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems
Vishal Venkataramani, Haizhou Shi, Zixuan Ke +6
Multi-Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification, which evaluates intermediate step…
Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models
Hao Wang, Hao Gu, Hongming Piao +6
The standard post-training recipe for large reasoning models, supervised fine-tuning followed by reinforcement learning (SFT-then-RL), may limit the benefits of the RL stage: while…
FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning
Yujie Feng, Hao Wang, Jian Li +6
Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. Memory replay methods are widely used for…
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
Zhiyong Cao, Dunqiang Liu, Qi Dai +9
Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-med…
Laser: Governing Long-Horizon Agentic Search via Structured Protocol and Context Register
Shuting Wang, Qiaolin Xia, Vich Wang +3
Recent advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs) have enabled agentic search systems that interleave multi-step reasoning with external tool use. H…
SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
Hao Wang, Jialun Zhong, Changcheng Wang +5
Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical…
A large language model system for the field of chemical engineering technology
Heng Zhang, Jibin Zhou, Feiyang Xu +6
The development of chemical engineering technology is a multi-stage process that encompasses laboratory research, scaling up, and industrial deployment. This process demands interd…
AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms
Zhenxing Xu, Yizhe Zhang, Weidong Bao +6
Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibit…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
Interpreting Fedspeak with Confidence: A LLM-Based Uncertainty-Aware Framework Guided by Monetary Policy Transmission Paths
Rui Yao, Qi Chai, Jinhai Yao +4
"Fedspeak", the stylized and often nuanced language used by the U.S. Federal Reserve, encodes implicit policy signals and strategic stances. The Federal Open Market Committee strat…
From Language to Logic: A Bi-Level Framework for Structured Reasoning
Keying Yang, Hao Wang, Kai Yang
Structured reasoning over natural language inputs remains a core challenge in artificial intelligence, as it requires bridging the gap between unstructured linguistic expressions a…
AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
Changze Lv, Jiang Zhou, Siyu Long +22
We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws,…
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Young-Jin Park, Kristjan Greenewald, Kaveh Alim +2
Process reward models (PRMs) play a central role in guiding inference-time scaling algorithms for large language models (LLMs). However, we observe that even state-of-the-art PRMs…
Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?
Yiyou Sun, Georgia Zhou, Haoyue Bai +4
Recent supervised fine-tuning (SFT) approaches have significantly improved language models' performance on mathematical reasoning tasks, even when models are trained at a small sca…
StableToolBench-MirrorAPI: Modeling Tool Environments as Mirrors of 7,000+ Real-World APIs
Zhicheng Guo, Sijie Cheng, Yuchen Niu +4
The rapid advancement of large language models (LLMs) has spurred significant interest in tool learning, where LLMs are augmented with external tools to tackle complex tasks. Howev…
Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents
Ivoline Ngong, Swanand Kadhe, Hao Wang +4
Conversational agents are increasingly woven into individuals' personal lives, yet users often underestimate the privacy risks associated with them. The moment users share informat…
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
Yuyan Chen, Hao Wang, Songzhou Yan +4
Emotional intelligence in large language models (LLMs) is of great importance in Natural Language Processing. However, the previous research mainly focus on basic sentiment analysi…
Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks
Jiayu Lin, Guanrong Chen, Bojun Jin +26
In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets. We organiz…
AutoFlow: Automated Workflow Generation for Large Language Model Agents
Zelong Li, Shuyuan Xu, Kai Mei +6
Recent advancements in Large Language Models (LLMs) have shown significant progress in understanding complex natural language. One important application of LLM is LLM-based AI Agen…
Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents
Zelong Li, Wenyue Hua, Hao Wang +2
Recent advancements on Large Language Models (LLMs) enable AI Agents to automatically generate and execute multi-step plans to solve complex tasks. However, since LLM's content gen…