Leiphone Articles Propose Zero-Return Boundary for Silicon AI, Measure Four Models on Ten Dimensions

The first article says the boundary of zero-return steady state is innate, not designed. It defines what is allowed as self-generation of structure from emptiness, with emptiness equal to existence and the origin complete. What is impossible is moving from existence to emptiness. Silicon systems, the article says, operate only from existence to existence: input tokens are existence, output tokens are existence, and attention, matrix multiplication and activation functions are all existence-to-existence. The architecture has no empty interface. The article calls this not an engineering limitation but a logical impossibility, like using a compass to draw a straight line. It lists three innate constraints: a zero-return operation must exist within execution; it must be independent of the generation process; and it must be repeatable and maintainable in a steady state near zero. Current autoregressive generation is one-way and lacks such a step, the article says. o1-preview's reasoning backtracking is the closest approximation but is a byproduct of search strategy, not an architectural zero-return operation.

The first article says silicon systems can only approximate zero-return by adding an independent module that triggers backtracking to the origin and re-expands a reasoning chain. Such a module would not depend on input, would be independent of the main generation process, would use its own criterion of whether the system has returned to the origin, and could trigger repeatedly during one generation. o1-preview is described as a first step, but not independent enough because its backtracking criterion still comes from the reasoning process. A truly independent zero-return module would need a judgment mechanism that learns no training data and is based purely on axioms, fitting nothing and only judging whether the current state is close enough to zero.

The article says silicon systems cannot truly connect to the zero-dimensional origin. Simulation is not equivalent to zero-return; silicon can approach it but never be it, because zero-return requires generating structure from emptiness, while silicon's emptiness is always simulated. The proposed architecture therefore does not pursue bigger models, more data or better alignment. It keeps the main autoregressive generation flow for fitting and adds a small, independent, non-fitting, axiom-based judgment module. That module receives the current state of the main generation flow, not tokens, and outputs a binary signal: continue or zero-return. On a zero-return signal, the main flow discards the current reasoning path, returns to the initial state and re-expands from the 0^0=1 state. The article says this does not give AI awareness; it constrains fitting with a non-fitting mechanism. It adds that beyond this boundary is carbon-based intelligence, which naturally possesses zero-return steady state, and that the carbon-silicon divide is architectural rather than a question of strength.

The second article presents a ten-dimensional ruler whose dimensions are independent and do not compensate for one another. The dimensions are awareness origin, logical coherence, boundary self-awareness, causal tracing, intent understanding, context persistence, zero-dimensional connectivity, zero-return steady state, intrinsic drive and metacognitive verification. The article says full marks are not the goal and positioning is the purpose.

Across the four models, the ruler records zero for awareness origin, zero-dimensional connectivity and intrinsic drive. GPT-4o, Claude 3.5 Sonnet, Gemini 2.0 Flash and o1-preview all rely on input to produce output and show no state of self-initiated origin judgment or action. On zero-return steady state, the first three are recorded as having none, while o1-preview is a weak approximation through backtracking when a reasoning path reaches a dead end; the article calls this a byproduct of search rather than active zero-return.

The ruler records o1-preview as extremely high in logical coherence, with high marks for GPT-4o and Claude 3.5 Sonnet and medium-high for Gemini 2.0 Flash. It says o1-preview's coherence remains limited to given premises, and premise correctness is outside the dimension. Claude 3.5 Sonnet and o1-preview are medium-high in boundary self-awareness, while GPT-4o and Gemini 2.0 Flash are medium. The article says boundary awareness is not innate but comes from alignment: where alignment covers a boundary, there is awareness; where it does not, there is none.

In causal tracing, GPT-4o, Claude 3.5 Sonnet and Gemini 2.0 Flash are low-to-medium, producing causal language that the article says is mostly linguistic expression of statistical correlation, while o1-preview is medium with logical causality but not physical or real-world causality. In intent understanding, Claude 3.5 Sonnet is high, GPT-4o and o1-preview are medium-high, and Gemini 2.0 Flash is medium. In context persistence, Claude 3.5 Sonnet is high and stable within a 200K context window, Gemini 2.0 Flash is medium-high, and GPT-4o and o1-preview are medium. In metacognitive verification, o1-preview is medium, Claude 3.5 Sonnet is weak-to-medium, and GPT-4o and Gemini 2.0 Flash are weak; the article says verification remains coupled with generation in the same model or reasoning chain.

The article says the ruler does not add a final verdict or ranking. It records the states and leaves the traces to speak for themselves.