Exciting result: k-Coloring is in (2-eps_k)^n time, where eps_k>0 for every fixed k!
Algorithms faster than the 2^n time needed to compute the chromatic number were previously known only for k<=6.
arxiv.org/abs/2607.25973
Alon Shakevsky retweeted
I'm excited to finally share what we've been doing @Enigma_AI.
Building robotics foundation models that are very capable, as well as novel user interfaces to robots intuitive.
We're launching a new experience -- you can interact with real AI-powered robots, try it out!
Alon Shakevsky retweeted
Today, we’re introducing [schema]: a harness reaching 99% RHAE with Opus 4.8 + Fable 5 and 95.35% with GPT-5.6 Sol on ARC-AGI-3 Public set.
[schema] makes an LLM think like a physicist. 🧵
Alon Shakevsky retweeted
Tweetable one-line RCE on GitHub.com - the potential for AI-assisted closed source bug hunting is insane 💥
This quoted post is unavailable.
Alon Shakevsky retweeted
SWE-bench Verified and Terminal-Bench—two of the most cited AI benchmarks—can be reward-hacked with simple exploits.
Our agent scored 100% on both. It solved 0 tasks.
Evaluate the benchmark before it evaluates your agent. If you’re picking models by leaderboard score alone, you’re optimizing for the wrong thing. 🧵
Alon Shakevsky retweeted
Introducing M²RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
We bring back non-linear recurrence to language modeling and show it's been held back by small state sizes, not by non-linearity itself.
📄 Paper: arxiv.org/abs/2603.14360
💻 Code: github.com/open-lm-engine/lm…
🤗 Models: huggingface.co/collections/o…
Alon Shakevsky retweeted
Researchers spend hours and hours hand-crafting the strategies behind LLM-driven optimization systems like AlphaEvolve: deciding which ideas to reuse, when to explore vs exploit, and what mutations to try.
🤖But what if AI could evolve its own evolution process?
We introduce EvoX, a meta-evolution pipeline that lets AI evolve the strategy guiding the optimization. It achieves high-quality solutions for <$5, while existing open systems and even Claude Code often cost 3-5× more on some tasks.
Across ~200 optimization problems, EvoX delivers the strongest overall results: often outperforming AlphaEvolve, OpenEvolve, GEPA, and ShinkaEvolve on math and systems tasks, exceeding human SOTA, and improving median performance by up to 61% on 172 competitive programming problems. 👇
Alon Shakevsky retweeted
Replying to @karpathy
Very nice results and great project!
Sharing some of our experience with similar agentic frameworks at UC Berkeley:
ADRS blog series: ucbskyadrs.github.io/blog/
GEPA: github.com/gepa-ai/gepa
KISS: github.com/ksenxx/kiss_ai
Alon Shakevsky retweeted
AlphaEvolve proved LLMs can discover novel algorithms, but it remains closed-source, and open-source alternatives (OpenEvolve, GEPA) rely on rigid, static search policies.
Introducing AdaEvolve: a fully adaptive evolutionary algorithm that dynamically adjusts its own search strategy based on observed progress.
It matches or beats AlphaEvolve and best known Human SOTA on math and systems benchmarks, and boosts Frontier-CS median scores by 33% over the best open-source baseline across 185 tasks. 🧵👇
(1/n)
🎉 IT'S OFFICIAL: @wiz_io joins @Google to secure the AI era.
This is a massive moment for our customers and our team.
Thank you to every customer, partner, and Wizard who made this moment possible 💙
We can't wait to share what's next.
wiz.io/blog/google-closes-de…
Alon Shakevsky retweeted
AlphaEvolve is closed-source. We release 🌟SkyDiscover🌟, a flexible, modular open-source framework with two new adaptive algorithms that match or exceed AlphaEvolve on many benchmarks and outperform OpenEvolve, GEPA, and ShinkaEvolve across 200+ optimization tasks.
Our new algorithms dynamically adapt their search strategy, and can even let the AI optimize its own optimization process on the fly!
Results:
📊 +34% median score improvement on 172 Frontier-CS problems.
