1 citations · 1 across the 9 of their papers we have counts for
11 papers
Selm: Self-Evolving Library Management for Multi-Skill Agent Optimization
Mengxi Hu, Yuxuan Hu, Jinbo Su +6
Large language model (LLM) agents increasingly use skills as a lightweight way to acquire task-specific knowledge. Recent work enables skills to evolve from task trajectories. Howe…
CARA: Concept-Aware Risk Attention for Interpretable Collision Anticipation
Zhishan Tao, Ruoyu Wang, Yucheng Wu +6
Collision anticipation in autonomous driving requires not only accurate early warnings but also interpretable reasoning about what risk factors are being tracked and how risk evolv…
Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning
Zhengbo Zhang, Changtao Miao, Jinbo Su +10
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex…
MemGround: Long-Term Memory Evaluation Kit for Large Language Models in Gamified Scenarios
Yihang Ding, Wanke Xia, Yiting Zhao +5
Current evaluations of long-term memory in LLMs are fundamentally static. By fixating on simple retrieval and short-context inference, they neglect the multifaceted nature of compl…
VisBrowse-Bench: Benchmarking Visual-Native Search for Multimodal Browsing Agents
Zhengbo Zhang, Jinbo Su, Zhaowen Zhou +14
The rapid advancement of Multimodal Large Language Models (MLLMs) has enabled browsing agents to acquire and reason over multimodal information in the real world. But existing benc…
TableCache: Primary Foreign Key Guided KV Cache Precomputation for Low Latency Text-to-SQL
Jinbo Su, Yuxuan Hu, Cuiping Li +4
In Text-to-SQL tasks, existing LLM-based methods often include extensive database schemas in prompts, leading to long context lengths and increased prefilling latency. While user q…
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts
Sijia Luo, Xiaokang Zhang, Yuxuan Hu +6
Reinforcement Learning (RL) has become essential for eliciting complex reasoning capabilities in Large Language Models (LLMs). However, the substantial memory overhead of storing K…
Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision
Ge Chang, Jinbo Su, Jiacheng Liu +7
Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving informative yet compact subgraphs that…
GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoning
Ge Chang, Jinbo Su, Jiacheng Liu +7
Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) techniques have exhibited remarkable performance across a wide range of domains. However, existing…
ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuning
Xinyi Wang, Jiashui Wang, Jinbo Su +9
Assembly code analysis and comprehension play critical roles in applications like reverse engineering, yet they face substantial challenges due to low information density and a lac…
KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models
Weijie Chen, Ting Bai, Jinbo Su +3
Large language models with retrieval-augmented generation encounter a pivotal challenge in intricate retrieval tasks, e.g., multi-hop question answering, which requires the model t…