Abstract
Premature semantic collapse -- the forced early commitment to a single meaning -- remains a core architectural limitation of current language models. Softmax-driven competition and greedy decoding cause models to discard valid interpretations before sufficient context is available, resulting in brittle reasoning and context failures. We introduce Non-Resolution Reasoning (NRR), a general computational framework that preserves semantic ambiguity during inference and performs resolution only when explicitly required. NRR integrates three components: (1) Multi-Vector Embeddings that maintain multiple viable interpretations per token, (2) Non-Collapsing Attention that prevents winner-take-all dynamics across layers, and (3) Contextual Identity Tracking (CIT), which assigns context-specific identities to recurring entities (e.g., distinguishing "Dr. Smith the cardiologist" from "Dr. Smith the researcher"). These mechanisms are unified by an external Resolution Operator $\rho$ that makes semantic commitment explicit, controllable, and task-dependent. Unlike standard architectures, NRR separates representation from resolution, allowing a single model to shift between creative, factual, and ambiguity-preserving reasoning without retraining. A synthetic evaluation demonstrates NRR's ability to preserve ambiguity and track context: CIT-enhanced models achieve 90.9% accuracy on out-of-distribution identity-shift tasks, compared to 9.1% for transformer baselines. NRR provides a principled alternative to premature collapse, reframing ambiguity as an explicit representational state rather than a failure mode. The question is not whether AI should resolve ambiguity, but when, how, and under whose control.
Abstract (translated)
过早的语义崩溃——即在有足够的上下文之前被迫提前承诺单一含义——仍然是当前语言模型的核心架构限制之一。由softmax驱动的竞争和贪婪解码导致模型在没有足够背景信息的情况下舍弃有效的解释，从而引发脆弱的推理和上下文理解失败。我们提出了一种通用计算框架：非解析推理（NRR），该框架在推断过程中保留语义模糊性，并仅在明确需要时进行解析。NRR整合了三个组成部分： 1. 多向量嵌入，为每个标记维持多个可行的解释。 2. 非崩溃注意力机制，阻止各层间的赢家通吃动态过程。 3. 上下文身份追踪（CIT），用于根据上下文环境赋予反复出现的实体特定的身份（例如区分“心脏病专家史密斯医生”和“研究员史密斯博士”）。 这些机制通过一个外部解析操作符$\rho$统一起来，该操作符使语义承诺变得显式、可控并依赖于任务需求。与标准架构不同的是，NRR将表示与解析分离，使得单一模型能够在创造性推理、事实性推理和保留模糊性的推理之间自由切换而无需重新训练。合成评估表明了NRR在保持模糊性和追踪上下文方面的有效性：增强CIT的模型在外来身份转变任务上达到90.9%的准确率，相比之下基于变换器的基础模型仅为9.1%。 NRR为过早崩溃提供了一个有原则的替代方案，将模棱两可视为一种显式的表示状态而不是失败模式。问题不再是AI是否应该解析模糊性，而是何时、如何以及在谁的控制下进行这种解析。
URL
https://arxiv.org/abs/2512.13478
