Claims Browser

The goal of general intelligence is to adapt to anything, not to be prepared for everything.

"The point is not to be prepared for everything, but to adapt to anything."

Intelligence is the ability to make sense of new problems on the fly, not a-priori competence.

"Not a-priori competence -- intelligence: the ability to make sense of the new problem on the fly."

General intelligence consists in showing intelligence regardless of the problem.

"What makes general intelligence "general" is that, *no matter the problem*, you should show intelligence."

Sam Altman's statements should not be taken at face value

"why do you take everything - or anything - Sam told you at face value?"

Claude models are better at inferring intent from design work where GPT is more rigid and needs more specific feedback for tweaks

"I think Claude models are better at inferring intent from design work where GPT is more rigid so it needs more specific feedback for tweaks."

LLM-generated text now exhibits at least 38 identifiable clichéd patterns

"My LLM cliché highlighter is up to 38 patterns now"

There are now 38 identifiable patterns of clichéd language produced by LLMs

"My LLM cliché highlighter is up to 38 patterns now"

Most machine learning approaches model chemical reactions through de novo generation or heuristic graph edits rather than through electron space transformations

"most machine learning approaches model them either through \textit{de novo} generation of product molecules or through heuristic graph edits that operate directly on molecular topology."

MAELLE naturally recovers mechanistic trajectories that align with known chemistry and can predict side products of a reaction

"because the learned flow operates over the full electron redistribution, MAELLE naturally recovers mechanistic trajectories that align with known chemistry and can predict side products of a reaction."

MAELLE maintains strong performance in out-of-distribution settings where existing methods degrade

"we evaluate robustness across two out-of-distribution settings - structural complexity and reaction type - and find that MAELLE maintains strong performance where existing methods degrade."

MAELLE achieves competitive performance on the USPTO-480K benchmark compared with leading reaction prediction models

"MAELLE achieves competitive performance on the USPTO-480K benchmark compared with leading reaction prediction models."

On DNA-to-amino-acid transduction, the method reduces runtime by several orders of magnitude compared to threshold-pruned beam summing and makes estimating prefix probabilities for long target strings feasible

"On a DNA-to-amino-acid transduction, it reduces runtime by several orders of magnitude relative to threshold-pruned beam summing and makes estimating prefix probabilities for long target strings feasible."

The proposed beam-summing algorithm achieves better compute-variance tradeoff on text and lower error on DNA compared to sequential Monte Carlo baselines

"We evaluate the method on encyclopedic text and DNA against sequential Monte Carlo baselines that resample with replacement. It achieves a better compute--variance tradeoff on text and lower error at the same maximum number of particles on DNA."

Resampling source prefixes without replacement and reweighting by inverse inclusion probability gives an unbiased estimator of target prefix probability in TLMs

"Instead, we resample source prefixes without replacement and reweight each selected prefix by the inverse of its inclusion probability. We show that applying this correction recursively gives an unbiased estimator of the target prefix probability and lets us estimate the mass lost by threshold pruning."

Prior threshold-pruned beam summing methods for TLMs produce lower bounds with unknown error

"Prior work uses a computational shortcut based on source prefix probabilities, then approximates the resulting sum with threshold-pruned beam summing. This produces a lower bound with unknown error."

Transduced language models can compute target prefix probabilities by composing a pretrained source language model with a functional finite-state transducer

"Transduced language models (TLMs) compose a pretrained \emph{source} language model with a functional finite-state transducer to induce a language model over \emph{target} strings."

The Latent Advertiser Mixture Auction (LAMA) mechanism satisfies Markov DSIC and IR properties while achieving near-optimal KL-regularized welfare

"We show that LAMA satisfies Markov DSIC and IR, and achieves near-optimal KL-regularized welfare."

Machine learning fails as a predictor for nitrogen rate optimization in winter wheat, with no model able to recover the best rate within farm tolerance

"On this bench, machine learning fails as a predictor. No model recovers the best rate within farm tolerance, and the benchmark noise shows none can."