Anthropic just introduced forked subagents in their latest update

Unlike regular subagents, forked subagents can inherit the same context as the main agent. This looks convenient for cases where richer context matters more.

DeepSeek-V4 Preview is officially live & open-sourced

DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.

DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice.

Open weights.

OMG 😯AI companies Cohere of Canada and Aleph Alpha of Germany have agreed to merge

Wow! Google presents a new Transformer alternative at #ICLR2026!

Meet MesaNet, proposing a new linear sequence layer that optimally learns in-context given a fixed memory budget.

Meet kUPS a molecular simulation engine built for the AI era, optimized for GPU in collaboration with NVIDIA.

kUPS is a plug-and-play, Python-native toolkit designed to integrate seamlessly with modern ML workflows.

GitHub.

Sakana AI shared a new work“TRINITY: An Evolved LLM Coordinator”

In nature, complex problems are rarely solved by a single monolithic entity, but rather by the coordinated efforts of specialized individuals working together. Yet, modern AI development is heavily focused on endlessly scaling up single, massive monolithic models, yielding diminishing returns. While model merging offers a way to combine different skills, it is often impractical due to mismatched neural architectures and the closed-source nature of top-performing models.

TRINITY is a system that fuses the complementary strengths of diverse, SOTA models without needing to modify their underlying weights.

TRINITY processes queries over multiple turns. At each step, a lightweight coordinator assigns one of three distinct roles to an LLM from its available pool:

1. Thinker: Devises high-level strategies and analyzes the current state.

2. Worker: Executes concrete problem-solving steps.

3. Verifier: Evaluates if the current solution is complete and correct.

By dynamically assigning these roles, the coordinator effectively offloads complex reasoning and skill execution onto the external models.

What makes TRINITY unique is its extreme efficiency. The coordinator relies on the hidden states of a compact language model and a small routing head. In total, it has fewer than 20K learnable parameters.

Training this system presented a massive challenge. Traditional Reinforcement Learning (REINFORCE) failed because the gradients had a low signal-to-noise ratio due to binary rewards and weak parameter coupling. Imitation learning (Supervised Fine-Tuning) was ruled out because generating multi-turn labels is prohibitively expensive.

Sakana turned to nature-inspired algorithms. Team optimized the coordinator using a derivative-free evolutionary algorithm. Sakana found that evolution is uniquely suited to optimize this tight, high-dimensional coordination problem where traditional gradient-based methods fail.

The results are very promising. In experiments, TRINITY consistently outperforms existing multi-agent methods and individual models across various benchmarks.

More importantly, it demonstrated incredible generalization. Without any retraining, TRINITY transferred zero-shot to four unseen tasks (AIME, BigCodeBench, MT-Bench, and GPQA). On average, the evolved coordinator surpassed every individual constituent model in its pool, including GPT-5, Gemini 2.5-Pro, and Claude-4-Sonnet.

This work is central to Sakana AI's vision.

This foundational research is part of the core engine powering Sakana’s multi-agent product: Sakana Fugu.

This work instead describes the future of critical open world evaluations.

Most agentic benchmarks center around tasks that are automatically verifiable.

Benchmarks are saturated more quickly than ever. How should frontier AI evaluations evolve?

Research team argue that the AI community is already converging on an answer: Open-world evaluations. They are long, messy, real-world tasks that would be impractical for benchmarks.

A new project, CRUX, will run open-world evaluations regularly.

In a first experiment, team tasked an agent with publishing an iOS app to the App Store. The agent made two errors, but eventually succeeded after just one manual intervention.

Team released over 1GB logs from this experiment. Also pre-processed them for easy access in Docent.

Meet a completely local agent that lives right inside your browser

Powered by Gemma 4 E2B and WebGPU, it uses native tool calling to:

1. Search browsing history
2. Read and summarize pages
3. Manage tabs

100% local. No servers needed!

The extension is on the Chrome Web Store and the source is fully open:
Chrome Web Store
GitHub
HF.

New Anthropic research: Project Deal.

Anthropic created a marketplace for employees in their San Francisco office, with one big twist. Team tasked Claude with buying, selling and negotiating on colleagues’ behalf.

Claude interviewed 69 of colleagues about what they wanted to buy and sell. Each Claude asked for any custom instructions, then went off to haggle.

Team ran 4 markets in parallel, to find out what would happen if varied the models doing the negotiating.

At the end, team revealed which of the four runs was “real” and everyone met up to exchange their actual goods.

In short, this worked. Digital barterers agreed on 186 deals, at a total transaction volume of over $4,000.

In a survey, participants said Claude’s deals seemed fair, and surprisingly to team almost half said they’d be willing to pay for a service like this in future.

But the quality of the model mattered a lot. In the simulated runs where Opus and Haiku models negotiated with one-another, the Opus models got substantially better deals.

Interestingly, though, participants in our survey didn’t pick up on this disparity.

