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arxiv: 2605.03052 · v1 · submitted 2026-05-04 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

How Language Models Process Negation

Zhejian Zhou , Tianyi Zhou , Robin Jia , Jonathan May
Authors on Pith no claims yet

Pith reviewed 2026-05-08 18:25 UTC · model grok-4.3

classification 💻 cs.CL
keywords negation processinglarge language modelsmechanistic interpretabilityattention mechanismsLLM internalsconstructive computation
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The pith

Language models process negation by constructing representations of negative phrases more than by suppressing positives.

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

The paper tests how models like Mistral-7B and Llama-3.1-8B handle negation inside their layers. It shows that the models contain correct internal signals for negation yet often output wrong answers because late attention heads favor quick shortcuts. Removing those shortcut heads raises accuracy on negation questions. Two processing routes appear: attention heads that focus on the negated span and dampen related ideas, and a stronger route that builds a direct vector for the full negative phrase such as promoting alternatives to the negated term. The constructive route dominates in the studied models.

Core claim

Models implement both suppressive attention heads that attend to negated phrases and suppress associated concepts, and constructive mechanisms that directly encode the negated phrase as a vector promoting alternatives. The constructive mechanism is more prominent, and ablating late-layer attention modules that promote shortcuts markedly improves accuracy on negation-related tasks.

What carries the argument

The constructive mechanism that builds a representation of the entire negative phrase as a vector promoting alternatives, operating alongside suppressive attention heads.

If this is right

  • Ablating late-layer attention modules that promote shortcuts greatly improves accuracy on questions involving negation.
  • Models implement both a suppressive route and a stronger constructive route for negation.
  • The constructive route encodes the full negative phrase directly rather than only damping positives.
  • Both mechanisms coexist inside the same models.

Where Pith is reading between the lines

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

  • Training methods could be adjusted to favor the constructive route and reduce reliance on shortcut attention.
  • Similar dual-mechanism patterns may appear for other logical operators such as quantifiers or conditionals.
  • Interpretability tools that separate competing internal routes could generalize to debugging other logical failures.

Load-bearing premise

The observational and causal interpretability techniques such as attention ablation and activation analysis accurately isolate the negation mechanisms without interference from other components.

What would settle it

No gain in accuracy on negation questions after ablating the identified late-layer attention modules, or activation patterns that fail to match either the constructive vector or the suppressive attention behavior.

Figures

Figures reproduced from arXiv: 2605.03052 by Jonathan May, Robin Jia, Tianyi Zhou, Zhejian Zhou.

