1 citations · 2 across the 16 of their papers we have counts for
Showing cs.AIShow all
2 papers · 1 filter
cs.AI2026
How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling
Dhananjay Ashok, Shantanu Agarwal, Vivek Datla +2
World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Langu…
cs.AI2026
Large Databases Need Small, Open-Weight Language Models
Parker Glenn, Alfy Samuel
Language model systems built around proprietary APIs often operate on a token-based cost model. This becomes prohibitively expensive in the context of large databases, where LM-enh…