1 citations · 3 across the 34 of their papers we have counts for
37 papers
DynThinker: Unifying Static 3D and Dynamic 4D Spatial Reasoning
Qihang Cao, Haoyuan Li, Zihan Guo +8
Achieving general spatial intelligence requires a unified understanding of both static 3D and dynamic 4D. However, we find that existing models struggle to excel at both: 3D post-t…
Building LLM Agent Systems the Deep Learning Way: From Modular Design to Architecture Search
Tao Feng, Pengrui Han, Zhongjie Dai +1
Large Language Models (LLMs) have revolutionized AI research and enabled exciting agent systems. To build a complex LLM agent system, most existing research relies on insights from…
CURIO: Curiosity-Driven Test-Time Learning for Open-Ended Discovery
Tao Feng, Fangxu Yu, Zijie Lei +5
Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a frozen large language model (LLM…
LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers
Tao Feng, Fangxu Yu, Haozhen Zhang +9
No single large language model (LLM) is optimal across all queries and budget constraints, making model routing essential for cost-effective deployment. Existing routers adopt dive…
EventConnector: Mining Social Event Relations through Temporal Graphs
Zijie Lei, Haofei Yu, Ge Liu +1
Understanding and retrieving related real-world events based on their temporal dynamics is a fundamental challenge in time-sensitive applications such as forecasting, information r…
-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues
Peixuan Han, Hongyi Du, Jiayu Liu +3
Personalization is a crucial capability of modern language agents. However, current research primarily positions personalized agents as passive responders to user preferences, limi…
ExpWeaver: LLM Agents Learn from Experience via Latent RAG
Tao Feng, Tianyang Luo, Jingjun Xu +5
Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. However, existing methods r…
ExpHarness: Model-Agnostic Experience Learning through a Trainable Harness
Tao Feng, Chongrui Ye, Fangxu Yu +11
Large language model (LLM) agents increasingly operate within a harness, the scaffolding that determines what enters the executor's context, yet the experience they accumulate acro…
ElasticMem: Latent Memory as a Learnable Resource for LLM Agents
Tao Feng, Chongrui Ye, Fangxu Yu +8
Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. However, existing memory-augmented…
LRanker: Scaling LLM Ranking to Millions of Candidates with Test-Time Scaling
Tao Feng, Zijie Lei, Zhigang Hua +4
Large language models (LLMs) offer strong semantic representations for ranking, but processing candidate texts within limited contexts and repeatedly invoking an LLM for candidate-…
RouteProfile: Graph-Based Profiling for Cold-Start LLM Routing
Jingjun Xu, Hongji Pu, Tao Feng +3
LLM routing is increasingly important for selecting suitable models under diverse user needs and deployment constraints, but its practical effectiveness depends on continual adapta…
GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs
Tao Feng, Haozhen Zhang, Zijie Lei +2
LLM routing has achieved promising results in integrating the strengths of diverse models while balancing efficiency and performance. However, to support more realistic and challen…
h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
Yanru Qu, Yijie Zhang, Wenjuan Tan +6
Accurate molecular representations are critical for drug discovery, and a central challenge lies in capturing the chemical environment of molecular fragments, as key interactions,…
Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems
Tao Feng, Pengrui Han, Guanyu Lin +2
Large language models (LLMs) have transformed AI research thanks to their powerful internal capabilities and knowledge. However, existing LLMs still fail to effectively incorporate…
MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels
Tianyang Luo, Tao Feng, Zhigang Hua +4
Reinforcement learning has emerged as a powerful paradigm for improving large language model (LLM) reasoning, where rollouts are sampled from the policy and reward signals computed…
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…
Probing the Knowledge Boundary: An Interactive Agentic Framework for Deep Knowledge Extraction
Yuheng Yang, Siqi Zhu, Tao Feng +2
Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far their knowledge boundary extends. Exist…
UniRec: Unified Multimodal Encoding for LLM-Based Recommendations
Zijie Lei, Tao Feng, Zhigang Hua +5
Large language models have recently shown promise for multimodal recommendation, particularly with text and image inputs. Yet real-world recommendation signals extend far beyond th…
ResearchArcade: Graph Interface for Academic Tasks
Jingjun Xu, Chongshan Lin, Haofei Yu +2
Academic research generates diverse data sources, and as researchers increasingly use machine learning to assist research tasks, a crucial question arises: Can we build a unified d…
PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modeling
Zhongjie Dai, Tao Feng, Jiaxuan You
The growing number of Large Language Models (LLMs) with diverse capabilities and response styles provides users with a wider range of choices, which presents challenges in selectin…
GTAlign: Game-Theoretic Alignment of LLM Assistants for Social Welfare
Siqi Zhu, David Zhang, Pedro Cisneros-Velarde +1
Large Language Models (LLMs) have achieved remarkable progress in reasoning, yet sometimes produce responses that are suboptimal for users in tasks such as writing, information see…
GMTRouter: Personalized LLM Router over Multi-turn User Interactions
Yihang Sun, Encheng Xie, Tao Feng +1
Large Language Model (LLM) routing has demonstrated strong capability in balancing response quality with computational cost. As users exhibit diverse preferences, personalization h…
Multi-Agent Evolve: LLM Self-Improve through Co-evolution
Yixing Chen, Yiding Wang, Siqi Zhu +5
Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs). However, the success of RL for LLMs heav…
AcademicEval: Live Long-Context LLM Benchmark
Haozhen Zhang, Tao Feng, Pengrui Han +1
Large Language Models (LLMs) have recently achieved remarkable performance in long-context understanding. However, current long-context LLM benchmarks are limited by rigid context…