A Statistical Physics of Language Model Reasoning Jack David Carson, Amir Reisizadeh: https://
arxiv.org/abs/2506.04374 #ArtificialIntelligence #DeepLearning #MachineLearning
MACHINE LEARNING
-
Statistical Physics Framework for Language Model Reasoning
By
–
-
DLBacktrace: Model-Agnostic Explainability Method for LLMs
By
–
Just dropped: Our webinar on DLBacktrace (DLB) — a model-agnostic explainability method for LLMs & deep learning models.
— Arya.ai (@arya_ai1) 9 juin 2025
✅ Works across any architecture
✅ No model retraining needed
✅ Designed for compliance & audit use-cases
📺 Watch now → https://t.co/3Dfq5sI6sg pic.twitter.com/jqrbx0jJgOJust dropped: Our webinar on DLBacktrace (DLB) — a model-agnostic explainability method for LLMs & deep learning models. ✅ Works across any architecture ✅ No model retraining needed ✅ Designed for compliance & audit use-cases 📺 Watch now → aryaxai.com/videos/inside-th…
-
Understanding Gaussian Splatting: Technical Explanation and Applications
By
–
Y si no conocéis qué es esto de los Gaussian Splatting no dejéis de ver este vídeo en el que explico la base de su funcionamiento!
-
Diffusion Transformer Designs via Grafting Method
By
–
Exploring Diffusion Transformer Designs via Grafting
Paper: https://
arxiv.org/pdf/2506.05340
v1.pdf
…
Project Page: https://
grafting.stanford.edu -
Grafting: Efficient Diffusion Transformer Architecture Redesign
By
–
Grafting: Efficiently Redesigning Diffusion Transformers with Pretrained Models Stanford and Liquid AI propose Grafting — a lightweight, low-cost method for exploring new model architectures using pretrained diffusion transformers (DiTs), without full retraining. Instead of
-
Text-to-LoRA: Instant Transformer Model Adaptation
By
–
Text-to-LoRA: Instant Transformer Adaption
Paper: https://
arxiv.org/pdf/2506.06105
.pdf
…
Code: https://
github.com/SakanaAI/text-
to-lora
… -
Reasoning Models Limitations: Understanding Problem Complexity Trade-offs
By
–
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Paper: https://
ml-site.cdn-apple.com/papers/the-ill
usion-of-thinking.pdf
… -
Logistic Regression Matches Frontier Models for Japanese Financial Fraud Detection
By
–
Our finding is that Logistic Regression is still a very strong baseline for detecting fraudulent Japanese financial statements, matching frontier models like Claude3.7, R1, o4-mini. Room for future improvement! GitHub: https://
github.com/SakanaAI/EDINE
T-Bench
…
HuggingFace: https://
huggingface.co/datasets/Sakan
aAI/EDINET-Bench
… -
EDINET-Bench: Evaluating LLMs on Japanese Financial Tasks
By
–
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements Paper: https://
pub.sakana.ai/edinet-bench/ We released a Japanese financial benchmark on @Huggingface
, designed to evaluate the performance of LLMs on financial tasks like fraud detection in Japan. -
AI Performance Over Human Reasoning: Beyond Traditional Thinking Models
By
–
Contrast this with the Illusion of Thinking paper from Apple. In the end, I don’t think it will matter if what the AI is doing fits into traditional understanding of reasoning / thinking. When it’s better than the best humans, how it does it will be irrelevant.