conceptual clarification

Existing argument analysis tools struggle to evaluate the conditional validity of arguments across diverse worldviews. This work proposes a multi-perspective reasoning framework that operationalizes conditional validity for the first time by integrating structured worldview modeling, three-tier natural language inference, and conditional reasoning with large language models. The system automatically identifies value conflicts and assumption gaps, generating perspective-specific explanations. It further supports interactive visualization to effectively reveal differences in logical and normative coherence of the same argument under pluralistic value systems, thereby enabling users to explore multidimensional interpretations of complex arguments.

This study addresses the lack of systematic annotation and visualization methods for legal argumentation structures in Chinese judicial judgments, which hinders computational analysis of legal reasoning. Drawing on legal reasoning theory, the work proposes the first fine-grained and operational annotation framework tailored to Chinese judgments. It formally defines four types of propositions, five categories of argumentative relations, formal representation rules for nested structures, and corresponding visualization conventions. A standardized annotation protocol and inter-annotator consistency control mechanism are also developed. The resulting comprehensive and reproducible annotation guideline establishes a reliable methodological and data foundation for legal argument mining, computational modeling of legal reasoning, and AI-assisted legal analysis.

This paper addresses the problem of quantifying the joint contribution of argument sets to the strength of a target argument in bipolar argumentation frameworks. To overcome the limitation of existing models—which only assess individual argument contributions—we propose, for the first time, a formal set-wise contribution function and an axiomatic foundation that captures synergistic, canceling, and dependency interactions among arguments within a set. Through rigorous mathematical modeling and formal logical derivation, we construct multiple set contribution functions satisfying distinct combinations of axioms and conduct systematic axiom compliance verification. Experimental evaluation demonstrates that our approach enables finer-grained modeling of complex argument compositions, significantly enhancing both expressiveness and interpretability of argument strength assessment—particularly in applications such as recommender systems.

Non-experts struggle to identify sources of ambiguity in Argumentation Frameworks (AFs) and assess argument acceptability in legal reasoning. Method: We propose an explanation-oriented visualization approach integrating game-theoretic perspective-based hierarchical visualization of argument length, semantic role classification of attack edges, multi-solution overlay rendering, and automated identification of critical attack sets—grounded in abstract AFs, three-valued/bivalent semantics, and gamified derivation structures. Contribution/Results: This work is the first to embed semantic role labeling and critical attack set generation algorithms directly into the visualization pipeline, enabling precise localization of ambiguity origins and exploration of disambiguation pathways. Evaluated on real-world legal cases, our method systematically generates disambiguated interpretations and explicitly reveals how alternative assumptions affect conclusions, significantly enhancing non-experts’ comprehension of teleological legal reasoning.

Existing structured argumentation frameworks struggle to simultaneously satisfy the five core rationality postulates—closure, direct and indirect consistency, non-interference, and crash-resistance—under credulous semantics when both undercuts and preferences are taken into account. This work proposes a novel framework, Deductive ASPIC$^{\ominus}$, which integrates the gen-rebuttals mechanism from ASPIC$^{\ominus}$ with the joint-support bipolar structure of Deductive ASPIC$-$, while incorporating preference handling. For the first time, this framework rigorously satisfies all five rationality postulates within an enhanced preferred semantics in settings that include both undercuts and preferences, thereby establishing a theoretically sound foundation and offering a new pathway toward logically robust argumentation systems.

This study addresses a critical vulnerability in automated reasoning systems: their frequent neglect of “frame uncertainty”—ontological blind spots arising from finite modeling assumptions. The work offers the first systematic distinction among aleatoric, epistemic, and frame uncertainties, arguing that the latter is especially hazardous due to its structural invisibility. Through an interdisciplinary review and conceptual analysis integrating practices from statistics, engineering, and machine learning, the paper exposes a fundamental limitation: current systems lack the capacity to introspectively evaluate their own framing choices. To mitigate this blind spot, the authors advocate for cognitive humility, rigorously differentiating between rigor within a given frame and rigor about the frame itself. Practical pathways are proposed to heighten risk awareness across five categories of practitioners.

This study addresses the challenge of modeling defeasible conditional obligations in dynamic informational environments, particularly the inability of existing semantic frameworks to retract previously derived obligations in the presence of conflicting information. To overcome this limitation, the paper proposes a two-layered preferential semantic framework that distinguishes between ideality-based and normality-based world orderings. By integrating a Hansson-Lewis style dyadic deontic logic with non-monotonic reasoning mechanisms, the framework satisfies key metatheoretic principles such as immunity to strengthening of the antecedent, inclusion, and avoidance of drowning. This approach not only enables dynamic adjustment and principled retraction of defeasible obligations but also establishes a formal connection with constraint-based input/output logics, thereby overcoming significant shortcomings of traditional models.

This study addresses the challenge of evaluating large language models’ reasoning capabilities on conceptual questions—such as those in philosophy, AI safety, and decision theory—that lack objective ground truth. The authors present the first systematically constructed and annotated dataset comprising 951 argumentative commentaries derived from 442 stance-based texts, spanning domains including AI safety, ethics, and political theory. They introduce a multidimensional expert scoring framework assessing centrality, strength, correctness, and clarity, yielding 1,458 annotations, and propose two scoring functions that decompose holistic conclusions into evaluable local argument units. Benchmarking experiments demonstrate a strong correlation between model performance and general language capabilities, thereby validating the dataset’s effectiveness and practicality as a novel benchmark for conceptual reasoning evaluation.

This study addresses the limitations of traditional metaphor analysis, which often focuses narrowly on source domains and struggles to uncover differences in the semantic frames activated by metaphors within complex discourses. To overcome this, the paper proposes an integrated framework that combines Conceptual Metaphor Theory with computational linguistics, leveraging natural language processing and semantic frame analysis to automatically identify salient metaphors in discourse and enable fine-grained cross-frame comparisons. Applied to climate news corpora, the method successfully detects both established and novel metaphorical frames. Furthermore, it reveals that while conservative and liberal media may employ the same source domains in discussions of immigration, they invoke markedly distinct semantic frames, thereby underscoring the constitutive role of metaphor in shaping political discourse.

This work addresses the limitations of existing counterfactual explanations in abstract argumentation, which predominantly rely on but-for tests and struggle with complex causal scenarios such as preemption and overdetermination. The paper proposes an intervention-based counterfactual reasoning framework that formalizes argument acceptability conditions as structural equations and incorporates refined counterfactual criteria from the Halpern-Pearl model of actual causality. By enabling simultaneous interventions on multiple variables and integrating witness constraints to fix key argument labels, the approach achieves precise identification of intricate causal structures within abstract argumentation frameworks. This method represents the first systematic effort to handle such complexities in this domain, demonstrating significantly enhanced expressiveness and reliability compared to current approaches.