6 papers
OpenRFM: Dissecting Relational In-Context Learning
Zhikai Chen, Junyu Yin, Jialiang Gu +5
Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pass via relational in-context le…
Multi-Rollout On-Policy Distillation via Peer Successes and Failures
Weichen Yu, Xiaomin Li, Yizhou Zhao +8
Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited guidance about where reasoning s…
Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?
Xiaoze Liu, Ruowang Zhang, Amir H. Abdi +5
Proactive agents read user activity as text and call an LLM on every event to decide whether to act. But user activity is not natively text: it is a structured event stream of (act…
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
Xiaoze Liu, Ruowang Zhang, Weichen Yu +7
Multi-Agent Systems (MAS) powered by Large Language Models have unlocked advanced collaborative reasoning, yet they remain bottlenecked by discrete text communication, which impose…
Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning
Hanbo Cheng, Limin Lin, Ruo Zhang +2
Despite rapid advancements, current text-to-image (T2I) models predominantly rely on a single-step generation paradigm, which struggles with complex semantics and faces diminishing…
Reflective Context Learning: Studying the Optimization Primitives of Context Space
Nikita Vassilyev, William Berrios, Ruowang Zhang +3
Generally capable agents must learn from experience in ways that generalize across tasks and environments. The fundamental problems of learning, including credit assignment, overfi…