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版本更新 · v3arXiv:2608.25735

Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale

Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming Xiao

原文摘要

Hosted retrieval-augmented generation (RAG) and semantic search allow users to query valuable provider-held corpora, raising two competing demands: to hide each query and chosen result, yet reveal only the documents that the user is authorized to receive. Existing cryptographic approaches either make this costly by processing the entire corpus for every query, or sacrifice quality for efficiency by scanning a few clusters. We repurpose learned deep hashing as a private filter: a randomized binary code points the provider to a short candidate list, while encrypted reranking and oblivious key transfer protect the precise query and final selection. This shortlist short-circuits full-corpus cryptographic search without sacrificing retrieval quality: with 200-500 candidates, it closely matches full-corpus retrieval across five zero-shot corpora spanning 25K to 5.4M documents. On the full 2.68M-passage NQ corpus over a 10-Gbps link, our protocol only adds 0.38 seconds, or 6.5%, to a 128-token Qwen3-32B RAG pipeline. The released code satisfies directional metric differential privacy (DP) and substantially reduces embedding-inversion and property-inference leakage, demonstrating that a carefully learned shortlist can make private dense retrieval both accurate and practical.

cs.CR · cs.AI · cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15499

Rateless Coding for Efficient Model Broadcast in Federated Learning

Zijun Qin, Zuolin Jin, Jingxuan Huang, Ming Xiao, Xianhao Chen

原文摘要

Model broadcast in federated learning (FL) with wireless channels faces significant challenges due to large model sizes and heterogeneous, unreliable channel conditions. While rateless coding has been widely adopted in modern broadcast communications to handle channel heterogeneity, its potential for model broadcast in FL remains largely unexplored. To fill this gap, we propose a joint source-channel coding scheme based on rateless coding to improve the robustness and efficiency of downlink model broadcast in FL. Specifically, we establish a rigorous convergence analysis of FL to inform the rateless code and quantization design under noisy downlink channel conditions. Moreover, by observing that the iterative process of FL naturally provides prior information about client updates, we leverage client-side local models as conditional prior information to aid the decoding process, thereby reducing transmission costs. Extensive simulations demonstrate that our rateless coding scheme significantly enhances downlink model broadcast efficiency in FL, while retaining comparable training accuracies for both homogeneous and heterogeneous data distributions.

cs.IT · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37307

EdgePace: Deadline- and Freshness-Aware LLM Scheduling on Unified-Memory Platforms

Teng Mei, Chengxuan Pei, Weijun Wang, Jie Wu, Marco Canini, Yunming Xiao

原文摘要

Large language models (LLMs) increasingly run on edge devices alongside tasks that continuously process sensor data. In many applications, these foreground tasks prepare observations as inputs to the LLM, whose outputs guide subsequent processing or actions. The observations must be processed by foreground deadlines, and LLM feedback must arrive while its source observations remain useful. Yet the CPU and accelerators share a unified-memory system, so LLM inference can delay foreground processing through memory-bandwidth contention even when compute resources remain available. We present EdgePace, a bandwidth-aware runtime that protects foreground deadlines while sustaining timely LLM feedback. EdgePace creates safe suspension points across prefill and decode, monitors foreground memory demand and stalls at low cost, and schedules inference according to contention risk, deadline slack, and output freshness. We evaluate EdgePace on a OnePlus 13 and three NVIDIA Jetson platforms using multi-camera processing and robotic target tracking. Relative to unmanaged inference, it reduces foreground deadline misses by 24.1%--96.1% while retaining 64.9%--98.5% of LLM throughput. All measured LLM outputs arrive within their task-defined freshness windows. On OnePlus 13, for example, camera deadline misses fall by 91.3% while 90.4% of LLM throughput is retained.

cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37271

An Interactive Mobile Augmented Reality System for Efficient Virtual Model-to-Patient Registration in Thoracic Surgery

Emmanuel Ajaero, Pascal Spiegler, George Rakovich, Marta Kersten-Oertal, Yiming Xiao

原文摘要

Video-Assisted Thoracoscopic Surgery (VATS) is a minimally invasive procedure, where precise surgical port placement is vital, yet it relies heavily on surgeon experience and mental mapping of pre-operative imaging in the operating room. To address this challenge, we present an accessible, interactive mobile augmented reality (AR) framework, HILMARTS, to enable accurate and efficient pre-operative virtual model-to-patient registration for AR-guided surgical navigation. Our system runs on a commercially available LiDAR-equipped iPad and supports a user-in-the-loop workflow through a two-stage coarse-to-fine registration process. First, the user performs a manual alignment using the AR view on the iPad. This initial maneuver is then refined through automatic point-cloud registration leveraging the LiDAR sensor. We further introduce a novel occupancy-grid-based Surface Coverage Score to provide real-time, actionable feedback to the operator on the geometric completeness of LiDAR data capture for reliable point-cloud-based alignment. In a phantom study with 16 participants, the system achieved a 3.31+/-1.26mm registration accuracy in ~4 min with a mean System Usability Scale (SUS) of 84.1. These findings highlight the system's accuracy, efficiency, robustness, and potential clinical usability, and could support its applicability to other AR-based surgical navigation tasks.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37080

Tessera: Structure-Completed Evidence Units for Web Retrieval-Augmented Generation

Peichun Hua, He Sun, Zirui Zhuang, Qi Qi, Yiming Qiu, Shinan Liu, Yunming Xiao

原文摘要

Web retrieval-augmented generation (RAG) splits a page into fixed-size text chunks or, in recent visual pipelines, screenshot tiles, and the retrieved item serves at once as the unit of indexing, ranking, and reading. We trace answer evidence from source HTML to reader input over the full English Wikipedia and find that this coupling loses evidence in text and screenshot pipelines alike: evidence present in the source page often fails to reach the reader intact, particularly structured content such as tables and infoboxes, which chunking separates from the headers and context that give it meaning. Stronger extraction, context expansion, and larger reader budgets leave the loss in place. We present Tessera, a training-free evidence delivery layer that decouples what a pipeline retrieves from what its reader reads. Given the pages behind any ranked list, Tessera rebuilds their HTML into fine-grained evidence units, each completed with its page title, section path, and table headers, selects the units relevant to the query, and packs them into the reader context at an unchanged token budget. The index, the retriever, and the reader stay as they are, so Tessera applies to text, screenshot, and agentic pipelines. Across three question answering benchmarks, Tessera improves answer accuracy by 5 to 6 points on text and screenshot pipelines, consistently across readers, retrievers, and reader budgets, and outperforms strong HTML-aware and structure-aware RAG baselines under matched budgets. The gains carry over to multi-step agentic search. Our code is publicly available at https://github.com/sarendis56/Tessera.

cs.IR

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arXiv ↗PDF ↗