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

• URL: http://arxiv.org/abs/2609.13807v1
• Date: Sat, 12 Sep 2026 08:35:45 GMT
• Title: Bypass Observation: A Conceptual Design of a Non-Intrusive Layer-Wise Semantic Extraction Architecture
• Abstract summary: We introduce Bypass Observation, a non-intrusive layer-wise readout architecture.<n>We derive a closed-form overhead approximation governed primarily by V/(12d)<n>We argue that Bypass Observation can make model iteration more observable while remaining only a partial, potentially misleading projection of hidden states.
• Score: 2.8943395720203386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation [58.47973015036709]
  DisPOSE is a self-supervised framework that approximates the inherently discrete multi-view person-assignment problem.<n>By employing differentiable Sinkhorn projections during denoising, our model learns to guide solutions toward valid and feasible assignments.<n>The proposed approach outperforms current state-of-the-art self-supervised methods on standard datasets.
  arXiv  Detail & Related papers  (2026-06-05T16:14:39Z)
• Diagnosing Visual Ignorance in Vision-Language Models [29.2851901986069]
  Vision-Language Models (VLMs) frequently rely on language priors, producing confident answers that are weakly grounded in visual evidence.<n>In this work, we study language-prior reliance from both mechanistic and behavioral perspectives.<n>We find that language-prior reliance is a systematic routing failure affecting both model internals and benchmark validity.
  arXiv  Detail & Related papers  (2026-06-05T04:16:01Z)
• STEP: Learning STructured Embeddings for Progressive Time Series [1.034052616244602]
  We present a novel method for learning interpretable representations of progressive time series.<n>Our approach uses a self-supervised contrastive objective to learn a low-dimensional latent space.<n>We evaluate the approach against the state of the art on diverse domains.
  arXiv  Detail & Related papers  (2026-05-29T09:33:34Z)
• Self-Consistent Latent Reasoning: Long Latent Sequence Reasoning for Vision-Language Model [56.21523258053447]
  SCOLAR (Self-COnsistent LAtent Reasoning) introduces a lightweight detransformer that generates auxiliary visual tokens in a single shot.<n>SCOLAR extends acceptable latent CoT length by over $30times$, achieves state-of-the-art among open-source models on real-world reasoning benchmarks.
  arXiv  Detail & Related papers  (2026-05-12T14:13:08Z)
• LogitDynamics: Reliable ViT Error Detection from Layerwise Logit Trajectories [1.4610038284393168]
  We study error prediction in Vision Transformers (ViTs)<n>Motivated by internal-signal hallucination detection in large language models, we investigate whether similar depth-wise signals exist inViTs.<n>By attaching lightweight linear heads to intermediate layers, we extract features from the last L layers that capture both the logits of the predicted class and its top-K competitors.<n>A linear probe trained on these features predicts the error indicator.
  arXiv  Detail & Related papers  (2026-04-12T13:43:40Z)
• A Progressive Training Strategy for Vision-Language Models to Counteract Spatio-Temporal Hallucinations in Embodied Reasoning [49.61652671596548]
  "Multi-image hallucination reasoning", where a massive performance drop between forward and temporal queries reveals a dependence on superficial shortcuts instead of genuine understanding.<n>To mitigate this, we develop a new dataset: Chain-of-temporal building on detailed reasoning into Chain steps and definitive judgments.<n>Our experiments demonstrate that this approach not only improves accuracy but also the forward-backward performance gap from over 70% to only 6.53%.
  arXiv  Detail & Related papers  (2026-04-12T07:48:44Z)
• LAYA: Layer-wise Attention Aggregation for Interpretable Depth-Aware Neural Networks [6.1253918397972]
  This paper revisits the role of the output layer and introduces LAYA, a novel output head that dynamically aggregates internal representations through attention.<n>Experiments on vision and language benchmarks show that LAYA consistently matches or improves the performance of standard output heads.<n>These interpretability signals emerge directly from the model's computation, without any external post hoc explanations.
  arXiv  Detail & Related papers  (2025-11-16T18:22:02Z)
• Understanding In-context Learning of Addition via Activation Subspaces [73.8295576941241]
  We study a structured family of few-shot learning tasks for which the true prediction rule is to add an integer $k$ to the input.<n>We then perform an in-depth analysis of individual heads, via dimensionality reduction and decomposition.<n>Our results demonstrate how tracking low-dimensional subspaces of localized heads across a forward pass can provide insight into fine-grained computational structures in language models.
  arXiv  Detail & Related papers  (2025-05-08T11:32:46Z)
• I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data? [76.15163242945813]
  Large language models (LLMs) have led many to conclude that they exhibit a form of intelligence.<n>We introduce a novel generative model that generates tokens on the basis of human-interpretable concepts represented as latent discrete variables.
  arXiv  Detail & Related papers  (2025-03-12T01:21:17Z)
• Semi-Supervised Unconstrained Head Pose Estimation in the Wild [57.11798881492183]
  We propose the first semi-supervised unconstrained head pose estimation method SemiUHPE.<n>Our method is based on the observation that the aspect-ratio invariant cropping of wild heads is superior to previous landmark-based affine alignment.<n>Our proposed method is also beneficial for solving other closely related problems, including generic object rotation regression and 3D head reconstruction.
  arXiv  Detail & Related papers  (2024-04-03T08:01:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.