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cs.LG2026
A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners
Patrick Emami, Nan Qiang, Peter Graf
Supervised fine-tuning (SFT) improves end-to-end classical planning in large language models (LLMs), but do these models also learn to represent and reason about the planning probl…
cs.LG2026
Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery
Renuka Chintalapati, Sid Raskar, Anurag Acharya +3
Coding agents accumulate extensive context during long-running tasks, yet fixed context windows force practitioners to choose between truncation and task failure. While numerous me…
cs.LG2025
SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems
Patrick Emami, Zhaonan Li, Saumya Sinha +1
Surrogate models are used to predict the behavior of complex energy systems that are too expensive to simulate with traditional numerical methods. Our work introduces the use of la…