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OpenAI Solves Millennium Math Problem Amid Credit Dispute While Meta and OpenAI Race to Dominate AI Market With New Launches

OpenAI Solves Millennium Math Problem Amid Credit Dispute While Meta and OpenAI Race to Dominate AI Market With New Launches

Sep 09, 2026
The Rundown AI

OpenAI claims a powerful unreleased AI model has solved one of math's greatest unsolved problems, the Navier-Stokes equation, using 10,000 agents over 88 hours, though a credit dispute clouds the milestone, as both OpenAI and Meta race to dominate AI with new product launches including Meta's always-on personal agent Muse …

Foundation Models Science Agents Image & Video
Same Benchmark Name, Different Results: Why Two MMLU Scores Can't Always Be Compared

Same Benchmark Name, Different Results: Why Two MMLU Scores Can't Always Be Compared

Sep 08, 2026
Dmitrii Zatona

Two MMLU benchmark scores from the same model family are deemed incomparable despite their similar numbers, exposing a critical flaw in AI evaluation: sharing a benchmark name means nothing if the testing conditions differ, as variables like prompt format, grader type, and dataset splits can shift accuracy by several percentage …

Research Foundation Models
Salesforce AI Releases Random Attention, a Signal-Free KV-Cache Tool That Outpaces Leading AI Memory Selectors

Salesforce AI Releases Random Attention, a Signal-Free KV-Cache Tool That Outpaces Leading AI Memory Selectors

Sep 07, 2026
GitHub

Salesforce AI Research unveils Random Attention, a surprisingly simple yet powerful KV-cache eviction tool that matches or beats leading AI memory selectors on major benchmarks — without ever reading attention scores or requiring calibration data — while also being the fastest option available in popular AI serving stacks.

Extropic's Z1T Chip Delivers 100x Energy Efficiency Over GPUs With New Sparse Transformer Architecture

Extropic's Z1T Chip Delivers 100x Energy Efficiency Over GPUs With New Sparse Transformer Architecture

Sep 07, 2026
Extropic

Extropic's new Z1T chip delivers over 100x energy efficiency gains compared to Nvidia's H100 GPU by using sparse, in-memory computing and a disaggregated inference pipeline, consuming just 294.52 nJ per token versus 40.9 µJ on the H100, while also achieving faster per-token latency — with open-source weights and training recipes …

Hardware Foundation Models Open Source
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