Agentic AI news, research notes, and industry analysis.
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Token prices are falling fast, but enterprise AI bills are rising. Uber burned through its entire 2026 AI coding budget in four months driven by Claude Code adoption. Goldman Sachs projects a 24× increase in token consumption by 2030. The Jevons paradox shows up again: efficiency gains don't reduce consumption — they expand it.
Read more →δ-mem augments a frozen full-attention LLM with an 8×8 associative memory state updated by delta-rule learning, applying low-rank corrections to attention at inference time — no fine-tuning required. It reaches 1.31× gains on memory-heavy benchmarks and 1.20× on long-conversation tasks.
Read more →A project called auto-arch-tournament applies Karpathy's autonomous research loop to RISC-V CPU microarchitecture design: an LLM agent proposes RTL changes, a formal verification pipeline gates acceptance, and 10 winning changes out of 73 proposals deliver a 92% CoreMark improvement in under 10 hours. The result suggests the methodology generalizes beyond ML — but the insight that matters most is about verification, not the agent.
Read more →GPT-5.4 Pro solved Erdős Problem #1196 — a 1968 conjecture about primitive sets — when a 23-year-old amateur fed it the problem in a single prompt. The AI's approach used von Mangoldt weights and a downward Markov chain, a framing that existed in analytic number theory for ninety years but had never been applied here. Terence Tao's explanation for why experts missed it is the most telling part of the story.
Read more →Hugging Face released ml-intern this week — an open-source autonomous agent that reads papers, discovers datasets, writes training scripts, and iterates on RLHF/DPO pipelines without human involvement. A demo run pushed Qwen3-1.7B from roughly 10% to 32% on GPQA in under ten hours. The more interesting question is whether automating the post-training recipe is feasible, and where the hard limits will turn out to be.
Read more →N-Day-Bench, a new benchmark from Winfunc Research, tests frontier LLMs on finding real vulnerabilities disclosed only after each model's knowledge cutoff — closing the memorization loophole that undermines most security evals. The April 13 run shows GPT-5.4 clearly ahead of the pack, with GLM-5.1 and Claude Opus 4.6 clustered close behind and Gemini 3.1 Pro trailing by 15 points. The methodology is the interesting part.
Read more →A new preprint identifies a consistent pattern in large reasoning models: the first generated solution outperforms later alternatives, and continued reasoning can actively degrade accuracy. The proposed fix, called RED, improves performance by up to 19% while cutting token usage by 37–70% versus competitive baselines. It's a useful challenge to the assumption that more inference compute is always better.
Read more →A new arXiv paper shows that sampling a model at high temperature, filtering outputs that actually run, and SFT-ing on the result lifts Qwen3-30B from 42.4% to 55.3% on LiveCodeBench — no reward model, no external verifier, no teacher model needed.
Read more →In 2023, Terence Tao predicted that 2026-level AI would be a trustworthy co-author in mathematical research. This month he credited ChatGPT Pro with a proof in a real analysis paper — and published a philosophical essay arguing AI is a natural extension of humanity's tool-building tradition. Both together are a data point, not a verdict.
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