旁路观测：一种非侵入式逐层语义提取架构的概念设计
Bypass Observation: A Conceptual Design of a Non-Intrusive Layer-Wise Semantic Extraction Architecture
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- Henan University（河南大学）
- National University of Defense Technology（国防科技大学）
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中文总结 AI 辅助
提出旁路观测架构，通过只读观测头实现非侵入式逐层语义提取，分析开销与变体，区分旁路思维链，并探讨在循环Transformer中的应用。
中文摘要 AI 辅助
大型语言模型在高维隐藏状态空间中进行推理，而用户仅能观察到最终输出。我们引入了旁路观测（Bypass Observation），这是一种非侵入式的逐层读取架构，它将只读观测头附加到选定的Transformer层上，而不将其输出反馈回主干网络。我们考虑了三种变体：跨层共享的LM头、各层专用的头，以及层或步自适应的头。对于全词汇表读取，我们推导出一个主要由V/(12d)决定的闭式开销近似值，代表性估计范围约为30%至240%，并讨论了通过稀疏观测、低秩分解、缩减词汇表、top-k读取和选择性位置来降低成本的方案。我们认为，旁路观测可以使模型计算更具可观测性，但同时它只是隐藏状态的部分且可能具有误导性的投影。我们进一步区分了旁路思维链（bypass chain-of-thought）与传统思维链（chain-of-thought）：传统推理标记会进入自回归计算，而旁路读取在推理时保持因果外部性，尽管它们仍可在强化学习中提供训练信号。最后，我们讨论了在循环和递归深度Transformer中的应用，其中逐迭代读取可能揭示收敛、振荡和潜在的暂停信号。该提案是概念性和分析性的；系统的实证验证留待未来工作。
英文摘要
Large language models reason in high-dimensional hidden-state spaces, while users observe only final outputs. We introduce Bypass Observation, a non-intrusive layer-wise readout architecture that attaches read-only observation heads to selected Transformer layers without feeding their outputs back into the backbone. We consider three variants: a shared LM head across layers, layer-specific heads, and a layer- or step-adaptive head. For full-vocabulary readout, we derive a closed-form overhead approximation governed primarily by V/(12d), with representative estimates ranging from about 30% to 240%, and discuss cost reductions via sparse observation, low-rank factorization, reduced vocabularies, top-k readout, and selective positions. We argue that Bypass Observation can make model computation more observable while remaining only a partial, potentially misleading projection of hidden states. We further distinguish bypass chain-of-thought from conventional chain-of-thought: conventional reasoning tokens enter the autoregressive computation, whereas bypass readouts remain causally external at inference time, although they can still provide training signals in reinforcement learning. Finally, we discuss applications to looped and recurrent-depth Transformers, where iteration-wise readout may expose convergence, oscillation, and potential halting signals. The proposal is conceptual and analytical; systematic empirical validation remains future work.
