5 citations · 24 across the 91 of their papers we have counts for
99 papers
Capability Scaling-Down Laws for LLM Compression
Xueqi Cheng, Liang Wu, Kelly Wan +2
LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable resource reductions can produ…
Experimental Experience Modeling for Autonomous Research
Wenda Wei, Yingchen Zhang, Ruqing Zhang +3
Autonomous research agents can generate hypotheses and conduct experiments, but experimentation remains a major source of computational cost. A fundamental challenge is deciding wh…
RAdapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG
Yucan Guo, Miao Su, Saiping Guan +6
Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla RAG efficiently handles simp…
HiDiffTIR: Hierarchical Difficulty-Aware Policy Optimization for Multi-Turn Tool-Integrated Reasoning
Yucan Guo, Xiaohan Wang, Miao Su +8
Tool-Integrated Reasoning (TIR) is a fundamental capability for LLM agents to solve complex tasks by interacting with external tools iteratively. Reinforcement Learning (RL) has be…
DeepRepro: State-Aware Subplanning for Paper-to-Code Reproduction in Evolving Repositories
Hongru Song, Ruqing Zhang, Jiafeng Guo +2
Recent advances in agentic large language models (LLMs) have enabled increasingly autonomous software engineering workflows, yet automatic machine learning (ML) paper-to-code repro…
Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents
Puji Wang, Yingchen Zhang, Ruqing Zhang +2
Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat assistants, unsafe content in thes…
Event Ontology Expansion via LLM-Based Conceptualization
Weicheng Ren, Zixuan Li, Long Bai +3
Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology.. Existing methods typically cluste…
SAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models
Yuchen He, Baolong Bi, Shenghua Liu +7
Although multi-objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing practice of static weighted summa…
Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Weiwei Ding, Zixuan Li, Long Bai +7
Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations. Existing LLM-KG integration frame…
Can LLM Rerankers Predict Their Own Ranking Performance?
Shiyu Ni, Keping Bi, Jiafeng Guo +3
Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available. Query performance prediction…
Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models
Lexin Wang, Shenghua Liu, Yiwei Wang +2
Can vision-language models predict what a 180° rotation would reveal from the original image alone? We study this ability through Rotated-Outcome Prediction: given an original imag…
SOMA: Efficient Multi-turn LLM Serving via Small Language Model
Xueqi Cheng, Qiong Wu, Zhengyi Zhou +3
Large Language Models (LLMs) are increasingly deployed in multi-turn dialogue settings where preserving conversational context across turns is essential. A standard serving practic…
LatentRouter: Can We Choose the Right Multimodal Model Before Seeing Its Answer?
Xueqi Cheng, Yushun Dong
Multimodal large language models (MLLMs) have heterogeneous strengths across OCR, chart understanding, spatial reasoning, visual question answering, cost, and latency. Effective ML…
ReAD: Reinforcement-Guided Capability Distillation for Large Language Models
Xueqi Cheng, Xugui Zhou, Tyler Derr +1
Capability distillation applies knowledge distillation to selected model capabilities, aiming to compress a large language model (LLM) into a smaller one while preserving the abili…
When and Why Grouping Attention Heads Accelerates Muon Optimization
Hongtao Zhang, Wenjie Zhou, Wei Chen +1
Muon orthogonalizes matrix updates, but multi-head attention naturally operates at the level of heads. This granularity mismatch raises the question of whether Muon should be appli…
EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer
Hao Zhang, Zhibin Zhang, Guangxin Wu +3
Large language models (LLMs) have achieved remarkable performance across diverse domains, yet their enormous computational and memory requirements hinder deployment in resource-con…
Detoxification for LLM: From Dataset Itself
Wei Shao, Yihang Wang, Gaoyu Zhu +4
Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself…
AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning
Hongru Song, Yu-An Liu, Ruqing Zhang +4
Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack surfaces. We study knowledge-base…
Relevance Supervision Can Teach Neural Retrievers to Prefer LLM-Generated Text
Wei Huang, Keping Bi, Yinqiong Cai +3
Recent studies show that neural retrievers often exhibit source bias, ranking LLM-generated passages above semantically similar human-written ones, which threatens the reliability…
HighlightBench: Benchmarking Markup-Driven Table Reasoning in Scientific Documents
Lexin Wang, Shenghua Liu, Yiwei Wang +5
Visual markups such as highlights, underlines, and bold text are common in table-centric documents. Although multimodal large language models (MLLMs) have made substantial progress…
PRISM-: Differential Subspace Steering for Prompt Highlighting in Large Language Models
Yuyao Ge, Shenghua Liu, Yiwei Wang +5
Prompt highlighting steers a large language model to prioritize user-specified text spans during generation. A key challenge of existing Key-editing approaches is extracting steeri…
Bagging-Based Model Merging for Robust General Text Embeddings
Hengran Zhang, Keping Bi, Jiafeng Guo +4
General-purpose text embedding models underpin a wide range of NLP and information retrieval applications, and are typically trained on large-scale multi-task corpora to encourage…
MI-PRUN: Optimize Large Language Model Pruning via Mutual Information
Hao Zhang, Zhibin Zhang, Guangxin Wu +3
Large Language Models (LLMs) have become indispensable across various domains, but this comes at the cost of substantial computational and memory resources. Model pruning addresses…
Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration
Guangxin Wu, Hao Zhang, Zhang Zhibin +2
Large Language Models (LLMs) have achieved remarkable success across a wide spectrum of natural language processing tasks. However, their ever-growing scale introduces significant…