From the 2 of 20 linked papers with an AI index.
5 papers · 1 filter
MemHarness: Memory Is Reconstructed, Not Replayed
Rong Wu, Daocheng Fu, Licheng Wen +10
The paper introduces MemHarness, a framework that lets large language model agents reconstruct and adapt retrieved past experiences to the current context instead of replaying them…
The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios
Daocheng Fu, Jianbiao Mei, Rong Wu +7
The rapid evolution of Multi-modal Large Language Models (MLLMs) has advanced workflow automation; however, existing research mainly targets performance upper bounds in static envi…
MemVerse: Multimodal Memory for Lifelong Learning Agents
Junming Liu, Yifei Sun, Weihua Cheng +11
Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember. Without reliable memory, agents catast…
TrustGeoGen: Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solving
Daocheng Fu, Jianlong Chen, Renqiu Xia +12
Geometric problem solving (GPS) requires precise multimodal understanding and rigorous, step-by-step logical reasoning. However, developing capable Multimodal Large Language Models…
KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models
Kemou Jiang, Xuan Cai, Zhiyong Cui +7
Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner. These LLM-enhanced methodologies excel…