Symbolic regression (SR) seeks to discover mathematical expressions directly from data, without requiring task-specific scientific descriptions. Despite tremendous progress in both conventional search-based methods and recent LLM-based methods, two critical limitations largely remain. First (Limited Generation Flexibil...
Kai-Feng Jin, Rui-Zhong Qiu, Zihao Li et al.· 0 citations
Sequential recommenders typically construct history inputs from item identifiers or content features using an embedding map shared across users. The same item therefore yields the same initial embedding representation, even when users have different relationships to the item. We propose personalized item representation...
Renzhi Wu, Zi-Kun Cui, Jun-Jie Yang et al.· 0 citations
Reinforcement learning with verifiable rewards (RLVR) has been shown to improve the reasoning capability of large language models (LLMs) across diverse reasoning tasks. However, group-based RLVR methods, such as GRPO, assign a uniform advantage to all tokens within rollouts of the same outcome. While existing works ref...
Qi Yu, Rui-Zhong Qiu, Zhichen Zeng et al.· 0 citations
Results characterize when runtime-controlled composition of coding-agent products improves execution accuracy, as well as when runtime-controlled composition of coding-agent products improves execution accuracy.
Zheng-Yu Chen, Lin-Feng Liu, Hong Li et al.· 0 citations
This work synthesizes a task-conditioned temporal workflow graph that jointly specifies agent connectivity and edge-level communication semantics, and introduces ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.
