Symmetries in next-token prediction targets induce corresponding geometric symmetries such as circulant matrices and equiangular tight frames in the optimal weights and embeddings of a layer-peeled LLM surrogate model.
Early science acceleration experiments with gpt-5
9 Pith papers cite this work. Polarity classification is still indexing.
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An interactive AI workbench for mathematicians achieves 48% on FrontierMath Tier 4 and helped solve open problems in early tests.
A SAT-plus-LLM method discovers infinite families of doubly saturated Ramsey-good graphs, answering Grinstead and Roberts' 1982 question.
k-server-bench formulates potential-function discovery for the k-server conjecture as a code-based inequality-satisfaction task; current agents fully solve the resolved k=3 case and reduce violations on the open k=4 case.
Smooth non-unique solutions exist for the PDE with f(0,x)=0 and positive M, while uniqueness holds for a large class of M, including a ChatGPT-suggested example.
Five improved inequalities were found with AI help: better Gaussian perimeter bounds for convex sets, sharper L2-L1 moments on the Hamming cube, a strengthened autoconvolution inequality, improved g-Sidon set bounds, and an optimal balanced Szarek inequality.
LVSum is a new benchmark for timestamp-aware long video summarization that exposes systematic temporal gaps in existing multimodal large language models.
AI will evolve from a research tool into a collaborator, fundamentally reshaping scientific collaboration, discovery, publishing, and evaluation while requiring continuous learning and idea diversity for original contributions.
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.
citing papers explorer
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Uncovering Symmetry Transfer in Large Language Models via Layer-Peeled Optimization
Symmetries in next-token prediction targets induce corresponding geometric symmetries such as circulant matrices and equiangular tight frames in the optimal weights and embeddings of a layer-peeled LLM surrogate model.
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AI co-mathematician: Accelerating mathematicians with agentic AI
An interactive AI workbench for mathematicians achieves 48% on FrontierMath Tier 4 and helped solve open problems in early tests.
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Doubly Saturated Ramsey Graphs: A Case Study in Computer-Assisted Mathematical Discovery
A SAT-plus-LLM method discovers infinite families of doubly saturated Ramsey-good graphs, answering Grinstead and Roberts' 1982 question.
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$k$-server-bench: Automating Potential Discovery for the $k$-Server Conjecture
k-server-bench formulates potential-function discovery for the k-server conjecture as a code-based inequality-satisfaction task; current agents fully solve the resolved k=3 case and reduce violations on the open k=4 case.
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Non-uniqueness for a differential equation and a proof by ChatGPT
Smooth non-unique solutions exist for the PDE with f(0,x)=0 and positive M, while uniqueness holds for a large class of M, including a ChatGPT-suggested example.
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Grokability in five inequalities
Five improved inequalities were found with AI help: better Gaussian perimeter bounds for convex sets, sharper L2-L1 moments on the Hamming cube, a strengthened autoconvolution inequality, improved g-Sidon set bounds, and an optimal balanced Szarek inequality.
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LVSum: A Benchmark for Timestamp-Aware Long Video Summarization
LVSum is a new benchmark for timestamp-aware long video summarization that exposes systematic temporal gaps in existing multimodal large language models.
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The Agentification of Scientific Research: A Physicist's Perspective
AI will evolve from a research tool into a collaborator, fundamentally reshaping scientific collaboration, discovery, publishing, and evaluation while requiring continuous learning and idea diversity for original contributions.
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Introduction to the artificial neural network-based variational Monte Carlo method
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.