Vol. I · No. 193THU, OCT 29, 2026
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Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)

Data selection is critical for training large language models on massive and heterogeneous corpora. Meta-learning for Training-data Selection offers a principled alternative to heuristic scoring by learning data weights from a target validation objective, but existing methods face a trade-off between fine-grained valuation and transferability to unseen data. A natural solution is to replace per-sample weights with a selection network. However, we find that directly incorporating such a network into existing MTS objectives leads to unstable optimization and poor generalization, caused by weigh...

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Judge dismisses antitrust lawsuits over Google’s AI Overviews

A federal judge has dismissed a pair of antitrust lawsuits filed by Chegg and Rolling Stone parent company Penske Media Corporation, which accused Google of driving away web traffic with its AI-powered search features, as reported earlier by Reuters. US District Judge Amit Mehta takes Google's side in a ruling on Wednesday, writing that PMC and Chegg's claims don't stand up to antitrust law. In lawsuits filed last year, Chegg and PMC accused Google of abusing its monopoly power by coercing publishers into supplying content for AI Overviews for free, or risk disappearing from search results. T...

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pwasm 0.2a0

Simon Willison releases pwasm 0.2a0, a pure Python WebAssembly engine improved via Claude Opus 5.5 code generation.

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The eternal complement

OpenAI argues advanced AI's primary economic value lies in automating routine work supporting breakthrough ideas, not replacing innovation itself.

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30 stories