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AI Ecosystem Intelligence Explorer
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:
Wan2.1 14B 480p I2V LoRAs - a Remade-AI Collection
A collection of Remade’s Wan2.1 14B 480p I2V LoRAs
Limit of RLVR
Reasoning LLMs Are Just Efficient Samplers: RL Training Elicits No Transcending Capacity
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
Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation
Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation
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.
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.
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.
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.