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cs.AI2026
From Table to Cell: Attention for Better Reasoning with TABALIGN
Tung Sum Thomas Kwok, Zeyong Zhang, Xinyu Wang +6
Multi-step LLM reasoning over structured tables fails because planning and execution share no explicit cell-grounding contract. Existing methods constrain the planner to a left-to-…
cs.AI2026
From History to State: Constant-Context Skill Learning for LLM Agents
Haoyang Xie, Xinyuan Wang, Yancheng Wang +2
Large language model (LLM) agents are increasingly used to operate browsers, files, code and tools, making personal assistants a natural deployment target. Yet personal agents face…
cs.AI2026
U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning
Christine P Lee, Xinyu Jessica Wang, Aws Albarghouthi +2
LLMs are increasingly used for end-user task planning, yet their black-box nature limits users' ability to ensure reliability and control. While recent systems incorporate verifica…