1 citations · 1 across the 13 of their papers we have counts for
14 papers
Inherit-MAS: Test-Time Evolution of Multi-Agent Systems through Workflow and Execution Inheritance
Songtao Wei, Yi Li, Zhichun Guo +1
Multi-agent systems (MAS) built from large language models coordinate specialized agents to tackle complex tasks, but effective workflows are difficult to design in advance. Test-t…
Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
Dongming Jiang, Yi Li, Bingzhe Li
Agentic memory is becoming essential for long-horizon AI agents, yet many existing systems rely on autoregressive LLMs to control how memories are organized, retrieved, and used, p…
A Universal Context-Reuse Layer for Cross-Model KV Sharing
Yi Li, Dongming Jiang, Yi Zhao +1
Modern large language model (LLM) serving systems increasingly operate over repeated or shared context, yet each model typically performs its own prefill computation even when anot…
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…
ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows
Alvaro Lopez Pellicer, Plamen Angelov, Marwan Bukhari +3
While interpretable prototype networks offer compelling case-based reasoning for clinical diagnostics, their raw continuous outputs lack the semantic structure required for medical…
DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs
Yi Li, Songtao Wei, Dongming Jiang +3
Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead.…
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…
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…
Hippocampus: An Efficient and Scalable Memory Module for Agentic AI
Yi Li, Lianjie Cao, Faraz Ahmed +2
Agentic AI require persistent memory to store user-specific histories beyond the limited context window of LLMs. Existing memory systems use dense vector databases or knowledge-gra…
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…
Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations
Dongming Jiang, Yi Li, Songtao Wei +8
Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and personalization beyond fixed conte…
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…
Knowledge-Driven Hybrid SSD Management Enhanced by Fine-Tuned LLMs
Qian Wei, Yi Li, Zehao Chen +4
A hybrid Solid-State Drives (SSDs) integrates different modes of flash cells (e.g., single-level cell (SLC) and Quad-Level Cell (QLC)) and enables them to convert between each othe…
YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training
Yi Li, Zhichun Guo, Guanpeng Li +1
Graph neural networks (GNNs) have become essential tools for analyzing non-Euclidean data across various domains. During training stage, sampling plays an important role in reducin…