7 citations · 39 across the 33 of their papers we have counts for
6 papers · 1 filter
Small but Mighty: Test Suites as Improvement Signals for Self-Evolving Agents
Yutong Cheng, Wei Cheng, Wenchao Yu +2
A self-evolving agent rewrites its own harness and must decide which rewrites to keep, so its improvement signal is the check a rewrite has to pass. Verifying a rewrite by agent ro…
MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
Dong Li, Yanchi Liu, Xujiang Zhao +6
Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly updating their li…
Coupled Hierarchical Search over Topology and Execution for Agentic Workflow Synthesis
Dong Li, Yanchi Liu, Xujiang Zhao +6
Although structured workflows empower Large Language Models (LLMs) to tackle complex problems, automating their creation is severely hindered by a vast combinatorial search space,…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement
Wangyang Ying, Yanchi Liu, Xujiang Zhao +5
Automatically extracting workflows as procedural graphs from natural language is promising yet underexplored, demanding both structural validity and logical alignment. While recent…
TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
Geon Lee, Wenchao Yu, Kijung Shin +2
Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associat…