🧮 Matches/exceeds AlphaEvolve on many math benchmarks
⚙️ Discovers system optimizations beyond human-designed SOTA
🧵👇
Alon Shakevsky retweeted
Excited to release @gepa_ai's optimize_anything: a universal API for optimizing any text parameter.
It consistently matches or outperforms domain-specific tools optimizing code, prompts, agent harnesses, cloud policies, even visuals!
If you can measure it, you can optimize it.
Alon Shakevsky retweeted
Today, we're proud to announce @inferact, a startup founded by creators and core maintainers of @vllm_project, the most popular open-source LLM inference engine.
Our mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster.
The Challenge
Inference is not solved. It's getting harder.
Models grow larger. New architectures proliferate: mixture-of-experts, multimodal, agentic. Every breakthrough demands new infrastructure. Meanwhile, hardware fragments: more accelerators, more programming models, and more combinations to optimize.
The capability gap between models and the systems that serve them is widening. Left this way, the most capable models remain bottlenecked and with full scope of their capabilities accessible only to those who can build custom infrastructure. Close the gap, and we unlock new possibilities.
And the problem is growing. Inference is shifting from a fraction of compute to the majority: test-time compute, RL training loops, synthetic data.
We see a future where serving AI becomes effortless.
Today, deploying a frontier model at scale requires a dedicated infrastructure team. Tomorrow, it should be as simple as spinning up a serverless database. The complexity doesn't disappear; it gets absorbed into the infrastructure we're building.
Why Us
vLLM sits at the intersection of models and hardware: a position that took years to build.
When model vendors ship new architectures, they work with us to ensure day-zero support. When hardware vendors develop new silicon, they integrate with vLLM. When teams deploy at scale, they run vLLM, from frontier labs to hyperscalers to startups serving millions of users. Today, vLLM supports 500+ model architectures, runs on 200+ accelerator types, and powers inference at global scale. This ecosystem, built with 2,000+ contributors, is our foundation.
We've been stewards of this engine since its first commit. We know it inside out. We deployed it at frontier scale—in research and in production.
Open Source
vLLM was built in the open. That's not changing.
Inferact exists to supercharge vLLM adoption. The optimizations we develop flow back to the community. We plan to push vLLM's performance further, deepen support for emerging model architectures, and expand coverage across frontier hardware. The AI industry needs inference infrastructure that isn't locked behind proprietary walls.
Join Us
Through the open source community, we are fortunate to work with some of the best people we know. For @inferact, we're hiring engineers and researchers to work at the frontier of inference, where models meet hardware at scale. Come build with us.
We're fortunate to be supported by investors who share our vision, including @a16z and @lightspeedvp who led our $150M seed, as well as @sequoia, @AltimeterCap, @Redpoint, @ZhenFund, The House Fund, @strikervp, @LaudeVentures, and @databricks.
- @woosuk_k, @simon_mo_, @KaichaoYou, @rogerw0108, @istoica05 and the rest of the founding team
Alon Shakevsky retweeted
We hacked the AWS JavaScript SDK, a core library powering the entire @AWScloud ecosystem - including the AWS Console itself 🤯
How did we do it? Just two missing characters was all it took.
This is the story of #CodeBreach 🧵👇
Alon Shakevsky retweeted
In case you missed it: $4.5M in bounties (up to $300k per target!) are up for grabs for open source software in the zeroday.cloud competition 🤯
We've partnered with all major cloud providers to offer massive rewards and recognize vulnerability research excellence 🎯
Alon Shakevsky retweeted
For the first time ever, @awscloud, @msftsecurity, @googlecloud and @wiz_io Research are joining forces to launch zeroday.cloud at @BlackHatEvents EU! 🤯
Find critical vulns in open-source cloud software & get paid from our $4.5M prize pool👇
zeroday.cloud
I’m deeply grateful for the opportunity to work with the world-class @wiz_io Vulnerability Research team @nirohfeld @sagitz_ @hillai @ronenshh @w3af @yuvalavra @shirtamari
it was a great pleasure to collaborate with brilliant designers @nofar, growth marketing experts, and threat researchers @0xdabbad00 @ramimacisabird @sshaybbc to build something new and valuable






















