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

arXiv: https://arxiv.org/abs/2607.14641 · HTML: https://mlsanity.com/paper/2607.14641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [All-but-the-Top: Simple and Effective Postprocessing for Word Representations](https://mlsanity.com/paper/1702.01417/) (2017)
- [Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approach](https://mlsanity.com/paper/2502.14000/) (2025)
- [From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning](https://mlsanity.com/paper/2507.13768/) (2025)
- [Quantum Abduction: A New Paradigm for Reasoning under Uncertainty](https://mlsanity.com/paper/2509.16958/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Minimal kappa--τ Logic for Risk-Sensitive Abduction](https://mlsanity.com/paper/2608.08192/) (2026)

## Abstract (arXiv)

Abductive reasoning operates in two directions. The synthetic mode builds explanations from available hypotheses; the analytic mode, conversely, identifies the latent factors whose interaction accounts for a complex observed state. This paper develops the analytic mode as a non-greedy, risk-sensitive discipline of commitment, in which candidate factors coexist and interact, resolving into committed conclusions only when explicit governance conditions are met. The formal core is the kappa-τ apparatus: kappa encodes the epistemic interaction among hypotheses, and τ sets a commitment threshold calibrated to the decision's stakes. The central contribution is the causal cluster, a structured object recording which latent factors participate in a decomposition, with what weights and interaction structure, together with a two-level architecture (intra-cluster kappa^*, inter-cluster kappa**) that guards against causal misattribution. Demonstrated in epidemiological crisis decomposition and adversarial cyber threat analysis, the framework's contribution to human-AI reasoning is the legibility of suspended decomposition as a shared coordination object, providing structural resistance to premature convergence. In practice, the decision-maker is handed not a single imposed answer but the competing explanatory scenarios, weighted by plausibility and paired with the evidence that would resolve between them, so that sound action is possible even before the ambiguity is resolved.