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AI Ecosystem Intelligence Explorer

3D
AI Detection
AI Fundamentals

21 of 182 articles

AI Responses May Include Mistakes

The other day I wanted to look up a specific IBM PS/2 model, a circa 1992 PS/2 Server system. So I punched the model into Google, and got this:

Harm and Risk
AI Fundamentals
 
5/31/2025

Wan2.1 14B 480p I2V LoRAs - a Remade-AI Collection

A collection of Remade’s Wan2.1 14B 480p I2V LoRAs

3D
Applied AI
 
5/28/2025

Limit of RLVR

Reasoning LLMs Are Just Efficient Samplers: RL Training Elicits No Transcending Capacity

LLM
AI Fundamentals
 
5/28/2025

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning

Machine unlearning is a promising approach to mitigate undesirable memorization of training data in ML models. In this post, we will discuss our work (which appeared at ICLR 2025) demonstrating that existing approaches for unlearning in LLMs are surprisingly susceptible to a simple set of benign rel

AI Fundamentals
 
5/25/2025

Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation

Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation

3D
Arts & Creative Industries
 
5/19/2025

LLM Inference Economics from First Principles

The main product LLM companies offer these days is access to their models via an API, and the key question that will determine the profitability they can enjoy is the inference cost structure.

LLM
Research
AI Fundamentals
 
5/17/2025

GitHub - SLAM-Handbook-contributors/slam-handbook-public-release: Release repo for our SLAM Handbook

Release repo for our SLAM Handbook. Contribute to SLAM-Handbook-contributors/slam-handbook-public-release development by creating an account on GitHub.

Computer Vision
3D
 
5/17/2025

GitHub - axbycc/LiveSplat: Live Gaussian Splatting for RGBD Camera Streams

Live Gaussian Splatting for RGBD Camera Streams. Contribute to axbycc/LiveSplat development by creating an account on GitHub.

3D
 
5/16/2025

Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards. Recent RLVR works that operate under the zero setting avoid supervision in labeling the reasoning process, but still depend on manually curated collections of questions and answers for training. The scarcity of high-quality, human-produced examples raises concerns about the long-term scalability of relying on human supervision, a challenge already evident in the domain of language model pretraining. Furthermore, in a hypothetical future where AI surpasses human intelligence, tasks provided by humans may offer limited learning potential for a superintelligent system. To address these concerns, we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external data. Under this paradigm, we introduce the Absolute Zero Reasoner (AZR), a system that self-evolves its training curriculum and reasoning ability by using a code executor to both validate proposed code reasoning tasks and verify answers, serving as an unified source of verifiable reward to guide open-ended yet grounded learning. Despite being trained entirely without external data, AZR achieves overall SOTA performance on coding and mathematical reasoning tasks, outperforming existing zero-setting models that rely on tens of thousands of in-domain human-curated examples. Furthermore, we demonstrate that AZR can be effectively applied across different model scales and is compatible with various model classes.

AI Fundamentals
 
5/12/2025
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