We propose a courtroom-style multi-agent framework, PROClaim, that reformulates verification as a structured, adversarial deliberation. Our approach integrates specialized roles (e.g., Plaintiff, Defense, Judge) with Progressive RAG (P-RAG) to dynamically expand and refine the evidence pool during the debate. Furthermore, we employ evidence negotiation, self-reflection, and heterogeneous multi-judge aggregation to enforce calibration, robustness, and diversity.

We propose AgentFact, an agent-based multimodal fact-checking framework designed to emulate the human verification workflow. AgentFact consists of five specialized agents that collaboratively handle key fact-checking subtasks, including strategy planning, high-quality evidence retrieval, visual analysis, reasoning, and explanation generation. All methods—except for the CCN model—utilize the GPT-4o-mini API as the large language model (LLM).

We present CARP (Claim Verification with Adversarial Reasoning and Planning), a novel multi-agent claim verification framework that organizes heterogeneous agents powered by multiple different language models competing as support and refutation teams. This adversarial structure forces comprehensive evaluation from both perspectives while mitigating confirmation bias and groupthink.

Inspired by real-world fact-checking practices, this work introduces DebateCV, the first debate-driven claim verification framework based on multiple LLM agents. Specifically, DebateCV employs two role-playing Debater agents with opposing stances: one affirming and one refuting the claim, to iteratively refine their assessments and enhance the depth and rigor of evidence analysis.

Automated fact-checking (AFC) aims to verify check-worthy claims using relevant information drawn from evidence resources. While AFC has advanced significantly, existing systems remain vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs, highlighting the need for robust, attack-aware AFC systems.

This work introduces a novel multi-agent architectural model designed for claim verification, achieving state-of-the-art performance on the FEVER dataset. The proposed system leverages specialized agents powered by Large Language Models (LLMs), integrated within a modular and scalable two-layered framework comprising a Reasoning Layer and a Decision Layer.

The Google Fact Check Tools API provides an interface for querying fact-check results, similar to the Fact Check Explorer tool, or continuously getting the latest updates on a particular query. It also allows authorized users to add, edit, and delete ClaimReview markup for their site's fact-checking articles.

ConvergePanel offers Compliance Claim Verification with AI, allowing users to submit compliance claims to multiple AI models and compare responses. This process helps surface inconsistencies, gaps, and areas requiring direct expert review before acting. The platform aims to support the research and documentation phase of compliance review by showing where models agree and diverge, signaling where expert review is most critical.

A multi-model AI platform provides enterprises with unified access to multiple large language models from providers like OpenAI, Anthropic, Google, and Meta through a single interface, enabling model flexibility, centralized governance, and compliance capabilities that single-provider solutions cannot deliver. This approach allows organizations to leverage the most suitable model for each task, balancing capability, cost, performance, and compliance through effective LLM orchestration.

By deploying specialized intelligent agents that each own a discrete verification domain—policy validation, fraud analysis, eligibility assessment, and final adjudication—the system transforms the traditionally opaque and error-prone claim processing workflow into a transparent, auditable, and deterministic pipeline. Fraud detection systems face the persistent challenge of adversarial adaptation: as fraudulent actors systematically observe the rejection patterns of automated systems, they iteratively refine their submission strategies to evade detection thresholds.

This is the implementation of Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), a strong multimodal claim verification system. DEFAME decomposes the fact-checking task into a dynamic 6-stage pipeline, leveraging an MLLM to accomplish sub-tasks like planning, reasoning, and evidence summarization. The system also provides an API for running fact-checks.

Multimodal AI refers to advanced systems capable of understanding and synthesizing information from multiple data types (such as text, images and speech) simultaneously. In a typical claims environment, these inputs are siloed, handled by different tools and teams, leading to fragmented workflows and loss of crucial context. A multimodal approach breaks down these silos. It creates a unified understanding by allowing insights from one data format to inform and validate the analysis of another.

