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Bypass Observation: A Conceptual Design of a Non-Intrusive Layer-Wise Semantic Extraction Architecture — arXiv:2609.13807

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13807
License (as archived): CC0-1.0
Archived: 2026-10-15T22:47:23.150Z
SHA-256 of the archived text: fcad1fe56b9dc7da2a21978b8abf772fce749c567308d43568db1fc14a6bf8e4
Stable URL of this page: https://metraipolis.com/documento/dc_6f41e4ea7fed

How to cite

Bypass Observation: A Conceptual Design of a Non-Intrusive Layer-Wise Semantic Extraction Architecture — arXiv:2609.13807 — arXiv (en), https://arxiv.org/abs/2609.13807 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6f41e4ea7fed (Metraipolis archive dc_6f41e4ea7fed, sha256 fcad1fe56b9dc7da2a21978b8abf772fce749c567308d43568db1fc14a6bf8e4)

BibTeX

@misc{dc6f41e4ea7fed,
  title = {Bypass Observation: A Conceptual Design of a Non-Intrusive Layer-Wise Semantic Extraction Architecture — arXiv:2609.13807},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13807},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6f41e4ea7fed; sha256 fcad1fe56b9dc7da2a21978b8abf772fce749c567308d43568db1fc14a6bf8e4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13807. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.13807 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T22:29:03.147Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).