The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Artificial Intelligence
arXiv:2606.07017 (cs)
[Submitted on 5 Jun 2026]
Title:The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective
View a PDF of the paper titled The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective, by Xiaoou Liu and 4 other authors
View PDF
HTML (experimental)
Abstract:Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap. While robotics and classical control have mature frameworks to address this gap, the foundation model community is treating agent robustness as an entirely novel phenomenon. Our paper proposes formalizing the foundation model agent evaluation and training gap as a classical sim-to-real problem structured entirely around the four elements of a Markov Decision Process, including Observation, Action, Transition, and Reward. In this paper, we set a comprehensive research agenda that translates classical discrepancies into the foundation model domain and advocates for adopting established solutions like domain randomization. We provide concrete examples, such as a multilingual tool calling to demonstrate how severe observation space gaps lead to operationally invalid actions despite correct semantic intent. Ultimately, this agenda aims to drive a paradigm shift, yielding a unified vocabulary and standardized stress test benchmarks to foster a new generation of highly trustworthy agents for reliable real-world applications.
| Comments: | 7 pages, 2 figures, 2 tables. Accepted by KDD 2026 Blue Sky Ideas Track |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Emerging Technologies (cs.ET) |
| MSC classes: | 68T50, 68T37, 68Q32 |
| ACM classes: | I.2.7; I.2.6; I.2.4 |
| Cite as: | arXiv:2606.07017 [cs.AI] |
| (or arXiv:2606.07017v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07017
arXiv-issued DOI via DataCite (pending registration)
|
|
| Related DOI: | https://doi.org/10.1145/3770855.3818660
DOI(s) linking to related resources
|
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
View a PDF of the paper titled The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective, by Xiaoou Liu and 4 other authors
Current browse context:
cs.AI
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.