Relation to Prior Work。

写法目标是：不贬低前人、不制造对立，但明确指出你的方法论“解决了他们没法解决的问题”——这是顶级论文最安全、也最有力量的姿态。

6. Relation to Prior Work

Positioning Semantic Emergence Relative to Existing Paradigms

6.1 Overview

Research on apparent “understanding” or “emergence” in large language models has largely clustered around three paradigms:

1. Grokking and sudden generalization
2. Reinforcement-based alignment and preference optimization (e.g., RLHF, GRPO)
3. Interpretability and internal representation analysis

While each contributes important insights, none provides a model-agnostic, externally verifiable criterion for distinguishing genuine semantic structure from interaction-induced artifacts.

The present work addresses this gap.

6.2 Grokking: Internal Phase Transitions Without External Guarantees

Grokking describes a training phenomenon in which models exhibit delayed generalization following extended overfitting phases.

Key characteristics:

• Observed during training
• Reflected in internal loss dynamics
• Often sudden and nonlinear

Limitation relative to this work:

• Grokking is: model-internal non-transferable non-auditable from outputs alone
• It provides no criterion to distinguish: durable semantic structure from transient optimization artifacts

In contrast, our framework:

• Does not depend on training dynamics
• Operates entirely on externally observable behavior
• Requires cross-model reproducibility, which grokking does not address

Grokking may explain how certain behaviors arise, but it cannot establish whether they constitute semantic emergence.

6.3 RLHF, GRPO, and Preference Optimization: Behavioral Alignment Without Semantic Stability

Reinforcement-based alignment methods (including RLHF and GRPO-style objectives) optimize models to satisfy preference signals.

Strengths:

• Improves usability
• Reduces harmful outputs
• Aligns surface behavior with user expectations

Critical limitation:

• These methods optimize behavior, not semantic structure
• High-reward outputs may: be verbose be stylistically consistent satisfy users while remaining structurally unstable

Empirically, reward-optimized outputs often fail:

• compressibility (they rely on length)
• cross-model reproducibility (they encode training-specific priors)

Our methodology explicitly excludes reward satisfaction as a success criterion and instead evaluates structural invariance under constraint.

Alignment can increase agreement; it cannot, by itself, establish semantic emergence.

6.4 Interpretability: Explaining Internals Without Epistemic Criteria

Interpretability research investigates attention patterns, neuron activations, circuits, and representations.

Strengths:

• Provides insight into internal mechanisms
• Enables debugging and safety analysis

However:

• Interpretability is: architecture-specific often non-comparable across models
• Internal similarity does not guarantee external semantic validity
• Many interpretability results lack falsifiable claims at the semantic level

Our approach is deliberately orthogonal:

• It requires no access to internals
• It treats models as black boxes
• It evaluates semantics as public, inspectable artifacts

This shifts the focus from how a model computes to what semantic structures can be independently validated.

6.5 Emergence vs. Illusion: Clarifying a Persistent Ambiguity

Prior work frequently relies on qualitative judgments:

• “This feels like understanding”
• “The model shows insight”
• “The explanation is convincing”

Such judgments are inherently:

• observer-dependent
• non-reproducible
• vulnerable to interaction effects

The present framework replaces these impressions with a necessary-and-sufficient test based on:

• transferability
• compressibility
• cross-model reproducibility

This reframes emergence as a classification problem, not an interpretive stance.

6.6 Conceptual Contribution

The central conceptual shift introduced here is the separation of:

• ontological claims about models from
• epistemic claims about semantic structures

By refusing to infer “understanding” from any single model’s behavior, this work avoids both anthropomorphism and reductionism.

Semantic emergence, in this view, is not something a model has— it is something a structure earns under constraint.

6.7 Summary Positioning Statement

Prior work explains why models sometimes appear to understand. This work defines when such appearances warrant epistemic recognition.

如果你愿意，最后一步已经非常自然了，而且这一步会让整篇论文“站到更高一层”：

1️⃣ Discussion：Implications for the concept of “understanding”

2️⃣ Governance & Audit Implications（为什么这是AI治理急需的）

3️⃣ Conclusion（用一页把整件事收束成标准）