Alibaba Introduced Qwen3

Open-weight Qwen3, latest large language models, including 2 MoE models and 6 dense models, ranging from 0.6B to 235B.

Qwen3-235B-A22B, achieves competitive results in benchmark evaluations of coding, math, general capabilities, etc., when compared to other top-tier models such as DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro.

Additionally, the small MoE model, Qwen3-30B-A3B, outcompetes QwQ-32B with 10 times of activated parameters, and even a tiny model like Qwen3-4B can rival the performance of Qwen2.5-72B-Instruct.

Trained on 36T tokens, covering 119 languages! Data extracted from PDFs, synthetic data, etc.
Thinking and non-thinking modes
Improved agentic, coding capabilities, support for MCP
Training pipeline similar to DeepSeek R1
Small distilled models, such as Qwen3-4B that can rival the performance of Qwen2.5-72B-Instruct, even a Qwen3-0.6B model

GitHub
HuggingFace
Modelscope

New work on automated prompt engineering for personalized text-to-image generation:

PRISM: Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation

Paper + Code

Prompt engineering for personalized image generation is labor-intensive or requires model-specific tuning, limiting generalization.

Key Idea: PRISM uses VLMs and iterative in-context learning to automatically generate effective, human-readable prompts using only black-box access to image generation models.

This approach shows strong generalization and versatility in generating accurate prompts for objects, styles and images across multiple T2I models, including Stable Diffusion, DALL-E, and Midjourney. It also enables easy editing and multi-concept prompt generation.

BCG 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝘁𝗲𝘀𝘁 𝗣𝗢𝗩 𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣)

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:

1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 𝗔𝗿𝗲 𝗠𝗼𝘃𝗶𝗻𝗴 𝗙𝗿𝗼𝗺 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗼 𝗥𝗲𝗮𝗹𝗶𝘁𝘆:
➜ Early deployments are already delivering 30–90% improvements in speed, productivity, and cost across coding, compliance, and supply chain domains.

2. 𝗠𝗖𝗣 𝗜𝘀 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗴𝗲𝗻𝘁𝘀:
➜ The Model Context Protocol (MCP) is the new open standard adopted by Anthropic, OpenAI, Microsoft, Google, and Amazon to expose tools, prompts, and resources reliably.

3. 𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗜𝘀 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝗥𝗮𝗽𝗶𝗱𝗹𝘆:
➜ Agents today can automate tasks up to one hour long — and this limit is doubling every seven months, pushing toward multi-day autonomous workflows by the end of the decade.

4. 𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝗠𝘂𝘀𝘁 𝗕𝗲 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆-𝗙𝗶𝗿𝘀𝘁:
➜ Security challenges grow as agents gain system access. OAuth, RBAC, permission isolation, eval-driven development, and real-time monitoring are mandatory to deploy agents safely.

5. 𝗧𝗵𝗲 𝗥𝗶𝘀𝗲 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁-𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀:
➜ Platforms like Azure Foundry, Vertex AI, Bedrock Agents, and Lindy are positioning themselves as the orchestration layer to create, manage, and scale enterprise agent ecosystems.

6. 𝗙𝗿𝗼𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘁𝗼 𝗙𝘂𝗹𝗹𝘆 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁𝘀:
➜ Enterprises are shifting from prompt chaining (rigid workflows) to fully autonomous agents capable of observing, reasoning, and acting dynamically based on real-world feedback.

7. 𝗠𝗖𝗣 𝗮𝗻𝗱 𝗔2𝗔 𝗪𝗶𝗹𝗹 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗘𝗰𝗼𝗻𝗼𝗺𝘆:
➜ MCP connects agents to tools and data. A2A (Agent-to-Agent communication) will enable agents to negotiate, collaborate, and coordinate across systems — forming true multi-agent networks.

U.S. Secretary of Commerce Howard Lutnick said the U.S. will accelerate bitcoin mining, support the construction of its own power infrastructure, and reduce reliance on the public power grid.

The U.S. will allow miners to build power plants and data centers near natural gas fields and consider incorporating bitcoin into the national economic account.

The U.K. government released consultation papers on crypto legislation.

It sees the creation of new regulated activities, such as operating a crypto exchange and stablecoin issuance.

Meta is releasing a standalone mobile app for its ChatGPT competitor, Meta AI.

There's a Discover feed that shows interactions that others (including your IG/FB friends) are having with the assistant. Meta tells the idea is to demystify AI and show “people what they can do with it."

OpenAI is working on a similar feed for ChatGPT.

Stanford and Google DeepMind released SWiRL: A synthetic data generation and multi-step RL approach for reasoning and tool use!

With SWiRL, the model’s capability generalizes to new tasks and tools. For example, a model trained to use a retrieval tool to solve multi-hop knowledge-intensive question answering tasks becomes significantly better at using Python to solve math problems (and vice versa).

As they scale the synthetic data size, the generalization gains continue to improve.

This suggests new possibilities for self improvement, where researchers use the model to synthetically generate data on multi-step tasks in more accessible (or affordable) domains and improve it on other domains.

Amazon introduces an architecture to migrate from various models to Amazon Nova models using DSPy and its MIPROv2 algorithm.

Turns out #DeepSeek does have a new release (671B math/prover model) but it's not R2.

