arXiv AI
Jul 24
A Counterfactual Cause in Situation Calculus
arXiv:2501. 06857v3 Announce Type: replace Abstract: Perhaps the most popular modern formulation of actual causality is the HP account by Halpern and Pearl.
Beyond But-for Test: Counterfactual Explanation in Abstract Argumentation via Actual Causality (Extended Version)
arXiv:2606. 31080v1 Announce Type: cross Abstract: Counterfactual explanation in abstract argumentation calls for an answer to the what-if query: would the topic argument still be accepted if the status of certain other arguments were changed?
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arXiv AI
Sep 1
DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark
arXiv:2608.30413v1 Announce Type: new Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of n...
arXiv Computation and Language
Sep 16
Autoformalizing Argumentative Material Inferences
The paper introduces GUARD, a neuro‑symbolic system that autoformalizes argumentative material by completing missing premises (guards) before formal verification. It uses large language models to generate candidate guards, Isabelle/HOL to verify them, and a contrastive test to ensure the proof depends on the original premises and does not over‑generalize. Experiments on Debatepedia and ARCT show that GUARD improves verified‑faithful scores by over 30 points and reduces leakage by about 20 points compared to prior LLM‑driven theorem proving methods.
Hugging Face Trending Papers
Aug 31
DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark
Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of newer evidence. Although recent studies have exami...
arXiv AI
Sep 10
Modus Tollens and Counterfactuals and Counterfactual Reasoning Based on Three Types of Negation
arXiv:2609.07483v1 Announce Type: new Abstract: Modus Tollens (MT) is a classical logical inference rule, while counterfactuals are hypothetical statements that are contrary to facts, and counterfact...
arXiv AI
Sep 1
Beyond Surface Forms: Symbolic Edits as a Test for Logical Reasoning with LLMs
arXiv:2608.30256v1 Announce Type: cross Abstract: Logical reasoning with large language models (LLMs) is a critical capability, as it reflects a system's ability to correctly deduce hypotheses from a...
arXiv Machine Learning
Jul 27
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
arXiv:2607. 22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome.
arXiv Computation and Language
Sep 4
Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG
The paper introduces Causal-Counterfactual RAG, a new framework that augments Retrieval-Augmented Generation with explicit causal graphs and counterfactual reasoning. By incorporating cause‑effect relationships into retrieval and evaluating both direct causal evidence and counterfactual scenarios, the approach aims to produce more robust, accurate, and interpretable answers. This method seeks to maintain contextual coherence, reduce hallucinations, and improve reasoning fidelity compared to traditional RAG systems.
arXiv AI
Aug 12
Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
arXiv:2602. 16481v2 Announce Type: replace Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions.
arXiv AI
Sep 10
When Tools Hurt LLM Reasoning: State-Dependent Belief Revision under External Evidence
arXiv:2508.15754v2 Announce Type: replace-cross Abstract: Tool use is often assumed to monotonically improve reasoning, where external evidence is expected to help when relevant and be ignored when i...
arXiv AI
Jul 16
Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution
arXiv:2607. 13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises.
arXiv AI
Jun 15
C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning
arXiv:2603. 05167v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as judges of chain-of-thought (CoT) reasoning, yet it remains unclear whether they can reliably assess process faithfulness rather than merely answer plausibility.