47 citations · 165 across the 90 of their papers we have counts for
24 papers · 1 filter
Coupled Variational Reinforcement Learning for Language Model General Reasoning
Xueru Wen, Jie Lou, Yanjiang Liu +6
While reinforcement learning has achieved impressive progress in language model reasoning, it is constrained by the requirement for verifiable rewards. Recent verifier-free RL meth…
AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing
Qingyu Zhang, Chunlei Xin, Xuanang Chen +7
Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a signifi…
MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning
Qianhao Yuan, Jie Lou, Zichao Li +6
LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compute cost and GPU memory overhead. To ad…
Beyond Turn Limits: Training Deep Search Agents with Dynamic Context Window
Qiaoyu Tang, Hao Xiang, Le Yu +11
While recent advances in reasoning models have demonstrated cognitive behaviors through reinforcement learning, existing approaches struggle to invoke deep reasoning capabilities i…
When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models
Yingzhi Mao, Chunkang Zhang, Junxiang Wang +6
Large Reasoning Models (LRMs) achieve strong performance on complex multi-step reasoning, yet they still exhibit severe safety failures such as harmful content generation. Existing…
PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing
Zhuoqun Li, Xuanang Chen, Hongyu Lin +5
As researchers delve more deeply into their work, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than…
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
Guozhao Mo, Wenliang Zhong, Jiawei Chen +7
Model Context Protocol (MCP) has become a key infrastructure for connecting LLMs with external tools, scaling to 10,000+ MCP servers with diverse tools. Unfortunately, there is sti…
RefCritic: Training Long Chain-of-Thought Critic Models with Refinement Feedback
Qiaoyu Tang, Hao Xiang, Le Yu +6
With the rapid advancement of Large Language Models (LLMs), developing effective critic modules for precise guidance has become crucial yet challenging. In this paper, we initially…
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing
Hao Xiang, Tianyi Tang, Yang Su +10
Recent advancements in Large Language Models (LLMs) have shown outstanding potential for role-playing applications. Evaluating these capabilities is becoming crucial yet remains ch…
Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction
Tianyun Zhong, Guozhao Mo, Yanjiang Liu +9
With the emergence of large language models (LLMs), there is an expectation that LLMs can effectively extract explicit information from complex real-world documents (e.g., papers,…
EmbedAgent: Benchmarking Large Language Models in Embedded System Development
Ruiyang Xu, Jialun Cao, Mingyuan Wu +6
Large Language Models (LLMs) have shown promise in various tasks, yet few benchmarks assess their capabilities in embedded system development. In this paper, we introduce EmbedAgen…
ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch
Jiawei Chen, Xinyan Guan, Qianhao Yuan +7
Current instruction data synthesis methods primarily focus on single-turn instructions and often neglect cross-turn coherence, resulting in context drift and reduced task completio…
Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Qiming Zhu, Jialun Cao, Xuanang Chen +6
Current research on large language models (LLMs) with retrieval-augmented code generation (RACG) has largely focused on single-language settings, leaving their cross-lingual effect…
ShortV: Efficient Multimodal Large Language Models by Freezing Visual Tokens in Ineffective Layers
Qianhao Yuan, Qingyu Zhang, Yanjiang Liu +6
Multimodal Large Language Models (MLLMs) suffer from high computational costs due to their massive size and the large number of visual tokens. In this paper, we investigate layer-w…
Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning
Ruoxi Xu, Yunjie Ji, Boxi Cao +7
Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domai…
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models
Qiao Liang, Yanjiang Liu, Weixiang Zhou +7
Does the prior knowledge of the vision encoder constrain the capability boundary of Multi-modal Large Language Models (MLLMs)? While most existing research treats MLLMs as unified…
DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking
Zhuoqun Li, Haiyang Yu, Xuanang Chen +6
Designing solutions for complex engineering challenges is crucial in human production activities. However, previous research in the retrieval-augmented generation (RAG) field has n…
PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides
Hao Zheng, Xinyan Guan, Hao Kong +7
Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods p…
SAISA: Towards Multimodal Large Language Models with Both Training and Inference Efficiency
Qianhao Yuan, Yanjiang Liu, Yaojie Lu +4
Multimodal Large Language Models (MLLMs) mainly fall into two architectures, each involving a trade-off between training and inference efficiency: embedding space alignment (e.g.,…
Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch
Xueru Wen, Jie Lou, Zichao Li +9
Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. However, most RM research is centered on English and relies heavily on synthetic r…
Scalable Oversight for Superhuman AI via Recursive Self-Critiquing
Xueru Wen, Jie Lou, Xinyu Lu +5
As AI capabilities increasingly surpass human proficiency in complex tasks, current alignment techniques, including SFT and RLHF, face fundamental challenges in ensuring reliable o…
DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
Xinyan Guan, Jiali Zeng, Fandong Meng +6
Large Language Models (LLMs) have shown remarkable reasoning capabilities, while their practical applications are limited by severe factual hallucinations due to limitations in the…
Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models
Yanjiang Liu, Shuhen Zhou, Yaojie Lu +6
Automated red-teaming has become a crucial approach for uncovering vulnerabilities in large language models (LLMs). However, most existing methods focus on isolated safety flaws, l…
From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs
Jialun Cao, Yaojie Lu, Meiziniu Li +9
The research in AI-based formal mathematical reasoning has shown an unstoppable growth trend. These studies have excelled in mathematical competitions like IMO and have made signif…