TempoBench: A verifiable benchmark for how AI reasons
How do we know if an AI is truly reasoning—and where it fails? TempoBench offers a clear answer.
TempoBench is a new, formally grounded and verifiable benchmark that lets researchers systematically probe multi-step reasoning, with difficulty you can dial up or down.
- Why it matters: Ad-hoc tests capture real decision chains but lack guarantees; proof systems are verifiable but don’t reflect agent-like tasks. TempoBench combines realism with rigor.
- How it works: Temporal Trace Evaluation (TTE) checks whether models can follow and simulate a step-by-step process. Temporal Causal Evaluation (TCE) tests cause-and-effect reasoning in multi-step systems.
- What they found: Today’s leading LLMs score 65.6% on TCE-normal but only 7.5% on TCE-hard—showing they 'get' the task yet struggle as system complexity rises.
- Open tools: Code and tasks are available: https://github.com/nik-hz/tempobench
For anyone building reliable AI agents, TempoBench is a practical lens to see not just what models answer—but how they think over time.
Paper: http://arxiv.org/abs/2510.27544v1
Register: https://www.AiFeta.com
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