54 citations · 131 across the 84 of their papers we have counts for
89 papers
Toolcompass: Guiding Tool Trialing, Not Suppressing It
Junlin Fang, Chong Zhang, Do Nguyen-Thanh +3
Large language model (LLM) agents must generalize from tools seen during training to unseen tools at deployment. A key challenge is tool trialing, i.e., excessive trials waste the…
Beyond Outcome Gaps: Process-Aware Fairness Diagnosis for LLM-based Multi-Agent Decision Systems
Yiran Zhao, Lu Zhou, Liming Fang +4
LLM-based multi-agent systems (MAS) are increasingly considered for high-stakes decision-making, yet outcome-based fairness audits can miss where risks arise within the decision tr…
LEAP: Likelihood Elicitation and Aggregation for LLM-based Probabilistic Forecasting
Yufei Chen, Yiran Zhao, Xiaogang Xu +3
LLM-based forecasting systems have improved on real-world tasks such as financial markets and sports outcomes, largely through stronger search and tool use. Many systems still ask…
Beyond Execution: Auditing Experimental Fidelity in LLM-Driven Scientific Research
Lezhi Yu, Xiaogang Xu, Yuhua Zhou +2
LLM agents used for scientific experimentation must do more than generate executable code: they must implement the reference method faithfully, design experiments that test the pap…
LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications
Xiaogang Xu, Jiaqi Tang, Jianmin Chen +12
Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tools, and iterative prediction.…
PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data
Zhenchao Tang, Xiaogang Xu, Tianxu Lv +16
Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here we show that cellular perturba…
ARAC: Benchmarking Auto-Research's Alignment and Completeness on End-to-End Researchs
Jiale Cui, Yueyao Yuan, Kaixi Zhong +3
The rapid advancement of Auto-Research has surfaced a fundamental evaluation challenge: how can we measure the alignment, logical coherence, and evolutionary completeness of its re…
Remember-R1: Mitigating Long-Context Visual Forgetting through Reinforcement Learning
Jianmin Chen, Jiaqi Tang, Wei Wei +9
Multimodal large language models (MLLMs) increasingly rely on long chain-of-thought reasoning for complex tasks. However, as reasoning sequences lengthen, models may gradually rely…
Streaming Communication in Multi-Agent Reasoning
Zhen Yang, Xiaogang Xu, Wen Wang +3
Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to scale linearly with pipeline depth. We introduce StreamMA, a multi-agent r…
Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models
Junlin Fang, Do Nguyen-Thanh, Xiaogang Xu +2
Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing t…
HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging
Weiyu Zhou, Tao Hu, Yijian Wang +3
Most existing multi-exposure HDR methods follow a fixed feed-forward reconstruction paradigm, making them prone to ghosting artifacts in complex dynamic scenes. To address this iss…
Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression
Tianhao Chen, Yuheng Wu, Kelu Yao +3
Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with long visual contexts. Existing compressio…
HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing
Ruyi Chen, Lu Zhou, Xiaogang Xu +3
Text-to-Image (T2I) models have made significant strides in visual realism and semantic consistency, yet they often perpetuate and amplify societal biases. Existing evaluation meth…
LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments
Chiyu Zhang, Huiqin Yang, Bendong Jiang +8
The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, whe…
AHPA: Adaptive Hierarchical Prior Alignment for Diffusion Transformers
Ruibin Min, Yexin Liu, Aimin Pan +5
Representation alignment has recently emerged as an effective paradigm for accelerating Diffusion Transformer training. Despite their success, existing alignment methods typically…
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
Xinlei Yu, Zhangquan Chen, Yongbo He +36
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an inc…
From Thinker to Society: Security in Hierarchical Autonomy Evolution of AI Agents
Xiaolei Zhang, Lu Zhou, Xiaogang Xu +5
Artificial Intelligence (AI) agents have evolved from passive predictive tools into active entities capable of autonomous decision-making and environmental interaction, driven by t…
Learning Latent Proxies for Controllable Single-Image Relighting
Haoze Zheng, Zihao Wang, Xianfeng Wu +5
Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and m…
FC-VFI: Faithful and Consistent Video Frame Interpolation for High-FPS Slow Motion Video Generation
Ganggui Ding, Hao Chen, Xiaogang Xu
Large pre-trained video diffusion models excel in video frame interpolation but struggle to generate high fidelity frames due to reliance on intrinsic generative priors, limiting d…
Fair in Mind, Fair in Action? A Synchronous Benchmark for Understanding and Generation in UMLLMs
Yiran Zhao, Lu Zhou, Xiaogang Xu +3
As artificial intelligence (AI) is increasingly deployed across domains, ensuring fairness has become a core challenge. However, the field faces a "Tower of Babel'' dilemma: fairne…
Low-Light Video Enhancement with An Effective Spatial-Temporal Decomposition Paradigm
Xiaogang Xu, Kun Zhou, Tao Hu +4
Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition s…
GDRO: Group-level Reward Post-training Suitable for Diffusion Models
Yiyang Wang, Xi Chen, Xiaogang Xu +2
Recent advancements adopt online reinforcement learning (RL) from LLMs to text-to-image rectified flow diffusion models for reward alignment. The use of group-level rewards success…
Class Incremental Medical Image Segmentation via Prototype-Guided Calibration and Dual-Aligned Distillation
Shengqian Zhu, Chengrong Yu, Qiang Wang +6
Class incremental medical image segmentation (CIMIS) aims to preserve knowledge of previously learned classes while learning new ones without relying on old-class labels. However,…
Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding
Jiaqi Tang, Jianmin Chen, Wei Wei +7
Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust ML…