arXiv AI
Memory-First Fact-Checking: A Knowledge-Graph-Grounded Multi-Agent System for Misinformation Detection
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arXiv AI
Jun 29
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
arXiv:2511. 03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information.
arXiv AI
Aug 24
Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
The paper introduces Trustworthy RAG, an evaluation agent designed to detect misinformation and knowledge poisoning in Retrieval-Augmented Generation systems. It combines natural language inference verification, a five-signal poison detector, and a weighted Trust Index to assess the reliability of retrieved content. Experiments on multiple LLMs show high accuracy and precision, with the agent effectively blocking unsafe advice in a secure-coding assistant scenario.
arXiv Computation and Language
Sep 15
DARE: Dialectical Agentic Reasoning for Structured Knowledge Fact Checking
arXiv:2609.13808v1 Announce Type: new Abstract: Structured knowledge fact checking aims to determine the truthfulness of natural language claims by reasoning over structured evidence. Recent program-...
arXiv Computation and Language
Aug 27
ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation via Reliability-Guided Reasoning Chains
ReliableRAG is a new framework for Retrieval-Augmented Generation that tackles misinformation in multi‑hop question answering. It extracts structured triples from retrieved documents, evaluates each triple’s reliability by combining semantic relevance to the query with credibility, and keeps only the top‑K reliable, non‑redundant triples. Using these refined triples, the system builds robust reasoning chains that filter out deceptive misinformation and produce accurate, trustworthy answers.
arXiv Computation and Language
Sep 25
Stochastic Semantic Evidence Graphs: Uncertainty Propagation and Governance for Agentic AI
The paper introduces Stochastic Semantic Evidence Graphs (SSEGs), a hierarchical stochastic directed acyclic graph that models uncertainty in AI-agent workflows, from evidence and retrieval to generation and decision mapping. SSEGs expand language nodes into autoregressive token subgraphs, optionally apply semantic reduction and calibration, and preserve uncertain claim–passage relations while propagating Fréchet bounds. The authors derive pathwise error bounds, use nodewise terms to trigger governance checks, and demonstrate through experiments that SSEGs can detect and quantify where uncertainty enters and propagates in AI outputs.
arXiv AI
Aug 14
ReflectFact: Self-Reflective Agents for Improving Comprehension and Reasoning in Multi-Hop Fact Verification
arXiv:2608. 12877v1 Announce Type: new Abstract: Multi-hop fact verification, which verifies claims by reasoning over multiple pieces of evidence, is critical for combating misinformation on social media yet remains highly challenging.