Google Research Introduces Retrieve-for-Train (R4T): An RL-Compiled Diffusion Retriever for 12× to 20× Faster Query Fan-Out

Search and recommendation systems increasingly need to return a set of results, not one best match. A query like ‘camping gear’ should return a tent, a sleeping bag, a stove, and a headlamp. It should not return 10 near-identical tents. Google Research has introduced Retrieve-for-Train (R4T) for this problem. The framework uses reinforcement learning (RL)…

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Nunchux AI Introduces VC-Attention: A Training-Free Low-Bit Attention Kernel That Speeds Up Video Diffusion Transformers

Nunchux AI has released VC-Attention, a training-free low-bit attention kernel built for video Diffusion Transformers (DiTs). It targets 2 problems at once: value quantization error and a slow softmax stage. Why Attention is the Video Bottleneck Video DiTs flatten a clip into 1 sequence of spatiotemporal tokens and run full self-attention at every layer. A…

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OpenAI Creates a New Framework to Disclose Dangerous AI Conduct

OpenAI introduced a new framework on Wednesday for the way it publicly discloses AI misalignment incidents, which the corporate says it hopes will assist inform comparable requirements throughout the {industry}. The corporate can also be releasing new details about a number of examples of AI mannequin misalignment it recognized previously 12 months. “As fashions advance…

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Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens

Knowledgator Engineering has released GLiFormer, a schema-conditioned encoder framework for information extraction. One model handles named-entity recognition (NER), text classification, relation extraction, nested JSON structuring, and text embeddings. You pass labels and extraction schemas at inference time. Two checkpoints are on Hugging Face. GLiFormer Base v1 has 264.2M parameters, and GLiFormer Large v1 has 575.6M….

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