End-to-End Fact-Checking Agents

Updated 1 July 2025

End-to-end fact-checking agents are automated systems that assess claim veracity by integrating detection, evidence retrieval, and inference processes.
They leverage modular architectures with advanced NLP and multimodal learning to produce clear justifications and verdicts.
Dynamic, iterative workflows reduce search costs while enhancing transparency, scalability, and adaptability in combating misinformation.

End-to-end fact-checking agents are automated systems designed to evaluate the factuality of natural language claims, often found in political speech, news, scientific discourse, and social media. These agents integrate a structured pipeline or modular architecture that encompasses claim detection, evidence retrieval from large corpora or web sources, factuality assessment using inference or reasoning models, and output generation such as verdicts or explanations. Modern developments in this field draw on advances in natural language processing, retrieval-augmented reasoning, LLM agents, and multimodal learning, with a growing emphasis on transparency, adaptability, and scalability. These agents have become central in efforts to mitigate misinformation and provide trustworthy, explainable AI-based decision support across domains.

1. Architectural Principles and Modular Pipelines

The typical architecture for end-to-end fact-checking agents is modular but interconnected, with subsystems aligning to core stages of the fact-checking process:

Claim Detection and Prioritization: This frontend component identifies sentences or spans needing verification, often using neural check-worthiness ranking models that integrate semantic (embedding-based) and syntactic (dependency-based) features (1903.08404). Attention models enhance interpretability and effectiveness, and weak supervision allows scaling in data-limited domains.
Evidence Retrieval: The system retrieves potentially relevant documents, sentences, or multimodal content (images) using information retrieval approaches:
- Static corpora (e.g., Wikipedia via Lucene or custom search APIs (1906.04164, 2109.00835)).
- Web-scale search using agents orchestrating external APIs (e.g., Cohere RAG, DuckDuckGo, SerpAPI) (2409.00009, 2411.00784, 2412.10510).
- Advanced retrievers employ dense bi-encoders and contrastive reranking, sometimes tailored to inference or answer-aware objectives using subquestions (2410.04657).
Verification/Inference: Verification models—often fine-tuned transformer architectures (e.g., XLM-RoBERTa, FinQA-RoBERTa)—conduct natural language inference (NLI) on claim-evidence pairs. Majority voting or aggregation methods reconcile multiple evidence pieces (2402.12147). In some frameworks, verification proceeds iteratively, fusing LLM internal knowledge with external evidence in an agent-guided loop (2411.00784).
Justification and Explanation Generation: Recent systems produce natural language rationales alongside verdicts, sometimes guided by reinforcement learning for label-explanation consistency (2205.12487, 2403.16662), or generate comprehensive, multimodal reports (2412.10510). Benchmark efforts emphasize evaluation at fine-grained, multi-step levels, including the correctness and reasoning quality of produced explanations (2311.09000).
Human-centric Reporting: Outputs include explicit veracity labels, justification text, highlighted supporting/refuting evidence, and, in advanced systems, interactive correction suggestions or dialogic clarification (2404.19482, 2412.10510, 2506.20876).

2. Advances in Retrieval and Reasoning

The field has evolved from basic text-based retrieval to more sophisticated, context-aware evidence aggregation:

Contrastive and answer-aware retrievers: Models such as the Contrastive Fact-Checking Reranker (CFR) explicitly optimize to surface evidence that enables direct veracity judgments for complex or indirect claims, leveraging subquestion decomposition, answer equivalence metrics (e.g., LERC), and distillation from LLM annotations (2410.04657).
Iterative Retrieval-Verification Loops: Agents such as FIRE (Fact-checking with Iterative Retrieval and Verification) integrate retrieval and verification in a unified, confidence-driven loop. The agent adaptively decides at each step whether to issue further search queries or finalize a verdict, efficiently leveraging both LLM internal knowledge and external evidence (2411.00784). This reduces computational and search costs by significant factors (up to 16.5x reduction in search cost).
Dynamic Planning and Tool Orchestration: DEFAME and similar frameworks incorporate a dynamic planner that selects among a suite of external tools (web search, reverse image search, geolocation), depending on the claim's modalities and present evidence gaps (2412.10510). The planner avoids redundant action, supports iteration for complex queries, and preserves traceability throughout the fact-checking process.

3. Multimodality, Multilinguality, and Domain Adaptation

Many real-world factual claims are multimodal or appear in multiple languages or scientific domains:

Multimodal Fact-Checking: DEFAME and comparable systems support claims combining text and images, retrieve and reason over both modalities, and produce multimodal, human-friendly reports. These agents invoke external visual and geographic tools as needed, integrating their outputs in the inference and justification stages (2412.10510).
Multilingual and Domain-Transfer Systems: Fine-tuned multilingual transformers (e.g., XLM-RoBERTa-Large) achieve strong claim detection and veracity inference across over 90 languages (2402.12147, 2404.19482). Agents incorporate translation, domain-adaptive pretraining, and alignment methods to support robust cross-lingual and cross-domain deployments.
Domain Adaptation Techniques: Robustness across domains is enhanced by adversarial retriever adaptation (to create domain-invariant embeddings), order-insensitive training for NLI readers, and careful alignment of feature representations between training and test domains, as shown in large-scale cross-topic evaluations (2403.18671).

