Claims Browser

BHI reframes biological-artificial integration as a system-level problem of computational allocation, coordination, and control

"BHI therefore reframes biological-artificial integration as a system-level problem of computational allocation, coordination, and control."

A spiking neural model combined with Bayesian inference principles creates a brain-like control algorithm capable of operating in uncertain environments

"By combining a biologically inspired spiking neural model with Bayesian inference principles, we propose a brain-like control algorithm capable of operating in uncertain environments."

There is an asymmetry in neural networks where forward computation has diversified significantly while scalable learning remains concentrated around backpropagation and related methods

"We call this asymmetry the forward-backward disconnect and develop a taxonomy spanning neural model families along three coupled axes: state-dynamics structure, credit-assignment mechanism, and biological grounding."

Across all neural network families, forward dynamics have diversified while the highest demonstrated scales remain concentrated in global or closely gradient-derived error-propagation mechanisms

"Across static, recurrent, attention-based, state-space, continuous-time, implicit, spiking, biologically plausible, and neuromorphic families, forward dynamics have diversified while the highest demonstrated scales remain concentrated in global or closely gradient-derived error-propagation mechanisms."

The proposed QHAdamW optimizer improves convergence, generalization, and forecasting performance compared to Adam for AQI forecasting using ANNs

"The proposed QHAdamW optimizer addresses the issues on convergence, generalization, and forecasting performance of Adam."

Qwen 3.8 27B is highly enjoyable and fun to play with as a local model

"I can't remember the last time I've had this much fun playing with a local model that runs on my own computers"

Open models will likely fill a long-tail ecosystem focused on efficiency and specialization rather than competing with closed models on the most valuable areas

"I put this mentally as my most likely outcome – open models are still incredibly useful, but fill a long-tail ecosystem relative to the closed counterparts that have monopoly ownership stakes in the most valuable areas like knowledge work collaboration, drug discovery, SWE, etc."

The established pretraining, midtraining, post-training lexicon may shift to pretraining, reasoning training, and post-training

"This could go so far as to change the established pretraining, midtraining, post-training lexicon that has been standard for a few years. It could come to be something closer to pretraining, reasoning training, and post-training."

LLM-driven multi-agent search can discover seismic reconstruction algorithms that outperform classical methods without requiring neural networks at inference time

"Starting from a classical reconstruction algorithm, an LLM-driven multi-agent search modifies only the components that the user has opened for editing, without prescribing the mechanism to be discovered. Candidates that violate the physical constraints of the task are rejected outright, and the remaining ones are scored by execution. The output is neither an agent system nor a neural network, but a standalone white-box algorithm that requires no agent or neural network at inference time."

The evolved POCS algorithm improves SNR by 3.49 dB on average over classic POCS for seismic data interpolation across 30-70% missing ratios

"For interpolation without added noise, the search discovered a residual-gated, phase-aligned dip-consistency projection; Evo-POCS improves the SNR over classic POCS by 3.49 dB on average across missing ratios from 30% to 70%."

The evolved MSSA algorithm improves reconstruction SNR by more than 7 dB over classic MSSA and more than 3 dB over stronger baselines for simultaneous interpolation and denoising

"For simultaneous interpolation and denoising, it discovered a reliability-grouped singular-value shrinkage; Evo-MSSA improves the average reconstruction SNR by more than 7 dB over classic MSSA and by more than 3 dB over a stronger rank-reduction baseline."

This is the first study to formulate seismic reconstruction operator design as a constrained, LLM-driven program evolution task

"To the best of our knowledge, this is the first study to formulate the design of a seismic reconstruction operator as a constrained, LLM-driven program evolution task."

Agentic algorithm evolution can complement deep learning in discovering explicit, inspectable, and deployable algorithms

"Agentic algorithm evolution can thus complement deep learning in discovering explicit, inspectable, and deployable seismic processing algorithms."

Deep learning methods encode reconstruction rules in learned weights that cannot be inspected and modified like explicit operators

"Deep-learning methods encode the reconstruction rules in learned weights rather than in an explicit operator that can be inspected and modified."

LLMs are getting worse at producing inspiring writing as they become more refined as tools rather than conversational assistants

"As LLMs get more refined as tools, rather than conversational assistants, I think we are actually going backwards on our goals of having models produce inspiring writing."

Pangram's AI detection has a false positive rate of 0.0041%, or 1 for every 24,000 instances

"They estimate the FP rate of the last version of the tool (Pangram 4.0, launched recently) at 0.0041%, or 1 for every 24,000 instances."

Models being stagnant in long-form non-fiction writing indicates they will struggle to autonomously solve grand open science problems

"Models being stagnant in long-form, non-fiction writing should be alarming to those reliant on models autonomously solving grand, open science problems in the near future. The models today struggle to organize and compellingly present some of the most established science in their area. This seems like a natural prerequisite that we should expect the models to master before they can solve broad, open-ended problems on their own."

Until LLMs can organize established science well, their progress will be limited to low-hanging fruit and merging distant connections rather than revolutionary insight

"Until this is solved, the progress of LLMs for science will look closer to solving low-hanging fruit and merging distant connections across fields, rather than any sort of revolutionary insight."