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cs.LG updates on arXiv.org

Recursive Flow Matching Model Merging on Loss Landscape: A Geometry Perspective JLT: Clean-Latent Prediction in Latent Diffusion Transformers Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes Balancing Plasticity and Stability with Fast and Slow Successor Features Rotation-Invariant Spherical Watermarking via 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Device Context Protocol: A Compact, Safety-First Architecture for LLM-Driven Control of Constrained Devices
Dongxu Yang · 2026-05-27 · via cs.LG updates on arXiv.org

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Abstract:Large language models are increasingly used as orchestrators of external tools via the Model Context Protocol (MCP), but MCP is built for software services with megabytes of memory and does not descend to the microcontrollers that dominate the long tail of physical devices. Recent work (IoT-MCP) ports MCP to edge gateways at 74 KB peak memory; this still excludes the smallest commodity MCUs and, critically, does not address the safety problem of giving an unreliable caller (an LLM that may hallucinate or be prompt-injected) direct control of physical hardware. We present the Device Context Protocol (DCP): a sub-50-byte typical frame (6-byte header + CBOR payload + optional 16-byte HMAC), a manifest schema in which capability scoping, range and type checks, dry-run evaluation, and units-as-types are protocol-layer primitives, and a host-side Bridge that rejects malformed or hallucinated calls before any byte reaches the device. Reference firmware measures 27.6 KB flash / 0.6 KB RAM on ESP32; the Python Bridge, ESP32 firmware, and a language-neutral conformance suite are MIT-licensed and public. An empirical study -- 675 tool calls produced by five LLMs across four vendors (DeepSeek, Alibaba, Zhipu, MiniMax) against six categories of adversarial prompts, with the injection category instantiating AgentDojo's attack templates -- shows DCP rejects 100% of capability-escalation attempts and 78% of prompt-injection attempts, versus 0--1% for Raw MCP and IoT-MCP, matching the expressiveness of a well-formed OpenAPI 3 schema at three orders of magnitude less firmware footprint. We position DCP as the missing layer between MCP (which is moving toward enterprise SaaS connectivity) and the physical devices it does not reach.
Comments: 15 pages, 5 figures. Reference implementation, Python package (pip install pydcp), and reproduction scripts at this https URL
Subjects: Networking and Internet Architecture (cs.NI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
ACM classes: C.2.4; K.6.5; D.4.6
Cite as: arXiv:2605.26159 [cs.NI]
  (or arXiv:2605.26159v1 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2605.26159

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dongxu Yang [view email]
[v1] Sun, 24 May 2026 12:37:19 UTC (141 KB)