A Minimal kappa--τ Logic for Risk-Sensitive Abduction
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Standard approaches to abductive reasoning can retain multiple candidate explanations, but they do not generally combine explicit compositional cross-hypothesis interaction with an internal, rival-sensitive commitment judgment. This paper argues that in risk-sensitive domains -- where premature commitment carries asymmetric downside costs -- the timing of commitment is itself a governed decision that the inferential apparatus should formally represent. We present a minimal kappa--τ logical framework built on two primitives: epistemic interaction among hypotheses (kappa) and a normative commitment threshold (τ). Hypotheses may coexist, reinforce or inhibit one another, and form emergent composite explanations, while collapse into committed conclusions is regulated by governance constraints rather than forced by inference alone. The logic is developed in two complementary modes sharing the interaction relation and the governance apparatus: a synthetic mode, in which atomic hypotheses are composed upward into emergent explanations, and an analytic mode, in which complex observed states of affairs are decomposed into causal clusters of latent factors, with commitment governed at both the cluster and the factor level. The framework provides formal machinery for domains in which the distinction between highly likely and commit-worthy is operationally consequential. The kappa--τ logic is positioned as the symbolic governance layer of a neurosymbolic architecture: its epistemic parameters are naturally estimated by neural components -- semantic embeddings and generative models, as demonstrated in existing computational realizations -- while its normative parameters remain under explicit human governance, yielding transparent and auditable abductive reasoning for deployment in high-stakes settings.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 5 of its 40 references that are in the map. First posted on arXiv 8 Aug 2026.
Builds on
5 of 40 references.
Closest ancestors · 1
Same direction, newest first
- 2026Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination16 Jul
Other references · 4
- 2020Neurosymbolic AI: The 3rd Wavecited by 253
- 2025From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoningcited by 4
- 2013Plausibility Measures: A User's Guidecited by 2
- 2025Quantum Abduction: A New Paradigm for Reasoning under Uncertaintycited by 2
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Propositional Abduction via Only-Knowing: A Non-Monotonic Approach
- 2026Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs
- 2025Complexity of Faceted Explanations in Propositional Abduction
- 2026Local verification cannot detect non-transportability: a cohomological theory of context preservation in agentic reasoning
- 2026Graph of States: Solving Abductive Tasks with Large Language Models
- 2025GEAR: A General Evaluation Framework for Abductive Reasoning
- 2024Abductive Reasoning in a Paraconsistent Framework
- 2026Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference