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 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 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
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.
arXiv AI
Jun 2
AtomEval: Validity-Aware Atomic Evaluation of Adversarial Claim Rewriting in Fact Verification
arXiv:2604. 07967v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can rewrite refuted claims to evade evidence-based fact verifiers, but conventional attack success rate (ASR) can be inflated when rewrites change, weaken, or correct the false proposition they are supposed to preserve.
arXiv AI
Jun 8
TRUE: A Trustworthy Unified Explanation Framework for Large Language Model Reasoning
arXiv:2602. 18905v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their decision-making processes remain difficult to interpret.
arXiv AI
Jul 31
A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models
arXiv:2607. 26102v1 Announce Type: cross Abstract: Mathematical chain of thought (CoT) evaluation is commonly reduced to whether the final answer matches a reference.
arXiv AI
Jul 14
CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps
arXiv:2607. 10562v1 Announce Type: new Abstract: Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge.
arXiv AI
Jun 10
Evaluating Research-Level Math Proofs via Strict Step-Level Verification
arXiv:2606. 10799v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs.
arXiv AI
Jun 29
CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching
arXiv:2602. 20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations.
arXiv Machine Learning
Jul 27
CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
arXiv:2607. 22511v1 Announce Type: cross Abstract: Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation.