1 citations · 1 across the 14 of their papers we have counts for
16 papers
REALHOP: Rethinking Multi-Hop Reasoning Evaluation via Behavioral Auditing
Jiawen Tao, Xiaokun Yuan, Yaoming Li +4
Complex questions often require multi-hop reasoning that connects facts distributed across sources or distant regions of a long context through intermediate steps. Benchmarks commo…
CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
Bowen Ye, Lei Li, Shicheng Li +16
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existi…
Semantic Head Specialization Guides Hybrid ViT Attention for Multimodal LLMs
Chenhong He, Lei Li, Shicheng Li +5
Hybrid attention dominates frontier LLMs, yet Vision Transformers (ViTs) in multimodal LLMs lack a satisfactory hybrid design, with no consensus on why certain attention patterns w…
PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems
Hanglong Lv, Dawei Zhu, Lei Li +11
Large language models are increasingly used as agentic workflow executors, yet existing training data and benchmarks largely assume informationally complete, single-turn queries. O…
Beyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training
Jiawen Tao, Miao Peng, Yaoming Li +7
Synthetic textbook data has improved language model pre-training, but prior work largely treats the benefit as a property of generated content or local rewriting style. We study a…
Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents
Bowen Ye, Rang Li, Qibin Yang +10
Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing agent benchmarks are limited by…
Fat-Cat: Document-Driven Metacognitive Multi-Agent System for Complex Reasoning
Tong Yang, Yemin Wang, Chaoning Zhang +1
The effectiveness of LLM-based agents is often limited not by model capacity alone, but by how efficiently contextual information is utilized at runtime. Existing agent frameworks…
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment
Zhewen Tan, Wenhan Yu, Jianfeng Si +9
In recent years, safety risks associated with large language models have become increasingly prominent, highlighting the urgent need to mitigate the generation of toxic and harmful…
MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use
Wenrui Liu, Zixiang Liu, Elsie Dai +5
Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (MCP) is considered a future tren…
ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents
Yilun Yao, Shan Huang, Elsie Dai +5
Large language models are increasingly deployed as research agents for deep search and long-horizon information seeking, yet their performance often degrades as interaction histori…
EntroCoT: Enhancing Chain-of-Thought via Adaptive Entropy-Guided Segmentation
Zihang Li, Yuhang Wang, Yikun Zong +6
Chain-of-Thought (CoT) prompting has significantly enhanced the mathematical reasoning capabilities of Large Language Models. We find existing fine-tuning datasets frequently suffe…
"Even GPT Can Reject Me": Conceptualizing Abrupt Refusal Secondary Harm (ARSH) and Reimagining Psychological AI Safety with Compassionate Completion Standard (CCS)
Yang Ni, Tong Yang
Large Language Models (LLMs) and AI chatbots are increasingly used for emotional and mental health support due to their low cost, immediacy, and accessibility. However, when safety…
Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection
Tianci Liu, Tong Yang, Quan Zhang +1
As AI advances, copyrighted content faces growing risk of unauthorized use, whether through model training or direct misuse. Building upon invisible adversarial perturbation, recen…
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
Ping Gong, Jiawei Yi, Shengnan Wang +13
Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to…
CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter
Zihang Li, Yangdong Ruan, Wenjun Liu +2
Although retrieval-augmented generation(RAG) significantly improves generation quality by retrieving external knowledge bases and integrating generated content, it faces computatio…
FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability
Linze Li, Sunqi Fan, Hengjun Pu +6
Over recent years, diffusion models have facilitated significant advancements in video generation. Yet, the creation of face-related videos still confronts issues such as low facia…