NRR-Core: Non-Resolution Reasoning as a Computational Framework for Contextual Identity and Ambiguity Preservation

TL;DR

This paper introduces NRR, a framework that allows AI systems to retain multiple interpretations simultaneously, challenging the traditional approach of early ambiguity resolution in neural architectures.

Contribution

NRR proposes principles and methods for preserving ambiguity and multiple interpretations in AI reasoning, enabling more flexible and context-aware understanding.

Findings

NRR maintains high entropy at ambiguous turns, indicating preserved interpretive flexibility.

Standard architectures collapse early, losing alternative interpretations quickly.

Case studies demonstrate NRR's effectiveness in paradox handling and creative generation.

Abstract

Current artificial intelligence systems exhibit a fundamental architectural limitation: they resolve ambiguity prematurely. This premature semantic collapse--collapsing multiple valid interpretations into single outputs--stems from classical identity assumptions in neural architectures. We propose Non-Resolution Reasoning (NRR), a framework treating ambiguity retention as a valid reasoning mode. NRR introduces three principles: (1) Non-Identity ($A \neq A$)--the same symbol refers to different entities across contexts; (2) Approximate Identity ($A \approx A$)--entities share partial structural overlap without being identical; (3) Non-Resolution--conflicting interpretations coexist without forced convergence. We formalize these through Multi-Vector Embeddings for context-dependent representation, Non-Collapsing Attention for parallel interpretation retention, and Contextual Identity Tracking (CIT) for maintaining $A \neq A$ across inference. We illustrate NRR through case studies in paradox handling, creative generation, and context-dependent reasoning. Functional verification in a synthetic two-turn disambiguation task shows NRR-lite maintains high entropy ($H = 0.91$ bits, near-maximum $1.0$) at ambiguous turns while standard architectures collapse early ($H = 0.15$ bits), preserving interpretive flexibility until context arrives. NRR challenges the assumption that meaning must collapse to be useful. In the narrow non-evaluative read adopted later in the series, the practical point is not that no judgment ever occurs, but that retained alternatives need not be implemented as repeated full branchwise comparative evaluation during retention while evidence is still incomplete. The question is not whether AI should resolve ambiguity, but when, how, and under whose control.