457 citations · 552 across the 18 of their papers we have counts for
23 papers
MemoWM: How World Models Change What Agents Need to Remember
Bingfan Zeng, Zhisheng Chen, Chenbo Sang +6
Long-term agents face growing storage demands as they accumulate experience. World models capture reusable regularities that can reduce the information stored for each experience.…
Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning
Wei Zhai, Xiang Liu, Qiang Huang +6
Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase spec…
UniCounting: Instance-Aware Proposal Consolidation for Image-Query-Free Multi-Category Counting
Jinshi Liu, Pan Liu, Lei He +2
Visual counting is commonly formulated as counting a single specified target, with a model receiving an image-specific exemplar, text query, or target category and returning a sing…
Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestration
Xutao Mao, Rui Qian, Linghan Chen +5
LLM agents now execute tasks end to end with permission to change real systems and increasingly orchestrate subagents that differ in capability and cost. Prior work treats the choi…
"You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accused
Xutao Mao, Rui Qian, Longxiang Wang +6
LLM agents increasingly keep working after a task succeeds as they resume after compaction or take over handoffs. Their finished work keeps receiving follow-up input that sometimes…
MemPrism: Task-Conditioned Relational Memory Views for Long-Horizon Agents
Zhisheng Chen, Bingfan Zeng, Bangde Cao +8
Long-horizon agents rely on memory to reuse experiences, yet existing memory systems often assume that evidence can be directly consumed through a fixed representation. This leads…
PMMC: Prospective Multimodal Memory Compilation for Long-Term LVLM Agents
Jingyu Sun, Yan Lin, Yuyang Xue +10
Long-term memory is essential for LVLM agents to maintain consistency and integrate information across extended multimodal interactions. Existing agent memory systems, however, oft…
Why Are GUI Agents Correct but Late? Decode on the Decision-Time Critical Path, Tested with Pre-Compiled Policy Trees
Zihan Dong, Rui Qian, Qishi Zhan +3
Computer-use agents often fail on transient GUI events because they produce the correct action only after the relevant window has already closed. We identify the main cause as expe…
How Benchmarks Mis-Score Computer-Use Agents
Zihan Dong, Zhiyuan Ma, Zekun Wang +5
Computer-use agents (CUA) are being deployed to browse the web and operate desktop software, yet their benchmark scores are still commonly produced by brittle scripted oracles. A s…
Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Self-Improving Personal Agents
Xutao Mao, Liangjie Zhao, Leyao Wang +6
Self-improving personal agents write profiles, memories, and reusable skills that carry over from one chat to the next. Prior works ask whether a model agrees with the user in its…
Seeing What Is Actually There: PriVE-Bench and PriVE-Tools for Counterfactual Evaluation of Agentic Visual Evidence in VLMs
Jingyu Sun, Jiachen Tu, Yuyang Xue +8
Vision-language models (VLMs) often answer visual questions using learned language and category priors rather than grounding their predictions in the image itself. Counterfactual i…
4DVLT: Dynamic Scene Understanding with Worldline-Centered Vision-Language Tracking
Chaoyue Li, Boxue Yang, Shengyao Zhou +4
Language-guided dynamic scene understanding requires identifying one physical entity across time, metric 3D space, and multiple viewpoints, a setting not captured by current multim…
Rubric-Guided Process Reward for Stepwise Model Routing
Shenghao Ye, Yu Guo, Zhengheng Li +2
Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequenti…
PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting
Yingbo Zhou, Yutong Ye, Zhiwei Ling +5
Reliable periodic patterns serve as a fundamental basis for accurate multivariate time series forecasting. However, existing methods either implicitly extract periodicity through c…
PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting
Yingbo Zhou, Yutong Ye, Shuhao Li +5
Real-world time series forecasting faces the fundamental challenge of non-stationary statistical properties, including shifts in mean and variance over time. While reversible insta…
AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation
Rui Qian, Chuanhang Deng, Qiang Huang +6
Reasoning segmentation requires models to ground complex, implicit textual queries into precise pixel-level masks. Existing approaches rely on a single segmentation token $\texttt{…
CoT2-Meta: Budgeted Metacognitive Control for Test-Time Reasoning
Siyuan Ma, Bo Gao, Zikai Xiao +6
Recent test-time reasoning methods improve performance by generating more candidate chains or searching over larger reasoning trees, but they typically lack explicit control over w…
Unrewarded Exploration in Large Language Models Reveals Latent Learning from Psychology
Jian Xiong, Jingbo Zhou, Zihan Zhou +6
Latent learning, classically theorized by Tolman, shows that biological agents (e.g., rats) can acquire internal representations of their environment without rewards, enabling rapi…
UGround: Towards Unified Visual Grounding with Unrolled Transformers
Rui Qian, Xin Yin, Chuanhang Deng +4
We present UGround, a \textbf{U}nified visual \textbf{Ground}ing paradigm that dynamically selects intermediate layers across \textbf{U}nrolled transformers as ``mask as prompt,''…
SolEval: Benchmarking Large Language Models for Repository-level Solidity Code Generation
Zhiyuan Peng, Xin Yin, Rui Qian +5
Large language models (LLMs) have transformed code generation. However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity lang…
Reasoning to Attend: Try to Understand How <SEG> Token Works
Rui Qian, Xin Yin, Dejing Dou
Current Large Multimodal Models (LMMs) empowered visual grounding typically rely on tokens as a text prompt to jointly optimize the vision-language model (e.g., LL…
3D Object Detection for Autonomous Driving: A Survey
Rui Qian, Xin Lai, Xirong Li
Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of percep…
BADet: Boundary-Aware 3D Object Detection from Point Clouds
Rui Qian, Xin Lai, Xirong Li
Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These methods typically comprise two steps: 1) Utilize a region proposal network to propose a ha…