It is crazy to me that some still don't see how big our GPU shortage is: – Most context window is <100k – Delayed rollouts of Agents, Codex – Full Sora never released – Veo 3 roll out taking weeks – Claude constant rate limits – Even big clouds default rate limits are
LLMS
-
AI Models Make Major Leaps in International Math Olympiad
By
–
Other AI models seem to have made big leaps in the International Math Olympiad, not just OpenAI. Not all announcements seem to be out yet.
-
Optimizing Reasoning Models Through Prompt Rewriting Fine-tuning
By
–
By this, I mean:
– Take an already-trained, frozen reasoning model (i.e. o4-mini via API)
– Add a smaller LLM that takes in a prompt, and rewrites it to improve how the frozen model performs
– Update the smaller LLM's weights, keep the larger LLM frozen The hope is that the -
Using RL to Optimize Prompts for Reasoning Models
By
–
Has anyone tried RL to rewrite prompts for reasoning models to further improve outputs? I'm assuming so, it feels pretty obvious, but if not I want to try it. If you know of any existing work here, pls lmk so I don't re-do something people have already done!
-
Beyond Compute: Alternative Methods for AI Improvements
By
–
What I’d like to know is how it was done other than by throwing more compute at it. Because the latter is not very interesting.
-
MathArena: Evaluating LLMs on Uncontaminated Math Competitions
By
–
MathArena: Evaluating LLMs on Uncontaminated Math Competitions Balunović et al.: https://
arxiv.org/abs/2505.23281 #ArtificialIntelligence #DeepLearning #MachineLearning -
Attention Mechanisms and Core Deep Learning Components Explained
By
–
Attention is all you need. Oh, and also MLPs, layer norm, resnets, positional encoding, tokenization, Adam, fine-tuning, GPUs, etc.
-
Context Engineering Survey: Optimizing LLM Information Systems
By
–
8. A Survey of Context Engineering for LLMs This survey defines Context Engineering as a formal discipline for optimizing information given to LLMs, outlining its core components, retrieval, processing, and management, and their integration in systems like RAG, memory, and
-
LLMs in AIOps: Survey of 183 Papers and Methods
By
–
10. A Survey of AIOps This survey analyzes 183 papers to evaluate how LLMs are being used in AIOps, focusing on data sources, task evolution, applied methods, and evaluation practices.
-
Scaling Reinforcement Learning for Enhanced Reasoning in Small Models
By
–
6. Scaling up RL This paper investigates how prolonged RL can enhance reasoning abilities in small language models across diverse domains.