← Back to all news
arXiv AI By Nicola Cogotti

No\=esis: Deterministic-First Retrieval with Two-Tier Context Hydration for Factuality-Critical Queries on Small Local Models

Read the original on arXiv AI →

Noesis is a retrieval-augmented architecture designed for small local language models that prioritizes deterministic judgments before generation. It employs a fact layer, positional addressing, provenance scoping, and two-tier context hydration to ensure factual integrity, achieving parity with larger models on exact value accuracy and eliminating confabulated numbers. The system delivers single-generation queries with traceable, source‑linked outputs, improving performance in regulated domains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Aug 24

RAG Deserves an Index: Why Ingest-Time Compilation Beats Query-Time Interpretation

The paper argues that retrieval‑augmented question‑answering systems should perform semantic compilation at ingest time rather than re‑deriving meaning at query time. By building a maintained structure—incrementally updated embeddings and validated atomic claims—read operations become far cheaper, with experimental results showing higher accuracy and lower token usage compared to traditional chunk‑based retrieval. The authors present two proofs: cheaper incremental updates and superior performance on broadcast‑interview transcripts, suggesting a new systems agenda for compilation and read planning.

By Kyle Wild, Yusuke Takahashi, Asako Uraki
llmsrag
More like this →
arXiv AI
Sep 28

CARGO: Context-Aware Retrieval-Gated Evaluation of Agentic AI in Production

CARGO is a framework for evaluating agentic AI systems in production that addresses the problem of reference-instance divergence (RID), where reference-based judges penalize correct answers that involve different entity identifiers. It treats retrieved references as procedural exemplars, grounds judgments in the live instance’s context, assigns a three-way status to claims, and gates evaluation by retrieval confidence. Using the CARGO-Bench diagnostic suite, CARGO eliminates false penalties and improves discrimination while revealing a limitation in detecting procedural corruptions.

By Mukul Chhabra, Shail Patel, Luigi Medrano
llmsagentssafety
More like this →
arXiv AI
Jun 24

Quantifying Prior Dominance in RAG Systems

arXiv:2606. 23695v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models in external knowledge, yet current evaluations rely on discrete heuristics that suffer from ''epistemic blindness'' - failing to distinguish genuine contextual information extraction from parametric memory recall.

By Barak Or
llmsragsafety
More like this →
arXiv AI
Jul 3

ContextNest: Verifiable Context Governance for Autonomous AI Agent

arXiv:2607. 02116v1 Announce Type: new Abstract: Autonomous AI agents increasingly depend on external knowledge stores, yet most retrieval pipelines provide relevance without durable guarantees of provenance, version identity, integrity, traceability, or point-in-time reconstruction.

By Misha Sulpovar (PromptOwl, LLC), Benn R. Konsynski (Goizueta Business School, Emory University), Qaish Kanchwala (IBM Research), Gabe Goodhart (IBM Research)
ragagents
More like this →