Will there be a significant advancement in frontier AI model architecture by end of year 2026?
IAOne-Off2m
Manifold MarketsSin KYCDatos de resolución verificadosCalibración aceptablePronóstico comunitario actualNo 75%
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Actualizado hace 8 horas
Desactualizado14 ago 25, 22:55 UTC31 dic 26, 23:59 UTC
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Follow-on from https://manifold.markets/Jasonb/will-a-gpt4-level-efficient-hrm-bas, since I'm interested in the possibility (or impossibility) of architectural innovations more broadly.
- Resolution criteria:
- The architecture must be meaningfully different from an auto-regressive transformer, either not transformer based at all, or a significant fusion of a transformer with other components.
- To clarify, something similar to incorporation of Mixture-of-Experts would not count, but diffusion based LLMs would (though they also need to meet the other criteria).
- The model must be significantly better than previous LLMs in some important aspect.
- E.g. for the same amount of training data it achieves much higher performance, or it can achieve similar performance to frontier models with far fewer parameters, or it lacks some failure mode common to current or future transformer-based LLMs.
