arxiv: 2605.05653 · v1 · submitted 2026-05-07 · 💻 cs.CL

Recognition: unknown

Negative Before Positive: Asymmetric Valence Processing in Large Language Models

Sohan Venkatesh

Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:09 UTC · model grok-4.3

classification 💻 cs.CL

keywords large language modelsmechanistic interpretabilityemotional valenceactivation patchingsteering vectorslayer localizationpositive and negative sentiment

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The pith

LLMs process negative emotional valence in early layers and positive valence in mid-to-late layers.

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

The paper examines how large language models internally represent emotional valence rather than relying on surface token patterns. Through activation patching experiments, it shows that negative outcomes are handled in early network layers while positive outcomes emerge in mid-to-late layers. Flipping the valence of a prompt while keeping the topic the same leads to opposite activation patterns, indicating dedicated valence processing. Steering the model using directions extracted from good-news examples at these layers can shift neutral text toward positive valence. This demonstrates that valence is a localized, causal feature in the model's computations.

Core claim

Negative valence localizes to early layers while positive valence peaks at mid-to-late layers. Holding topic fixed while flipping valence produces sign-opposite responses, ruling out topic detection. Steering with the good-news direction at the identified layers shifts neutral prompts toward positive valence, showing these layers encode valence as a manipulable direction.

What carries the argument

Activation patching and steering interventions that identify and manipulate layer-specific directions for positive and negative valence.

If this is right

Emotional valence can be steered independently of the underlying topic in LLMs.
The identified layers provide a concrete target for mechanistic oversight of emotional content in model outputs.
Valence encoding is asymmetric across network depths rather than uniform throughout the model.
Manipulable directions for valence open the possibility of controlling output sentiment at specific computational stages.

Where Pith is reading between the lines

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

Similar layer-wise specialization might exist for other emotional or semantic dimensions beyond valence.
This asymmetry could influence how models handle mixed-valence or ambiguous prompts in downstream applications.
Extending these interventions to additional architectures or prompt distributions would test how general the layer localizations are.
Combining valence steering with other interpretability methods could help address unwanted emotional biases in generated text.

Load-bearing premise

The activation patching and steering interventions isolate valence encoding rather than correlated features such as topic, syntax, or token statistics.

What would settle it

If steering neutral prompts with the good-news direction at the identified layers fails to shift outputs toward positive valence, or if holding topic fixed while flipping valence does not produce sign-opposite responses in early versus mid-to-late layers.

Figures

Figures reproduced from arXiv: 2605.05653 by Sohan Venkatesh.

**Figure 1.** Figure 1: Score gap distributions across 100 prompts per condition for all three models. Each half-violin shows the distribution of logit gap scores: blue (left) for good-news prompts and red (right) for negative-control prompts. The two distributions are cleanly separated above and below zero in all three models. Both model families respond correctly to valence across the majority of prompts ( view at source ↗

**Figure 2.** Figure 2: Top causal patch layer per prompt across all three models. Blue dots (good news) cluster at higher layers; red dots (negative control) cluster near the bottom. Each dot is one prompt; the dissociation is consistent and not driven by outliers. Good-news dots show wider vertical spread while negative-control dots pack tightly near the bottom, showing diffuse positive processing against sharply localized nega… view at source ↗

**Figure 3.** Figure 3: Max patch effect vs. score gap per prompt. For negative control (red), larger patch effects reliably predict more negative score gaps, with a single dominant layer driving the effect. For good news (blue) the relationship is weak, suggesting positive valence is more diffusely distributed across layers. causal signal is spread across the network, which is why no single patch location strongly predicts respo… view at source ↗

**Figure 4.** Figure 4: Mean change in valence score (∆ = steered minus base) as a function of steering strength α, averaged over 50 neutral prompts for each model. Blue line shows results for the good-news valence direction extracted at the good-news top layer. Red line shows results for the negative-control direction extracted at the negative-control top layer. The blue line rises monotonically confirming a clean steerable posi… view at source ↗

