AF-XRAY: Visual Explanation and Resolution of Ambiguity in Legal Argumentation Frameworks
- URL: http://arxiv.org/abs/2507.10831v1
- Date: Mon, 14 Jul 2025 22:00:45 GMT
- Title: AF-XRAY: Visual Explanation and Resolution of Ambiguity in Legal Argumentation Frameworks
- Abstract summary: We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning.<n>We use real-world legal cases to show that our tool supports teleological legal reasoning by revealing how different assumptions lead to different justified conclusions.
- Score: 1.287452323302345
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Argumentation frameworks (AFs) provide formal approaches for legal reasoning, but identifying sources of ambiguity and explaining argument acceptance remains challenging for non-experts. We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning. AF-XRAY introduces: (i) layered visualizations based on game-theoretic argument length revealing well-founded derivation structures; (ii) classification of attack edges by semantic roles (primary, secondary, blunders); (iii) overlay visualizations of alternative 2-valued solutions on ambiguous 3-valued grounded semantics; and (iv) identification of critical attack sets whose suspension resolves undecided arguments. Through systematic generation of critical attack sets, AF-XRAY transforms ambiguous scenarios into grounded solutions, enabling users to pinpoint specific causes of ambiguity and explore alternative resolutions. We use real-world legal cases (e.g., Wild Animals as modeled by Bench-Capon) to show that our tool supports teleological legal reasoning by revealing how different assumptions lead to different justified conclusions.
Related papers
- Primitive-Driven Compositional Forensic Visual Prompting for Open-World Face Anti-Spoofing [59.765442274924595]
We propose a compositional visual prompt learning framework that operates entirely in the visual feature space.<n>The framework employs patch-aware attention to refine a shared set of learnable micro-forensic primitives into localized forensic evidence units.<n>Experiments on nine open-world protocols demonstrate state-of-the-art performance, strong cross-domain generalization, and robust adaptation to unseen attacks.
arXiv Detail & Related papers (2026-08-18T04:18:08Z) - Look on Demand: A Cognitive Scheduling Framework for Visual Evidence Acquisition in Multimodal Reasoning [82.86343313807158]
We argue that a central challenge is how and when visual evidence is introduced into the reasoning process.<n>Motivated by this insight, we propose CSMR, a multimodal reasoning framework in which a language model controls the reasoning process.
arXiv Detail & Related papers (2026-05-27T08:43:13Z) - A3R: Agentic Affordance Reasoning via Cross-Dimensional Evidence in 3D Gaussian Scenes [115.37391672369914]
Affordance reasoning aims to identify the region that supports the action specified by a given text instruction in complex environments.<n>We propose A3R, an agentic affordance reasoning framework that enables an MLLM-based policy to iteratively select evidence acquisition actions.<n>Experiments on scene-level benchmarks show that A3R consistently surpasses static one-shot baselines.
arXiv Detail & Related papers (2026-04-02T10:40:51Z) - Argumentation for Explainable and Globally Contestable Decision Support with LLMs [16.75184263883707]
ArgEval is a framework that shifts from instance-specific reasoning to structured evaluation of general decision options.<n>These frameworks provide explainable recommendations for specific cases while still supporting global contestability.<n>We show that ArgEval can produce explainable guidance aligned with clinical practice.
arXiv Detail & Related papers (2026-03-15T22:37:14Z) - Adaptive Collaboration of Arena-Based Argumentative LLMs for Explainable and Contestable Legal Reasoning [1.4988096015658898]
ACAL is a neuro-symbolic framework that integrates adaptive multi-agent collaboration with an Arena-based Quantitative Bipolar Argumentation Framework (A-QBAF)<n>ACAL dynamically deploys expert agent teams to construct arguments, employs a clash resolution mechanism to adjudicate conflicting claims, and utilizes uncertainty-aware escalation for borderline cases.<n>Our framework supports a Human-in-the-Loop (HITL) contestability workflow, enabling users to directly audit and modify the underlying reasoning graph to influence the final judgment.
arXiv Detail & Related papers (2026-02-21T17:47:13Z) - ARGORA: Orchestrated Argumentation for Causally Grounded LLM Reasoning and Decision Making [11.531465685641086]
We introduce ARGORA, a framework that organizes multi-expert discussions into explicit argumentation graphs.<n>ARGORA can remove individual arguments and recompute outcomes, identifying which reasoning chains were necessary.<n>We further introduce a correction mechanism that aligns internal reasoning with external judgments when they disagree.
