Contrastive Representations for Temporal Reasoning (2508.13113v1)

Abstract: In classical AI, perception relies on learning state-based representations, while planning, which can be thought of as temporal reasoning over action sequences, is typically achieved through search. We study whether such reasoning can instead emerge from representations that capture both perceptual and temporal structure. We show that standard temporal contrastive learning, despite its popularity, often fails to capture temporal structure due to its reliance on spurious features. To address this, we introduce Combinatorial Representations for Temporal Reasoning (CRTR), a method that uses a negative sampling scheme to provably remove these spurious features and facilitate temporal reasoning. CRTR achieves strong results on domains with complex temporal structure, such as Sokoban and Rubik's Cube. In particular, for the Rubik's Cube, CRTR learns representations that generalize across all initial states and allow it to solve the puzzle using fewer search steps than BestFS, though with longer solutions. To our knowledge, this is the first method that efficiently solves arbitrary Cube states using only learned representations, without relying on an external search algorithm.

• The paper introduces CRTR, which uses in-trajectory negative sampling to capture temporal structures and mitigate reliance on traditional search algorithms.
• It employs a novel contrastive loss that optimizes conditional mutual information, effectively aligning latent representations with true temporal distances.
• Experimental results show that CRTR achieves higher success rates in tasks like Sokoban and Rubik’s Cube while reducing computational search requirements.

Contrastive Representations for Temporal Reasoning

Introduction

The paper "Contrastive Representations for Temporal Reasoning" (2508.13113) introduces a method for learning representations from data that capture both perceptual and temporal structures required for efficient reasoning in combinatorial environments. The authors propose a novel approach, termed Contrastive Representations for Temporal Reasoning (CRTR), which addresses the limitations of standard contrastive learning techniques that often rely on spurious features. This work is distinguished by its aim to reduce the dependency on traditional search algorithms like Best First Search (BestFS) by providing representations that inherently capture the temporal dynamics of the problem.

Motivation and Problem Definition

In classical AI, effective planning is achieved through search, however, search algorithms like A* and BestFS are computationally expensive and not well-suited for tasks requiring structured combinatorial reasoning. This paper posits whether representations that jointly capture perceptual and temporal structures can obviate the need for search. Traditional contrastive learning (CL) methods fail in combinatorial tasks because they often overfit to static features, rather than capturing essential temporal dynamics.

The proposed methodology involves constructing representations by employing a novel negative sampling strategy—within-trajectory negatives—preventing reliance on static features such as wall patterns or visual cues often present in puzzles like Sokoban. The theoretical foundation for CRTR is based on optimizing the conditional mutual information (MI), ensuring that the learned representations discard unimportant contextual information while maintaining the temporal structure central to decision-making processes.

Implementation and Methodology

CRTR employs a unique sampling strategy for training: state pairs from within the same trajectory serve as negatives, compelling the model to focus on distinguishing temporally distant states. This is implemented via a straightforward algorithmic change to existing contrastive frameworks, as illustrated in the provided pseudocode (Algorithm 1).

The training involves:

• Sampling state pairs from trajectories.
• Introducing temporal distinctions by sampling multiple positive pairs per trajectory.
• Computing a contrastive loss that balances between maximizing state temporal closeness and context irrelevance.

The method leverages a neural encoder to formulate a critic function that estimates similarity in a latent space and uses this to drive the contrastive objective.

Experimental Results

CRTR was evaluated across a range of combinatorial environments including Sokoban, Rubik’s Cube, and others. Key metrics such as Spearman's rank correlation were used to gauge the alignment of representation space distance with the actual temporal distance across trajectories. The results evident in Figures 1 and 14 show that CRTR outperforms standard contrastive and supervised baselines in correctly capturing temporal dynamics and improving planning efficiency.

• Success rates were consistently higher with CRTR across different combinatorial tasks.
• The representations did not require extensive search, maintaining high accuracy in solving tasks using less computation.
• NOTABLY, in Rubik's Cube, CRTR learned representations capable of achieving similar performance to BestFS but with reduced reliance on search.

Theoretical Implications

This approach provides a novel perspective on the interplay between representation learning and temporal reasoning, demonstrating that metric learning frameworks like CRTR can effectively capture complex problem dynamics. The use of conditional mutual information optimization ensures the representations are robust to irrelevant context while emphasizing crucial temporal transitions—a result with potential applications in areas such as robotic assembly and chemical synthesis.

Conclusion

The CRTR method innovatively addresses the shortcomings of traditional contrastive learning techniques in capturing temporal structures by introducing in-trajectory negatives. This research demonstrates the potential to reduce the reliance on search algorithms in combinatorial reasoning tasks. The insights found here may extend beyond the evaluated environments, offering new avenues for representation learning in broader AI applications where temporal reasoning is essential. Future work may explore the adaptability of this approach to environments with stochastic dynamics, seeking to further bridge the gap between representation learning and efficient decision-making.