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COMPANY · ENTITY #4821

MiniLM

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2

RESEARCH · 1 SOURCE · Hugging Face

ProvenanceGuard: source-aware factuality verification for MCP-based LLM agents

The paper ProvenanceGuard proposes a post-generation verifier that preserves source identity from Model Context Protocol (MCP) tool traces to check both whether each claim is supported and whether it is attributed to the correct tool output. The method decomposes answers into claims, finds the most relevant MCP source for each, verifies support (using models in the authors' local setup such as MiniLM and DeBERTa), compares claimed attribution, and can block or prompt source-grounded repairs; experiments used 281 real medical-agent traces.

7.0

RESEARCH · 1 SOURCE · Apple Machine Learning Research

Glyph: multi-strategy agentic system for column description and sensitivity-ontology tagging

Glyph is a production system framing column-description generation and column-type annotation as cooperating stateful LLM agents. The Descriptor grounds descriptions in pipeline source code retrieved from enterprise GitHub via an active RAG loop, while the Tagger runs three parallel strategies (description-based, regex-based, and a fine-tuned MiniLM contrastive metadata encoder over a vector DB) and fuses ranked outputs with Reciprocal Rank Fusion; the paper reports large retrieval gains (NDCG@10 0.55→0.92, MAP@100 0.19→0.90) and evaluates end-to-end multi-label tagging with ablations and provenance-enabled, value-free, code-grounded design choices.

7.0