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arxiv: 2605.11206 · v2 · submitted 2026-05-11 · 💻 cs.CL

Recognition: unknown

Instructions Shape Production of Language, not Processing

Andreas Waldis , Leshem Choshen , Yufang Hou , Yotam Perlit
Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:58 UTC · model grok-4.3

classification 💻 cs.CL
keywords language modelsinstructionsprobingattention interventionsoutput productioninput processingprompting effectsasymmetry
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The pith

Instructions primarily shape how language models produce outputs rather than how they process inputs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors show that instructions create a clear asymmetry in language models between handling input and generating output. Task-specific information stays mostly fixed in the sample input tokens no matter how the prompt varies, and it links only weakly to what the model actually does. The same information in output tokens shifts a lot with prompting changes and tracks behavior much more closely. Blocking the flow of instruction details to output tokens cuts both the information and the model's performance, while blocking it only for sample tokens leaves both almost unchanged. This pattern appears across different models and tasks, and it grows stronger as models get larger or receive more instruction tuning.

Core claim

Instructions trigger a production-centered mechanism in language models. Through layer-wise probing of task-specific information across five binary judgment tasks, the information in sample tokens remains largely stable across prompting variations and correlates only weakly with behavior, whereas the information in output tokens varies substantially and correlates strongly with behavior. Attention-based interventions confirm this causally by showing that blocking instruction flow to all subsequent tokens reduces both behavior and information in output tokens, whereas blocking it only to sample tokens has minimal effect. The asymmetry generalizes across model families and tasks and becomes sh

What carries the argument

Layer-wise probing of task-specific information at sample versus output token positions, combined with attention-based blocking of instruction flow to isolate effects on production.

If this is right

  • Task-specific information in output tokens predicts model behavior more reliably than the same information in input sample tokens.
  • Blocking instruction signals from reaching output tokens reduces both information content and task performance.
  • The production-centered asymmetry grows stronger in larger models and in models that have undergone instruction tuning.
  • Assessing model capabilities requires measuring both internal representations and observable behavior while separating input processing from output production.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Prompt engineering may work mainly by steering the generation steps rather than by changing how inputs are understood.
  • The same processing-production split could appear in other sequential tasks such as code generation or planning.
  • Disrupting output pathways in isolation might reveal instruction sensitivity even when input encoding remains intact.

Load-bearing premise

The layer-wise probing isolates task-specific information at specific token positions without interference from other positions or model components, and the attention blocking cleanly separates instruction effects on sample versus output tokens.

What would settle it

If blocking instruction flow only to sample tokens were found to substantially alter model behavior or the task-specific information present in output tokens, that would falsify the claimed separation between processing and production effects.

Figures

Figures reproduced from arXiv: 2605.11206 by Andreas Waldis, Leshem Choshen, Yotam Perlit, Yufang Hou.

Figure 1
Figure 1. Figure 1: We analyze behavior (top) and internals across the computational stages of processing instruction and sample tokens (bottom left) and producing output tokens (bottom right). Probing reveals an asymmetry: task-specific information in sample representations (⃗hS) stays stable across prompting variations and is decoupled from behavior, while information in output representations (⃗hO) varies and tracks behavi… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Layer-wise task-specific information for sample tokens averaged across tasks and models, area indicates deviation across prompting variations. (b) Layer-wise task-specific information for output tokens averaged across tasks and models, area indicates deviation across prompting variations. (c) Behavioral results for the three prompting variations averaged across models and tasks. (d) Instance-level agre… view at source ↗
Figure 3
Figure 3. Figure 3: (a) We intervene on the attention flow by either blocking it between instruction and sample tokens (prompt-only) or between instruction and all subsequent tokens (full). (b) Intervention results of selectively disabling attention flow between instruction and sample tokens (prompt-only) or all subsequent tokens (full). Deltas show the change relative to the unmodified evaluation (P↶). Interventions confirm … view at source ↗
Figure 4
Figure 4. Figure 4: (a) Layer-wise task-specific information in sample and output tokens for Llama-3.1, OLMO-2, and Qwen-2.5. (b) Behavioral performance across prompting variations for those models. (c) Impact of the prompt-only intervention on information in sample and output tokens and model behavior. confirming that the asymmetry between processing and production is not architecture-specific. However, the layer-wise streng… view at source ↗
Figure 5
Figure 5. Figure 5: (a) Emergence of task-specific information with growing model size, focusing on sample and output tokens. (b) Effect of scaling model size on behavioral performance. behavioral performance steadily improves with model size ( [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of pre-trained (base) and instruction-tuned LMs focusing on the model internals (a) and the behavioral (b) perspective. Instruction-tuning largely preserves the pro￾cessing stage. For sample tokens, base and instruction-tuned models show highly similar layer￾wise patterns ( [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: (a) Task-specific information in sample tokens, output tokens, and behavioral performance (EM) across judgment tasks, averaged across models and prompting variations. (b) Layer-wise pairwise represen￾tation agreement heatmaps per task, for sample tokens (top) and output tokens (bottom). Each cell (i, j) indicates mean agreement between probing predictions at layers i and j, averaged across instances. tion,… view at source ↗
Figure 8
Figure 8. Figure 8: Validation of the probing setup across model layers, averaged across tasks and models. [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Validation of the probing setup with no intermediate hidden layer ( [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Sanity checks of the production-centered mechanism, averaged across tasks and models. [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Instance-level probing–prompting alignment, averaged across tasks and models. [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Layer-wise task-specific information for [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Behavioral performance (EM) across the three prompting variations ( [PITH_FULL_IMAGE:figures/full_fig_p025_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Effect of the prompt-only intervention on task-specific information in [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Comparison of pre-trained (base, dotted) and instruction-tuned models (solid) per judgment task. (a) Layer-wise task-specific information in sample (top) and output (bottom) tokens for each task. sample token curves are nearly identical across conditions for all tasks, while output token curves show task-dependent gains from instruction-tuning, largest for knowledge and reasoning tasks. (b) Behavioral per… view at source ↗
Figure 16
Figure 16. Figure 16: Layer-wise probing–prompting consistency distributions per judgment task, for [PITH_FULL_IMAGE:figures/full_fig_p027_16.png] view at source ↗
read the original abstract

Instructions trigger a production-centered mechanism in language models. Through a cognitively inspired lens that separates language processing and production, we reveal this mechanism as an asymmetry between the two stages by probing task-specific information layer-wise across five binary judgment tasks. Specifically, we measure how instruction tokens shape information both when sample tokens, the input under evaluation, are processed and when output tokens are produced. Across prompting variations, task-specific information in sample tokens remains largely stable and correlates only weakly with behavior, whereas the same information in output tokens varies substantially and correlates strongly with behavior. Attention-based interventions confirm this pattern causally: blocking instruction flow to all subsequent tokens reduces both behavior and information in output tokens, whereas blocking it only to sample tokens has minimal effect on either. The asymmetry generalizes across model families and tasks, and becomes sharper with model scale and instruction-tuning, both of which disproportionately affect the production stage. Our findings suggest that understanding model capabilities requires jointly assessing internals and behavior, while decomposing the internal perspective by token position to distinguish the processing of input tokens from the production of output tokens.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that instructions in language models primarily shape the production of output tokens rather than the processing of sample tokens. Layer-wise probing across five binary judgment tasks shows task-specific information in sample tokens remains largely stable with only weak behavioral correlation, while the same information in output tokens varies substantially and correlates strongly with behavior. Attention-based blocking interventions confirm the asymmetry causally: blocking instruction flow to all subsequent tokens reduces both behavior and output-token information, whereas blocking it only to sample tokens has minimal effect. The pattern generalizes across model families and sharpens with scale and instruction-tuning.

