V4-Flash实验：思考让指针追踪0/16→14/16，孪生任务仍50%。换接口比堆算力更重要。
该论文提出符号化—基底论题（Symbolization–Substructure Thesis）和涌现不变性公式 R_s^*=R_φ^*+C_s，认为规模扩展只能缩小补偿差 C_s，却无法消除任务接口下限 R_φ^*。作者用 DeepSeek V4-Flash 做了匹配实验：加入思考后指针追踪成绩从 0/16 提高到 14/16；观察孪生任务仍停在 50% 的构造下限；恢复关键记忆后成绩从 50% 升到 100%。这些结果支持“在同一接口内扩展规模”与“精化接口本身”之间的区别。
Emergence Invariance: From Symbolized Thought to Interface Refinement
Language can be viewed as a formalized subset of thought: a consequence-governed symbolic structure projected from wider situated cognition. Large language models trained at scale exhibit compensatory emergence: sparse architectural primitives support in-context learning, multi-step reasoning, tool use, and chain of thought. Yet a language-first probabilistic architecture inherits substantive, substrate, and high-level incompletenesses relative to human cognition. Their coexistence makes an LLM a human-like thought-form generator that reconstructs increasingly human-like reasoning forms from an incomplete substrate. We ask whether emergence can compensate for every missing distinction. We formalize the philosophical premise as the Symbolization--Substructure Thesis and introduce emergence invariance. For a scale-indexed family acting through a shared task interface $φ$, $\mathcal{R}_s^*=\mathcal{R}_φ^*+C_s$: scale can reduce the compensation gap $C_s$, while a positive interface floor $\mathcal{R}_φ^*$ persists. We prove that, under a fixed input law, one interface is universally no less informative exactly when its completed information $σ$-field refines the other, and that total compensation occurs exactly when both the interface floor and asymptotic compensation gap vanish. The framework unifies existing results on grounding, memory, position, attention, Bayesian inheritance, scientific abduction, and reasoning control. In a matched DeepSeek V4-Flash API study, thinking improves pointer chasing from $0/16$ to $14/16$ when relevant distinctions are available; exact observational twins remain at their $50\%$ construction floor; and restoring decisive memory moves matched performance from $50\%$ to $100\%$. These results provide initial evidence for the predicted separation between scaling within an interface and refining the interface itself.