Learning Structural Convergence: A Neuro-Symbolic Benchmark for Temporal Reasoning

• URL: http://arxiv.org/abs/2607.22365v1
• Date: Fri, 24 Jul 2026 14:50:19 GMT
• Title: Learning Structural Convergence: A Neuro-Symbolic Benchmark for Temporal Reasoning
• Abstract summary: This paper introduces TRACTA, a benchmark for temporal structural reasoning in high-complexity event-driven systems.<n>The benchmark includes three tasks: early_warning, pattern_detection, and run_classification.<n>Results show that raw event-level learning remains informative, but learned temporal modeling over semantic capability achieves the highest aggregate point estimates.
• Score: 2.7644691581661287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-complexity operational environments require methods that detect and anticipate temporally distributed patterns rather than classify isolated events. This paper introduces TRACTA (Temporal Reasoning and Capability-Trajectory Analysis), a controlled synthetic benchmark for temporal structural reasoning in high-complexity event-driven systems, instantiated through Multi-Domain Operations (MDO)-like scenarios. The benchmark includes three tasks: early_warning, pattern_detection, and run_classification, and compares raw-event neural models, a contract-lite semantic baseline, and a neuro-symbolic configuration operating on semantically grounded trajectories. Results show that raw event-level learning remains informative, but learned temporal modeling over semantic capability and contextual direct-impact trajectories achieves the highest aggregate point estimates, with the largest margins on the temporal tasks. Ablation analysis indicates that capability dynamics, contextual impacts, and temporal structure contribute complementary information. Shortcut diagnostics indicate that the most direct cross-run global-identifier shortcut is controlled in the primary neural input view, while residual shallow signals remain. Overall, the findings support a bounded methodological conclusion: in controlled synthetic settings, semantically grounded trajectories provide an effective representation for temporal structural reasoning, supporting further investigation of semantic interfaces between event data, structured representations, and temporal learning.

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