4 citations · 8 across the 31 of their papers we have counts for
22 papers · 1 filter
MemTools: A Unified Research Framework for Interoperable Agent Memory
Chengfeng Zhao, Jinhui Chen, Sirui Liang +4
While memory systems are essential for agent architectures, pervasive architectural fragmentation restricts systematic research. Existing implementations typically couple different…
Trace-Based On-Policy Distillation for Masked Diffusion Language Models
Haolin Ren, Ziyang Huang, Chenhao Yuan +2
Diffusion large language models (dLLMs) are a promising alternative to autoregressive generation. However, reasoning-oriented post-training for dLLMs remains challenging. Supervise…
Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering
Chengfeng Zhao, Yuqiao Tan, Shizhu He +3
While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires continuous environmental interact…
SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models
Shiqiang Cai, Nianhong Niu, Shizhu He +2
Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A high-quality scientific taxon…
DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
Jinxiang Meng, Shaoping Huang, Fangyu Lei +17
Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from cod…
GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum
Shuwen Xu, Yao Xu, Jiaxiang Liu +4
Agentic knowledge graph question answering (KGQA) requires an agent to iteratively interact with knowledge graphs (KGs), posing challenges in both training data scarcity and reason…
WideSeek: Advancing Wide Research via Multi-Agent Scaling
Ziyang Huang, Haolin Ren, Xiaowei Yuan +6
Search intelligence is evolving from Deep Research to Wide Research, a paradigm essential for retrieving and synthesizing comprehensive information under complex constraints in par…
Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing
Cunli Mao, Xiaofei Gao, Ran Song +4
Large language models (LLMs) based Multilingual Knowledge Graph Completion (MKGC) aim to predict missing facts by leveraging LLMs' multilingual understanding capabilities, improvin…
SparK: Query-Aware Unstructured Sparsity with Recoverable KV Cache Channel Pruning
Huanxuan Liao, Yixing Xu, Shizhu He +6
Long-context inference in large language models (LLMs) is increasingly constrained by the KV cache bottleneck: memory usage grows linearly with sequence length, while attention com…
Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Fangyu Lei, Jinxiang Meng, Yiming Huang +5
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with nu…
Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement
Yuqiao Tan, Shizhu He, Huanxuan Liao +2
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving relevant documents from external sources and incorporating them into the context. While it…
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models
Yiming Huang, Jianwen Luo, Yan Yu +8
We introduce DA-Code, a code generation benchmark specifically designed to assess LLMs on agent-based data science tasks. This benchmark features three core elements: First, the ta…
CITI: Enhancing Tool Utilizing Ability in Large Language Models without Sacrificing General Performance
Yupu Hao, Pengfei Cao, Zhuoran Jin +4
Tool learning enables the Large Language Models (LLMs) to interact with the external environment by invoking tools, enriching the accuracy and capability scope of LLMs. However, pr…
Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning Tasks
Huanxuan Liao, Shizhu He, Yao Xu +3
In this paper, we propose ural-mbolic ollaborative istillation (), a novel knowledge distillation method for lear…
: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models
Huanxuan Liao, Shizhu He, Yupu Hao +4
Small Language Models (SLMs) are attracting attention due to the high computational demands and privacy concerns of Large Language Models (LLMs). Some studies fine-tune SLMs using…
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
Wangtao Sun, Chenxiang Zhang, XueYou Zhang +7
Although Large Language Models (LLMs) have demonstrated strong ability, they are further supposed to be controlled and guided by in real-world scenarios to be safe, accurate, and i…
From Instance Training to Instruction Learning: Task Adapters Generation from Instructions
Huanxuan Liao, Shizhu He, Yao Xu +5
Large language models (LLMs) have acquired the ability to solve general tasks by utilizing instruction finetuning (IFT). However, IFT still relies heavily on instance training of e…
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Wangtao Sun, Haotian Xu, Xuanqing Yu +4
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited a…
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering
Huanxuan Liao, Shizhu He, Yao Xu +4
Retrieval-Augmented-Generation and Generation-Augmented-Generation have been proposed to enhance the knowledge required for question answering with Large Language Models (LLMs) by…
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
Tongxu Luo, Jiahe Lei, Fangyu Lei +4
Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks. Nonetheless, the process of updating billions of parameters demands s…
ControlLM: Crafting Diverse Personalities for Language Models
Yixuan Weng, Shizhu He, Kang Liu +2
As language models continue to scale in size and capability, they display an array of emerging behaviors, both beneficial and concerning. This heightens the need to control model b…
Assessing Knowledge Editing in Language Models via Relation Perspective
Yifan Wei, Xiaoyan Yu, Huanhuan Ma +4
Knowledge Editing (KE) for modifying factual knowledge in Large Language Models (LLMs) has been receiving increasing attention. However, existing knowledge editing methods are enti…