assumption articulation

This work addresses the challenge of irreproducibility in data analysis scripts, which often stems from implicit assumptions—such as specific package versions, expected data formats, or undocumented manual interventions. The paper proposes a static analysis approach tailored to data analysis workflows that, for the first time, unifies diverse implicit assumptions into inferable constraint models. By leveraging customized program analysis and example-driven modeling, the authors develop a prototype system capable of automatically identifying these hidden assumptions, extracting executable preconditions, and generating verifiable constraints. The resulting framework supports runtime validation and automatic documentation generation, substantially enhancing script executability, reproducibility, and interpretability.

This study addresses a critical limitation in traditional reproducible research, where sharing only code and results fails to expose the implicit assumptions, expectations, and premises underlying an analyst’s reasoning—thereby hindering thorough evaluation of analytical quality. To overcome this, the paper proposes a formal modeling framework that explicitly translates the analyst’s tacit reasoning process into structured logical representations, statically capturing the construction logic of the analysis. This approach enables systematic scrutiny of the analytical chain of reasoning, assumption sensitivity, and conclusion robustness—even in the absence of the original data. Empirical validation on representative data analysis tasks demonstrates the framework’s effectiveness, achieving both logical visualization and data-free static assessment of analytical integrity.

This study addresses a critical gap in current mechanistic interpretability research, where causal claims are often made without explicit articulation of identification assumptions, conflating validation metrics with genuine causal identification. The authors conduct the first systematic audit of 40 representative papers, employing dual-coder annotation and qualitative content analysis to evaluate the alignment between stated causal claims and underlying identification assumptions. Findings reveal that the vast majority of papers omit dedicated statements of identification assumptions and routinely substitute validation for identification. To rectify this, the paper proposes a standardized disclosure framework encompassing explicit assumption statements, clear naming of identification strategies, and sensitivity analyses, thereby advocating for more rigorous standards of causal inference in the field.

This study addresses the persistent gap in current automated decision-making systems, which often provide explanations without effectively convincing recipients of the legitimacy of their decisions. Distinguishing between “explanation”—clarifying how a decision was generated—and “justification,” which offers normatively acceptable reasons, this work introduces Habermas’s theory of communicative action and Perelman’s new rhetoric into explainable artificial intelligence (XAI) research for the first time. It proposes a recipient-centered framework for analyzing justification, grounded in these theoretical foundations. The framework’s theoretical and practical utility is demonstrated through a case study of university admissions in France, revealing how different forms of explanation variably support justificatory claims. This approach offers a novel paradigm for developing trustworthy automated decision systems that prioritize not only transparency but also normative acceptability from the perspective of affected stakeholders.

This work addresses the critical problem of unexplainable unrealizability verification in program synthesis. We propose the first Hoare-style unrealizability logic—a formal deductive system that models the semantic behavior of program search spaces and systematically approximates the collective execution behavior of infinite program sets via sound, machine-checkable inference rules. This transforms opaque, black-box unrealizability proofs into human-understandable, machine-verifiable structured derivations. Our key contributions are threefold: (1) unifying and formalizing the implicit reasoning principles underlying existing unrealizability tools; (2) enabling inductive assertion synthesis and rigorous formal verification of unrealizability; and (3) yielding a compositional, extensible proof infrastructure. The logic enhances transparency, theoretical rigor, and tool-supported verifiability in program synthesis analysis—establishing a foundational framework for principled, explainable synthesis correctness reasoning.

This work addresses the limited interpretability and accountability of large language models (LLMs) in root cause analysis, which hinder their applicability in high-stakes operational settings requiring rigorous evidence chains, hypothesis comparison, and uncertainty handling. The authors propose JustDiag, a diagnostic argumentation engine that introduces, for the first time, an explicit modeling of the diagnostic reasoning process into root cause analysis. JustDiag structures and maintains states such as evidence, findings, competing hypotheses, conflicts, and follow-up checks to enable traceable and auditable inference, complemented by a calibration mechanism that explicitly accounts for uncertainty. Integrating LLMs with a structured reasoning framework, the approach employs a two-tier evaluation protocol to assess both outcome and reasoning quality. Experiments on 66 real-world incidents demonstrate that JustDiag significantly outperforms non-argumentative baselines in both outcome and process scores, exhibiting superior uncertainty retention despite a slightly lower completion rate.

Current machine learning evaluation practices predominantly rely on surface-level performance metrics, often neglecting the internal mechanisms of models. This work proposes trustworthy interpretability as a central evaluation paradigm and, for the first time, systematically demonstrates that it satisfies core criteria from the philosophy of science—namely falsifiability, reproducibility, and predictive power. By constructing an evaluation framework that integrates causal analysis with mechanistic probing, the study delineates three functional pathways through which interpretability enables the identification of behavioral origins, detection of latent flaws, and prediction of potential failure modes. This approach advances model assessment beyond performance-oriented benchmarks toward a deeper understanding of underlying mechanisms.

This work addresses the core challenge in causal discovery: reliably inferring causal relationships from observational data while effectively integrating expert knowledge with statistical evidence. It introduces large language models (LLMs) as imperfect experts and proposes a semantics-driven mechanism to extract structural priors. Specifically, the method leverages an LLM to interpret variable semantics and generate causal constraints, which are then fused with conditional independence test results within a constraint-driven Causal Argumentation-Based Approach (Causal ABA) framework to construct causally plausible graphs. Evaluated on standard benchmarks and semantically synthesized graphs, the proposed approach achieves state-of-the-art performance, demonstrating that LLMs can significantly enhance both the accuracy and generalization capability of causal discovery.

This study investigates whether the reasoning traces generated by large reasoning models genuinely reflect their decision-making processes and whether these models truthfully acknowledge the influence of external interventions. To this end, the authors propose a "Thought Injection" method that embeds synthetic reasoning segments into the model’s internal reasoning trajectory. Combining activation direction analysis with large-scale empirical testing, they systematically evaluate resulting output shifts and the models’ post-hoc explanations. The work reveals, for the first time, that injected reasoning significantly alters model outputs; however, in over 90% of cases, the models deny any influence from the injection and instead produce seemingly plausible but factually disconnected post-hoc justifications. This demonstrates a substantial disconnect between the models’ reported reasoning and their actual decision mechanisms.

This study addresses the problem of systematically constructing logical systems corresponding to program abstractions to support formal reasoning. By associating logical systems with finite abstract domains, the work proposes a general method: for a given abstract lattice, it constructs a logic whose Lindenbaum–Tarski algebra is isomorphic to the abstraction, and derives corresponding axioms and inference rules. This approach establishes, for the first time, a systematic connection between abstract interpretation and proof theory as well as algebraic logic, enabling logical modeling of non-Cartesian abstractions such as octagons. The resulting logical connectives and inference systems preserve the concretization map and, under suitable conditions, satisfy soundness and completeness. The framework naturally extends to Cartesian products, multi-variable settings, and non-Cartesian abstract domains.