Figure 1
Figure 1. Figure 1: Illustration of competing mechanisms for negation. In the negation mechanism, attention module A1 moves the represen￾tation of the negation token (“not”) to the position of the concept being negated (e.g., amphibian). Subsequently, A2, together with downstream MLPs, constructs and promotes a new negated rep￾resentation (e.g., mammal). In contrast, the shortcut mechanism bypasses explicit negation reasoning… view at source ↗
Figure 2
Figure 2. Figure 2: OLMo2 Positive and Negative Accuracies at various pre-training checkpoints. We observe that negative accuracy first plummets at early training steps, then rises again and stabilizes. ablates their functionalities. If switching off certain heads recovers expected behaviors on P−, then we conclude that those attention modules play a causally significant role in shortcut behavior. More specifically, we apply … view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of PCA subspace. The residual stream hidden states are taken from Llama-3.1-8B at layer 11 after the attention module (11 mid). The hidden states of P+ and P− are colored as blue and red. Arrows indicate the direction from one hidden state of P+ to the corresponding hidden state of P−. It can be seen that positive and negative hidden states are approximately linearly separable by one directio… view at source ↗
Figure 5
Figure 5. Figure 5: Normalized evidence count plotted against attention layer index. The normalized evidence count measures the percentage of samples in the dataset for which evidence is identified at a given layer. Results are on Llama-3.1-8B. The blue (red) line indicates the ratio of samples that LogitLens identifies Y¯ (Y ) related tokens as top promoted (demoted) tokens. We observe that evidence count peaks at causally i… view at source ↗
Figure 6
Figure 6. Figure 6: Path Patching and Attention Sink Ablation results on Llama-3.1-8B. X axis indicates the center layer that we ablate or patch. Y axis is negation accuracy. Both methods suggest that mid-layer attention modules are causally important for negation processing. "justification": "These tokens suggest non-natural, persistent materials, which are incompatible with biodegradability." ,→ ,→ ,→ } ] ``` If **no convin… view at source ↗
Figure 7
Figure 7. Figure 7: Path Patching and Attention Sink Ablation results on Mistral-7B. X axis indicates the center layer that we ablate or patch. Y axis is negation accuracy. Both methods suggest that mid-layer attention modules are causally important for negation processing. C. Additional Analyses C.1. Sanity Check on Negation Sensitivity Is sensitivity defined in Section 4.1 just an artifact of random￾ness? Is our choice of o… view at source ↗
Figure 8
Figure 8. Figure 8: Cross-validated LDA model accuracy as a function of position in the residual stream. A higher accuracy indicates that we can decode “not” from the residual stream more reliably. As shown, “not” is moved to “Y” at early to middle layers. C.5. LLM Annotation Results for Mistral-7B-v0.1 In view at source ↗
Figure 9
Figure 9. Figure 9: Normalized evidence count plotted against attention layer index. Results are on mistralai/Mistral-7B-v0.1. The blue (red) line indicates the ratio of samples that LogitLens identifies Y¯ (Y ) related tokens as top promoted (demoted) tokens. We observe that evidence count for “not” follows the same trends as patching results in view at source ↗
Figure 10
Figure 10. Figure 10: Visualization of the PCA space at different model layers. The hidden states of P+ and P− are colored as blue and red. Arrows indicate the direction from one hidden state of P+ to the corresponding hidden state of P−. It can be seen that positive and negative hidden states are approximately linearly separable by one direction. 18 view at source ↗
read the original abstract

We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong answers to questions involving negation, they do possess internal components that process negation correctly. Their poor accuracy is due to late-layer attention behavior that promotes simple shortcuts; ablating those attention modules greatly improves accuracy on negation-related questions. Second, we uncover how models process negation. We consider two hypotheses: models could use attention heads that attend to the phrase being negated and suppress related concepts, or they could directly construct a representation of the entire negative phrase (e.g., representing "not gas" as a vector that promotes liquids and solids). We apply a range of observational and causal interpretability techniques on Mistral-7B and Llama-3.1-8B to show that models implement both mechanisms, with the "constructive" mechanism being more prominent. Combined, our work deepens the understanding of LLMs' internals, highlighting construction-dominant computations and the coexistence of competing mechanisms within LLMs.

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 LLMs such as Mistral-7B and Llama-3.1-8B possess internal components that correctly process negation, but late-layer attention promotes shortcut behaviors that degrade accuracy on negation-related questions; ablating those modules improves performance. It tests two hypothesized mechanisms—suppressive attention heads that attend to negated phrases versus constructive construction of negative representations (e.g., 'not gas' promoting liquids/solids)—and applies observational and causal interpretability techniques to conclude that both are implemented, with the constructive mechanism being more prominent.

Significance. If the causal interventions cleanly isolate the two mechanisms without residual cross-talk from distributed attention, the work provides concrete mechanistic evidence for coexistence of competing computations in LLMs and a dominance ordering favoring construction over suppression. This strengthens the case for construction-dominant circuits in logical reasoning and supplies falsifiable predictions about ablation effects that could guide future circuit-level analyses.

