4 citations · 5 across the 17 of their papers we have counts for
8 papers · 1 filter
Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks
Tianyi Xu, Yaolun Zhang, Yiran Wu +6
Training-free self-improvement lets an agent use feedback to revise the response it is about to return and to update the harness state that shapes how it handles later tasks. On op…
EvalResearchBench: Can AI Agents Build Their Own Feedback for Recursive Self-Improvement?
Yaolun Zhang, Tianyi Xu, Yujie Zhao +3
Recursive self-improvement (RSI) relies on evaluation feedback to assess progress and guide further research, yet repeatedly running complex benchmarks is costly and slows iteratio…
EvoPool: Evolutionary Programmatic Annotation for Label-Efficient Specialized Supervision
Tianyi Xu, Yaolun Zhang, Xuan Ouyang +1
Large language models excel at general tasks but underperform smaller supervised models in specialized, high-stakes domains where training labels are costly. We address this regime…
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning
Yaolun Zhang, Yujie Zhao, Nan Wang +6
Automatic multi-agent systems aim to instantiate agent workflows without relying on manually designed or fixed orchestration. However, existing automatic MAS approaches remain only…
EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
Yaolun Zhang, Tianyi Xu, Shengyu Dai +3
We argue that multi-agent test-time evolution is not single-agent evolution replicated N times. A single-agent learner can only evolve its own context and memory. A multi-agent sys…
Kirchhoff index of a nested geometric graph with weighted multiple edges
Da-yeon Huh
Kirchhoff index, Kf(G), introduced by Klein and Randic in 1993, represents the total effective resistances between all pairs of vertices in a graph G, where each edge is regarded a…
Live-Evo: Online Evolution of Agentic Memory from Continuous Feedback
Yaolun Zhang, Yiran Wu, Yijiong Yu +2
Large language model (LLM) agents are increasingly equipped with memory, which are stored experience and reusable guidance that can improve task-solving performance. Recent \emph{s…
Construct, Align, and Reason: Large Ontology Models for Enterprise Knowledge Management
Yao Zhang, Hongyin Zhu
Enterprise-scale knowledge management faces significant challenges in integrating multi-source heterogeneous data and enabling effective semantic reasoning. Traditional knowledge g…