@kaggle
Account based inUnited States!
About this account
- Account based in
- United States
- Connected via
- Web
X says this location may be affected by a proxy or VPN.
Account-level information from X, not a live location or the device used for a specific post.
The leading platform for crowdsourced benchmarks, competitions, and hackathons where the community shapes real-world progress in AI.
San Francisco
Joined October 2009
- Tweets5.7K
- Following297
- Followers321K
- Likes3.2K
Could we get more from a quantum computer by asking it to do less? ⚛️
Verifiable Quantum Circuit Compilation, hosted by @GoogleQuantumAI and Kaggle, is live! Build search and AI methods to compile quantum circuits for real-world bottlenecks in chemistry, fusion energy, and numerical analysis.
💰 Prize Pool: $150,000
⏰ Entry Deadline: January 13, 2027
Your work can directly lower the threshold for useful quantum computing.
Join the competition here: kaggle.com/competitions/goog…
Read more about the open source verification tool behind the competition and its three benchmarks in Tanuj Khattar and Ryan Babbush's post on the Google Open Source Blog (@GoogleOSS):
goo.gle/kaggle-quantum-comp
How tightly can you pack (n) squares into a square? 📦
Lining squares up in a grid often wastes space, and tilting just a few of them at the right angle can make the box smaller. In Squares in Squares, you'll pack 1 to 1,000 unit squares into the smallest square container you can. Only 59 of those puzzles have proven best answers, so the rest are open.
💰 Prize Pool: $50,000
⏰ Entry Deadline: July 11, 2027
🏆 Prize Milestones: At 3, 6, and 9 months
This is a well-studied open problem in combinatorial geometry: the best-known solutions come from decades of work by mathematicians and programmers.
Join the competition here: kaggle.com/competitions/squa…
Can frontier models actually predict how real people behave? 🕺
Today, in collaboration with @RunRehearsals, we are launching Human Behavior Bench, the first public benchmark that tests whether AI can understand human preference and everyday decision-making.
AI models have gotten increasingly good at coding, but knowing for whom to build is the next frontier of challenges for AI models to capture. Human Behavior Bench evaluates models on predicting real human behavior across health, entertainment, recreation, values, and consumption.
To build this baseline, we gathered ground truth data directly from real people and evaluated models across a wide range of everyday scenarios:
• Ground truth baseline: 1,244 US adults surveyed (June-Sept 2026)
• Some cohorts are traditional demographic indicators such as age, income, and location
• Other filters are behavioral e.g. GLP-1 patients and recent car buyers
• 750 cases total (149 public / 601 private) across finance, health, recreation, values, and consumption
• Scored using 1-TVD (total variation distance)
Base models still have a significant gap to close to accurately predict human behavior by cohort. LLMs’ predictions tend to converge towards the mean, while the true distribution of human preferences vary greatly.
Against a human noise ceiling of ~93, the current leaderboard scores are: Rehearsals’ Engram 1.0 (AI Twins) tops the leaderboard at 94.1%, followed by Gemini 4 Argon at 80.6% Claude Opus 5 at 80.2%, GPT-5.6 Sol at 79.5%.
ALT A leaderboard graphic from Kaggle titled "Rehearsals: Human Behavior Bench". The ranking chart lists nine AI models and their corresponding scores, measured on a scale from 70% to 85%. The models are ranked as follows: 1. Gemini 4 Argon with a score of 80.6% 2. Claude Opus 5 with a score of 80.2% 3. GPT-5.6 Sol with a score of 79.5% 4. Claude Opus 5.5 with a score of 79.3% 5. Gemini 3.8 Flash with a score of 79.2% 6. Gemini 3 Flash Preview with a score of 78.5% 7. GPT-5.6 Terra with a score of 77.8% 8. Gemini 3.5 Flash-Lite with a score of 75.9% 9. Grok 4.7 with a score of 75.2% The background features a subtle gradient with warm yellow and beige tones, and the source is cited at the bottom as the Human Behavior Bench Leaderboard on Kaggle.
Off-the-shelf models perform well on the general population (scoring around 85), but fail when predicting deeper, specific cohorts (scoring closer to 55-60) where they drift back to the "average American."
Where base models struggle most:
• Hypothetical reactions (e.g., pricing increases or medical decisions)
• Abstract value sentiments and attitudes
• Testing novel concepts or products
Where the accuracy impacts understanding real people:
• 55–64 year olds in the Northeast, after a $4/mo streaming price hike: 70% of people actually keep their plan. But every model predicts that only 36–47% would.
• Older Midwest shoppers paying by phone/watch: 25% in reality. Every model says 6% or less.
• Rural drivers who renew car insurance without shopping around: 24% in reality. Every model claims around 50%.
Ready to identify molecules from mass spectra in the Enveda CASMI 2026 competition? 🧪
Check out this starter notebook by haideptry. It walks you through candidate retrieval and reranking across four evidence channels, and is a great starting point to search spectral libraries, match structural analogs, simulate fragmentation, and rank candidates with a neural fingerprint model.
kaggle.com/code/haideptry/en…
Which model outputs the best pelican riding a bike? 🪽🚲
Inspired by @simonw's pelican test, models generate an SVG and humans rate the results. GPT-6 Astra currently leads, with Gemini 4 Argon close behind.
Vote now for the best one at kaggle.com/benchmarks/kaggle…
We’ve teamed up with Google DeepMind and Zapier to host the inaugural Bench Summit by Kaggle.
Gain hands-on insights from the AI startups and researchers defining today’s evaluation standards, and get closer to the frontier.
Secure your spot or apply to speak:
rsvp.withgoogle.com/events/b…
ALT Announcement graphic from Kaggle for the Bench Summit. The image has a light cream background with a stylized, artistic illustration of a mountain peak overlaid with yellow contour lines and subtle geometric wire frame arrows pointing upward. Logo at the top reads "kaggle." Text at the bottom includes a red pin icon for the location "San Francisco", a calendar icon for "October 28, 2026", and the main heading: "Shape the next generation of evaluation @ Bench Summit".
Can you identify which LLM wrote a passage from the text alone? 👀
Each AI lab leaves behind distinct stylistic markers in vocabulary, phrasing, and punctuation. In Name That LLM, you'll identify which of 18 leading models from 6 organizations (Anthropic, Google, Moonshot AI, OpenAI, Qwen, and ZAI) wrote each unlabeled passage.
💰 Prize Pool: $50,000
⏰ Entry Deadline: January 6, 2027
Your solutions will show how reliably today's frontier models can be fingerprinted from short text alone, helping researchers audit open benchmarks and understand what makes each model family distinct.
Enter the competition here: kaggle.com/competitions/name…
Huge congratulations to Francis Halzen on the Nobel Prize, a landmark moment in astrophysics! 🏆
It was an honor for our Kaggle community to collaborate with the IceCube Neutrino Observatory in 2023, building open-source ML models to help predict the direction of neutrinos.
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2026 #NobelPrize in Physics to Francis Halzen “for decisive contributions to the IceCube Neutrino Observatory and the discovery of high-energy neutrinos of astrophysical origin.”
Check out the 2023 competition here: kaggle.com/competitions/icec…
Look beyond conventional scouting wisdom: your submission could help coaches and evaluators identify hidden talent, avoid costly draft busts, and design position-specific player development protocols.
Join the Open or University Track here: kaggle.com/competitions/nfl-…
