13 citations · 35 across the 32 of their papers we have counts for
34 papers
Cavity-tunable Pseudospin-Hall Topology and Edge Transport
Minghui Niu, Yan Meng, Chengle Fan +5
Topological edge states are a hallmark of topological phases, with their existence tied to bulk band topology through bulk-edge correspondence. In photonic pseudospin-Hall systems,…
SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles
Yuyao Ge, Yiwei Wang, Yuchen He +5
Memory-augmented reinforcement learning strengthens LLM agents' ability to solve complex long-horizon tasks. Skills are one such form of memory, pairing instructions with an applic…
Omni-Decision: Evidence-Ledger Planning for Omni-Modal Agents
Ming Ma, Yi Zhu, Yiran Zhong +7
Omni-modal agents must seek evidence across video, audio, web pages, and computation to answer questions. Their main bottleneck is planning: noisy multimodal observations accumulat…
Multimodal Reward Hacking in Reinforcement Learning
Jiayu Yao, Yiwei Wang, Anmeng Zhang +5
Reinforcement learning (RL) is increasingly used to align multimodal large language models (MLLMs), but higher rewards do not always imply better task performance. This risk is amp…
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…
PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding
Baolong Bi, Yuyao Ge, Shenghua Liu +9
Reliable AI systems require large language models (LLMs) to exhibit behaviors aligned with human preferences and values. However, most existing alignment approaches operate at trai…
Gated Differentiable Working Memory for Long-Context Language Modeling
Lingrui Mei, Shenghua Liu, Yiwei Wang +7
Long contexts challenge transformers: attention scores dilute across thousands of tokens, critical information is often lost in the middle, and models struggle to adapt to novel pa…
Reward and Guidance through Rubrics: Promoting Exploration to Improve Multi-Domain Reasoning
Baolong Bi, Shenghua Liu, Yiwei Wang +6
Recent advances in reinforcement learning (RL) have significantly improved the complex reasoning capabilities of large language models (LLMs). Despite these successes, existing met…
Not in Sync: Unveiling Temporal Bias in Audio Chat Models
Jiayu Yao, Shenghua Liu, Yiwei Wang +5
Large Audio Language Models (LALMs) are increasingly applied to audio understanding and multimodal reasoning, yet their ability to locate when events occur remains underexplored. W…
A Survey of Vibe Coding with Large Language Models
Yuyao Ge, Lingrui Mei, Zenghao Duan +12
The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding agents, enabling a novel development methodology…
Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning
Yuyao Ge, Shenghua Liu, Yiwei Wang +6
Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in complex visual environments. While existing enhanc…
AdaptFlow: Adaptive Workflow Optimization via Meta-Learning
Runchuan Zhu, Bowen Jiang, Lingrui Mei +8
Recent advances in large language models (LLMs) have sparked growing interest in agentic workflows, which are structured sequences of LLM invocations intended to solve complex task…
A Survey of Context Engineering for Large Language Models
Lingrui Mei, Jiayu Yao, Yuyao Ge +12
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a f…
RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs
Baolong Bi, Shenghua Liu, Xingzhang Ren +7
The foundational capabilities of large language models (LLMs) are deeply influenced by the quality of their pre-training corpora. However, enhancing data quality at scale remains a…
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
Juan Chen, Baolong Bi, Wei Zhang +5
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating their parametric knowledge with external retrieved content. However, knowledge conflicts c…
Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation
Jiayu Yao, Shenghua Liu, Yiwei Wang +5
Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustne…
Large Language Models as Computable Approximations to Solomonoff Induction
Jun Wan, Lingrui Mei
The rapid advancement of large language models (LLMs) calls for a rigorous theoretical framework to explain their empirical success. While significant progress has been made in und…
a1: Steep Test-time Scaling Law via Environment Augmented Generation
Lingrui Mei, Shenghua Liu, Yiwei Wang +5
Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical errors, and inability to self-correct during com…
Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking
Yuyao Ge, Shenghua Liu, Yiwei Wang +4
Recent advances in Large Language Models (LLMs) have introduced Reasoning Large Language Models (RLLMs), which employ extended thinking processes with reflection and self-correctio…
Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models
Baolong Bi, Shenghua Liu, Yiwei Wang +4
Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and…
VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model
Jiani Zheng, Lu Wang, Fangkai Yang +7
Training Vision-Language Models (VLMs) for Graphical User Interfaces (GUI) agents via Reinforcement Learning (RL) faces critical challenges: environment-based RL requires costly in…
Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach
Yurong Wu, Fangwen Mu, Qiuhong Zhang +8
Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates th…
Context-DPO: Aligning Language Models for Context-Faithfulness
Baolong Bi, Shaohan Huang, Yiwei Wang +11
Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intenti…
You Know What I'm Saying: Jailbreak Attack via Implicit Reference
Tianyu Wu, Lingrui Mei, Ruibin Yuan +3
While recent advancements in large language model (LLM) alignment have enabled the effective identification of malicious objectives involving scene nesting and keyword rewriting, o…