5 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…
DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs
Longxuan Yu, Yunshu Wu, Yu Fu +5
Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length and quality: with a fixed step…
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…
CausalEval: Towards Better Causal Reasoning in Language Models
Longxuan Yu, Delin Chen, Siheng Xiong +6
Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While language models (LMs) can generate rat…