3 citations · 10 across the 20 of their papers we have counts for
25 papers
MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents
Haozhen Zhang, Haodong Yue, Quanyu Long +6
Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent memory systems construct memory…
From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation
Quanyu Long, Xiao Chen, Jianda Chen +4
Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce. We explor…
The Shape of Time: Video-Token Contrast for Temporal Understanding in VideoLMs
Yumeng Shi, Quanyu Long, Yin Wu +1
Seeing frames in order does not mean representing time. Modern VideoLMs receive ordered video streams, yet their main supervision acts on generated text rather than video-token rep…
When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents
Qisheng Hu, Quanyu Long, Wenya Wang
Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in external memory, seemingly sides…
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +47
As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that ma…
Self-Verification Dilemma: Experience-Driven Suppression of Overused Checking in LLM Reasoning
Quanyu Long, Kai Jie Jiang, Jianda Chen +3
Large Reasoning Models (LRMs) achieve strong performance by generating long reasoning traces with reflection. Through a large-scale empirical analysis, we find that a substantial f…
Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
Haozhen Zhang, Haodong Yue, Tao Feng +8
Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory const…
MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents
Haozhen Zhang, Quanyu Long, Jianzhu Bao +4
Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors ab…
Programming over Thinking: Efficient and Robust Multi-Constraint Planning
Derrick Goh Xin Deik, Quanyu Long, Zhengyuan Liu +2
Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints. Existing large language mod…
From Competition to Synergy: Unlocking Reinforcement Learning for Subject-Driven Image Generation
Ziwei Huang, Ying Shu, Hao Fang +5
Subject-driven image generation models face a fundamental trade-off between identity preservation (fidelity) and prompt adherence (editability). While online reinforcement learning…
Causality Matters: How Temporal Information Emerges in Video Language Models
Yumeng Shi, Quanyu Long, Yin Wu +1
Video language models (VideoLMs) have made significant progress in multimodal understanding. However, temporal understanding, which involves identifying event order, duration, and…
Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification
Qisheng Hu, Quanyu Long, Wenya Wang
Multi-hop claim verification is inherently challenging, requiring multi-step reasoning to construct verification chains while iteratively searching for information to uncover hidde…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…
Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering
Yumeng Shi, Quanyu Long, Wenya Wang
Video question answering benefits from the rich information in videos, enabling various applications. However, the large volume of tokens generated from long videos presents challe…
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking
Qisheng Hu, Quanyu Long, Wenya Wang
Large language model pipelines have improved automated fact-checking for complex claims, yet many approaches rely on few-shot in-context learning with demonstrations that require s…
Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries
Yin Wu, Quanyu Long, Jing Li +2
Retrieval-augmented generation (RAG) is a paradigm that augments large language models (LLMs) with external knowledge to tackle knowledge-intensive question answering. While severa…
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts
Ziwei Huang, Wanggui He, Quanyu Long +8
Evaluating the quality of synthesized images remains a significant challenge in the development of text-to-image (T2I) generation. Most existing studies in this area primarily focu…
Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance?
Qisheng Hu, Quanyu Long, Wenya Wang
Fact-checking pipelines increasingly adopt the Decompose-Then-Verify paradigm, where texts are broken down into smaller claims for individual verification and subsequently combined…
Large Language Models Know What Makes Exemplary Contexts
Quanyu Long, Jianda Chen, Wenya Wang +1
In-context learning (ICL) has proven to be a significant capability with the advancement of Large Language models (LLMs). By instructing LLMs using few-shot demonstrative examples,…
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning
Quanyu Long, Yin Wu, Wenya Wang +1
In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs). By instructing LLMs using few-shot demonstrative…
Backdoor Attacks on Dense Retrieval via Public and Unintentional Triggers
Quanyu Long, Yue Deng, LeiLei Gan +2
Dense retrieval systems have been widely used in various NLP applications. However, their vulnerabilities to potential attacks have been underexplored. This paper investigates a no…
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning
Quanyu Long, Wenya Wang, Sinno Jialin Pan
Large language models (LLMs) have showcased their capability with few-shot inference known as in-context learning. However, in-domain demonstrations are not always readily availabl…
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation
Quanyu Long, Tianze Luo, Wenya Wang +1
In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach. One of the difficulties is how to learn task discrimination in the absence of…
Generative Imagination Elevates Machine Translation
Quanyu Long, Mingxuan Wang, Lei Li
There are common semantics shared across text and images. Given a sentence in a source language, whether depicting the visual scene helps translation into a target language? Existi…