Seattle Times and Newsday sue OpenAI and Microsoft for copyright infringement. Two regional US newspapers filed suit against OpenAI and Microsoft, alleging their journalism was used to train models without permission or payment. The case joins a growing pile of similar suits from news publishers. The timing is notable: OpenAI's GPT-6 Astra began rolling out the same week, keeping the company's training data practices in the public eye. The outcome of these cases will shape what publishers can demand retroactively for content already ingested. (The Verge)
Brief
Monday, September 7, 2026 · 5 min read
IFM Ships Six Apache-Licensed Models, Up to 375B Parameters
Good morning. Labor Day in the US, which means most of the industry is theoretically offline. The models, however, did not get the memo. A six-model open-weight fleet dropped over the weekend, a newspaper lawsuit landed, and researchers published a paper arguing that smarter agents make financial markets more fragile, not less. Quiet holiday.
Today's reading time is 5 minutes.
OPEN WEIGHTS
Most open-weight releases give builders one model size and a benchmark table; the gap between what fits on a laptop and what runs on a cluster has stayed largely unfilled by any single release.
Driving the news: The Institute of Foundation Models, the frontier lab backed by Abu Dhabi's MBZUAI, released K2 Horizon last week: six models under the Apache 2.0 license, spanning 0.9B, 3.7B, 7B, 32B, 36B-A4B (a mixture-of-experts model activating 4B parameters per token), and 375B-A23B. Apache 2.0 means commercial use with no royalty and no special agreement required. The full range ships together, so a team can prototype on the small end and deploy on the large end without switching ecosystems.
- The 375B-A23B is a sparse mixture-of-experts model, meaning it activates roughly 23B parameters per forward pass despite its 375B total weight count.
- IFM was launched in May 2025, making K2 Horizon one of its first major public releases.
Zoom in: Open-weight releases have clustered at the 7B and 70B marks for most of the past two years, with very large sparse models staying proprietary or carrying restrictive licenses. MBZUAI has been building toward a frontier-capable open lab since announcing IFM in 2025, positioning the UAE as something other than a compute-for-hire market. A six-checkpoint release covering three orders of magnitude is an unusual move: it signals that the lab wants to be useful to hobbyists running a 0.9B model locally and to enterprises evaluating a 375B deployment.
- Mixture-of-experts architecture at the large end keeps inference costs lower than a dense model of equivalent parameter count.
- Apache 2.0 is the permissive end of the open-source spectrum; it is notably less restrictive than the custom community licenses attached to several competing large releases.
Why it matters: For builders, a matched family under one license simplifies the path from experiment to production: the same fine-tuning approach and tooling can carry across sizes. The 36B-A4B slot is particularly useful for teams that need more capability than a 7B but cannot afford the memory footprint of a dense 32B. Commercial teams that previously had to negotiate licenses for large open models now have a 375B option they can use without a legal review.
Bottom line: Six models, one license, zero royalties: IFM just made 'we need a bigger model' a much cheaper problem to solve.
MarkTechPost ↗Get this every weekday
Two minutes, 7am ET. No fluff.
Also happening
OpenAI says its internal coding agents have measurably accelerated research velocity. OpenAI published internal data showing that AI coding agents are now running experiments, writing code, and handling tasks that researchers previously did manually, with measurable increases in experiment throughput. The post does not name specific productivity figures but describes agents handling multi-step research tasks autonomously. It is a company talking about its own tools, so the framing is favorable, but the underlying claim, that agentic coding is changing how frontier AI research gets done, is consistent with what other labs have reported anecdotally. The post appeared yesterday. (OpenAI)
H Company releases NeoMME, a multimodal encoder that skips the vision tower entirely. NeoMME comes in 260M and 800M versions and processes text and raw 32x32 image patches inside a single Transformer, with no separate pretrained vision encoder and no causal decoder. That architecture makes it cheaper to train and easier to deploy for retrieval tasks than systems that bolt a vision tower onto a language model. H Company trained it with masked discrete-diffusion objectives rather than next-token prediction. At 800M parameters it is small enough to run on modest hardware while handling multilingual text alongside images. (MarkTechPost)
OpenAI, AIRPPU, and WAN-IFRA launch an AI program for Ukrainian independent news organizations. The program is designed to help Ukrainian newsrooms build AI capabilities and maintain editorial independence under wartime conditions. It is a partnership between OpenAI, a Ukrainian press freedom organization, and the World Association of News Publishers. No specific tools or funding figures are named in the announcement. The initiative sits alongside OpenAI's ongoing legal exposure from news publishers in other markets, which makes the optics of supporting journalism abroad while being sued by publishers at home somewhat complicated. (OpenAI)
The thread
Open weights are filling in the gaps
IFM's K2 Horizon ships six model sizes under a single permissive license, covering the range from a laptop-friendly 0.9B to a 375B sparse model. H Company's NeoMME ships at 260M and 800M with an architecture that removes two components, the vision tower and the causal decoder, that previously required separate infrastructure. Both releases push capability into smaller, cheaper, more deployable packages. For builders, the practical effect is that 'we need a different size' and 'we need a different modality' are increasingly problems you can solve by downloading a checkpoint rather than training one.
From the publisher
Snippt
Image, video and voice tools in one place
On our radar
- A new arXiv paper argues that deploying more capable LLM agents in financial markets can degrade system-level stability even as individual model performance improves, because better agents coordinate in ways that amplify volatility rather than dampen it.
- Researchers published a removal-based method for improving LLM explanation faithfulness at test time, targeting the gap between what a model says it reasoned and what it actually computed.
- HarvestBench is a new benchmark that puts a literal price on an agent avoiding harm to animals during a farm simulation task, the first benchmark to attach a cost to a named side effect rather than just measuring whether the side effect occurred.
- EXAONE Forecast for Finance is a new financial time-series foundation model from LG AI Research, aimed at zero-shot forecasting tasks where general-purpose models have historically underperformed domain-specific ones.
- A paper on quantization in recurrent networks finds that low-precision state storage corrupts temporal inference in ways that do not appear in feedforward models, because the quantized state feeds back into the next time step.
- Local inference users on r/LocalLLaMA are reporting that a recent Qwen model variant generates extremely long reasoning chains on single-turn coding tasks, with some users seeing 13 minutes of thinking time at 150 tokens per second on consumer hardware.
- Victor Gao, vice chairman of the China Energy Fund Committee, stated publicly that China will not allow any country to achieve dominance in AI, per South China Morning Post weekend coverage.
Get the brief in your inbox
Every weekday morning. Two minutes, no fluff.