The Ledger

Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.

clear all ✕

why aren't all 5 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable

Insight
Beam: Science acts as an infinite token generator for AI
“Science is as an infinite token generator to train models at scale.”
Andy Beam Jul 16, 2026 ▶ 2:46 🔬 RL with Verifiable Rewards, but the Verifier is a Lab — Lila Sciences
Insight
Beam: Chain of thought is an unreliable narrator of model computation
“It actually thinks in latent space, it emits tokens. So, like, the chain of thought is often an unreliable narrator for what the model, the computation of the model is actually doing.”
Andy Beam Jul 16, 2026 ▶ 27:51 🔬 RL with Verifiable Rewards, but the Verifier is a Lab — Lila Sciences
Insight
Beam: Verified scientific reasoning traces lift models despite parameter disadvantages
“So like we have just seen incredible lift from showing the model that even if we're at like a parameter disadvantage relative to the frontier models, just showing it an experimentally verified reasoning, reasoning trace, you see just immediate lift when we do …”
Andy Beam Jul 16, 2026 ▶ 1:24:12 🔬 RL with Verifiable Rewards, but the Verifier is a Lab — Lila Sciences
Insight
Beam: AI safety cannot wait because capability curves are sigmoid-shaped
“Safety is not something you can procrastinate on because capability curves tend to be sigmoid shaped and it can look like everything's fine and then all of a sudden there's something that you didn't Anticipate the model being able to do.”
Andy Beam Jul 16, 2026 ▶ 16:35 🔬 RL with Verifiable Rewards, but the Verifier is a Lab — Lila Sciences
Insight
Beam: Cross-domain training reduces domain data requirements in science models
“And so again, the core bet that we're making is that is true for science. That if the model is trained on an increasingly broad set of data, the amount of data that you need in a given domain, that data requirement is reduced. In some cases will be reduced to …”
Andy Beam Jul 16, 2026 ▶ 30:20 🔬 RL with Verifiable Rewards, but the Verifier is a Lab — Lila Sciences
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 200 episodes transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.