https://aijourney.space/deepseek/news/ Changes at DeepSeek, logged from its own announcements, documentation and price pages, never from another publisher, each with what it means for you. en https://aijourney.space/updates/2026-10-03-predictive-credit-measuring-what-scientific-explanations-add-to-experime/ 2026-10-03-predictive-credit-measuring-what-scientific-explanations-add-to-experime Sat, 03 Oct 2026 00:00:00 GMT What happened. On 29 September 2026, researchers posted a paper to arXiv testing whether scientific explanations from research agents add measurable predictive credit beyond a plain experiment description, running paired forecasts across 336 prospective states, 12 Tox21 endpoints and 24 OpenML tasks, with DeepSeek V4 Pro and DeepSeek V4 Flash as the requested models under test. Sources. arXiv preprint, Predictive Credit: Measuring What Scientific Explanations Add to Experimental Forecasts, submitted 29 September 2026: https://arxiv.org/abs/2610.00314 (read 3 Oct 2026) our DeepSeek API price table, which lists V4 Pro and V4 Flash peak-hour rates per million tokens: https://aijourney.space/deepseek/#api-prices (read 3 Oct 2026) Why it matters. For anyone paying for an evaluation run rather than reading about one, the results show a real workflow cost: swapping DeepSeek V4 Flash in to save money moved matched point MAE from .01823 to .02020 and still missed the equivalence test, so a cheaper model is not a free substitute when credit, not just correctness, is what gets measured. DeepSeek's own API price table lists V4 Pro at $3.96 per million output tokens at peak and V4 Flash at a third of that, a saving this paper's own numbers suggest comes with a measurable accuracy cost rather than none. https://aijourney.space/updates/2026-09-24-automated-extraction-of-records-of-processing-activities-ropa-using-hybr/ 2026-09-24-automated-extraction-of-records-of-processing-activities-ropa-using-hybr Thu, 24 Sep 2026 00:00:00 GMT What happened. On September 23, 2026, researchers from a Vietnamese university group posted a paper describing RoPA Manager, a hybrid retrieval system built to automatically extract Records of Processing Activities required under Vietnam's Personal Data Protection Law, which took effect January 1, 2026, and comparing a cloud-hosted DeepSeek model against a locally deployed model on that extraction task. Sources. arXiv paper on RoPA Manager, hybrid RAG and locally deployed LLMs, submitted 23 September 2026: https://arxiv.org/abs/2609.27359 (read 24 Sep 2026) Why it matters. For a compliance team deciding whether sensitive processing records can leave the building, this test gives a concrete number rather than a guess: a locally run open model on a single 24 GB card came within a fraction of a percentage point of the cloud service, so the sovereignty requirement in the new decree does not have to cost accuracy. That changes the calculus for smaller organizations that cannot run large GPU clusters but still need to keep processing logs on premises, since a modest local card becomes a workable substitute for a paid API rather than a compromise they have to accept. https://aijourney.space/updates/2026-09-16-grounded-adjudication-of-variations-across-extracted-timelines-gavel-com/ 2026-09-16-grounded-adjudication-of-variations-across-extracted-timelines-gavel-com Wed, 16 Sep 2026 00:00:00 GMT What happened. On September 11, 2026, researchers Jack Cummins, Sayantan Kumar, Ketan Tamirisa, and Jeremy C. Weiss submitted a paper describing GAVEL, a protocol that has a large language model compare two extracted clinical timelines against the original case report and rule on each discrepancy rather than scoring output against a single fixed reference annotation. Sources. arXiv preprint 2609.13475, submitted 11 September 2026: https://arxiv.org/abs/2609.13475 (read 16 Sep 2026) Why it matters. Clinical NLP teams building extraction pipelines usually validate against one expert-written reference, a workflow that inherits every gap and slip in that annotation; a report-grounded judge instead lets two competing extractors, including outputs from DeepSeek V3.2, be checked against the primary text itself, which changes how a team decides whether to trust or merge a pipeline's output.