Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task

The evolution of deep learning has led to models capable of complex pattern recognition, notably in NLP and strategic games. Early works like BERT (Devlin et al., 2018) and GPT (Radford et al., 2018, 2019) have shown impressive performance, but their internal mechanisms remain opaque. Toshniwal et al. (2021) demonstrated that language models trained on chess move sequences could predict legal moves and implicitly track board states, hinting at internal world representations. However, the nature, causality, and interpretability of these representations are still debated. Prior research often focused on natural language understanding or simple tasks, lacking systematic causal validation. This study advances the field by exploring a simplified strategy environment—Othello—and employing rigorous probing and intervention techniques to uncover and validate internal world models.

Despite evidence that large language models can implicitly learn structured representations, it remains unclear whether such internal states are causally involved in decision-making, especially in environments lacking explicit rules. The core challenge is to determine if models develop internal, nonlinear representations of environment states that influence their predictions, and whether these representations can be manipulated to control model outputs. Addressing this question is crucial for interpretability, safety, and controllability of AI systems, particularly in complex decision-making scenarios where understanding internal reasoning is vital.

This research introduces several innovations: 1) training a GPT model on a synthetic, rule-free environment (Othello) to test emergent internal representations; 2) employing nonlinear probes (MLPs) to detect complex, nonlinear encoding of environment states; 3) developing a gradient-based intervention method to causally manipulate internal representations and observe effects on predictions; 4) creating latent saliency maps via attribution, visualizing the influence of internal states on decision-making. These methods collectively provide a comprehensive framework for analyzing and controlling deep models’ internal reasoning, surpassing prior work limited to surface-level analysis.

• �� Construct an 8-layer GPT model trained on move sequences from synthetic and championship Othello datasets, with no explicit rule encoding.
• �� Use cross-entropy loss to optimize move prediction, with input as tile indices.
• �� Apply nonlinear probes (MLPs) to internal activations to predict the current board state, assessing the complexity of internal representations.
• �� Perform gradient-based interventions: select a layer, compute gradients of the probe’s score for a specific tile, and adjust activations to flip the tile’s state.
• �� Validate causal effects by comparing model predictions before and after intervention, measuring alignment with targeted board states.
• �� Generate latent saliency maps by measuring prediction sensitivity to interventions on each tile, visualizing key decision regions.

The model trained on synthetic data achieved an error rate of 0.02%, while the championship-trained model had an error of 5.17%, both vastly outperforming random guessing. Nonlinear probes across layers predicted board states with errors below 4.8%, confirming complex internal encoding. Intervention experiments successfully manipulated internal states to produce predicted move changes aligned with targeted board configurations, demonstrating causality. Latent saliency maps highlighted critical tiles influencing predictions, showing distinct patterns between synthetic and championship models. These experiments validate the emergence of internal world representations and their causal role in decision-making.

The models learned to predict legal moves with high accuracy, indicating internal understanding rather than memorization. Nonlinear probes revealed complex, layered encoding of board states, with errors below 4.8%. Gradient interventions effectively altered predictions by changing internal representations, confirming causality. Latent saliency maps provided intuitive visualizations of decision-critical regions, differing between rule-based and strategy-based models, thus offering interpretability and control tools.

This framework can be applied to interpret and control AI systems in strategic environments, such as game-playing agents or autonomous decision-makers. It also offers a pathway to understanding internal reasoning in natural language models, aiding transparency and trust. The causal manipulation techniques could enhance AI safety by enabling targeted interventions and debugging in complex systems.

The current approach is validated only in a simplified, synthetic environment, limiting direct generalization to natural language or real-world tasks. Gradient-based interventions are computationally intensive and may not scale efficiently. The robustness of internal representations under adversarial conditions remains untested, necessitating further research to ensure stability and reliability in diverse scenarios.