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Verification of the Implicit World Model in a Generative Model via Adversarial Sequences

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
generative sequence modelimplicit world modeladversarial sequenceschess
Abstract:

Generative sequence models are typically trained on sample sequences from natural or formal languages. It is a crucial question whether—or to what extent—sample-based training is able to capture the true structure of these languages, often referred to as the "world model". Theoretical results indicate that we can hope for soundness at best, that is, generating valid sequences, but not necessarily all of them. However, it is still important to have practical tools that are able to verify whether a given sequence model is sound. In this study, we focus on chess, as it is a domain that provides enough complexity while having a simple rule-based world model. We propose adversarial sequence generation for verifying the soundness of the sequence model. Our adversaries generate valid sequences so as to force the sequence model to generate an invalid next move prediction. Apart from the falsification of soundness, this method is also suitable for a more fine-grained analysis of the failure modes and the effects of different choices during training. To demonstrate this, we propose a number of methods for adversarial sequence generation and evaluate the approach on a large set of chess models. We train models on random as well as high-quality chess games, using several training recipes. We find that none of the models are sound, but some training techniques and dataset choices are able to improve soundness remarkably. We also investigate the potential application of board state probes in both our training and attack methods. Our findings indicate that the extracted board states have no causal role in next token prediction in most of the models.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes an adversarial framework to verify soundness of implicit world models in generative sequence models, using chess as a testbed. Within the taxonomy, it occupies the 'Adversarial Testing and Soundness Verification' leaf under 'World Model Discovery and Representation Learning'. Notably, this leaf contains only the original paper itself—no sibling papers are present. This isolation suggests the adversarial falsification approach represents a relatively unexplored direction within the broader field of world model verification, which encompasses 50 papers across approximately 36 topics.

The taxonomy reveals that neighboring leaves focus on complementary verification strategies: 'Linear and Nonlinear Representation Probing' (3 papers) examines internal representations through probing classifiers, 'Formal Evaluation Metrics' (1 paper) applies automata-theoretic principles, and 'Latent Representation Interpretation' (1 paper) uses multimodal explanation techniques. The paper's adversarial approach diverges from these by actively generating sequences designed to induce failures rather than passively analyzing learned representations. This positions the work at the intersection of verification and stress-testing, bridging the gap between discovery-oriented probing methods and the application-focused branches like 'Reinforcement Learning with World Models'.

Among 23 candidates examined through semantic search and citation expansion, none clearly refute the three main contributions. The adversarial framework for soundness measurement examined 10 candidates with 0 refutable matches; the large-scale empirical study likewise examined 10 candidates with no overlaps; and the board state probe causality analysis examined 3 candidates, also without refutation. This limited search scope suggests that within the top-K semantically similar papers, the specific combination of adversarial generation for soundness falsification in chess-based sequence models appears distinctive, though the analysis does not claim exhaustive coverage of all potentially relevant prior work.

The analysis indicates the paper occupies a sparse research direction within a moderately populated field. While world model verification has attracted substantial attention (50 papers total), the adversarial falsification angle remains underexplored based on the taxonomy structure and the absence of closely overlapping work among examined candidates. However, these findings are constrained by the limited search scope and do not preclude the existence of relevant work outside the top-23 semantic matches or in adjacent research communities not captured by this taxonomy.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Verification of implicit world models in generative sequence models. This field examines how generative models—particularly large language models and sequence predictors—develop internal representations of the environments or systems they model, and how these implicit world models can be discovered, validated, and tested for correctness. The taxonomy reveals a rich landscape organized around several complementary themes. World Model Discovery and Representation Learning focuses on uncovering and interpreting the latent structures that emerge during training, with works like Emergent Linear Representations[1] and Emergent World Representations[4] demonstrating that models can spontaneously learn coherent internal states. World Model Training and Application addresses how to build and deploy these models in domains ranging from autonomous driving (GAIA-1[5]) to interactive environments (Othello World Model[45], Web World Models[46]). Meanwhile, branches such as Latent Space Design and Optimization, Generative Models for 3D and Spatial Reasoning, and Domain-Specific Generative Applications explore architectural choices, geometric reasoning capabilities, and specialized use cases, while Theoretical and Conceptual Foundations and Uncertainty and Robustness examine the principles and reliability of these learned representations. A particularly active line of work investigates whether emergent world models are faithful to the underlying dynamics they purport to capture. Studies like Evaluating Implicit World[2] and Critiques World Models[7] probe the accuracy and limitations of these representations, while Latent Syntax Weaving[3] explores how structured knowledge can be woven into latent spaces. The original paper, Adversarial Sequences[0], sits squarely within the World Model Discovery and Representation Learning branch, specifically under Adversarial Testing and Soundness Verification. Its emphasis on constructing adversarial test cases to expose failures in implicit world models complements the diagnostic approaches of Evaluating Implicit World[2] and contrasts with the more interpretive focus of Emergent Linear Representations[1]. By systematically challenging the robustness of learned representations, Adversarial Sequences[0] addresses a critical gap: ensuring that the world models discovered in generative sequence models are not only present but also sound and reliable under stress-testing conditions.

Claimed Contributions

Adversarial framework for measuring soundness of implicit world models

The authors introduce a new methodology based on adversarial sequence generation to verify whether generative models adhere to the true world model. The adversary generates valid sequences designed to force the model to predict invalid continuations, thereby testing soundness without requiring threshold parameters to define the generated language.

10 retrieved papers
Large-scale empirical study with multiple training schemes

The authors conduct extensive experiments training 24 models using different training objectives (next token, probability distribution, joint probe) and datasets (random games, curated high-quality games) of varying sizes to evaluate how these choices affect the implicit world model quality.

10 retrieved papers
Analysis of board state probe causality in model predictions

The authors investigate whether board state probes have a functional causal role in next-token prediction through gradient-based alignment analysis and adversarial attacks. They find that probes operate largely independently of the next-token predictor head, with gradients being nearly orthogonal.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Adversarial framework for measuring soundness of implicit world models

The authors introduce a new methodology based on adversarial sequence generation to verify whether generative models adhere to the true world model. The adversary generates valid sequences designed to force the model to predict invalid continuations, thereby testing soundness without requiring threshold parameters to define the generated language.

Contribution

Large-scale empirical study with multiple training schemes

The authors conduct extensive experiments training 24 models using different training objectives (next token, probability distribution, joint probe) and datasets (random games, curated high-quality games) of varying sizes to evaluate how these choices affect the implicit world model quality.

Contribution

Analysis of board state probe causality in model predictions

The authors investigate whether board state probes have a functional causal role in next-token prediction through gradient-based alignment analysis and adversarial attacks. They find that probes operate largely independently of the next-token predictor head, with gradients being nearly orthogonal.

Table of Contents

Verification of the Implicit World Model in a Generative Model via Adversarial Sequences | Novelty Validation