# Quantum Abduction: A New Paradigm for Reasoning under Uncertainty

arXiv: https://arxiv.org/abs/2509.16958 · HTML: https://mlsanity.com/paper/2509.16958/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective](https://mlsanity.com/paper/2003.00330/) (2020)
- [Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approach](https://mlsanity.com/paper/2502.14000/) (2025)
- [Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research](https://mlsanity.com/paper/2307.10250/) (2023)
- [From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning](https://mlsanity.com/paper/2507.13768/) (2025)
- [Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics](https://mlsanity.com/paper/2501.14634/) (2025)

## Built on by (most cited first, 2 of 2)

- [Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination](https://mlsanity.com/paper/2607.14641/) (2026)
- [A Minimal kappa--τ Logic for Risk-Sensitive Abduction](https://mlsanity.com/paper/2608.08192/) (2026)

## Abstract (arXiv)

Abductive reasoning - the search for plausible explanations - has long been central to human inquiry, from forensics to medicine and scientific discovery. Yet formal approaches in AI have largely reduced abduction to eliminative search: hypotheses are treated as mutually exclusive, evaluated against consistency constraints or probability updates, and pruned until a single "best" explanation remains. This reductionist framing overlooks the way human reasoners sustain multiple explanatory lines in suspension, navigate contradictions, and generate novel syntheses. This paper introduces quantum abduction, a non-classical paradigm that models hypotheses in superposition, allows them to interfere constructively or destructively, and collapses only when coherence with evidence is reached. Grounded in quantum cognition and implemented with modern NLP embeddings and generative AI, the framework supports dynamic synthesis rather than premature elimination. Case studies span historical mysteries (Ludwig II of Bavaria, the "Monster of Florence"), literary demonstrations ("Murder on the Orient Express"), medical diagnosis, and scientific theory change. Across these domains, quantum abduction proves more faithful to the constructive and multifaceted nature of human reasoning, while offering a pathway toward expressive and transparent AI reasoning systems.