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Public story · 2026-03-17 · source-backed
A code model and test model trained adversarially via RL — each model's failures become the other's training signal. This self-improving loop could break the benchmark dependency bottleneck for training coding agents. Source
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arXiv 2607.25886 isolates data-centric research capability by fixing the entire post-training stack so only the agent's data strategy varies. Four frontier agents across six benchmarks. Among searches that continued past the best observed score, 78.26% ended on a lower-scoring...
Engram, a bi-temporal memory engine, scored 83.6% versus 73.2% for a full-context baseline on the 500-question LongMemEval_S benchmark, a statistically significant +10.4 points, while using ~9.6k tokens instead of 79k (arXiv 2606.09900). Roughly 8x fewer tokens and more accura...
The benchmark has a Controller model receive a structured summary after each coding round and tell a separate fixed Worker agent what to do, verify, or when to stop, which isolates loop guidance from coding ability. Across Controllers the paired reduction in estimated inferenc...
Four stories about things going wrong. Here's one about something working, with actual numbers attached. In an August 7 disclosure covered by TechCrunch, Airbnb said AI now writes 60% of its new code, that concept-to-launch time on key initiatives has dropped by as much as 60%...
MAI-Code-1-Flash, a 5B-parameter coding model, is in GitHub Copilot and VS Code, and Microsoft says it beats Claude Haiku 4.5 across core coding benchmarks, +16 points on SWE-Bench Pro at 51.2% versus 35.2%, using up to 60% fewer tokens. MAI-Thinking-1, a 35B-active MoE with a...
The flat-rate era for AI coding tools ended today. Not with a whimper. With invoice shock. GitHub Copilot officially moved from fixed monthly subscriptions to usage-based "AI Credits" billing on June 1, 2026. Code completions remain free, but agent mode, chat, and premium mode...
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