arXiv:2605.26789v1 Announce Type: new Abstract: Post-training is routinely evaluated through aggregate benchmark scores that treat multi-hop reasoning as a single capability -- as if a model that answers more questions correctly must be better at assembling facts. We show that this assumption can be misleading: recipes with statistically indistinguishable atomic knowledge produce composition behaviour separated by over 40 percentage points, a phenomenon we call composition collapse: the systematic failure to assemble stably-known facts into chains, invisible to aggregate metrics. We introduce
This research emerges as AI models become increasingly complex and evaluation methods struggle to keep pace with capabilities beyond simple factual recall, highlighting a crucial blind spot.
It reveals that current evaluation benchmarks for AI may be fundamentally flawed, misrepresenting models' true understanding and reasoning abilities, which has significant implications for AI development and deployment.
The understanding of AI model capabilities shifts from merely statistical accuracy on individual facts to a more nuanced view where factual knowledge does not inherently guarantee complex reasoning, especially in critical applications.
- · Researchers developing advanced AI evaluation methods
- · AI developers focusing on compositional reasoning
- · Companies requiring robust, reliable AI for complex tasks
- · AI developers relying solely on aggregate benchmark scores
- · Stakeholders who assume high factual recall equals high reasoning
- · Benchmarks that do not test compositional reasoning explicitly
AI development priorities will likely shift towards improving compositional reasoning explicitly, rather than just increasing factual knowledge.
New, sophisticated benchmarks and evaluation methodologies will become standard, leading to a more accurate assessment of AI capabilities.
This could accelerate the development of truly intelligent, agentic AI systems able to robustly handle complex, multi-step problems.
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Read at arXiv cs.AI