12 citations · 37 across the 53 of their papers we have counts for
57 papers
Benchmarking and Error Analysis of Deep Search and Deep Research LLM Agents
Zixuan Yu, Zhenheng Tang, Xiaowen Chu +1
As Large Language Models (LLMs) evolve into autonomous Deep Search and Deep Research Agents capable of executing long-horizon, multi-step tasks, evaluating their reliability become…
Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement
Xutao Mao, Jianing Zhu, Jinman Zhao +4
Reinforcement learning (RL) has substantially improved the reasoning capabilities of vision-language models (VLMs), yet it can induce chain-of-thought (CoT) obfuscation, an operati…
Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaigns
Yijie Wang, Zhen-Yu Yin, Zhenheng Tang +1
Long-running molecular simulation campaigns require repeated continuation from saved states, provenance-aware progression, adaptive assessment, and occasional interpretation of wor…
SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction
Guofan Yu, Sitian Chen, Zhenheng Tang +2
Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges. We present SNI-GNN, a SmartNIC-ass…
Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training
Zhiqiang Tan, Maoxin Wang, Sijie Wang +4
It is well established that the reasoning capabilities of large language models (LLMs) can be improved by applying reinforcement learning (RL) in a post-training stage. In a standa…
Zellige: Moldable Sequence Placement for Mixed Image-Video DiT Training
Guangyu Xiang, Xueze Kang, Minwei Zhao +4
High-quality video generation requires training Diffusion Transformers (DiTs) jointly on image and video data, posing a mixed-length sequence training problem across GPUs. Existing…
CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection
Qihua Pan, Zhenheng Tang, Peijie Dong +4
Evaluating Large Language Models (LLMs) incurs prohibitive computational overhead during continuous development processes. While coreset selection accelerates evaluation, existing…
DRNOISE: Benchmarking Deep Research Agents in Misleading Evidence Environments
Jun Nie, Zhiqin Yang, Zhenheng Tang +4
Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents. Existing evaluatio…
Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration
Xueze Kang, Guangyu Xiang, Suyi Li +4
Diffusion models are increasingly deployed as production visual-generation services, where serving high-resolution image and long video generation is often limited by GPU memory. P…
KernelFlume: Elastic Core-Attention Scaling for Agentic Long-Context Decoding
Guangyu Xiang, Xueze Kang, Lin Zhang +4
LLM serving is increasingly dominated by long and dynamic decode workloads from agents, reasoning models, and extended conversations. When bursty long-context demand exceeds deploy…
Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning
Kai Liu, Peijie Dong, Xinchen Xie +5
The rapid progress of reasoning and agentic large language models (LLMs) has increased the demand for long-context inference, but self-attention (SA) scales quadratically with cont…
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production
Xiang Liu, Shimiao Yuan, Zhenheng Tang +5
LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the releva…
Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs
Yujia Chen, Yang Ye, Xiao Chu +2
Reinforcement learning (RL) with verifiable rewards has proven effective at post-training LLMs for coding, yet deploying separate task-specific specialists incurs costs that scale…
MDN: Parallelizing Stepwise Momentum for Delta Linear Attention
Yulong Huang, Xiang Liu, Hongxiang Huang +5
Linear Attention (LA) offers a promising paradigm for scaling large language models (LLMs) to long sequences by avoiding the quadratic complexity of self-attention. Recent LA model…
VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing
Xiaoyan Su, Peijie Dong, Zhenheng Tang +8
Despite the rapid advancements in Vision-Language Models (VLMs), a critical gap remains in their ability to handle structured, controllable diagrammatic tasks essential for profess…
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
Wenxiang Lin, Xinglin Pan, Ruibo Fan +2
Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been proposed to reduce communication…
SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation
Song Tang, Kaiyong Zhao, Yuliang Li +5
Automatically generating interactive 3D indoor scenes from natural language is crucial for virtual reality, gaming, and embodied AI. However, existing LLM-based approaches often su…
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Haotian Ye, Haowei Lin, Jingyi Tang +30
Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply gen…
Is Your LLM-as-a-Recommender Agent Trustable? LLMs' Recommendation is Easily Hacked by Biases (Preferences)
Zichen Tang, Zirui Zhang, Qian Wang +3
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce recommendation, and job recruitment. In the…
Are Dilemmas and Conflicts in LLM Alignment Solvable? A View from Priority Graph
Zhenheng Tang, Xiang Liu, Qian Wang +3
As Large Language Models (LLMs) become more powerful and autonomous, they increasingly face conflicts and dilemmas in many scenarios. We first summarize and taxonomize these divers…
Rethinking Deep Research from the Perspective of Web Content Distribution Matching
Zixuan Yu, Zhenheng Tang, Tongliang Liu +3
Despite the integration of search tools, Deep Search Agents often suffer from a misalignment between reasoning-driven queries and the underlying web indexing structures. Existing f…
OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation
Yehua Huang, Penglei Sun, Zebin Chen +2
Academic peer review remains the cornerstone of scholarly validation, yet the field faces some challenges in data and methods. From the data perspective, existing research is hinde…
Dissecting Outlier Dynamics in LLM NVFP4 Pretraining
Peijie Dong, Ruibo Fan, Yuechen Tao +11
Training large language models using 4-bit arithmetic enhances throughput and memory efficiency. Yet, the limited dynamic range of FP4 increases sensitivity to outliers. While NVFP…
On the Spectral Flattening of Quantized Embeddings
Junlin Huang, Wenyi Fang, Zhenheng Tang +5
Training Large Language Models (LLMs) at ultra-low precision is critically impeded by instability rooted in the conflict between discrete quantization constraints and the intrinsic…