The first infrastructure benchmark for agentic AI has arrived. An AI agent chains tens to hundreds of AI model calls, using tools, gathering context, and iterating until the task is complete. Existing benchmarks
RESEARCH
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Humanity’s final exam and FrontierMath incoming
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Humanitys very last exam and Very FrontierMath incoming
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Fable 5’s lead until GPT-5.6 and benchmark saturation
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Looking at the graph, I think Fable 5 will only maintain its lead up to GPT-5.6. And secondly, I think the benchmark will soon be completely saturated.
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Debate on the effect of fallback in averaged benchmarks
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To those in the replies who say 'but opus 4.8 is weaker so without fallback, the score would be even higher': this is not necessarily true because of how any benchmark works – which is an average of queries – and what is called 'the x.com/ClementDelangu…'
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The absence of fallback does not necessarily increase the benchmark score
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To those in the replies who say 'but opus 4.8 is weaker so without fallback, the score would be even higher': this is not necessarily true because of how any benchmark works – which is an average of queries – and what is called 'the x.com/ClementDelangu…'
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The erroneous reasoning about fallback and benchmarks
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To those in the replies who say "but opus 4.8 is weaker so without fallback, the score would be even higher": this is not necessarily true because of how any benchmark works – which is an average of queries – and what is called "the x.com/ClementDelangu…"
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Refutation of the argument that a weaker model increases the score
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To the people in the replies who say "but opus 4.8 is weaker, so without fallback, the score would be even higher": this is not necessarily true because of how any benchmark works – which is an average of queries – and what is called "the x.com/ClementDelangu…"
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Benchmarks: why a weaker model does not necessarily improve the score
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To those in the replies who say 'but opus 4.8 is weaker so without fallback, the score would be even higher': this is not necessarily true because of how any benchmark works – which is an average of queries – and what is called 'the x.com/ClementDelangu…'
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Asynchronous AI Cuts Energy by Orders of Magnitude while Learning Continuously
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Asynchronous #AI cuts computing energy by orders of magnitude while learning continuously
by Daegan Miller @TechXplore_com Learn more: https://
bit.ly/4fuihmz #MachineLearning #ArtificialIntelligence #DL #ML -
Debate on the impact of fallback in benchmarks
By
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To the people in the replies who say 'but opus 4.8 is weaker so without fallback, the score would be even higher': that is not necessarily true because of how any benchmark works – which is an average of queries – and what is called 'the x.com/ClementDelangu…'