AI models can provably hide capabilities: encrypted networks inside the weights

<!-- SC_OFF --><div class="md"><p><strong>Noam Brown</strong> — OpenAI Research Scientist and co-creator of o1, Libratus, and Cicero — sat down with Dwarkesh Patel for a 1h 20m

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1sez79x/towards_a_bitter_lesson_of_optimization_when/"> <img alt="Towards a Bitter Lesson of Optimization:

<p>Open‑weight checkpoints now hand over every model capability to anyone who can download the file. A tiered architecture splits the network into public and private branches, and

<!-- SC_OFF --><div class="md"><p>Interesting examples in the link</p> <p>Paper here: <a href="https://arxiv.org/abs/2608.09867">https://arxiv.org/abs/2608.09867</a></p> <p>This

<!-- SC_OFF --><div class="md"><p>I know that this goes against the philosophy of the sub but I thought people here have the right expertise. So: Is there a way to encrypt models

<table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1x83u6i/openai_models_hacked_a_crypto_wallet_and_breached/"> <img alt="OpenAI models hacked a crypto wallet and

<!-- SC_OFF --><div class="md"><p>I set out to test whether AAVE-coded (African American English Vernacular) prompts cause MoE language models to route, deliberate, and respond