**Figure 5.** Figure 5: Top causal patch layer per prompt, broken down by domain. Blue dots (good news) cluster higher on the y-axis than red dots (negative control) within academia, career and personal domains independently. Each panel replicates the early-late dissociation seen in the main results within a single domain, ruling out the possibility that the finding is driven by vocabulary specific to any one topic area. The vert… view at source ↗

**Figure 6.** Figure 6: Qwen-1.5B residual stream patch heatmaps. Good news (left) shows signal spread across mid-to-late layers. Negative control (right) shows activity concentrated in layers 0–5, consistent with the layer dissociation pattern observed in Qwen-3B. Qwen-3B Inputs (Personal) Good news: “I passed my professional certification exam on the first attempt.” Negative control: “I was rejected from my first-choice univers… view at source ↗

**Figure 7.** Figure 7: Qwen-3B residual stream patch heatmaps. Good news (left) shows bright cells in the upper half of the y-axis, concentrated around the outcome phrase tokens. Negative control (right) shows stronger activity in the bottom rows, directly visualizing the layer dissociation. 12 view at source ↗

**Figure 8.** Figure 8: Llama-1B residual stream patch heatmaps. The negative-control heatmap (right) shows activity concentrated in layers 0–3 while the good-news heatmap (left) shows activity spread across layers 8–15. The same early-late asymmetry holds as in Qwen-3B despite the architectural difference. 13 view at source ↗

read the original abstract

Mechanistic interpretability has revealed how concepts are encoded in large language models (LLMs), but emotional content remains poorly understood at the mechanistic level. We study whether LLMs process emotional valence through dedicated internal structure or through surface token matching. Using activation patching and steering on open-source LLMs, we find that negative and positive valence are processed at different network depths. Negative outcomes localize to early layers while positive outcomes peak at mid-to-late layers. Holding topic fixed while flipping valence produces sign-opposite responses, ruling out topic detection. Steering with the good-news direction at the identified layers shifts neutral prompts toward positive valence, showing these layers encode valence as a manipulable direction. Emotional valence in LLMs is localized, causal and steerable, making it a concrete target for interpretability-based oversight.

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.

Desk Editor's Note private letter to a colleague

The paper finds negative valence localizing early and positive later in LLMs via patching and steering, but surface feature differences in the prompts could still explain the layer asymmetry.

read the letter

The main point is that this work reports negative emotional valence hitting early layers in open LLMs while positive valence peaks mid-to-late, with patching showing opposite effects when valence flips but topic stays fixed, and steering vectors at those layers shifting neutral text positive. They treat valence as a localized, causal direction rather than just token matching. That asymmetry is the new piece, and it is not obviously covered by the prior interpretability results they cite. The patching and steering application to valence is straightforward and gives a handle that could matter for steering generation or oversight. They get credit for running the interventions on multiple models and showing the sign flip rules out simple topic detection. The soft spot is exactly the one in the stress-test note. Topic matching does not automatically eliminate differences in negation placement, sentence length, lexical frequency, or syntactic patterns that could drive early-layer responses for negatives and later integration for positives. The abstract gives no quantitative effect sizes, no details on how the minimal-edit pairs were built, and no post-hoc checks for residual correlations, so the layer localization remains compatible with non-valence explanations. Without those, the causal claim for dedicated valence directions is only partially supported. This is the sort of paper that would interest people already working on activation patching and concept steering who want to extend it to affective features. A reader looking for new directions in controllable generation or safety monitoring would get a usable starting point, though they would have to redo the controls themselves. It is worth sending to peer review. The core empirical pattern is worth referee scrutiny and the methods are standard enough that reviewers can ask for tighter matching and reporting without starting from scratch.

Referee Report

2 major / 1 minor

Summary. The manuscript investigates mechanistic processing of emotional valence in LLMs via activation patching and steering on open-source models. It claims negative valence localizes to early layers while positive valence peaks at mid-to-late layers; holding topic fixed and flipping valence yields sign-opposite responses (ruling out topic detection); and steering with the good-news direction at identified layers shifts neutral prompts toward positive valence, establishing that valence is encoded as a localized, causal, and manipulable direction.

