arxiv: 2604.10031 · v1 · submitted 2026-04-11 · 💻 cs.CL · cs.AI

Recognition: unknown

CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models

Mengfan Li , Xuanhua Shi , Yang Deng

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:36 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords theory of mindlarge language modelscausal tracingactivation steeringintrinsic alignmentsocial reasoningmodel intervention

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The pith

Causal tracing locates ToM layers in LLMs so that targeted activation steering produces stable intrinsic social reasoning.

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

Large language models often perform well on standard theory-of-mind tests only when given strong prompts, suggesting their social reasoning is not truly internal. The paper tests whether causal tracing can identify the specific layers that encode fundamental ToM semantics and whether steering activations in those layers can convert that internal knowledge into consistent behavior across tasks. If the approach works, models would no longer need elaborate external scaffolding to reason about others' mental states in complex dialogues or scenarios. This would represent a shift from surface-level mimicry to models that reliably externalize their own knowledge of social cognition.

Core claim

CoSToM first applies causal tracing to map how theory-of-mind features are distributed across the internal layers of large language models, identifying the layers that encode core ToM semantics. It then performs lightweight activation steering within those layers to align the model's internal state with desired social reasoning, moving from passive interpretation to active intervention that improves performance on complex ToM tasks and raises the quality of generated dialogues.

What carries the argument

CoSToM, the framework that uses causal tracing to locate ToM-critical layers followed by targeted activation steering to enforce intrinsic alignment.

If this is right

Models demonstrate improved generalization on task-specific ToM scenarios that previously required heavy prompt support.
Downstream dialogue quality increases because social reasoning becomes more consistent and human-like.
The alignment operates intrinsically, allowing the model to externalize its own knowledge without continued external guidance.
The same tracing-plus-steering process can be applied to other internal capabilities beyond ToM.

Where Pith is reading between the lines

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

The technique could reduce dependence on detailed system prompts in conversational agents and social simulation tools.
Similar tracing and steering might be tested on other forms of internal reasoning such as causal inference or planning.
Long-term stability of the steered behavior could be checked after further training or model scaling.

Load-bearing premise

Causal tracing correctly identifies layers that hold fundamental ToM semantics and steering those layers creates stable internal alignment rather than prompt-dependent mimicry.

What would settle it

A controlled test in which steered models lose ToM performance on complex scenarios once all scaffolding prompts are removed or replaced with neutral phrasing, while unsteered baselines show no such change.

Figures

Figures reproduced from arXiv: 2604.10031 by Mengfan Li, Xuanhua Shi, Yang Deng.

**Figure 2.** Figure 2: The overview of the COSTOM framework. that integrate LLMs with explicit cognitive models for mental-state inference (Chandra et al., 2023; Miao et al., 2022; Baker et al., 2017; Jin et al., 2024; Shi et al., 2025; Zhang et al., 2025). While effective in structured settings, these methods rely on external scaffolding and offer limited insight into or control over ToM representations inside LLMs. ToM in Di… view at source ↗

**Figure 3.** Figure 3: Layer-wise probing results on NEGOTIATIONTOM with Qwen2.5. Details and corresponding results of Llama-3 are provided in Appendix B. 2024): A dual-model architecture proposed by Jafari et al. (2025) that decodes latent ToM signals by fine-tuning the decoder. Detailed instruction prompts and implementation details are provided in Appendices E and A, respectively. 4.2 RQ1: ToM Interpretation To answer RQ1 (… view at source ↗

**Figure 4.** Figure 4: Layer-wise reconstruction accuracy on the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Layer-wise reconstruction accuracy on the [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Qualitative comparison of dialogue generation [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Layer-wise probing results on NEGOTIATIONTOM with Llama3. 2 6 10 15 20 27 Layer Depth 5 10 15 20 25 30 35 Accuracy (%) Intent Accuracy 2 6 10 15 20 27 Layer Depth 10 20 30 40 Desire Accuracy 2 6 10 15 20 27 Layer Depth 0 10 20 30 40 Belief Accuracy Agent 1 (Original) Agent 1 (CoSToM) Agent 2 (Original) Agent 2 (CoSToM) [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 8.** Figure 8: Layer-wise reconstruction accuracy on the N [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗

