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arxiv: 2605.20423 · v1 · pith:B6T2DHXBnew · submitted 2026-05-19 · 💻 cs.AI

OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind

Sharmin Sultana Srishty , Kazi Mahathir Rahman , Malaika Parizat Sakkhi , Samia Shahid Prianna , Shaikhul Islam Sinat This is my paper

Pith reviewed 2026-05-21 07:06 UTC · model grok-4.3

classification 💻 cs.AI
keywords Theory of MindObserver-Self ConflictReinforcement LearningAdversarial GenerationLarge Language ModelsRecursive ReasoningBelief ModelingSocial AI
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The pith

OSCToM generates observer-self belief conflicts with RL to let smaller LLMs reach 76% accuracy on asymmetric Theory of Mind tasks.

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

The paper introduces OSCToM to create high-order Theory of Mind scenarios that test recursive beliefs and information asymmetries in LLMs. It focuses on observer-self conflicts, where an observer's view of another agent clashes with the observer's own belief state, demanding multi-layered reasoning beyond basic perspective taking. The method uses reinforcement learning, an extended domain-specific language, and compositional surrogate models to produce targeted adversarial examples. Experiments show that an 8B model trained this way outperforms prior systems on the FANToM benchmark and matches them on Hi-ToM and BigToM while synthesizing data six times more efficiently.

Core claim

OSCToM models nested belief conflicts by generating observer-self conflicts through RL-guided adversarial generation with an extended domain-specific language and compositional surrogate models; this produces training data that enables an 8B model to achieve 76% accuracy on information-asymmetric FANToM tasks compared to the 0.2% reported for prior systems, while remaining competitive on Hi-ToM and BigToM and using six times less data synthesis effort.

What carries the argument

Observer-self conflict: a scenario in which an observer's view of another agent conflicts with the observer's own belief state, generated via RL-guided adversarial search over an extended domain-specific language and compositional surrogate models to force recursive reasoning.

Load-bearing premise

The RL-guided adversarial generation creates genuine high-order ToM scenarios that require true recursive belief reasoning rather than surface patterns or artifacts that models can exploit without nested understanding.

What would settle it

Models trained on OSCToM data achieve high accuracy on the generated benchmark but drop to near-chance performance on a fresh set of human-validated high-order ToM problems that contain no reusable surface cues and explicitly require tracking at least three levels of nested beliefs.

Figures

Figures reproduced from arXiv: 2605.20423 by Kazi Mahathir Rahman, Malaika Parizat Sakkhi, Samia Shahid Prianna, Shaikhul Islam Sinat, Sharmin Sultana Srishty.

Figure 1
Figure 1. Figure 1: The OSCToM system architecture. The left module depicts the Adversarial Story Generator (combining [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The 8-stage end-to-end OSCT verification and training workflow, mapping the progression from initial [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Learning curve for the optimized DQN generator. The vertical axis represents the mean episodic reward [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Histogram of aggregate hardness scores across [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Per-dimension surrogate complexity scores across the six cognitive modules. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Training loss across both curriculum stages of the OSCToM-8B fine-tuning pipeline. Stage 1 optimizes on 1st [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Bar chart comparison of benchmark accuracy across all evaluated models on ToMi, Hi-ToM, BigToM, and [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Scatter plot of model size (parameters) versus mean benchmark accuracy. OSCToM-8B shows the best [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Inference throughput comparison across models. OSCToM-8B achieves a 5.7x reduction in average latency [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Optuna optimization history. The optimal reward of 0.900 was reached at Trial 4 and confirmed by [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Fanova-estimated hyperparameter importance [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: Action distribution across 300 timesteps. All [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
read the original abstract

Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult. This paper presents OSCToM (Observer-Self Conflict Theory of Mind), an approach for modeling nested belief conflicts in LLM-based ToM tasks. The key case is one in which an observer's view of another agent conflicts with the observer's own belief state. Such cases go beyond simple perspective-taking and require recursive, multi-layered reasoning. OSCToM combines reinforcement learning (RL), an extended domain-specific language, and compositional surrogate models to generate observer-self conflicts. In our experiments, OSCToM-8B gives the best overall result among the systems tested. It improves on the reported ExploreToM results on FANToM and remains competitive on Hi-ToM and BigToM. On the information-asymmetric FANToM benchmark, OSCToM reaches 76% accuracy, compared with the 0.2% reported by ExploreToM. The data-synthesis procedure is also 6x more efficient, indicating that targeted training data can help smaller models handle advanced cognitive reasoning. The project code is available at https://github.com/sharminsrishty/osct.

