Nexar introduced a new AI model designed to predict and prevent car crashes — BADAS 1.0.

It beat SOTA models by learning from 10B+ real miles and 60M+ real events, not simulations

Based on Meta FAIR's V-JEPA 2.

OpenAI just dropped browser—ChatGPT Atlas.

Agent mode in Atlas completes tasks faster as you browse the web.

Available in preview for Plus, Pro, and Business users.

Available today on macOS. ChatGPT can see the page you’re on and answer your questions right there via the Ask ChatGPT sidebar.

ChatGPT can offer suggestions wherever you’re typing on the web. Ask ChatGPT to open, close, reopen, bookmark or revisit any of your tabs.

Meta showed how sparsely finetuning memory layers enables targeted updates for continual learning, w/ minimal interference with existing knowledge.

While full finetuning and LoRA see drastic drops in held-out task performance, memory layers learn the same amount with far less forgetting (-11%).

Airbnb CEO Brian Chesky: “We’re relying a lot on Alibaba’s Qwen model.

It’s very good. It’s also fast and cheap... We use OpenAI’s latest models, but we typically don’t use them that much in production because there are faster and cheaper models.”

A new Morgan Stanley report on BCI reveals a future that's closer than we think. A report from 2024 here.

Here are the key takeaways:

1. The core thesis isn't just medical. It's existential. As AI accelerates exponentially, BCI is seen as humanity's "chance to keep up." The ultimate goal is a seamless symbiosis, merging human consciousness with machine intelligence.

2. The path to mass adoption runs through medicine. With a US healthcare TAM of ~$400 Billion, BCIs will first restore sight to the blind, movement to the paralyzed, and speech to the voiceless. This addresses a dire need, creates a willing patient base, and accelerates regulatory approval.

3. Neuralink isn't just a player; it's the pacesetter. With 12 human patients already using its "Telepathy" device to control computers with their minds, the company is demonstrating a viable product.
Roadmap: From "Telepathy" (mind-control of devices) to "Blindsight" (restoring vision) by 2030.
Vertical Integration: Their secret sauce is controlling the entire stack—the chip, the surgical robot, and the software.
Funding & Hype: Recently raised $650M at a $9BN valuation, backed by top-tier VCs.

4. Key competitors are taking different, less invasive approaches:
Synchron: Uses blood vessels to place its Stentrode implant (no open-brain surgery).
Precision Neuroscience: Places a thin film on the brain's surface.
Merge Labs (by Sam Altman): Exploring non-invasive sonogenetics (using ultrasound).

5. The Inevitable Challenges & Risks
The "Neuro-Elite": Will this create a new class divide between enhanced and non-enhanced humans?
Data Security: How do we protect the most personal data imaginable—our neural signals from hacking?
Ethical Quagmire: The transition from therapy to human enhancement will be the defining ethical debate of the coming decades.

Google together with UCL made a free AI Research Foundations curriculum – available now on Google Skills.

Tahoe Therapeutics has built AI bio's largest virtual cell model. And it's giving it away for free.

Tahoe-x1 is a foundation AI model trained to simulate entire cell behaviors, including gene expression, drug responses, and interactions across biological contexts. It’s touted as the largest of its kind in AI-driven biotechnology and is open-source to foster community research.

The model was trained on over 300 million unique cells, incorporating:
- Tahoe-100M а dataset of 100 million cells with 60,000 drug-cell interactions (1,200 molecules tested on 50 cancer cell lines).

This was a "world record" at the time, built with Parse Biosciences (sample prep) and Ultima Genomics (sequencing).

- Future Goals: Tahoe aims to scale to 1 billion single-cell datapoints and 1 million drug-patient interactions, likened to a "GPT moment" for biology, akin to large language models for text.

Models built on Tahoe-100M (e.g., by Arc Institute) showed 2x better accuracy than competitors. Tahoe-x1 advances this further by integrating diverse data across species, tissues, and perturbations (external stimuli like drugs).

