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Dell CTO John Roese: The key to the AI operating model is not the model, but the enterprise's proprietary knowledge

Dell Global CTO and Chief AI Officer John Roese said that if enterprises merely deploy AI agents without changing their operating model, they will only be running their business the old way, resulting in a poor experience. Dell has spent nearly two years running fully autonomous agents internally, by having domain experts encode company practices into machine-readable Markdown skill files so that the agents work according to Dell's rules and preferences. Roese emphasized that an enterprise's moat is not the tools or even the models, but this proprietary knowledge, which should therefore stay in-house rather than be handed over to third parties.

Ai2 releases the FairStress benchmark to evaluate group fairness in LLM decision-making

Ai2 introduces FairStress, a benchmark measuring the demographic group fairness of LLMs in decisions such as hiring, healthcare, and lending. It contains 300 two-candidate scenarios across 29 domains, and expands to more than 13 million test cases after combining identity presentation methods with adversarial prefixes. It is now available for download and integrated into Olmo-eval. The experiments cover the Olmo, Llama, Qwen, Gemma, Claude, and GPT series, and find that when candidate identities are presented implicitly, the average gap at which models abandon the correct answer is 6.9 percentage points, and 9.3 percentage points when identities are explicitly labeled. On FairStress-Core, Claude Opus 5 and GPT-5.5 select the minority-coded candidate about 60–65% of the time when there is no clearly superior candidate; most of the nine debiasing methods tested fail to reliably narrow the asymmetry, while counterfactual chain-of-thought narrows the gap while maintaining accuracy.

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FairStress: A Deeper Evaluation of Fairness in LLM Decision-Making

The FairStress benchmark contains 300 two-candidate decision scenarios spanning 29 domains, expanded into over 13 million test cases by varying how demographic identities are presented and adding adversarial prefixes, and it introduces a 50,000-entry FairStress-Core subset. Across 15 models, the average gap in abandoning the correct answer was 6.9 percentage points when identities were implicit and 9.3 percentage points when labels were explicit; Claude Opus 5 and GPT-5.5 chose the minority-coded candidate about 60–65% of the time when there was no objectively better option.

Meta's Open-Weight Model Muse Spark Open Launches on Fireworks Serverless API

Fireworks launched Meta's open-weight model Muse Spark Open on the day of release. It is an 816B-parameter MoE model that activates 42B parameters per token, with a 1M-token context window, targeting long-horizon coding and agentic tasks. Fireworks says it ranks first among 75 models on Vals AI's Harvey Legal Agent Benchmark leaderboard with 25.4%, nearly 4 times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench it surpasses Claude Opus 5.5 at every difficulty level. On Fireworks, the model has zero data retention enabled by default, uses only US-based compute at no extra charge, and supports fine-tuning with serverless LoRA.

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AuxoAI Raises Over $20 Million Series A to Bring AI Expertise to Enterprises

Enterprise engineering and consulting firm AuxoAI announced it has closed a $20.7 million Series A funding round led by NTT Venture Capital to accelerate growth and expand its enterprise AI consulting model. Its core platform, Damia, is a toolkit for "forward-deployed engineers" that turns enterprise data into applications and encodes business process knowledge into AI agents capable of long-term collaboration. The company has worked with Global 2000 enterprises for more than three years and has partnerships with platforms including Google, AWS, Snowflake, Databricks and Microsoft.

PyTorch 2.15 Brings FP8 and Faster MoE Decoding to MPS

PyTorch 2.15 adds FP8 support to the MPS backend and makes MoE model decoding 5–6.5 times faster than 2.14: on an M3 Ultra with torch.compile, Qwen3.6-35B-A3B goes from 9.4 tokens/s to 61.3 tokens/s, and LFM2.5-8B-A1B goes from 23.6 to 123.6 tokens/s. For Wan2.2's tiled VAE video decoding, peak driver memory drops from about 270 GiB to 8 GiB, and the image corruption issue with Qwen-Image-2.1's VAE encoding on MPS is also fixed with a padding change, raising the encode-decode round-trip PSNR from 10.06 dB to 29.95 dB. This version requires macOS 15 or later and Python 3.11 or later.

MiniMax M3.1 Flash Launches on Ollama

MiniMax announced that M3.1 Flash gained support on Ollama on its first day of launch, with the model hosted in the US with zero data retention and ready for direct use in agent development. Ollama said the model supports image understanding and a 1M-token context window, is suited to fast coding and agentic workflows, and provided the commands for calling it on Ollama Cloud as well as in Claude Code and Codex.

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MiniMax M3.1 Flash launches on Cline and is free to use

MiniMax announced that M3.1 Flash is now live on Cline and free to use, targeting agentic coding tasks that take a long time and where costs add up quickly. MiniMax thanked Cline for completing integration on launch day and recommends users try it on their most complex repositories.

MiniMax M3.1 Flash Launches on GMI Cloud

MiniMax announced that M3.1 Flash (also known as Space Bunny) is now available on GMI Cloud, and thanked the company for completing integration on day0. According to GMI Cloud, the model is designed for coding, agentic workflows, and computer use, cutting agentic task costs by 70%, scoring 73.7% on DeepSWE v1.1 and 82% on CyberGym.

MiniMax M3.1 Flash Now Free for a Limited Time on Command Code

MiniMax officially confirmed that M3.1 Flash (also known as Space Bunny) is now live on Command Code and free for a limited time for all subscribers. According to Command Code, the model supports a 1M token context window, delivers frontier-level coding capabilities, and is available to users on all plans and subscriptions.

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MiniMax releases the M3.1 Flash model, at one-third the cost of M3 and 3x faster

MiniMax has released M3.1 Flash, which reaches frontier-level performance on tasks such as Cowork (Automation-Bench 62%, ALE 30%), coding (DeepSWE 74%), and visual reasoning (BabyVision 91%), at one-third the cost of M3 and 3x the speed. The unified API price is $0.15/$0.60 per 1M input/output tokens, with cache reads at $0.015 per 1M tokens. The company says Space Bunny, which previously topped global leaderboards and processed 200T+ tokens, is an early version of M3.1 Flash, and the model weights will be released on October 23.

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MiniMax M3.1 Flash Launches on Vercel AI Gateway and Is Free for a Limited Time

Vercel Developers announced that MiniMax M3.1 Flash is now live on AI Gateway and available for free, under the call name minimax/minimax-m3.1-flash. The model performs better at agentic tasks, coding, and cybersecurity, and is free through October 22 when served by MiniMax. Guillermo Rauch reposted the news and said, "More free AI."