AI10 min read
JEPA-Anything Uses One Recipe For Seven Fields
Researchers released JEPA-Anything to fix capacity issues in world models. It applies Orthogonal Predictive Factorization across seven different domains.
From MarkTechPost
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AI10 min read
Researchers released JEPA-Anything to fix capacity issues in world models. It applies Orthogonal Predictive Factorization across seven different domains.
From MarkTechPost
AI1 min read
Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he said something that’s been stuck in m...
From MIT Technology Review AI
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Alibaba's Qwen went from an invite-only chatbot in April 2023 to a 2.4-trillion-parameter open-weight model in August 2026. This is the full story, release by release: every major model, its key feature, and how its license changed. Each...
From MarkTechPost
AI1 min read
Aleph Alpha has released Kolibri, a 78.1B-parameter English-German Mixture-of-Experts model that activates only 3.46B parameters per token. It has a 1M-token context and per-request reasoning effort, and its Apache 2.0 FP8 weights run on...
From MarkTechPost
AI1 min read
NVIDIA, MIT, and Oxford developed Physis-Lang, a framework that adds physics reasoning to video captions. It improves physics accuracy in models.
From MarkTechPost
AI1 min read
Alibaba's Qwen team launched Qwen-Audio-3.1-Realtime, a full-duplex voice model for agents. It supports tool calling, has a large context window, and offers significant price reductions.
From MarkTechPost
AI1 min read
The US government wants to spend $30.3 million over the next five years on an improved form of lie detector, according to a Department of Defense budget request. The program, called Polygraph+ or Polygraph Next, will focus on scoring alg...
From MIT Technology Review AI
AI1 min read
Kyutai has released Voice of Reason, 2 open-weight speech-to-speech models built on GLM-4-Voice-9B. Supervised fine-tuning and reinforcement learning lift spoken GSM8K accuracy from 27.3% to 77.1%. There is no transcription step and no t...
From MarkTechPost
AI1 min read
NVIDIA researchers have released SoL-Pi, 4 harness mechanisms for the open-source Pi coding agent, discovered by an AI running auto-research loops across 535 environments. On EdgeBench, SoL-Pi cuts token traffic by 44.7% to 49.0% and API...
From MarkTechPost
AI1 min read
Google released Retrieve-for-Train to generate diverse result sets quickly.
From MarkTechPost
AI1 min read
Nunchux AI launched VC-Attention to speed up video diffusion models without retraining. It uses low-bit quantization and replaces the slow softmax stage.
From MarkTechPost
AI1 min read
Stanford researchers released Paper2Agent to convert papers into testable AI agents.
From MarkTechPost
AI1 min read
Knowledgator released GLiFormer, an encoder that extracts nested JSON without generating tokens.
From MarkTechPost
AI1 min read
Sakana AI researchers introduced PC-ALM, a layer-local training method for deep networks that achieves performance comparable to backpropagation up to 1000 layers on MNIST. The research provides a JAX implementation for experimentation and benchmarking.
From MarkTechPost
AI1 min read
A new architecture, Recurrent Looped Transformer (RLT), developed by a Princeton researcher, allows for unbounded temporal depth in decoder-only LLMs by carrying decoder state across all tokens. This design addresses limitations in traditional attention mechanisms and offers a potential path to improved reasoning capabilities.
From MarkTechPost
AI1 min read
The Fly Language Model (FLM) integrates the complete MaleCNS fly connectome into a frozen 1.2B LLM. Experiments demonstrate that the connectome’s influence is minimal compared to a direct-input control, highlighting the importance of the underlying language model.
From MarkTechPost
AI1 min read
Research from ByteDance Seed demonstrates that LLMs struggle to engineer robust agent harnesses, with only 34 out of 64 changes generalizing across benchmarks. The study evaluates the ability of various LLMs to autonomously build and refine agent execution frameworks, revealing significant variation in performance and highlighting the challenges of automated agent design.
From MarkTechPost
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