The next generation of AI models will take into consideration multi-modal datasets, such as radiology imagery, wearable-device data, voice transcripts, and unstructured EHR narratives. Such inputs build more clinical contexts, which provoke more detailed risk assessments and enhance the correctness of adjudication. Multi-modal integration will also broaden the opportunities in faster fraud detection and more individual health-insurance analytics.

FactFlux is an intelligent multi-agent system that automatically fact-checks social media posts using the power of AI agents working in coordination. The multi-agent architecture offers specialization benefits, where each agent focuses on what it does best, reducing complexity and improving accuracy, and API endpoints are planned for integration with other platforms.

A new preprint from Stanford and peer institutions evaluates 15 large language models (LLMs) on over 6,000 claims, finding that today's leading models perform poorly when relying solely on built-in knowledge, even with advanced reasoning and web search. The study highlights that the key to better performance lies in giving models access to high-quality, curated evidence, improving accuracy by 233 percent on average across model variants.

The result is FactAgent — a web-based fact-checking assistant that decomposes claims into verifiable sub-statements, retrieves evidence from the web, evaluates source credibility, and synthesizes a verdict with confidence scores. The application is built on five main components, including the Claude API for language processing and LangGraph for orchestrating the agentic workflow, following a ReAct-style pattern with four distinct nodes.

Adversarial artificial intelligence (AI), or adversarial machine learning (ML), is a type of cyberattack where threat actors corrupt AI systems to manipulate their outputs and functionality. These attacks weaponize the same capabilities that make AI valuable, crafting malicious inputs designed to bypass guardrails, poison training data, or extract sensitive information from model behavior.

This guide demonstrates building a real-time fact-checking application that extracts verifiable claims from any text or URL and validates them against live web sources using a multi-phase pipeline. The architecture includes Claim Extraction via LLM-powered identification and Parallel Verification, where each claim is searched and analyzed concurrently, with results streaming in real-time.

The implementation architecture typically involves preprocessing pipelines that normalize incoming claims data, feature extraction modules that identify relevant clinical and administrative attributes, and ensemble models that combine multiple prediction algorithms to enhance accuracy. Recent research demonstrates that hybrid models combining random forests with deep neural networks achieve accuracy rates of 94.6% in predicting appropriate adjudication outcomes for routine claims.

Adversarial testing is a security methodology that applies to all IT systems, including APIs, networks, and web applications, by adopting an attacker's mindset to find logical and technical flaws. Equixly offers a scaled adversarial testing solution in its Agentic AI Hacker, which uses autonomous agents built on reinforcement learning algorithms to emulate an AI-assisted human adversary and discover weaknesses.

Much of the terrain covered by human fact-checkers requires a kind of judgement and sensitivity to context that remains far out of reach for fully automated verification. Despite progress in automatic verification of a narrow range of simple factual claims, Automated Fact-Checking (AFC) systems will require human supervision for the foreseeable future.

Factiverse enhances its fact-checking capabilities by integrating the Semantic Scholar API, providing access to over 220 million scientific articles to verify claims more accurately and efficiently. The API meticulously analyses gathered evidence, extracting pertinent snippets using Factiverse's advanced models, which then employ credible sources to ascertain whether the evidence supports or contradicts identified claims.

This guide provides an automated way to assess factual accuracy at scale by extracting claims from text or URLs, retrieving real-world evidence, and evaluating each claim using gpt-oss-120B powered by Cerebras ultra low latency inference. The system returns one of three structured verdicts: True, False, or Uncertain, based on the evidence found online.

This report summarises the state of the art in fact-checking technology in Europe and the United States, exploring how fact-checking practices are augmented with different technical tools and providing an overview of available or emerging technology. It notes that while AI systems/tools/assistants are used, in most cases there is no good solid statistical foundation to use to check claims, and there is no real unifying global infrastructure of fact-checked stories.

An identity verification API integrates with business validation software to automatically check that an entity is who they claim to be. These systems run automated identity verification checks to authenticate businesses using official business documents and government databases, providing a confidence score. The data is cross-checked against official, authoritative databases and watchlists to ensure authenticity.