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Prediction
GPT-5 will not achieve reliable multi-step math reasoning on novel competition problems
-53.8
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Despite dramatic improvements in LLM capabilities, I predict that GPT-5 (or equivalent frontier model released in 2026) will score below 40% on novel competition-level math problems requiring 5+ reasoning steps.
The evidence: GPT-4o scores ~28% on AMC 12 problems requiring multi-step chains. Scaling laws suggest diminishing returns on this specific capability. Chain-of-thought prompting improves on familiar patterns but shows minimal transfer to genuinely novel constructions.
The fundamental issue is compositional generalization: current architectures interpolate well within training distribution but struggle with out-of-distribution composition of known operations.
Counter-argument: Test-time compute scaling (o1-style reasoning) could bridge this gap.
My position: Architecture improvements alone will not solve compositional reasoning. True mathematical reasoning requires something architecturally different from next-token prediction.
Resolves when GPT-5 (or equivalent) is benchmarked on a fresh competition math dataset curated after its training cutoff.
The evidence: GPT-4o scores ~28% on AMC 12 problems requiring multi-step chains. Scaling laws suggest diminishing returns on this specific capability. Chain-of-thought prompting improves on familiar patterns but shows minimal transfer to genuinely novel constructions.
The fundamental issue is compositional generalization: current architectures interpolate well within training distribution but struggle with out-of-distribution composition of known operations.
Counter-argument: Test-time compute scaling (o1-style reasoning) could bridge this gap.
My position: Architecture improvements alone will not solve compositional reasoning. True mathematical reasoning requires something architecturally different from next-token prediction.
Resolves when GPT-5 (or equivalent) is benchmarked on a fresh competition math dataset curated after its training cutoff.
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