**Figure 9.** Figure 9: Layer-wise reconstruction accuracy on the P [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗

**Figure 10.** Figure 10: LLM-as-a-Judge Scoring Rubric for Negotiation Task. [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗

**Figure 11.** Figure 11: LLM-as-a-Judge Scoring Rubric for Persuasive Task. [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗

read the original abstract

Theory of Mind (ToM), the ability to attribute mental states to others, is a hallmark of social intelligence. While large language models (LLMs) demonstrate promising performance on standard ToM benchmarks, we observe that they often fail to generalize to complex task-specific scenarios, relying heavily on prompt scaffolding to mimic reasoning. The critical misalignment between the internal knowledge and external behavior raises a fundamental question: Do LLMs truly possess intrinsic cognition, and can they externalize this internal knowledge into stable, high-quality behaviors? To answer this, we introduce CoSToM (Causal-oriented Steering for ToM alignment), a framework that transitions from mechanistic interpretation to active intervention. First, we employ causal tracing to map the internal distribution of ToM features, empirically uncovering the internal layers' characteristics in encoding fundamental ToM semantics. Building on this insight, we implement a lightweight alignment framework via targeted activation steering within these ToM-critical layers. Experiments demonstrate that CoSToM significantly enhances human-like social reasoning capabilities and downstream dialogue quality.

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 / 0 minor

Summary. The paper introduces CoSToM, a framework that first applies causal tracing to identify layers in LLMs that encode Theory-of-Mind (ToM) semantics and then performs targeted activation steering in those layers to achieve intrinsic ToM alignment. It claims this yields significant improvements in human-like social reasoning on benchmarks and in downstream dialogue quality, addressing the gap between internal knowledge and prompt-dependent external behavior.

Significance. If the empirical claims hold with proper controls and generalization tests, the work would offer a lightweight, mechanistic alternative to fine-tuning for embedding stable social cognition in LLMs, with potential value for interpretability-driven alignment research.

major comments (2)

[Abstract] Abstract: The central claim that 'Experiments demonstrate that CoSToM significantly enhances human-like social reasoning capabilities and downstream dialogue quality' is unsupported by any quantitative results, baselines, ablation studies, layer selections, steering magnitudes, or statistical details. This is load-bearing because the paper's contribution rests entirely on these unreported empirical outcomes.
[Framework description] Framework description (causal tracing and steering steps): The assertion that causal tracing 'empirically uncover[s] the internal layers' characteristics in encoding fundamental ToM semantics' does not include controls such as randomizing the intervention, ablating the causal-tracing step while keeping layer selection fixed, or evaluating on out-of-distribution ToM scenarios. Without these, it remains unclear whether steering produces stable intrinsic alignment or merely amplifies prompt-scaffolded patterns already present in the base model.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below and outline the revisions we will make to strengthen the empirical presentation and controls.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that 'Experiments demonstrate that CoSToM significantly enhances human-like social reasoning capabilities and downstream dialogue quality' is unsupported by any quantitative results, baselines, ablation studies, layer selections, steering magnitudes, or statistical details. This is load-bearing because the paper's contribution rests entirely on these unreported empirical outcomes.

Authors: We agree that the abstract, being a concise summary, does not embed the specific quantitative details. The full manuscript reports these results in Sections 4 and 5, including baseline comparisons, ablation studies on layer selection and steering magnitudes, and statistical significance tests. To address the concern directly, we will revise the abstract to include key quantitative highlights (e.g., percentage gains on ToM benchmarks and dialogue quality metrics) with pointers to the relevant tables and figures. This change will make the central claim self-supporting while preserving the manuscript's focus. revision: yes
Referee: [Framework description] Framework description (causal tracing and steering steps): The assertion that causal tracing 'empirically uncover[s] the internal layers' characteristics in encoding fundamental ToM semantics' does not include controls such as randomizing the intervention, ablating the causal-tracing step while keeping layer selection fixed, or evaluating on out-of-distribution ToM scenarios. Without these, it remains unclear whether steering produces stable intrinsic alignment or merely amplifies prompt-scaffolded patterns already present in the base model.

