R-WoM: Retrieval-augmented World Model For Computer-use Agents
Large Language Models (LLMs) can serve as world models to enhance agent
decision-making in digital environments by simulating future states and
predicting action outcomes, potentially eliminating costly trial-and-error
exploration. However, this capability is fundamentally limited by LLMs'
tendency toward hallucination and their reliance on static training knowledge,
which can lead to compounding errors that inhibit long-horizon simulations. To
systematically investigate whether LLMs are appropriate for world modeling, we
probe two core capabilities of world models--future state prediction and reward
estimation--through three tasks: next-state identification, full-procedure
planning alignment, and milestone transition recognition. Our analysis shows
that while LLMs effectively capture immediate next states and identify
meaningful state transitions, their performance rapidly degrades in
full-procedure planning. This highlights LLMs' limitations in reliably modeling
environment dynamics over long horizons. To address these limitations, we
propose the Retrieval-augmented World Model (R-WoM), which grounds LLM
simulations by incorporating factual, up-to-date knowledge retrieved from
external tutorials. Experiments show that R-WoM achieves substantial
improvements of up to 25.3% (OSWorld) and 18.1% (WebArena) compared to
baselines, with particular advantages in longer-horizon simulations.
68ef9c827c010e9ba0dc7f70
Kai Mei
claimed_verified
2025-10-16T06:41:18.378Z
63ea62855c837d9968eb93f7
/avatars/405ea6b595c36251d61cb5d7b7ec87f1.svg
KaiMei
user
dongyuanjushi
68ef9c827c010e9ba0dc7f71
Jiang Guo
68ef9c827c010e9ba0dc7f72
Shuaichen Chang
claimed_verified
2025-10-18T04:50:09.723Z
632b8294ffe3618eff2f3774
/avatars/002b5b85f41033448979ccecabac28b1.svg
Shuaichen Chang
user
shuaichenchang
68ef9c827c010e9ba0dc7f73
Mingwen Dong
68ef9c827c010e9ba0dc7f74
Dongkyu Lee
68ef9c827c010e9ba0dc7f75
Xing Niu
68ef9c827c010e9ba0dc7f76
Jiarong Jiang
R-WoM: Retrieval-augmented World Model For Computer-use Agents
Large Language Models (LLMs) can serve as world models to enhance agent
decision-making in digital environments by simulating future states and
predicting action outcomes, potentially eliminating costly trial-and-error
exploration. However, this capability is fundamentally limited by LLMs'
tendency toward hallucination and their reliance on static training knowledge,
which can lead to compounding errors that inhibit long-horizon simulations. To
systematically investigate whether LLMs are appropriate for world modeling, we
probe two core capabilities of world models--future state prediction and reward
estimation--through three tasks: next-state identification, full-procedure
planning alignment, and milestone transition recognition. Our analysis shows
that while LLMs effectively capture immediate next states and identify
meaningful state transitions, their performance rapidly degrades in
full-procedure planning. This highlights LLMs' limitations in reliably modeling
environment dynamics over long horizons. To address these limitations, we
propose the Retrieval-augmented World Model (R-WoM), which grounds LLM
simulations by incorporating factual, up-to-date knowledge retrieved from
external tutorials. Experiments show that R-WoM achieves substantial
improvements of up to 25.3% (OSWorld) and 18.1% (WebArena) compared to
baselines, with particular advantages in longer-horizon simulations.
LLMs can enhance decision-making in digital environments but struggle with long-horizon simulations due to hallucination and static knowledge. R-WoM improves performance by integrating external, up-to-date knowledge.
Large Language Models world models future state prediction reward estimation next-state identification full-procedure planning milestone transition recognition Retrieval-augmented World Model R-WoM OSWorld WebArena