4. Transparency, Justification, and Evaluation

Transparency and rationale generation have emerged as key principles for trustworthy deployment:

Justification Production: Nearly all advanced agents mandate not only a verdict but a natural-language justification that references explicit evidence and reasons through intermediate steps (2412.10510, 2311.09000, 2502.17924). Multi-agent evaluation frameworks (e.g., FACT-AUDIT) now score both veracity and justification quality, surfacing subtle reasoning errors in otherwise correct predictions.
Fine-Grained Benchmarks: Factcheck-Bench and FACT-AUDIT offer document-level, sentence-level, and claim-level annotations, supporting detailed diagnostic evaluation and error localization—a requisite for both research progress and safe real-world deployment (2311.09000, 2502.17924). Metrics such as Insight Mastery Rate (IMR) and Justification Flaw Rate (JFR) quantify reasoning and explanation reliability.
Human-AI Interaction: Research in high-stakes fields like medicine reveals fundamental obstacles for end-to-end agents: unverifiable claims due to lack of evidence, ambiguous queries, and subjective ground-truth labels, even among experts. These findings suggest that a communicative, interactive approach—where the agent clarifies ambiguity and transparently communicates limitations—is essential in such settings (2506.20876).

5. Agentic Workflows and Multi-Agent Collaboration

A key development is the shift from monolithic black-box predictors to agentic, modular, and sometimes collaborative multi-agent systems:

LLM-powered Agentic Approaches: FactAgent executes a structured, expert-informed workflow where the LLM applies specialized sub-tools (e.g., for bias detection, commonsense, evidence aggregation) in an explicit sequence, mimicking expert reasoning. At each sub-step, the LLM documents findings, culminating in a transparent final decision (2405.01593).
Web Retrieval and Observation: Agent-based frameworks combine an offline LLM with a web search agent, allowing the LLM to decompose queries, invoke search iteratively, and integrate real-time evidence before making a prediction and quantifying uncertainty. This yields significant improvements in macro F1 over stand-alone LLMs (2409.00009).
Multi-Agent Evaluation and Data Generation: FACT-AUDIT’s collaborative agents (appraiser, inquirer, quality inspector, evaluator, prober) generate challenging, adaptive audit datasets, evaluate both verdicts and justifications, and iteratively refine scenario taxonomies to expose LLM weaknesses (2502.17924).

6. Challenges, Limitations, and Future Outlook

Despite substantial progress, multiple intrinsic challenges persist:

Dataset and Task Construct Validity: In open and high-stakes domains like medicine, most user queries cannot be resolved in an end-to-end fashion, often for lack of scientific evidence, claim ambiguity, or subjective interpretation (2506.20876). This suggests fact-checking agents in such fields require integration with dialogic clarifications and transparent abstention mechanisms.
Retrieval Bottlenecks and Reasoning Complexity: The effectiveness of the overall system is often bottlenecked by evidence retrieval, especially for non-obvious, indirect, or multi-hop reasoning claims. Augmenting retrieval with answer-aware supervision, contrastive signals, and subquestion decomposition is essential but remains imperfect (2410.04657).
Evaluation Methodology: Traditional accuracy metrics are insufficient; full evaluation must consider reasoning quality, justification completeness, credibility of evidence, and, in adversarial settings, robustness to deliberately deceptive or adversarial examples.
Scalability and Efficiency: Practically deployable agents (e.g., FIRE) must dramatically reduce computational and search costs, maintain efficiency at web scale, and enable real-time or interactive use scenarios (2411.00784).
Human Oversight and Responsible Use: Automated fact-checking agents—especially those making impactful decisions—should remain subject to expert oversight, given the continuing limitations in reasoning, evidence coverage, and interpretive nuance (2410.04657, 2412.10510).

7. Exemplary Implementations and Resources

A selection of notable systems, datasets, and benchmarks:

System/Dataset	Multimodality	Multilingual	Modular Agentic Workflow	Justification	Fine-grained Evaluation
DEFAME (2412.10510)	Text+Image	Adaptive	Dynamic Planner, Tools	Yes	Yes
FactCheck Editor (2404.19482)	Text	90+ langs	Transformer + LLM	Yes	Yes
FIRE (2411.00784)	Text	Flexible	Iterative Retrieval-Verification	Partial	Yes
FACT-AUDIT (2502.17924)	Text	Flexible	Multi-agent	Yes	Yes (incl. justification)
MOCHEG (2205.12487)	Multimodal	English	Modular	Yes	Yes
CrowdChecked (2210.04447)	Text	English	Large-scale retrieval	N/A	No

Conclusion

End-to-end fact-checking agents have transitioned from static, text-oriented pipelines to dynamic, explainable, and often agent-based systems, integrating retrieval, inference, and justification across text, images, and multilingual input. Recent advances focus on enhanced retrieval, multimodal capabilities, human-centric explainability, robust evaluation, and scalable design. Persistent challenges include evidence scarcity for open-domain claims, ambiguity in high-stakes fields, and the need for responsible, transparent, and interactive communication with users. The trajectory of the field points toward increasingly flexible, modular, and transparent agents capable of augmenting human fact-checkers as well as empowering the public against misinformation.