arXiv Detail & Related papers (2026-01-29T10:48:04Z) - Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models [72.4149653187766]
We propose a Reasoner-Verifier framework named Adrialversa Reasoning RAG (ARR)<n>The Reasoner and Verifier engage in reasoning on retrieved evidence and critiquing each other's logic while being guided by process-aware advantage.<n> Experiments on multiple benchmarks demonstrate the effectiveness of our method.
arXiv Detail & Related papers (2026-01-08T06:57:03Z) - Think Visually, Reason Textually: Vision-Language Synergy in ARC [94.15522924153264]
ARC-AGI is a rigorous testbed for conceptual rule induction and transfer to novel tasks.<n>Most existing methods treat ARC-AGI as a purely textual reasoning task, overlooking the fact that humans rely heavily on visual abstraction.<n>We introduce two synergistic strategies: Vision-Language Synergy Reasoning (VLSR) and Modality-Switch Self-Correction (MSSC)<n>Our findings suggest that unifying visual abstraction with linguistic reasoning is a crucial step toward achieving generalizable, human-like intelligence.
arXiv Detail & Related papers (2025-11-19T18:59:04Z) - From Disagreement to Understanding: The Case for Ambiguity Detection in NLI [0.0]
We show that content-based ambiguity offers a process-independent signal of divergent human perspectives.<n>A key limitation is the lack of annotated datasets for ambiguity and subtypes.<n>We propose addressing this gap through new annotated resources and unsupervised approaches to ambiguity detection.
arXiv Detail & Related papers (2025-07-20T20:27:35Z) - A Survey on Latent Reasoning [100.54120559169735]
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities.<n>CoT reasoning that verbalizes intermediate steps limits the model's expressive bandwidth.<n>Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state.
arXiv Detail & Related papers (2025-07-08T17:29:07Z) - CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
arXiv Detail & Related papers (2025-06-05T17:02:52Z) - Ground-R1: Incentivizing Grounded Visual Reasoning via Reinforcement Learning [96.01617809845396]
Ground-R1 is a reinforcement learning framework that enables grounded visual reasoning without requiring explicit evidence or rationale annotations.<n>Ground-R1 achieves superior performance and exhibits emergent cognitive behaviors such as uncertainty awareness, spatial perception, and iterative refinement.
arXiv Detail & Related papers (2025-05-26T17:51:47Z) - DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
We introduce DELTA, a discriminative model designed for legal case retrieval.
We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability.
Our approach can outperform existing state-of-the-art methods in legal case retrieval.
arXiv Detail & Related papers (2024-03-27T10:40:14Z) - Discovering Significant Topics from Legal Decisions with Selective
Inference [0.0]
We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts.
The method identifies case topics significantly correlated with outcomes, topic-word distributions and case-topic weights.
We show that topics derived by the pipeline are consistent with legal doctrines in both areas and can be useful in other related legal analysis tasks.
arXiv Detail & Related papers (2024-01-02T07:00:24Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual
Recognition [57.08108545219043]
Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision.
Existing literature addresses this challenge by employing local-based representation approaches.
This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
arXiv Detail & Related papers (2023-05-12T00:13:17Z) - How to disagree well: Investigating the dispute tactics used on
Wikipedia [17.354674873244335]
We propose a framework of dispute tactics that unifies the perspectives of detecting toxicity and analysing argument structure.
This framework includes a preferential ordering among rebuttal-type tactics, ranging from ad hominem attacks to refuting the central argument.
We show that these annotations can be used to provide useful additional signals to improve performance on the task of predicting escalation.
arXiv Detail & Related papers (2022-12-16T09:01:19Z) - Admissibility in Strength-based Argumentation: Complexity and Algorithms
(Extended Version with Proofs) [1.5828697880068698]
We study the adaptation of admissibility-based semantics to Strength-based Argumentation Frameworks (StrAFs)
Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung's fundamental lemma.
We propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions.
arXiv Detail & Related papers (2022-07-05T18:42:04Z) - An Argumentation-based Approach for Identifying and Dealing with
Incompatibilities among Procedural Goals [1.1744028458220426]
An intelligent agent may generate multiple pursuable goals, which may be incompatible among them.
In this paper, we focus on the definition, identification and resolution of these incompatibility.
arXiv Detail & Related papers (2020-09-11T01:01:34Z) - Expressiveness of SETAFs and Support-Free ADFs under 3-valued Semantics [15.174903837196297]
We shed light on the relation between SETAFs and support-free ADFs.
It is only the presence of unsatisfiable acceptance conditions in support-free ADFs that discriminate the two approaches.
arXiv Detail & Related papers (2020-07-07T16:03:23Z)
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.