Mechanics of Learned Reasoning 1: TempoBench, A Benchmark for Interpretable Deconstruction of Reasoning System Performance (2510.27544v1)

Abstract: LLMs are increasingly excelling and outpacing human performance on many tasks. However, to improve LLM reasoning, researchers either rely on ad-hoc generated datasets or formal mathematical proof systems such as the Lean proof assistant. Whilst ad-hoc generated methods can capture the decision chains of real-world reasoning processes, they may encode some inadvertent bias in the space of reasoning they cover; they also cannot be formally verified. On the other hand, systems like Lean can guarantee verifiability, but are not well-suited to capture the nature of agentic decision chain-based tasks. This creates a gap both in performance for functions such as business agents or code assistants, and in the usefulness of LLM reasoning benchmarks, whereby these fall short in reasoning structure or real-world alignment. We introduce TempoBench, the first formally grounded and verifiable diagnostic benchmark that parametrizes difficulty to systematically analyze how LLMs perform reasoning. TempoBench uses two evaluation benchmarks to break down reasoning ability. First, temporal trace evaluation (TTE) tests the ability of an LLM to understand and simulate the execution of a given multi-step reasoning system. Subsequently, temporal causal evaluation (TCE) tests an LLM's ability to perform multi-step causal reasoning and to distill cause-and-effect relations from complex systems. We find that models score 65.6% on TCE-normal, and 7.5% on TCE-hard. This shows that state-of-the-art LLMs clearly understand the TCE task but perform poorly as system complexity increases. Our code is available at our \href{https://github.com/nik-hz/tempobench}{GitHub repository}.

• The paper introduces TEMPOBENCH, a novel benchmark for evaluating temporal reasoning in LLMs using two tasks: Temporal Trace Evaluation and Temporal Causality Evaluation.
• It quantifies performance with precision, recall, and F1 scores, highlighting model strengths in standard conditions and limitations in complex causal tasks.
• Findings advocate for improved architectures and training data to enhance LLMs' causal credit assignment and multi-step planning in dynamic scenarios.

Summary

The paper "Mechanics of Learned Reasoning 1: TempoBench, A Benchmark for Interpretable Deconstruction of Reasoning System Performance" introduces TEMPOBENCH, a novel benchmark aimed at evaluating and enhancing the reasoning capabilities of LLMs with a focus on temporal reasoning tasks. Developed by researchers at Columbia University, TEMPOBENCH bridges the gap between ad-hoc datasets and complex mathematical proof systems to provide a verifiable, synthetic framework for disambiguating reasoning performance over temporal tasks. This approach allows researchers to systematically explore factors affecting reasoning difficulty, thereby offering insights into the structural features that impede or facilitate effective reasoning in LLMs.

Benchmark Design

TEMPOBENCH comprises two major tasks: Temporal Trace Evaluation (TTE) and Temporal Causality Evaluation (TCE).

Temporal Trace Evaluation (TTE)

TTE assesses an LLM's ability to determine whether a sequence of inputs satisfies temporal constraints defined by finite-state machines. This involves parsing the sequence of transitions to verify acceptance by the automaton, thus testing the LLM's capability to model runtime verification and underlying world representations.

Temporal Causality Evaluation (TCE)

TCE evaluates the ability of the LLM to infer causal dependencies over time in complex systems. Given a trace from a temporal reasoning task and an observed effect, the LLM must identify the minimum set of causal inputs required for that effect to occur. This task explicitly requires understanding causal relationships and credit assignment through sophisticated reactive synthesis mechanisms.

Evaluation Framework

The evaluation metrics for TEMPOBENCH are grounded in precision, recall, and F1 scores derived from formally verified ground truths. These metrics allow for rigorous statistical analysis of reasoning performance beyond simple correctness measures. The paper highlights performance metrics across model variants, revealing significant insights into the behavior and failure modes of LLMs in complex reasoning scenarios.

Results

The results from TEMPOBENCH demonstrate distinct performance scaling across different LLM models and benchmark tasks. Models show competence in normal task conditions but struggle with increased complexity in hard conditions, underscoring the challenges LLMs face with deep temporal dependencies. Notably, state-of-the-art LLMs, while adept at understanding task formulations, exhibit limitations in predicting exact causal relations, as evidenced by substantial performance drops on TCE-hard tasks compared to TCE-normal setups.

Implications and Future Work

TEMPOBENCH provides a structured approach to dissecting LLM reasoning capabilities and offers avenues for future developments. By isolating critical features that determine task difficulty, researchers can refine LLM architectures to enhance performance on temporal reasoning tasks. The paper posits that training data derived from TEMPOBENCH can improve causal credit assignment, planning, and multi-step prediction tasks in LLM agents, paving the way for more reliable AI systems in real-world applications.

Conclusion

TEMPOBENCH stands out as a formally verified and diagnostically focused benchmark that transcends traditional leaderboard paradigms by providing deep insights into reasoning performance. Its innovative approach enables the segmentation of reasoning tasks into quantifiable components, ultimately facilitating targeted improvements in the design and implementation of LLMs. The findings are pivotal in advancing the understanding of temporal causality within reactive systems, illustrating the path to enhanced reasoning capabilities and broader AI applicability in dynamic environments.