Significance. If the central asymmetry holds, the work offers a useful decomposition of LLM behavior into processing versus production stages, supported by both correlational probing and causal interventions. The cross-model generalization and the observation that effects strengthen with scale and tuning provide concrete, falsifiable predictions that could inform future analyses of instruction following. The empirical focus on token-position-specific information flow is a strength.

major comments (2)
  1. [Intervention results] Intervention description (likely §3.2): zeroing attention from instruction positions to sample tokens does not remove the instruction hidden states from the residual stream; residual connections and subsequent feed-forward layers can still propagate task-specific information to output positions. This undercuts the claim that minimal behavioral change demonstrates instructions bypass sample-token processing.
  2. [Probing analysis] Probing results (likely §4.1): the reported stability of task-specific information in sample tokens and its weak correlation with behavior rests on the assumption that layer-wise probes isolate position-specific signals without leakage from other token positions or residual components; no ablation of this assumption is described.
minor comments (2)
  1. [Figures] Include error bars or confidence intervals on all layer-wise probing and behavioral plots to allow assessment of the reported stability and correlations.
  2. [Results] Clarify the exact set of models and tasks in the generalization section; the abstract mentions five tasks and multiple families but the main text should list them explicitly with sample sizes.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below, providing our strongest honest defense of the manuscript while noting where clarifications or additions will improve the work.

read point-by-point responses

  1. Referee: [Intervention results] Intervention description (likely §3.2): zeroing attention from instruction positions to sample tokens does not remove the instruction hidden states from the residual stream; residual connections and subsequent feed-forward layers can still propagate task-specific information to output positions. This undercuts the claim that minimal behavioral change demonstrates instructions bypass sample-token processing.

    Authors: We appreciate the referee's careful analysis of the intervention mechanics. However, the design and results still support the production-centered interpretation. Zeroing attention from instruction positions specifically to sample tokens prevents direct attention-based incorporation of instruction signals into sample-token representations. The preserved instruction hidden states in the residual stream enable direct influence on output positions (via subsequent attention from output tokens to instruction tokens), which is precisely the bypass of sample-token processing that our claim describes. The key evidence is the asymmetry: blocking instruction flow only to sample tokens yields minimal change in behavior and output information, while blocking to all subsequent tokens (including output positions) produces large reductions. This pattern indicates that task-specific information need not be routed through sample processing. We will add a clarifying paragraph in the revised §3.2 explicitly discussing residual propagation and distinguishing direct versus indirect pathways. revision: partial

  2. Referee: [Probing analysis] Probing results (likely §4.1): the reported stability of task-specific information in sample tokens and its weak correlation with behavior rests on the assumption that layer-wise probes isolate position-specific signals without leakage from other token positions or residual components; no ablation of this assumption is described.

    Authors: We agree that an explicit check for position-specific isolation would strengthen the probing results. Although the probes are trained exclusively on activations extracted from designated sample or output token positions, residual-stream mixing could introduce some leakage. In the revision we will add an ablation subsection (new §4.2) that (i) trains control probes on randomly shuffled or masked position activations and (ii) reports cross-position probe accuracy and mutual information. These controls will quantify any leakage and confirm that the reported stability in sample tokens and strong correlation in output tokens are position-dependent. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on direct empirical measurements and interventions

full rationale

The paper presents no mathematical derivation chain or fitted model whose outputs are forced by its own inputs. Its central claims follow from layer-wise probing of task-specific information (measured via classifiers on hidden states) and attention-masking interventions performed on five binary judgment tasks across model families. These are experimental observations of stability in sample-token representations versus variability in output-token representations, with causal tests via blocking. No equation reduces a prediction to a fitted parameter by construction, no uniqueness theorem is imported from self-citation, and no ansatz is smuggled in. The work is self-contained against external benchmarks (multiple tasks, scales, and model families) and therefore receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard interpretability assumptions about what layer activations encode; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Task-specific information can be measured from layer-wise activations in a way that distinguishes processing from production stages.
    This is the core premise of the probing and intervention design described in the abstract.

pith-pipeline@v0.9.0 · 5488 in / 1194 out tokens · 58382 ms · 2026-05-14T20:58:03.405667+00:00 · methodology

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