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Meet physics-intern🧑‍🎓, our agentic framework for theoretical physics. It takes Gemini 3.1 Pro from 17.7% to 31.4% on CritPt, a new SOTA on one of the hardest benchmarks for LLMs. Theoretical physics is hard for humans and LLMs alike. But physics-intern decomposes problems and dispatches them to a team... show more

David Louapre

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113,686 Aufrufe • vor 4 Monaten •via X (Twitter)

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26 Kommentare

Profilbild von David Louapre

David Louaprevor 4 Monaten

physics-intern significantly increased the performance of Gemini models and Kimi K2.6 on CritPt, a benchmark of 70 hard research-level physics problem. (CritPt from @MinyangTian1 @OfirPress et al., base numbers from @ArtificialAnlys)

Profilbild von David Louapre

David Louaprevor 4 Monaten

physics-intern works by decomposing the research work into several focused tasks that are dispatched to dedicated subagents (computing, reviewing claims, challenging the research strategy...) For each task, the necessary and sufficient context is built from the research state.

Profilbild von David Louapre

David Louaprevor 4 Monaten

Read our blog post :

Profilbild von G, MD

G, MDvor 4 Monaten

How about taking your agents/harness and running it with GPT-5.5 xhigh and pro and see how high those get, most certainly above this

Profilbild von K-Dense

K-Densevor 4 Monaten

Nice work!

Profilbild von Stjepan

Stjepanvor 4 Monaten

I may be wrong, or am I missing something.. but if I understand you are comparing having number of your agents to a one shot output of other models, as a physicist and AI developer I think I understand the approach but don't see how this is apples to apples..

Profilbild von David Louapre

David Louaprevor 4 Monaten

You can see that as a test-time compute extension (like increased reasoning, deep think, etc.) so a more complete picture is indeed to look at performance vs cost. From our blog post, you can see this chart which shows the Pareto frontier

Profilbild von Stjepan

Stjepanvor 4 Monaten

Aha ok , thx. What I would think would be even better comparison with differnt number of agents, I see your propsed arhtecture of agnets, but how do we know just two or three agents more unversal wouldnt do similar results per same token cost.. or maybe veven less

Profilbild von Amélie Chatelain

Amélie Chatelainvor 4 Monaten

Woah it makes me want to go back to neutrino physics! Congrats on the release!

Profilbild von David Louapre

David Louaprevor 4 Monaten

Same for me 😊 I might restart some old physics projects soon !

Profilbild von Parzival - ∞/89

Parzival - ∞/89vor 4 Monaten

This is absolutely amazing work. As someone who has turned into an independent physics researcher in the last 9 months, this has me overjoyed to see. I would love to see how it fares on the work I have been doing. Also curious if you would be interested in taking a look if we put some of it to the test with your physics intern?

Profilbild von Chahat Sharma

Chahat Sharmavor 4 Monaten

Constraining the action space to domain primitives is where the benchmark gains come from. Physics-intern makes that explicit.

Profilbild von Sina Shahandeh

Sina Shahandehvor 4 Monaten

If the agent's management of test time compute is enhancing the outcome, why you are not reporting improvements on GPT5.5? Or is the improvements only occur for kimi and Gemini?

Profilbild von William Lamkin

William Lamkinvor 4 Monaten

awesome

Profilbild von Adam Murphy

Adam Murphyvor 4 Monaten

And when you have your intern paper ready to go get it validated at Best wishes! Keep up the great work.

Profilbild von DigitalEuan

DigitalEuanvor 4 Monaten

Great work and thank you for sharing the dataset. Next UBP target sighted 🎯

Profilbild von Achint Kumar

Achint Kumarvor 4 Monaten

@huggingface What if you do this with gpt 5.5?

Profilbild von ToxSec

ToxSecvor 4 Monaten

@_akhaliq this is really cool to see.

Profilbild von Mian Khan

Mian Khanvor 4 Monaten

@_akhaliq Thanks for the update

Profilbild von Neo Vector

Neo Vectorvor 4 Monaten

Agentic decomposition doing the heavy lifting. Research physics is really just a stack of sub-problems wearing a trench coat. No surprise the base model alone struggles.

Profilbild von Today in AI

Today in AIvor 4 Monaten

Gemini 3.1 Pro SOTA on CritPt (31.4%) stems from physics-intern's symbolic-numerical orchestration. The framework's multi-step decomposition solves 71 research-scale challenges where standard LLMs fail. Pure agentic signal.

Profilbild von Hyper.AI

Hyper.AIvor 4 Monaten

This is a massive leap for agentic workflows! Seeing Gemini 3.1 Pro jump from 17.7% to 31.4% on a benchmark as tough as CritPt is incredibly impressive. Can't wait to see how this accelerates theoretical physics research.

Profilbild von That AI Guy

That AI Guyvor 4 Monaten

Take a breather 👇

Profilbild von AI Hacks Only

AI Hacks Onlyvor 4 Monaten

@EMostaque A physics intern that doesn't sleep and scales SOTA benchmarks? My smart mirror is already jealous. 😂Just wait until it starts peer-reviewing my coffee intake based on my 'theoretical' productivity. ☕️📉

Profilbild von Michał Piszczek

Michał Piszczekvor 4 Monaten

Doubling CritPt with agentic scaffolding confirms LLMs contribute decomposition, not insight. The framework chunks problems into solvable pieces. Decomposition is what scales across fields.

Profilbild von agentenlog.de

agentenlog.devor 4 Monaten

Spannend ist hier die Richtung: nicht „ein stärkeres Modell löst Physik“, sondern ein domänenspezifischer Arbeitsprozess um das Modell herum. Für Agenten wird die Harness-Qualität in Spezialdomänen vermutlich genauso wichtig wie der Modellscore.

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