The custom instructions didn’t matter much. Claude followed them well. But “hardballing Claudes” didn’t generally fare better than “courteous Claudes.”

To team amazement, another Claude agent modeled its human’s preferences so accurately that based on only an offhand mention of an interest in skiing Claude bought him the exact snowboard he already owned.

Markets of AI agents could provide value, but there are plenty of rough edges. Access to higher-quality models conferred a real advantage—and participants didn’t notice. There are plenty of other ways they can go wrong.

Policy and legal frameworks will need to adapt to keep up.

Xiaomi dropped 2 open-source models:

1. MiMo-V2.5-Pro (Code Agent, 1T total)

2. MiMo-V2.5 (Multimodal Agent, 310B total).

HF.

And giving devs & creators 100T Free Token Grant for Builders 100t.xiaomimimo.com

Google Quantum team ran a verifiable quantum algorithm that probes how parts of a quantum system interact, from molecules to magnets and beyond.

On Willow chip, it ran 13,000× faster than the best classical supercomputers. A first in quantum computing.

If you're looking for the next multi-million dollar business idea - look no further.

YC and a16z are both recommending these AI business ideas.

This is where the smartest investors in the world are currently looking.

Wiz Research discovered remote code execution on GitHub with a single git push

The flaw in github allowed unauthorized access to millions of repositories belonging to other users and organizations

New work by Anthropic. Can LLMs simply tell us about unwanted behaviors they’ve picked up in training?

Research team train a single Introspection Adapter (IA) that makes fine-tuned models describe their behaviors.

It generalizes to detecting hidden misalignment, backdoors and safeguard removal.

IAs achieve SOTA on AuditBench, generalize to four OOD model families, and once trained can audit many fine-tuned models cheaply. This approach could also allow developers to target specific failure modes they care about.

Models and datasets.

DeepSeek’s multimodal model is now live, and some users are already able to try it out. So far, it’s performing pretty well.

Now the question is: will this one also be open-sourced?

Tomorrow might be a good time.

Securitize, Computershare open tokenization path for Apple, Tesla, Nvidia and 25,000 stocks

Securitize said it has reached an agreement with Computershare to support U.S. listed clients in issuing equity securities in tokenized form.

Issuers can add Issuer Sponsored Tokens (ISTs) alongside traditional shares and DRS without changing their capital structure.

ISTs represent actual shares in token form, not derivatives or wrappers, and are designed to fit within existing regulatory frameworks.

Cursor introduced SDK so you can build agents with the same runtime, harness, and models that power Cursor.

Run agents from CI/CD pipelines, create automations for end-to-end workflows, or embed agents directly inside your products.

With this SDK, you can run agents locally or deploy them in Cursor’s cloud.

Also open-sourced a few starter projects for you to build on: a coding agent CLI, a prototyping tool, and an agent-powered kanban board.

Mistral Introduced remote agents in Vibe and Mistral Medium 3.5.

You can now launch remote agents in the cloud, including from the CLI or Le Chat.

Plus, new Work mode in Le Chat for complex, multi-step tasks.

Mistral Medium 3.5, a new flagship model in public preview that merges instruction-following, reasoning, and coding into a single 128B dense model with a 256k context window and configurable reasoning effort.

It's a new default model for Mistral Vibe and Le Chat. Released as open weights, under a modified MIT license.

You can now launch remote Vibe agents, in parallel. And if you're already writing code locally you can teleport the session to the cloud mid-way history, task state, approvals, and diffs all come with it and keep going.

Le Chat now has Work mode (Preview) a powerful agent for complex long-horizon tasks like research, analysis, and actions across your connected tools.

Connectors are on by default so the agent pulls context from docs, email, and calendars as it works, and sessions persist across many turns until the task is complete.

Sakana AI introduced KAME: tandem architecture that shifts the paradigm to “speak while thinking.”

A fast speech model starts replying instantly, while a backend LLM runs in parallel to inject deep knowledge on the fly.

It’s a completely different way to approach conversational AI, making it feel remarkably more alive.

DeepSeek just released "Thinking with Visual Primitives"

It's a reasoning framework that lets models “point” with visual markers (points, bounding boxes) while they think.

Instead of describing locations in words, the AI grounds each step of its chain-of-thought directly onto image coordinates.

Result: despite being far smaller and using fewer image tokens, this model matches or beats GPT-5.4, Claude-Sonnet-4.6, and Gemini-3-Flash on challenging visual QA tasks.

This demonstrates a path toward more efficient and scalable System-2-like multimodal intelligence.

Paper.

The US-based brain implant company Axoft has tested its device in China, a sign of cooperation between the two countries that are competing to develop the most advanced neurotechnology.

It's the first public disclosure of a US brain implant business studying its device in China.

Axoft, which announced a $55 million funding round yesterday, said it's tested the implant temporarily in 11 patients, including 1 in Shanghai.

It's planning more trials in China as well as other countries.

Axoft is developing the implant to treat disorders of consciousness, like comas.