The Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth Scaling
arXiv:2606.29278v1 Announce Type: new Abstract: We introduce the Complexity Ceiling Benchmark (CCB), a controlled evaluation of how language-model reasoning decays as the number of required sequential steps grows. CCB fixes the semantic content of a task and varies only its depth N in {5,...,50} across three structurally distinct regimes: grounded spatial state-tracking, abstract symbolic pointer manipulation, and transitive relational inference. Across 6,000 trials over five frontier and open-weight LLMs we find a consistent pattern of geometric per-step decay with widely separated domain cei
The release of the Complexity Ceiling Benchmark provides a methodology to quantify the limitations of current LLMs in sequential reasoning, a critical step as AI foundational models become more complex and attempt more intricate tasks.
A strategic reader should care because understanding the 'depth ceiling' of LLMs in sequential reasoning helps in identifying where current AI applications will fail and what future research priorities are needed for more robust AI agents.
This research provides a standardized metric to evaluate the decay of LLM reasoning with increased sequential steps, offering a clearer picture of current model capabilities and limitations beyond just superficial performance metrics.
- · AI research labs focused on reasoning
- · Developers of more robust AI architectures
- · Companies with complex, sequential tasks
- · Companies overestimating current LLM reasoning capabilities
- · AI applications requiring deep, sequential inference
- · Investors funding unscalable LLM approaches
The benchmark will likely become a standard tool for evaluating and comparing advanced LLMs beyond simple task performance.
This improved understanding of reasoning limitations will guide the development of new AI architectures specifically designed for multi-step tasks, potentially hybrid systems.
Advances in sequential reasoning could accelerate the development of truly autonomous AI agents capable of complex planning and execution across diverse domains.
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Read at arXiv cs.AI