EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression — Paper

Abstract

Recent advances in multimodal large language mod- els (MLLMs) have opened new opportunities for edge intelligence by enabling reasoning across heterogeneous sensor modalities, such as vision, text, and telemetry data. However, deploying these capabilities on resource-constrained edge platforms remains challenging due to the substantial computational, memory, and communication demands of modern MLLMs. Rather than transmitting raw sensor observations or partitioning neural networks at intermediate layers, Edge Multi-Modal Intelligence (EMMI) communicates a compact representation between edge devices and server resources, enabling communication-efficient edge MLLM inference. To achieve this, EMMI performs modality-specific encoding, cross-modal representation fusion, and learned compression at the edge, transmitting only a compact latent representation to server-side resources for high-capacity MLLM reasoning. This representation-centric design reduces communication overhead, preserves local data privacy, and provides a fixed-size interface between heterogeneous edge devices and server-side MLLMs. Evaluation on a representative multimodal benchmark demonstrates that EMMI can reduce the communication payload by 32x while maintaining comparable downstream accuracy, resulting in up to a 3.4x reduction in estimated end-to-end inference latency under bandwidth-constrained edge conditions.

Authors 2

Motahare Mounesan, Irfan Khan

Specification

Official page: arxiv.org/abs/2609.11058
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
arXiv id: 2609.11058
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
Categories: cs.LG, cs.DC
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
PDF: arxiv.org/pdf/2609.11058
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 35 min agohigh

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9

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T19

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35 min ago

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Authors: Motahare Mounesan, Irfan Khan

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Value	Valid from → to	Status	Source	Confidence	Extractor
Motahare Mounesan, Irfan Khan	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperEMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression
New paper: EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	35 min ago	1

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