llm-hallucination

[JMLR 2026] "UQLM: A Python Package for Uncertainty Quantification in Large Language Models"

[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

This repository contains the code implementation for the paper "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)

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.

一键核验论文与报告里的所有引用，10 秒出红绿表

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.

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

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.

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.

Emperor Time - a full-rigor coding harness skill/plugin: micro-waterfall SDLC gates, scientific-method claim ledgers to cut hallucination, mandatory critique, and consent-based orchestration of other coding agents.

Official PyTorch implementation of a mechanistic interpretability framework for Self-Explaining LLMs. Generates real-time mathematical provenance and logical explanations for model outputs to detect, track, and eliminate hallucinations in critical domains.