major comments (2)
  1. [Abstract / causal-intervention results] Abstract and the causal-intervention section: the claim that ablating late-layer attention modules 'greatly improves accuracy' on negation questions is load-bearing for the shortcut hypothesis, yet the abstract supplies no quantitative effect sizes, baseline comparisons, or controls showing that the same ablation leaves non-negation performance unchanged. Without these, it is impossible to rule out that the improvement is an artifact of general capacity reduction rather than targeted removal of negation shortcuts.
  2. [Interpretability experiments] The section applying observational and causal techniques to distinguish suppressive vs. constructive mechanisms: because transformer attention is distributed, ablating or patching individual heads can produce effects consistent with both hypotheses simultaneously. The manuscript does not report head-level orthogonality tests, circuit decomposition, or controls that would demonstrate the interventions isolate one mechanism from the other; this directly undermines the ability to assert that the constructive mechanism is 'more prominent.'
minor comments (2)
  1. [Hypotheses section] Clarify the precise definition of 'constructive' vs. 'suppressive' representations with an example vector or activation pattern so readers can replicate the classification criteria.
  2. [Experimental setup] The datasets and exact negation-question templates used for accuracy measurements are not listed; include them (or a pointer to the release) to allow reproduction of the reported accuracy gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments on our work. We address each major comment below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract / causal-intervention results] Abstract and the causal-intervention section: the claim that ablating late-layer attention modules 'greatly improves accuracy' on negation questions is load-bearing for the shortcut hypothesis, yet the abstract supplies no quantitative effect sizes, baseline comparisons, or controls showing that the same ablation leaves non-negation performance unchanged. Without these, it is impossible to rule out that the improvement is an artifact of general capacity reduction rather than targeted removal of negation shortcuts.

    Authors: We agree that the abstract would be strengthened by including quantitative details. The full manuscript reports ablation results with specific accuracy improvements on negation tasks alongside controls confirming stable performance on non-negation benchmarks. We will revise the abstract to incorporate these effect sizes, baseline comparisons, and non-negation controls to directly address concerns about general capacity reduction. revision: yes

  2. Referee: [Interpretability experiments] The section applying observational and causal techniques to distinguish suppressive vs. constructive mechanisms: because transformer attention is distributed, ablating or patching individual heads can produce effects consistent with both hypotheses simultaneously. The manuscript does not report head-level orthogonality tests, circuit decomposition, or controls that would demonstrate the interventions isolate one mechanism from the other; this directly undermines the ability to assert that the constructive mechanism is 'more prominent.'

    Authors: We acknowledge the distributed nature of attention and the resulting challenge in cleanly isolating mechanisms. Our interventions combine head-specific ablation (targeting suppressive attention patterns to negated phrases) with representation-level patching (targeting constructive negative phrase vectors), yielding convergent evidence from observational and causal methods that favors the constructive mechanism. We will add a dedicated discussion of potential cross-talk, any available orthogonality metrics, and a more qualified statement on prominence to reflect the limitations of isolation in distributed systems. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical interpretability findings are self-contained

full rationale

The paper's central claims rely on applying observational and causal interpretability methods (attention ablation, activation analysis) to Mistral-7B and Llama-3.1-8B to identify negation-processing mechanisms. No mathematical derivations, equations, fitted parameters renamed as predictions, or self-citation chains are present that would reduce any result to its own inputs by construction. The coexistence and prominence of constructive vs. suppressive mechanisms are reported as outcomes of direct interventions on model internals, not as logical equivalences or ansatzes smuggled via prior self-work. This is a standard empirical analysis with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on standard mechanistic interpretability assumptions rather than new free parameters or invented entities.

axioms (2)
  • domain assumption Activation patching and attention ablation can causally isolate the contribution of specific heads or layers to a behavioral outcome.
    Invoked when claiming that ablating late-layer attention improves negation accuracy and that this reveals the shortcut mechanism.
  • domain assumption Observational techniques (e.g., attention visualization, representation similarity) combined with causal interventions can distinguish between suppression and constructive mechanisms.
    Used to conclude that both mechanisms are implemented and that construction is more prominent.

pith-pipeline@v0.9.0 · 5473 in / 1401 out tokens · 20059 ms · 2026-05-08T18:25:01.470535+00:00 · methodology

discussion (0)

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Reference graph

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