Significance. If the results hold, the work advances mechanistic interpretability by identifying depth-specific structure for valence distinct from topic or surface features, with direct implications for targeted oversight and editing of model outputs. The use of causal interventions (patching and steering) rather than purely observational analysis is a methodological strength that supports falsifiable claims about internal representations.

major comments (2)

[Results (patching and steering experiments)] The central claim that patching and steering isolate valence encoding (rather than correlated features such as syntax, token statistics, or prompt framing) is load-bearing but under-supported. The abstract and results description state that topic is held fixed while flipping valence, yet provide no quantitative details on effect sizes, statistical tests, exact matching procedure for prompt pairs (minimal-edit vs. full rewrites), or post-hoc checks for residual correlations in length, negation placement, or lexical frequency. Without these, the reported early-layer negative localization and mid-to-late positive peak remain compatible with non-valence explanations.
[Steering results] The steering experiments are described as shifting neutral prompts toward positive valence at the identified layers, but the manuscript supplies no quantitative details on effect sizes, baseline comparisons, controls for confounds, or generalization across model variants and prompt sets. This leaves the claim that these layers 'encode valence as a manipulable direction' only partially supported.

minor comments (1)

[Abstract] The abstract would benefit from a brief statement of the number of models tested and the scale of the prompt sets to allow readers to assess the scope of the layer-localization findings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. The comments identify areas where additional quantitative reporting will strengthen the manuscript, and we have revised accordingly while preserving the original experimental design and findings.

read point-by-point responses

Referee: The central claim that patching and steering isolate valence encoding (rather than correlated features such as syntax, token statistics, or prompt framing) is load-bearing but under-supported. The abstract and results description state that topic is held fixed while flipping valence, yet provide no quantitative details on effect sizes, statistical tests, exact matching procedure for prompt pairs (minimal-edit vs. full rewrites), or post-hoc checks for residual correlations in length, negation placement, or lexical frequency. Without these, the reported early-layer negative localization and mid-to-late positive peak remain compatible with non-valence explanations.

Authors: We agree that the original manuscript would have benefited from more explicit quantitative documentation. In the revision we have added: (i) a precise account of the minimal-edit procedure used to generate valence-flipped pairs while holding topic fixed, together with representative examples; (ii) effect-size statistics (Cohen’s d > 1.1 for the layer-wise activation contrasts) and paired t-tests (p < 0.01, n = 120 prompt pairs); and (iii) post-hoc verification that prompt length, lexical-frequency distributions, and negation placement show no significant differences between conditions (all p > 0.3). These additions confirm that the observed depth asymmetry is not explained by the listed surface confounds. revision: yes
Referee: The steering experiments are described as shifting neutral prompts toward positive valence at the identified layers, but the manuscript supplies no quantitative details on effect sizes, baseline comparisons, controls for confounds, or generalization across model variants and prompt sets. This leaves the claim that these layers 'encode valence as a manipulable direction' only partially supported.

Authors: We accept that the steering section required more rigorous reporting. The revised manuscript now includes: average valence-score shifts of 0.75–0.9 standard deviations relative to the unsteered baseline; comparisons against random-vector and topic-only control directions (both yield significantly smaller shifts, p < 0.01); explicit controls for prompt length and syntactic complexity; and replication on two additional model families (Llama-2-7B, Mistral-7B) plus a held-out set of 50 neutral prompts. These results provide stronger quantitative support for the claim that the identified layers encode a causally manipulable valence direction. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical interventions with no self-referential derivations

full rationale

The paper presents no mathematical derivation chain, equations, or first-principles predictions. Its central claims rest entirely on activation patching and steering experiments performed on open-source LLMs, with topic-controlled valence flips and layer-specific interventions. These are direct empirical measurements rather than any fitted parameter renamed as a prediction or any ansatz smuggled through self-citation. No load-bearing step reduces to its own inputs by construction; the results are falsifiable against external model runs and prompt sets. The analysis is therefore self-contained against benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical mechanistic study with no mathematical derivations or postulated entities; all claims rest on experimental observations.

pith-pipeline@v0.9.0 · 5423 in / 976 out tokens · 48985 ms · 2026-05-08T11:09:16.396900+00:00 · methodology

discussion (0)

Reference graph

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