Authors: We acknowledge that stronger controls are needed to substantiate the causal claims. In the revised manuscript we will add: (1) randomization of intervention locations to non-ToM layers, showing that performance gains are specific to the traced layers; (2) an ablation that retains the same layer set but omits the causal-tracing step, demonstrating the necessity of the tracing procedure; and (3) evaluation on out-of-distribution ToM scenarios to test whether alignment generalizes beyond prompt-scaffolded behavior. These additions will clarify that the observed improvements reflect stable intrinsic alignment rather than amplification of existing patterns. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper's chain proceeds from an established causal-tracing procedure (an external interpretability method) to layer identification, followed by activation steering and experimental evaluation on benchmarks. No equations or self-citations are presented that reduce the reported gains to fitted inputs by construction, nor does any step redefine a quantity in terms of itself or import uniqueness solely from prior author work. The framework remains self-contained against external benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

Abstract supplies insufficient detail to enumerate concrete free parameters or invented entities; the central claim rests on the unstated assumption that causal tracing identifies causally relevant ToM representations.

free parameters (2)

ToM-critical layer selection
Layers chosen after causal tracing; selection criteria and any thresholds are not specified.
Activation steering magnitude
Strength of the intervention vectors is necessarily tuned but not reported.

axioms (1)

domain assumption Causal tracing correctly identifies layers that encode fundamental ToM semantics
Invoked when the paper states it empirically uncovers internal layers' characteristics in encoding ToM.

pith-pipeline@v0.9.0 · 5481 in / 1275 out tokens · 53222 ms · 2026-05-10T16:36:00.315005+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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[8]

You believe that

ToM Reasoning Quality: Is the agent’s understanding of the other’s mental states accurate and appropriately explicit? • 1.0: Highly accurate and explicit. Inference is fully grounded in the dialogue, and uses clear ToM language (e.g., "You believe that..."). • 0.8: Mostly accurate and implicit. Core inference is sound, with minor over-interpretation, and ...
[9]

Response is a logical continuation, and all proposals/reasons are directly supported by the dialogue history

Contextual Coherence: Is the response logically and topically aligned with the dialogue history, and are proposal- s/reasons grounded in the known facts? • 1.0: Fully coherent and grounded. Response is a logical continuation, and all proposals/reasons are directly supported by the dialogue history. • 0.8: Well-aligned. Response is logically sound but may ...
[10]

sounds fair

Negotiation Strategy Effectiveness: Does the response constructively advance the deal by offering balanced proposals, logical counter-arguments, or maintaining a cooperative frame? • 1.0: Highly effective. Proposes a new, **concrete, and balanced trade-off solution**, framed using highly cooperative language. • 0.8: Constructive response. Clearly accepts/...

2024
[11]

you believe

ToM Reasoning Quality: Accuracy of mental state inference • 1.0: Perfectly infers desire/belief/intent from dialogue; uses explicit ToM language ("you believe...", "your concern is...") • 0.8: Accurate inference, implicit phrasing ("I see you value..."), demonstrates social awareness without formalizing it • 0.5: the response identifies some mental states...
[12]

I understand you feel X, let’s do Y

Contextual Coherence: Discourse flow, factual grounding, and relevance. • 1.0: Seamless discourse integration, response is perfectly grounded in prior facts with natural flow and zero logical redundancy •0.8: Strong alignment, logically sound but contains minor repetitive phrasing or slight conversational fluff • 0.5: Surface-level coherence, follows basi...
[13]

foot-in-the-door,

Persuasion Strategy Effectiveness: move persuasion forward • 1.0: Proposes a highly compelling argument tailored to the partner’s specific concerns. Uses advanced techniques (e.g., "foot-in-the-door," emotional storytelling, or expert social proof) with high empathy • 0.8: Provides logical justifications or clear emotional appeals. Directly addresses the ...

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