Public institutions hold large volumes of sensitive documents and support tickets that cannot leave the premises, ruling out cloud-hosted language models entirely. We report on RAGAL, a retrieval-augmented assistant for the technical-support team of AFIR, the Romanian Agency for Financing Rural Investments, built and operated under three hard constraints: zero data egress (no external API calls, even for synthetic data), a read-only mandate (the assistant drafts, humans execute), and a single 8 GB consumer laptop as the only development and training machine.
The paper introduces REVA, a method for compressing retrieval-augmented generation (RAG) prompts by aggregating historical query–document–model interactions into reusable evidence views. REVA mines attention traces from the target generator, maps token-level attention to readable words, aggregates importance across repeated document accesses, and produces budget‑specific plain‑text views that maintain document order and the standard RAG interface. Experiments on four benchmarks with modern LLMs show that REVA improves generation quality by 1.0–5.8 points over existing compressors while reducing compression overhead by 5.3 to 15.6 times and adding less than 40 ms of latency.
By Tuan Nguyen, Qiran Hu, Banruo Liu, Khoa D. Doan, Kok-Seng Wong, Fan Lai
arXiv:2606. 28361v1 Announce Type: cross Abstract: Multi-step retrieval-augmented generation (RAG) has been widely deployed as LLM-powered web services for complex question answering, where iterative retrieval-reasoning rounds deliver strong multi-hop accuracy.
By Kuan Yan, Zhiqing Tang, Tian Wang, Weijia Jia
arXiv:2609.22124v1 Announce Type: new
Abstract: This paper describes the system submitted to the UNLP 2026 Shared Task on Multi-Domain Document Understanding. The challenge required extracting precis...
By Illya Havrylov
Spruce is a system that enables secure, private retrieval of large document collections outsourced to untrusted clouds by learning compact binary embeddings that preserve search quality while drastically reducing computation and communication. It replaces expensive corpus-wide embedding scoring with efficient Hamming-distance calculations under a two-server multi-party computation protocol, and introduces a fixed-radius protocol, private cluster pruning, and a one-core dealer to further cut latency and bandwidth usage. Across corpora ranging from 383K to 5.42M documents, Spruce maintains original search quality, achieving up to 6.7× faster full scans and 22.9× speedups with pruning, while retaining over 94% of the original NDCG.
Spruce is a system that enables scalable private outsourced retrieval by learning compact binary embeddings and using efficient Hamming-distance computation under a two‑server multi‑party computation protocol. It replaces costly corpus‑wide embedding scoring with a fixed‑radius protocol that avoids multi‑round candidate selection, and introduces private cluster pruning and a one‑core dealer to reduce computation and eliminate OT preprocessing bottlenecks. Across corpora of 383K–5.42M documents, Spruce maintains original search quality while achieving up to 31.5× higher throughput and reducing query times to a few seconds.
By Peichun Hua, Yunming Xiao
arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.
By Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar Hanawal
The paper introduces X-CoSD, a communication‑efficient framework for collaborative speculative decoding that allows a small on‑device language model to draft tokens while a server‑side large language model verifies them, even when the two models use different vocabularies. X-CoSD employs hybrid resampling to limit distribution exchange to only the common vocabulary, and its enhanced variant X-CoSD‑E further reduces communication by sending only server‑sampled replacement candidates for device verification. Both variants preserve the server model’s distribution and, according to experiments, accelerate token generation without sacrificing quality.
By Jaeduk Lee, Wan Choi
arXiv:2608. 15919v1 Announce Type: cross Abstract: Retrieval-Augmented Generation over knowledge graphs (Graph-RAG) has emerged as a powerful paradigm for grounding large language models in domain-specific corpora.
By Nicola Cogotti
arXiv:2609.23371v1 Announce Type: cross
Abstract: Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persisten...
By Yifan Wang, Dejing Dou
arXiv:2606. 00426v1 Announce Type: new Abstract: Federated continual learning (FCL) lets distributed clients adapt language-model heads to evolving NLP tasks without sharing raw text.
By Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj Sharma
FlexP-SFT introduces an aggregation-free framework for personalized split federated fine-tuning of large language models, eliminating the client-side aggregation step that traditionally causes communication bottlenecks and straggler issues. The method employs a layer‑flexible alignment strategy to balance personalization and generalization without global synchronization, and formulates split‑ratio selection as a resource‑aware discrete optimization problem. Experiments demonstrate that FlexP-SFT improves both accuracy and latency compared to baselines, achieving a superior resource‑accuracy trade‑off.
By Jiaxiang Geng, Tianjun Yuan, Pengchao Han, Ying Gao, Xianhao Chen, Bing Luo