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Ken Griffin donates $3B to Carnegie Mellon as university plots Miami campus

ft.com·3 pts·bearseascape·
0

A somewhat optimistic view of AI in mathematics

proofsandprompts.com·1 pts·bearseascape·
0

To Serve Man: AI, Math, and Navier–Stokes

ml5885.github.io·4 pts·bearseascape·
1

Model Spec Midtraining: Improving How Alignment Training Generalizes

alignment.anthropic.com·2 pts·bearseascape·
0

Following the Text Gradient at Scale (2025)

ai.stanford.edu·9 pts·bearseascape·
1

Transformers Are Inherently Succinct (2025)

arxiv.org·62 pts·bearseascape·
9

Slople – Can you tell real ML papers from AI-generated ones?

ml5885.github.io·3 pts·bearseascape·
1

Benchmarking Culture

argmin.net·1 pts·bearseascape·
0

Why one small American town won't stop stoning its residents to death

archiveofourown.org·2 pts·bearseascape·
1

The most complex model we understand [video]

youtube.com·2 pts·bearseascape·
0

Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs

arxiv.org·1 pts·bearseascape·
0

MooseAgent: A LLM Based Multi-Agent Framework for Automating Moose Simulation

arxiv.org·13 pts·bearseascape·
0

Automated Researchers Can Subtly Sandbag

alignment.anthropic.com·2 pts·bearseascape·
0

Auditing Language Models for Hidden Objectives

anthropic.com·1 pts·bearseascape·
0

Policy for LLM Writing on LessWrong

lesswrong.com·2 pts·bearseascape·
0

Towards Understanding Distilled Reasoning Models: A Representational Approach

arxiv.org·3 pts·bearseascape·
0

Transformers Learn to Implement Multistep Gradient Descent with Chain of Thought

arxiv.org·1 pts·bearseascape·
0

(Mis)Fitting: A Survey of Scaling Laws

arxiv.org·2 pts·bearseascape·
0

Resurrecting saturated LLM benchmarks with adversarial encoding

arxiv.org·1 pts·bearseascape·
0

Deep Double Descent: Where Bigger Models and More Data Hurt

openai.com·2 pts·bearseascape·
0

Value-Based Deep RL Scales Predictably

arxiv.org·68 pts·bearseascape·
3