llm-hallucination

[JMLR 2026] UQLM: A Python package for LLM hallucination detection using uncertainty quantification

[NeurIPS 2025] SECA: Semantically Equivalent and Coherent Attacks for Eliciting LLM Hallucinations

KIPRIS 특허·실용신안·상표·디자인 검색 MCP — 자유검색/항목검색/출원인/권리자/서지상세 7개 도구 | KIPRIS Korean patent·utility·trademark·design search → 7 MCP tools

[ICML 2026] REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations

[UAI 2026 Oral] SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory, which aims to detect hallucinated content in LLM-generated text.

(개요) 국가법령정보센터와 알리오의 공공기관 내부규정을 검색·비교·분석하는 MCP. (도구) 법제처 87 + ALIO 공공기관 규정 23 = 110개 MCP 도구. (데이터) 1,600 법률, 10,000 행정규칙, 수만건 판례, 344개 공공기관 35,000 내부규정.

RAG Hallucination Detecting By LRP.

CRoPS (TMLR)

Emperor Time: Agent Skills coding harness for Claude Code / OpenCode. Tiny-by-default micro-waterfall SDLC gates, claim ledgers against hallucination, mandatory critique, consent-based multi-agent orchestration.

Novel Hallucination detection method

A formally-grounded governance framework for Kilo Code. Establishes explicit policy foundations and experimental guardrails across Kilo’s Architect and Code modes to eliminate model hallucination, unauthorized scope creep, and unverified architecture drift.

Hallucination Detection in Pretrained Generative AI Models for Bengali Text - A benchmark dataset of 7,043 annotated samples and fine-tuned transformer architectures (BanglaBERT & mBERT).

Make your AI coding agent prove every claim with a file:line citation, then machine-check each one offline. A verified-citation gate (CLI + MCP server + GitHub Action) that catches LLM hallucinations before merge.

Identifiers that resist hallucination, survive repeated LLM copying, and repair themselves when damaged — or fail honestly when they can't. The specification and its conformance vectors.

Semi-supervised pipeline to detect LLM hallucinations. Uses Mistral-7B for zero-shot pseudo-labeling and DeBERTa for efficient classification.

Research paper on how agentic debate pipelines can be constructed to reduce hallucinations in LLMs with open-source and commercial models

Batch-verify every citation, DOI, arXiv ID, CVE, commit SHA, and issue reference in a paper or PR against real registries; red/green report, JSON sidecar, GitHub Action.

Build your own open-source REST API endpoint to detect hallucination in LLM generated responses.

A theoretical framework for embedding lightweight, controllable AI into enterprise information systems (ERP, finance, supply chain) without relying on general-purpose LLMs. L0-L5 evolution model + hallucination control + human-in-the-loop.（2026-06-04 | PSSXiv:202606.02680V1）

Aether by SF2X — AI trust verification layer. 3-model tribunal that catches LLM hallucinations. 91/100, AUC 1.0. Chrome extension, API, GitHub Action, and public playground.