activity
20242026
most citedMAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents

1 citations · 1 across the 5 of their papers we have counts for

collaborators

10 papers

cs.AI2026

V-Mem: Modality-Routed Retrieval for Long-Term Multimodal Agentic Memory

Dingyi Kang, Dongming Jiang, Yi Li +2

Interaction between users and LLM agents is increasingly multimodal: conversations interleave text with images, and a later question may target either. Yet most agent memories are…

cs.DC2026

Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference

Yafan Huang, Sheng Di, Guanpeng Li

Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as cod…

cs.AI2026

HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution

Dongming Jiang, Yi Li, Guanpeng Li +2

Memory retrieval in agentic large language model (LLM) systems is often treated as a static lookup problem, relying on flat vector search or fixed binary relational graphs. However…

cs.LG2026

LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models

Songtao Wei, Yi Li, Zhikai Li +7

Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated Chain-of-Thought (CoT) trajec…

cs.AI20261 cited

MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents

Dongming Jiang, Yi Li, Guanpeng Li +1

Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity…

cs.LG2026

CoSA: Compressed Sensing-Based Adaptation of Large Language Models

Songtao Wei, Yi Li, Bohan Zhang +6

Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such…