2 citations · 2 across the 8 of their papers we have counts for
Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
Rethinking Memory as Continuously Evolving Connectivity
Jizhan Fang, Buqiang Xu, Zhixian Wang +12
Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic e…
cs.CL2026
Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks
Tianze Xu, Yanzhao Zheng, Pengrui Lu +11
Rubric-based Reinforcement Learning (RL) has emerged as a promising approach for aligning Large Language Models (LLMs) with complex, open-domain instruction following tasks. Howeve…
cs.CL2024★ 2 cited
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models
Haoran Sun, Lixin Liu, Junjie Li +4
The ability of large language models (LLMs) to follow instructions is crucial to real-world applications. Despite recent advances, several studies have highlighted that LLMs strugg…