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 Need an LLM to Decide When to Act?
Xiaoze Liu, Ruowang Zhang, Amir H. Abdi +5
Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipelines repeatedly interpret activity hist…
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…
Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
Xiaoze Liu, Ruowang Zhang, Weichen Yu +7
Heterogeneous multi-agent systems combine models with different capabilities through a common communication interface. Exchanging internal states directly requires translating betw…