Trification: A Comprehensive Tree-based Strategy Planner and Structural Verification for Fact-Checking
- URL: http://arxiv.org/abs/2512.00267v1
- Date: Sat, 29 Nov 2025 01:12:24 GMT
- Title: Trification: A Comprehensive Tree-based Strategy Planner and Structural Verification for Fact-Checking
- Authors: Anab Maulana Barik, Shou Ziyi, Yang Kaiwen, Yang Qi, Shen Xin,
- Abstract summary: We propose a novel automated fact-checking framework called Trification.<n>It begins by generating a comprehensive set of verification actions to ensure complete coverage of the claim.<n>It then structured these actions into a dependency graph to model the logical interaction between actions.
- Score: 1.4537814945365966
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Technological advancement allows information to be shared in just a single click, which has enabled the rapid spread of false information. This makes automated fact-checking system necessary to ensure the safety and integrity of our online media ecosystem. Previous methods have demonstrated the effectiveness of decomposing the claim into simpler sub-tasks and utilizing LLM-based multi agent system to execute them. However, those models faces two limitations: they often fail to verify every component in the claim and lack of structured framework to logically connect the results of sub-tasks for a final prediction. In this work, we propose a novel automated fact-checking framework called Trification. Our framework begins by generating a comprehensive set of verification actions to ensure complete coverage of the claim. It then structured these actions into a dependency graph to model the logical interaction between actions. Furthermore, the graph can be dynamically modified, allowing the system to adapt its verification strategy. Experimental results on two challenging benchmarks demonstrate that our framework significantly enhances fact-checking accuracy, thereby advancing current state-of-the-art in automated fact-checking system.
Related papers
- Beyond Accuracy: A Cognitive Load Framework for Mapping the Capability Boundaries of Tool-use Agents [11.65679508751598]
We introduce a framework grounded in Cognitive Load Theory to move from simple performance scoring to a diagnostic tool.<n>Our framework deconstructs task complexity into two quantifiable components: Intrinsic Load and Extraneous Load.<n>Our evaluation reveals distinct performance cliffs as cognitive load increases, allowing us to precisely map each model's capability boundary.
arXiv Detail & Related papers (2026-01-28T09:17:51Z) - Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
arXiv Detail & Related papers (2025-10-16T05:35:37Z) - Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics [89.1999907891494]
We present WebDetective, a benchmark of hint-free multi-hop questions paired with a controlled Wikipedia sandbox.<n>Our evaluation of 25 state-of-the-art models reveals systematic weaknesses across all architectures.<n>We develop an agentic workflow, EvidenceLoop, that explicitly targets the challenges our benchmark identifies.
arXiv Detail & Related papers (2025-10-01T07:59:03Z) - Towards Robust Fact-Checking: A Multi-Agent System with Advanced Evidence Retrieval [1.515687944002438]
The rapid spread of misinformation in the digital era poses significant challenges to public discourse.<n>Traditional human-led fact-checking methods, while credible, struggle with the volume and velocity of online content.<n>This paper proposes a novel multi-agent system for automated fact-checking that enhances accuracy, efficiency, and explainability.
arXiv Detail & Related papers (2025-06-22T02:39:27Z) - DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts [35.952854524873246]
Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME) is a zero-shot MLLM pipeline for open-domain, text-image claim verification.<n>DEFAME operates in a six-stage process, dynamically selecting the tools and search depth to extract and evaluate textual and visual evidence.
arXiv Detail & Related papers (2024-12-13T19:11:18Z) - A SAT-based approach to rigorous verification of Bayesian networks [13.489622701621698]
We introduce a verification framework tailored for Bayesian networks, designed to address these drawbacks.
Our framework comprises two key components: (1) a two-step compilation and encoding scheme that translates Bayesian networks into Boolean logic literals, and (2) formal verification queries that leverage these literals to verify various properties encoded as constraints.
We benchmark the efficiency of our verification scheme and demonstrate its practical utility in real-world scenarios.
arXiv Detail & Related papers (2024-08-02T03:06:51Z) - OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs [59.836774258359945]
OpenFactCheck is a framework for building customized automatic fact-checking systems.<n>It allows users to easily customize an automatic fact-checker and verify the factual correctness of documents and claims.<n>CheckerEVAL is a solution for gauging the reliability of automatic fact-checkers' verification results using human-annotated datasets.
arXiv Detail & Related papers (2024-05-09T07:15:19Z) - Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification [31.38942054994932]
Federated Learning (FL) systems are susceptible to adversarial attacks.<n>RedJasper is a two-staged anomaly detection method specifically designed for real-world FL deployments.<n>It identifies suspicious activities in the first stage, then activates the second stage conditionally to further scrutinize the suspicious local models.
arXiv Detail & Related papers (2023-10-06T07:09:05Z) - Synthetic Disinformation Attacks on Automated Fact Verification Systems [53.011635547834025]
We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings.
We show that these systems suffer significant performance drops against these attacks.
We discuss the growing threat of modern NLG systems as generators of disinformation.
arXiv Detail & Related papers (2022-02-18T19:01:01Z) - Fingerprint recognition with embedded presentation attacks detection:
are we ready? [6.0168714922994075]
The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack algorithms (PAD) into such systems.
Current research did not state much about their effectiveness when embedded in fingerprint verification systems.
This paper proposes a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially.
arXiv Detail & Related papers (2021-10-20T13:53:16Z) - Higher Performance Visual Tracking with Dual-Modal Localization [106.91097443275035]
Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy.
We propose a dual-modal framework for target localization, consisting of robust localization suppressingors via ONR and the accurate localization attending to the target center precisely via OFC.
arXiv Detail & Related papers (2021-03-18T08:47:56Z) - Generating Fact Checking Explanations [52.879658637466605]
A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process.
This paper provides the first study of how these explanations can be generated automatically based on available claim context.
Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.
arXiv Detail & Related papers (2020-04-13T05:23:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.