arXiv:2606. 15179v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has emerged as a pivotal technique for improving language models by incorporating external knowledge at inference time.
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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.
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.
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...
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.