Key Achievements and Partnerships
1. Tahoe-100M contributed to the Arc Virtual Cell Atlas (300+ million cells), a public resource launched with Arc Institute in February 2025, making data accessible to researchers worldwide.

2. Tahoe-x1 has identified new drug candidates for cancer (including "undruggable" targets) and novel therapeutic targets. The company is advancing its pipeline toward clinical trials and seeking one strategic partner (pharma or AI company) for co-development.

3. Tahoe competes with initiatives like the Chan Zuckerberg Initiative, which also aims for 1 billion cell datasets for AI modeling.

Ant group introduced Ring-1T, a 1T-parameter MoE reasoning model with ~50B params active per token.

It’s trained with a long-CoT SFT phase, a verifiable-rewards reasoning RL phase, then a general RLHF phase, and introduces three pieces that make trillion-scale RL actually run:

- IcePop to stabilize updates

- C3PO++ to keep GPUs busy under a token budget

- ASystem to unify high-throughput RL stack

On benchmarks, it leads open weights on AIME-25, HMMT-25, ARC-AGI-1, LiveCodeBench, CodeForces, and ArenaHard v2.

It reaches silver-medal level on IMO-2025 using only natural-language reasoning.

Microsoft introduced 12 new Copilot features. With Groups, Copilot goes multiplayer.

You can now collaborate in real time with your team + Copilot to brainstorm, co-write, plan, or study together.

Plan and collaborate with the new Groups feature—perfect for organizing study sessions, family vacations, or a night out. Copilot makes your crew more productive by answering questions, assigning tasks, and getting everyone moving.

More engaging conversations with Copilot through the new Mico appearance. It brings that much more personal experience to the notion of your AI companion.

And, shop smarter online with the new Edge, AI browser. Copilot, with your permission, can evaluate your open tabs to make more confident decisions and then take action to help you book reservations, travel, or make smarter shopping decisions.

You spend $1B training a model A.
Someone on your team leaves and launches their own model API B.

You're suspicious. Was B was derived (e.g., fine-tuned) from A?
But you only have blackbox access to B.

With this paper, you can still tell with strong statistical guarantees (p-values < 1e-8).

Idea: test for independence of A's training data order with likelihoods under B.

There are crazy amounts of metadata about training process baked into the model that can't be washed out, like a palimpsest.

Stanford and Tsinghua University presented Ctrl-World is a controllable world model that generalizes zero-shot to new environments, cameras, and objects.

Build on pre-trained video model, Ctrl-World adds key designs to make it compatible with modern VLA:

1) Fully controllable via low-level action conditioning.
2) Multi-view prediction including wrist-view.
3) Context as memory for consistency.

Despite these promising progress, also notice WM can still:

- Fail in modeling complex physical interactions.
- Sensitive to initial observations.

Tahoe introduced Tahoe-x1 (Tx1) a 3B parameter, single-cell foundation model that learns unified representations of genes, cells, and drugs, achieving state-of-the-art performance across cancer-relevant cell biology benchmarks, open-sourced on HuggingFace.

Tx1 is the first billion-parameter, compute-efficient foundation model trained on perturbation-rich single-cell data. And it is fully open-source with open weights.

Tx1 is a Transformer (scGPT-inspired self-supervised objective) that stays practical to train at 3B parameter scale, enabling empirical search for optimal architectures and hyperparameters for modeling cells. It is 3-30x more compute efficient than other cell state models.

To make it so, researchers borrowed the best tricks from LMs (FlashAttention v2, FSDP, Dataset streaming, Mixed-precision + multi-node scaling). But even cooler: researchers improved the attention operation at the heart of these models.

Researchers designed new benchmarks to evaluate its performance in cancer-relevant discovery and translational tasks.

Tx1 achieves SOTA on key discovery tasks. It outperforms other models (and for the first time matches with linear baselines) in predicting gene essentiality as measured by the landmark DepMap dataset, a key piece of data in identifying subtype-specific targets.

