Skip to content
AI News HubLIVE
Original source2 min read

Collaborative In-Context Learning for Indoor Radio Map Estimation

Summary

This paper formulates indoor radio map estimation (RME) as a collaborative in-context learning (ICL) problem for the first time. It proposes Poly-Ripple, a framework where multiple clients perform ICL with large autoregressive vision models (LAVMs) without model updates, using ripple-ordered autoregressive modeling and importance-aware aggregation. Experiments on a public indoor RME benchmark show consistent improvements over representative ICL baselines, aligning with theoretical analysis.

SourcearXiv Machine LearningAuthor: Yuanzhe Peng, Jie Xu
Collaborative In-Context Learning for Indoor Radio Map Estimation
Report an error

The correction channel is not available yet. You can copy the article reference below for later.

Correction instructions
Read article

[Submitted on 21 Oct 2026]

Title:Collaborative In-Context Learning for Indoor Radio Map Estimation

View a PDF of the paper titled Collaborative In-Context Learning for Indoor Radio Map Estimation, by Yuanzhe Peng and 1 other authors

View PDF HTML (experimental)

Abstract:Indoor radio map estimation (RME) is essential for wireless applications such as coverage planning, localization, and network deployment. However, most existing methods follow a supervised learning paradigm that requires costly labeled data and task-specific model training. Moreover, indoor environments involve privacy-sensitive information, such as building layouts and structures, that often cannot be centralized. To address these challenges, we make the first attempt to formulate indoor RME as a collaborative in-context learning (ICL) problem. We propose Poly-Ripple, a framework in which multiple clients perform ICL using large autoregressive vision models (LAVMs) without model updates. Each client is prompted by its private demonstrations and predicts the radio map for the same scene query. To capture the causal structure of wireless propagation outward from the transmitter (Tx), we adopt ripple-ordered autoregressive modeling. We further introduce importance-aware aggregation to exploit heterogeneous contextual knowledge distributed across clients under the context length constraint. We provide theoretical analysis and conduct extensive experiments on a public indoor RME benchmark. The experimental results show that Poly-Ripple consistently outperforms representative ICL baselines, while aligning with our theoretical analysis.

Comments: Accepted as a regular paper at the 2026 IEEE International Conference on Big Data (IEEE BigData 2026)

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2610.31702 [cs.LG]

(or arXiv:2610.31702v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.31702

arXiv-issued DOI via DataCite

Submission history

From: Yuanzhe Peng [view email] [v1] Wed, 21 Oct 2026 18:29:15 UTC (737 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Collaborative In-Context Learning for Indoor Radio Map Estimation, by Yuanzhe Peng and 1 other authors

View PDF

HTML (experimental)

TeX Source

view license

Additional Features

Audio Summary

Current browse context:

cs.LG

new | recent | 2026-10

Change to browse by:

cs cs.AI

References & Citations

SciX

Google Scholar

Semantic Scholar

Loading...

Data provided by:

Bibliographic Tools

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, Media

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

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

IArxiv recommender toggle

IArxiv Recommender (What is IArxiv?)

Author

Venue

Institution

Topic

About arXivLabs

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.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

Key points and analysis

Article intelligence

ResearchersAdvanced

Key points

  • First to formulate indoor RME as a collaborative ICL problem, avoiding costly labeled data and task-specific training.
  • Poly-Ripple enables multiple clients to perform ICL with LAVMs without model updates, each prompted by private demonstrations.
  • Ripple-ordered autoregressive modeling captures the causal structure of propagation from the transmitter, and importance-aware aggregation exploits heterogeneous contextual knowledge under context length constraints.
  • Consistently outperforms representative ICL baselines on a public indoor RME benchmark, matching theoretical analysis.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

More recent stories on this topic · Research