Thank you! I'm quite happy with the core_eval.py rewrite. I wanted to evaluate my base model with the DCLM "core score" as described in their paper, but what felt like it should surely be a simple thing of ~300 lines of code actually required me to pip install and depend on a
MACHINE LEARNING
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Memory’s Role in AGI Development Race
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Memory is bigger than agents, whoever solves this wins the race to AGI
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DiT Convergence: Width Over Depth Matters
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Diffusion Transformers struggled at first. Why? Their width was too small for RAE’s high-dimensional latents. The fix: scale width ≥ latent dimension. Once model width ≥ 768, DiT started converging instantly. Depth didn’t matter width unlocked training stability.
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RAEs improve diffusion models over VAEs
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Today, most diffusion models still use VAEs built on 2021 tech. They compress images into low-dimensional latents (like 4 channels). That’s why diffusion models lose global structure and texture fidelity. RAEs fix this by encoding rich semantic features directly from pretrained
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Open datasets trend enables independent model training
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I love the diversity of trending open datasets these days. There’s no excuse anymore not to train your own models! – Fineweb and a shuffle of it by @karpathy – Webscale-RL, a large-scale reinforcement learning dataset from @salesforce – SVQ, an audio dataset from @Google –
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Importance of In-Context Learning in AI Reasoning
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Why would in-context learning be that important in AI? (i'm not talking about just "storing new info", which could be achieved for LLMs by enriching prompt, but about "updating reasoning processes") Like, for a human, I get it, we are limited by both lifespan and memory size
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Share Insights on LLMs AI Agents and Machine Learning
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If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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alphaXiv Resources Tab Now Available for Research Papers
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Check out http://
alphaXiv.org and click on the resources tab for any paper! -
EgoAgent: First-Person AI Perception and Action Model
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How can we build AI agents that perceive, predict, and act from a first-person view, just like humans?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 18 octobre 2025
Inspired by the human perception-action loop, researchers propose EgoAgent, a unified agent model that simultaneously learns to represent the environment, predict the future,… pic.twitter.com/Gd4JWaZBTRHow can we build AI agents that perceive, predict, and act from a first-person view, just like humans? Inspired by the human perception-action loop, researchers propose EgoAgent, a unified agent model that simultaneously learns to represent the environment, predict the future,
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RBI’s DPIP AI Platform Detects Digital Payment Fraud Prevention
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RBI’s New Weapon- ‘DPIP’ The AI Platform That Will Spot Digital Fraud Before They Can Happen! https://
the420.in/rbi-ai-digital
-payment-fraud-detection-dpip/
… via @The420in #RBI #DPIP #fraud #scam #AI #CyberSec #artificial_intelligence @SpirosMargaris @PawlowskiMario @mvollmer1 @gvalan @ipfconline1 @LaurentAlaus