New work Anthropic and Thinking Machines

AI companies use model specifications to define desirable behaviors during training. Are model specs clearly expressing what we want models to do? And do different frontier models have different personalities?

The problem: specifications can be inherently ambiguous, or scenarios can force tradeoffs between competing principles, causing models to make wildly different choices.

The experiments show that high disagreement among frontier models strongly correlates with specification issues—indicating important gaps in current behavioral guidelines.

Datasets.

MiniMax’s M2 achieves a new all-time-high Intelligence Index score for an open weights model and offers impressive efficiency with only 10B active parameters (200B total)

Key takeaways:

1. Efficiency to serve at scale: MiniMax-M2 has 200B total parameters and is very sparse with only 10B active parameters per forward pass. Such few active parameters allow the model to be served efficiently at scale (DeepSeek V3.2 has 671B total and 37B active, Qwen3 has 235B total and 22B active). The model can also easily fit on 4xH100s at FP8 precision

2. Strengths focus on agentic use-cases: The model’s strengths include tool use and instruction following (as shown by Tau2 Bench and IFBench). As such, while M2 likely excels at agentic use cases it may underperform other open weights leaders such as DeepSeek V3.2 and Qwen3 235B at some generalist tasks. This is in line with a number of recent open weights model releases from Chinese AI labs which focus on agentic capabilities, likely pointing to a heavy post-training emphasis on RL. Similar to most other leading open weights models, M2 is a text only model - Alibaba’s recent Qwen3 VL releases remain the leading open weights multimodal models

3. Cost & token usage: MiniMax’s API is offering the model at a very competitive per token price of $0.3/$1.2 per 1M input/output tokens. However, the model is very verbose, using 120M token to complete our Intelligence Index evaluations - equal highest along with Grok 4. As such, while it is a low priced model this is moderated by high token usage

4. Continued leadership in open source by Chinese AI labs: MiniMax’s release continues the leadership of Chinese AI labs in open source that DeepSeek kicked off in late 2024, and which has been continued by continued DeepSeek releases, Alibaba, Z AI and Moonshot AI

Google introduced next-generation conversational agents, including:

1 - a new, easy-to-use low-code visual builder
2 - natural-sounding voices
3 - reduced deployment time
4 - unified governance

Neuralink announced the first participant in the UK

Paul, who is paralyzed due to motor neuron disease, received his Neuralink implant at UCLH earlier this month and was able to control a computer with his thoughts just hours after surgery.

Clifford Chance and Deutsche Bank looked at the growing intersection of AI and DLT in a new report.

AI predictability and DLT immutability could create autonomous systems that move beyond efficiency to self-verifiability, capable of executing, auditing and learning simultaneously.

AI-augmented smart contracts translate business logic into executable code through LLMs, lowering technical barriers and accelerating deployment. Deutsche Bank’s own pilot with finaXai for tokenized fund servicing illustrates how AI can automate code audits and stress-test logic across multi-jurisdictional workflows.

AI-powered blockchain oracles enhance market-data integrity and anomaly detection, evolving from passive data relays to predictive control layers. The Chainlink–Euroclear–Swift initiative demonstrates how AI-driven oracle networks could synchronise fragmented corporate-action data, cutting validation costs that currently exceed USD 3–5 million annually across custodians.

Tokenized data marketplaces use DLT to enable verifiable AI training through traceable data provenance and federated learning allowing hospitals or banks to train models locally without exposing raw data. This architecture could unlock high-value private datasets while aligning with GDPR, the EU AI Act, and MiCA’s data-governance provisions.

Agentic AI with blockchain wallets gives autonomous systems controlled financial agency. By combining multi-signature wallets with programmable limits, these agents could conduct low-value transactions independently, laying the groundwork for AI-to-AI commerce within a USD 36 trillion digital-payments economy, from automated insurance claims to real-time treasury optimization

Yet convergence amplifies regulatory tension.