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University of Molise

Academic institutioneurope · it
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Research library13linked papers
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Selected work

Representative Papers

A Minimal $κ$--$τ$ Logic for Risk-Sensitive Abduction

Aug 08, 2026

Traditional abductive reasoning struggles to model interactions among hypotheses and risk-sensitive timing of commitments, limiting its applicability in high-stakes decision-making. This work proposes the first logical framework—denoted κ–τ—that explicitly integrates commitment-timing control by introducing hypothesis interaction parameters (κ) and normative commitment thresholds (τ), thereby distinguishing between “highly plausible” and “worthy of commitment.” The framework incorporates a dual-mode mechanism of synthesis and analysis to support governable reasoning. Implemented as a neurosymbolic architecture, it enables neural components to estimate cognitive parameters while allowing human agents to specify normative ones. A preliminary computational implementation demonstrates its potential to deliver transparent, auditable, and formally grounded abductive tools tailored for high-risk scenarios.

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From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

Jul 21, 2026

This work addresses the tendency of large language models (LLMs) to generate outputs lacking verifiable syntactic structure, often resulting in structural errors and hallucinations. The authors propose a neurosymbolic framework that, for the first time, dynamically aligns the incremental derivation mechanism of Combinatory Categorial Grammar (CCG) with the prefix-driven generation process of LLMs. By leveraging the Curry–Howard isomorphism, the approach lifts model outputs into typed compositional derivations. This enables unified structural reconstruction across both natural language and formal languages—including SQL, Solidity, and OWL—and incorporates a two-tier verification mechanism to enforce structural consistency and enable early detection of factual inaccuracies in generated content.

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Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination

Jul 16, 2026

This work addresses the challenges of premature convergence and insufficient interpretability in human-AI collaboration under complex observations by proposing a non-greedy, risk-sensitive abductive reasoning framework. The approach leverages causal cluster structures and a dual-level κ-architecture (κ* and κ**) to enable accurate causal decomposition while avoiding misattribution. A novel κ–τ mechanism is introduced, wherein κ models cognitive interactions among competing hypotheses and τ dynamically adjusts commitment thresholds based on decision risk. Undetermined decompositions serve as shareable coordination artifacts to enhance transparency. Empirical validation in epidemic crisis and adversarial cyber threat scenarios demonstrates that the method generates multiple coexisting, evidence-supported explanatory pathways, thereby facilitating robust decision-making under ambiguity.

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Recent publications

Latest Papers

A Minimal $κ$--$τ$ Logic for Risk-Sensitive Abduction

Aug 08, 2026

Traditional abductive reasoning struggles to model interactions among hypotheses and risk-sensitive timing of commitments, limiting its applicability in high-stakes decision-making. This work proposes the first logical framework—denoted κ–τ—that explicitly integrates commitment-timing control by introducing hypothesis interaction parameters (κ) and normative commitment thresholds (τ), thereby distinguishing between “highly plausible” and “worthy of commitment.” The framework incorporates a dual-mode mechanism of synthesis and analysis to support governable reasoning. Implemented as a neurosymbolic architecture, it enables neural components to estimate cognitive parameters while allowing human agents to specify normative ones. A preliminary computational implementation demonstrates its potential to deliver transparent, auditable, and formally grounded abductive tools tailored for high-risk scenarios.

0 citationsRead paper

From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

Jul 21, 2026

This work addresses the tendency of large language models (LLMs) to generate outputs lacking verifiable syntactic structure, often resulting in structural errors and hallucinations. The authors propose a neurosymbolic framework that, for the first time, dynamically aligns the incremental derivation mechanism of Combinatory Categorial Grammar (CCG) with the prefix-driven generation process of LLMs. By leveraging the Curry–Howard isomorphism, the approach lifts model outputs into typed compositional derivations. This enables unified structural reconstruction across both natural language and formal languages—including SQL, Solidity, and OWL—and incorporates a two-tier verification mechanism to enforce structural consistency and enable early detection of factual inaccuracies in generated content.

0 citationsRead paper

Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination

Jul 16, 2026

This work addresses the challenges of premature convergence and insufficient interpretability in human-AI collaboration under complex observations by proposing a non-greedy, risk-sensitive abductive reasoning framework. The approach leverages causal cluster structures and a dual-level κ-architecture (κ* and κ**) to enable accurate causal decomposition while avoiding misattribution. A novel κ–τ mechanism is introduced, wherein κ models cognitive interactions among competing hypotheses and τ dynamically adjusts commitment thresholds based on decision risk. Undetermined decompositions serve as shareable coordination artifacts to enhance transparency. Empirical validation in epidemic crisis and adversarial cyber threat scenarios demonstrates that the method generates multiple coexisting, evidence-supported explanatory pathways, thereby facilitating robust decision-making under ambiguity.

0 citationsRead paper