Editorial analysis

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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 / 1 minor

Summary. The paper introduces OSCToM, a method that combines reinforcement learning, an extended domain-specific language, and compositional surrogate models to generate observer-self conflict scenarios for evaluating and training high-order Theory of Mind reasoning in LLMs. It reports that OSCToM-8B achieves the best overall results among tested systems, substantially improving on ExploreToM baselines on the information-asymmetric FANToM benchmark (76% accuracy vs. 0.2%) while remaining competitive on Hi-ToM and BigToM, and that the data-synthesis procedure is 6x more efficient. The project code is released at https://github.com/sharminsrishty/osct.

Significance. If the generated scenarios genuinely require recursive nested belief attribution without exploitable surface patterns, the approach could meaningfully advance the creation of challenging ToM benchmarks and targeted training data for LLMs. The public release of code is a clear strength that supports reproducibility and further work in this area.

major comments (2)
  1. [§3] §3 (Generation Procedure): The RL-guided adversarial generation with the extended DSL and surrogate models is presented as producing genuine observer-self conflicts that demand multi-layered recursive reasoning, yet the manuscript provides no control experiments, distribution analyses, or ablation studies checking for detectable lexical markers, fixed narrative templates, or statistical regularities that models could exploit via pattern matching. This directly undermines the interpretation of the 76% FANToM accuracy as evidence of high-order ToM capability.
  2. [§4.2] §4.2 (FANToM Results): The headline comparison reports OSCToM-8B at 76% versus the 0.2% cited for ExploreToM on information-asymmetric cases, but without error bars, multiple random seeds, or confirmation that the test scenarios are free of generation artifacts, it is impossible to determine whether the gap reflects improved reasoning or differences in scenario distribution.
minor comments (1)
  1. [Abstract and §4] The efficiency claim of '6x more efficient' in the abstract and §4 is not accompanied by a precise definition of the baseline or the metric (e.g., scenarios per GPU-hour).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects of experimental rigor needed to support claims about high-order ToM reasoning. We address each major comment below and will revise the manuscript accordingly to incorporate additional analyses and statistical reporting.

read point-by-point responses

  1. Referee: [§3] §3 (Generation Procedure): The RL-guided adversarial generation with the extended DSL and surrogate models is presented as producing genuine observer-self conflicts that demand multi-layered recursive reasoning, yet the manuscript provides no control experiments, distribution analyses, or ablation studies checking for detectable lexical markers, fixed narrative templates, or statistical regularities that models could exploit via pattern matching. This directly undermines the interpretation of the 76% FANToM accuracy as evidence of high-order ToM capability.

    Authors: We agree that explicit verification against surface-level exploitation is necessary to substantiate the ToM interpretation. The extended DSL and compositional surrogate models were designed to promote diversity and recursion beyond fixed templates, and the RL objective targets observer-self conflicts specifically. However, the original manuscript did not include dedicated ablations or lexical analyses. In revision we will add a new subsection with (i) n-gram and template-frequency comparisons against ExploreToM, (ii) performance on semantically perturbed versions of the generated scenarios, and (iii) distribution statistics over nesting depth and information asymmetry. These controls will directly test whether the reported gains can be attributed to pattern matching rather than recursive belief reasoning. revision: yes

  2. Referee: [§4.2] §4.2 (FANToM Results): The headline comparison reports OSCToM-8B at 76% versus the 0.2% cited for ExploreToM on information-asymmetric cases, but without error bars, multiple random seeds, or confirmation that the test scenarios are free of generation artifacts, it is impossible to determine whether the gap reflects improved reasoning or differences in scenario distribution.

    Authors: We concur that variance reporting and artifact checks are required for reliable interpretation of the performance gap. The 76% figure was obtained on the information-asymmetric FANToM split using OSCToM-generated training data, but the manuscript omitted multi-seed statistics and explicit artifact audits. In the revision we will (i) rerun all FANToM evaluations across five random seeds and report means with standard deviations, (ii) provide side-by-side distributional comparisons (nesting depth, lexical diversity, information-asymmetry metrics) between OSCToM and ExploreToM scenarios, and (iii) include a brief artifact analysis confirming absence of obvious generation regularities. These additions will clarify whether the accuracy difference stems from improved reasoning or from shifts in scenario statistics. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical gains on external benchmarks are not forced by construction

full rationale

The paper presents a data-generation pipeline (RL + extended DSL + compositional surrogates) whose output is used to produce training examples for an 8B model, followed by direct accuracy measurement on the pre-existing FANToM, Hi-ToM and BigToM benchmarks. The headline 76 % vs. 0.2 % comparison is therefore an external empirical result rather than a quantity that reduces to the generation procedure by definition or by a fitted parameter being relabeled as a prediction. No self-definitional equations, load-bearing self-citations, or uniqueness theorems appear in the provided text; the derivation chain remains self-contained against the cited external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based on abstract only; no free parameters, axioms, or invented entities are specified in the provided text. The approach assumes standard RL and LLM capabilities from prior literature without detailing new postulates.

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Reference graph

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