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🧵 1/7 The real bottleneck in frontier research isn’t information. It’s judgment: what do you trust when the evidence is incomplete, conflicting, and expensive to validate? Recently, Apodex-FutureFlow ( Apodex )—driven by their open 35B model as its backbone—ranked #1 on the FutureX prediction leaderboard for 3 of the...

45,339 Aufrufe • vor 5 Tagen •via X (Twitter)

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Haaland finished his World Cup with seven goals. Messi and MbappĆ© are now level on eight, with one match left each. So I asked Apodex Apodex a simple question with a not-so-simple answer: who wins the Golden Boot? It first gave Messi a 65% chance, MbappĆ© 25%, and the field 10%. But the useful part wasn’t the pick. It was what happened when I challenged the reasoning. I asked Apodex to audit its own forecast. It found that the first analysis had underweighted the difficulty of Messi’s final, while not fully translating the more open—but rotation-sensitive—third-place match into Mbappé’s chances. The result: Messi fell from 65% to 61%, while MbappĆ© rose from 25% to 29%. I then made it remove every claim that couldn’t be traced to reliable sources. Using only five verified inputs—goals, assists, minutes played, FIFA’s tiebreak rules, and the remaining fixtures—it settled at: Messi: 60% MbappĆ©: 30% Others: 10% The core logic is surprisingly clean: both have eight goals, but Messi leads 4–3 in assists. If they finish level on goals and that assist gap remains, Messi wins. Mbappé’s clearest route is to outscore him. That’s what made this test interesting. Apodex didn’t pretend to know the future—it exposed the evidence, the uncertainty, and the exact reason its confidence changed. This evidence-driven approach has also been validated by Apodex’s recent performance on the FutureX forecasting benchmark. Timestamped on July 16. I’ll come back after the final and check the result. This is a probability forecast based on current evidence, not betting advice. Try it:

Ashlyn He

55,580 Aufrufe • vor 8 Tagen

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - ā€œimportant discovery per joulesā€ - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 Aufrufe • vor 2 Monaten