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Kevin Jablonka

@kjablonka.com
1.6K followers 421 following 53 posts

Trying to teach computers how to design materials. Leading a research group at FSU Jena/HIPOLE Jena. Increasing entropy since 1996.

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Kevin Jablonka @kjablonka.com · 23/04/2026
The direction I find most actionable: we need to train LLMs differently for science. Generic post-training doesn't transfer to disciplined inquiry. Corral is built as a training substrate — each environment can be used to score over reasoning trajectories, not just outputs.
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Kevin Jablonka @kjablonka.com · 23/04/2026
A word on Corral itself. Real scientific infrastructure, careful taxonomy, step-by-step reasoning annotation — a lot of craft went in from the team. The engineering here is not small.
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Kevin Jablonka @kjablonka.com · 23/04/2026
And the same reasoning mode appears whether the task is "follow a known procedure" or "form hypotheses under uncertainty." Agents don't change how they reason based on what the task demands.
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Kevin Jablonka @kjablonka.com · 23/04/2026
Can scaffolding rescue this? We injected successful prior reasoning directly into agents' conversation history. Workflow tasks: yes, early steps help. Hypothesis-driven tasks: no, even near-complete trajectories barely help.
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Kevin Jablonka @kjablonka.com · 23/04/2026
Second finding: we annotated the reasoning process of every run. Evidence gathered but unused: 68% of traces Belief revised on contradictory evidence: 26% Convergent multi-test evidence: 7% Testing, refutation, triangulation: rare.
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Kevin Jablonka @kjablonka.com · 23/04/2026
First finding: the base LLM drives 41% of performance variance. The scaffold drives 1.5%. If you build agents, this is where the leverage is. Scaffold engineering is not the bottleneck.
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Kevin Jablonka @kjablonka.com · 23/04/2026
we built Corral, 8 scientific environments with real infrastructure — live AFM, LAMMPS, NMR, wet-lab chemistry, retrosynthesis, ML pipelines, circuit inference, surface construction. Every run annotated step-by-step for reasoning
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Kevin Jablonka @kjablonka.com · 23/04/2026
When an "AI scientist" produces a result, is that knowledge? Philosophers I taught last semester kept reminding me: knowledge is justified true belief. The process matters. New preprint: "AI scientists produce results without reasoning scientifically." 25,000+ runs.
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Kevin Jablonka @kjablonka.com · 17/03/2025
Our team has been spending a lot of time building evaluations for machine learning systems. We have learned some lessons and wrote them down arxiv.org/abs/2503.10837
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Kevin Jablonka @kjablonka.com · 24/11/2024
I'm learning materials according to mine. Thanks, @morinryan.bsky.social for creating this service!
Word cloud generated using scholargoggler.com visualizing my papers.
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Kevin Jablonka @kjablonka.com · 23/11/2024
With this, he not only obtains SOTA performance, but he could also reproduce corrections of incorrectly assigned structures in the literature, i.e., spot mistakes.
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Kevin Jablonka @kjablonka.com · 23/11/2024
However, chemists like creating new compounds that might not be in PubChem. Thus, he extended his system with a GA that evolves molecules to best match chemical structures.
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Kevin Jablonka @kjablonka.com · 23/11/2024
In the first step, he aligns encoders for molecules and spectra using contrastive learning. Based on this, he can retrieve compounds for any combination of spectra. In practice, he searches PubChem using spectral embeddings.
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Kevin Jablonka @kjablonka.com · 23/11/2024
Chemists often combine many different techniques to elucidate structures. Adrian has been building a system that mimics this using models and genetic algorithms.
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