Xiaomi MiMo-7B: a 7B reasoning model series trained from scratch, outperforms 32B+ baselines on math and code via dense RL

- pretrained on 25T tokens w/ multi-token prediction
- RL rewards from rule-verifiable math/code tasks
- cold-start RL model (MiMo-7B-RL-Zero) hits 93.6% MATH-500, 49.1% LCB v5
- SFT→RL variant matches OpenAI o1-mini
- also open: base + SFT checkpoints
- seamless rollout engine: 2.29× faster RL training
- vLLM + MTP inference ready
- strong AIME 2025 (55.4%) and LCB v6 (49.3%) results

Google DeepMind introduced the SAS prompt: LLM as Numerical Optimizers for Robot Self-Improvement

Large language models like Gemini have an inherent ability to problem solve, without needing to retrain for specific jobs.

Robots can use these models to improve how they operate over time, by interacting with the world, and learning from those interactions.

With the SAS prompt, you can now use language models like Gemini to learn from a robot's history.

This allows the model to analyze parameter effects, and suggest ways to improve - similar to a real-life table tennis coach.

Also Google released a dataset of table tennis ball throws, and a simple MuJoCo simulation environment able to replicate trajectories from the real world, with data on specific serves and rallies.

Paper.

Sam Altman's World Brings Biometric Verification and Digital Payments to US Market

World (formerly Worldcoin) has chosen six key innovation hubs for its American debut: Atlanta, Austin, Los Angeles, Miami, Nashville, and San Francisco. Americans in these cities can now:

1. Verify their unique World ID using the company's advanced biometric technology

2. Access the complete World App experience

3. Claim the Worldcoin (WLD) token airdrop.

The company's signature NVIDIA-powered Orbs — the biometric verification devices that distinguish humans from AI — will be available across the USA via standalone World Spaces and partner locations including Razer stores.

Alongside its identity verification system, World has announced the World Card a financial product that connects directly to users' World App wallets, enabling them to spend digital assets anywhere Visa is accepted.

Key features include:

1. Seamless integration with verified human identities through World ID
2. Ability to spend digital assets at over 150 million Visa-accepting locations globally
3. Merchants receive fiat currency without needing to understand crypto.

4. A rewards program specifically optimized for the AI economy, with enhanced rewards on AI subscriptions and services

5. Rewards paid directly in WLD tokens to connected wallets.

World emphasizes that its architecture places Americans in complete control of their digital identity:

- Personal data remains exclusively on users' devices through "Personal Custody"

- Advanced cryptographic systems, including Anonymized Multi-Party Computation and zero-knowledge proofs, ensure data privacy
- Verification of humanity without compromising personal information

Morgan Stanley plans to offer crypto trading to E-Trade clients.

Morgan Stanley is working on a plan to add cryptocurrency trading to its E-Trade platform, in what would be the most significant move by a major US bank to help everyday customers buy into the asset class since the Trump administration began removing regulatory barriers.

The project is nascent and executives envision launching the service sometime next year, according to people familiar with the matter. The firm is considering partnering with one or multiple established crypto firms as it sets up the mechanics for the brokerage’s clients to buy and sell popular tokens including Bitcoin and Ether.

Microsoft Introduced Phi-4-reasoning, adding reasoning models to the Phi family of SLMs.

The model is trained with both supervised finetuning (using a carefully curated dataset of reasoning demonstration) and Reinforcement Learning.

- Competitive results on reasoning benchmarks with much larger top-tier models up to DeepSeek R1.

- Strong performance on new tests released after data collection (AIME 2025, HMMT).

- Reasoning transfers/generalizes well to new domains even with only SFT (e.g. k-SAT, Mae Solving, Calendar Planning, etc.)

- Retains and often significantly improves general-purpose capabilities (e.g. instruction following).

HuggingFace Phi-4-reasoning
HF Phi-4-reasoning-plus
Hf Phi-4-mini-reasoning

Microsoft is getting ready to host Elon Musk’s Grok AI model. Microsoft has been in discussions with xAI to make Grok AI available on Azure's AI Foundry service.

In recent weeks Microsoft has been in discussions with xAI to host the Grok AI model and make it available to customers and Microsoft’s own product teams through the Azure cloud service.

The move could prove controversial internally and further inflame tensions with Microsoft’s partner OpenAI.

Huawei is building a 7nm fab in Shenzhen for its smartphone and Ascend chips, its first effort to manufacture its own high-end chips.

The Guanlan site is part of a sprawling network of new chip manufacturing sites all working on various elements of Huawei's push to become a semiconductor champion, from equipment to fabrication.

Huawei wasn't considered a serious player in chip manufacturing before it was sanctioned in 2019. The move kickstarted massive investment to localise chip technology, aided by state funds and led by the tech giant. The Guanlan network is part of this effort.

Cisco's Foundation AI released Foundation-Sec-8B

Built on Llama 3.1, the LLM matches Llama 3.1-70B & GPT-4o-mini on multiple security tasks

It will help with use cases like threat detection, vulnerability assessment, security automation, and more.

Carnegie Mellon University started company with only AI employees

They got OpenAI, Gemini, Anthropic, etc models and gave them job roles. They were retarded and costed a fuckton. Claude 3.5 was the best employee and only did 24% of its tasks.

Paper.

Google DeepMind presented Evaluating Frontier Models for Stealth and Situational Awareness:
- 5 evals of ability to reason about and circumvent oversight
- 11 evals for measuring a model’s ability to instrumentally reason about itself, its environment and its deployment

No SotA model currently shows concerning levels of either capabillity.

Anthropic launched a new "AI for Science" program

Under the initiative, the company will provide up to $20,000 in free API credits (for 6 months) to researchers in “high-impact” scientific fields like drug discovery, genomics, and agriculture