3 citations · 9 across the 27 of their papers we have counts for
29 papers
VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness
Zihao Lin, Haoyu Dong, Haobo Xu +10
LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reasoning, and memory. Recursive h…
Hierarchical Credit Assignment for RLVR on Fused Gromov-Wasserstein Geometry
Qi Yu, Ruizhong Qiu, Zhichen Zeng +6
Reinforcement learning with verifiable rewards (RLVR) has been shown to improve the reasoning capability of large language models (LLMs) across diverse reasoning tasks. However, gr…
Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
Ming Li, Dai Li, Xuying Ning +10
Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale re…
Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
Tianxin Wei, Zhan Shi, Minhua Lin +14
Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowledge from accumulated traject…
EvoHarness-RL: Learning Runtime Harness Coordination for Self-Evolving Agents
Xuying Ning, Dongqi Fu, Tianxin Wei +17
Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, recover from failures, and reuse experience across extended interactions.…
Multi-Turn Agentic Scientific Literature Search via Workflow Induction
Jisen Li, Bingxuan Li, Nanyi Jiang +10
Scientific literature search often requires more than retrieving papers from a single query: users' intents are underspecified, preference-dependent, and evolve through interaction…
Harnessing Generalist Agents for Contextualized Time Series
Zihao Li, Kaifeng Jin, Yuanchen Bei +8
Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end workflows for analyzing tempor…
Code as Agent Harness
Xuying Ning, Katherine Tieu, Dongqi Fu +39
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineerin…
Heterogeneous Scientific Foundation Model Collaboration
Zihao Li, Jiaru Zou, Feihao Fang +6
Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability…
PaperMind: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs
Yanjun Zhao, Tianxin Wei, Jiaru Zou +7
Understanding scientific papers requires more than answering isolated questions or summarizing content. It involves an integrated reasoning process that grounds textual and visual…
Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Xiao Lin, Zhicheng Tang, Weilin Cong +14
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long…
MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains
Xuying Ning, Dongqi Fu, Tianxin Wei +7
With the increasing demand for step-wise, cross-modal, and knowledge-grounded reasoning, multimodal large language models (MLLMs) are evolving beyond the traditional fixed retrieve…
FeDecider: An LLM-Based Framework for Federated Cross-Domain Recommendation
Xinrui He, Ting-Wei Li, Tianxin Wei +5
Federated cross-domain recommendation (Federated CDR) aims to collaboratively learn personalized recommendation models across heterogeneous domains while preserving data privacy. R…
A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents
Tianxin Wei, Ting-Wei Li, Zhining Liu +26
Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilitie…
ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplification
Xiao Lin, Philip Li, Zhichen Zeng +6
Despite rich safety alignment strategies, large language models (LLMs) remain highly susceptible to jailbreak attacks, which compromise safety guardrails and pose serious security…
Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents
Yuanchen Bei, Tianxin Wei, Xuying Ning +7
Long-term memory is a critical capability for multimodal large language model (MLLM) agents, particularly in conversational settings where information accumulates and evolves over…
Subspace Alignment for Vision-Language Model Test-time Adaptation
Zhichen Zeng, Wenxuan Bao, Xiao Lin +8
Vision-language models (VLMs), despite their extraordinary zero-shot capabilities, are vulnerable to distribution shifts. Test-time adaptation (TTA) emerges as a predominant strate…
AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
Chengming Cui, Tianxin Wei, Ziyi Chen +6
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling pr…
CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation
Tianxin Wei, Xuying Ning, Xuxing Chen +6
In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific items. However, existing generative recomme…
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Tianxin Wei, Noveen Sachdeva, Benjamin Coleman +12
Statefulness is essential for large language model (LLM) agents to perform long-term planning and problem-solving. This makes memory a critical component, yet its management and ev…
Graph4MM: Weaving Multimodal Learning with Structural Information
Xuying Ning, Dongqi Fu, Tianxin Wei +2
Real-world multimodal data usually exhibit complex structural relationships beyond traditional one-to-one mappings like image-caption pairs. Entities across modalities interact in…
Harnessing Consistency for Robust Test-Time LLM Ensemble
Zhichen Zeng, Qi Yu, Xiao Lin +6
Different large language models (LLMs) exhibit diverse strengths and weaknesses, and LLM ensemble serves as a promising approach to integrate their complementary capabilities. Desp…
Flow Matching Meets Biology and Life Science: A Survey
Zihao Li, Zhichen Zeng, Xiao Lin +9
Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological…
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Junyu Zhang, Runpei Dong, Han Wang +8
This paper presents AlphaOne (1), a universal framework for modulating reasoning progress in large reasoning models (LRMs) at test time. 1 first introduces moment, which…