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) once, offline, to learn good query fan-out. It then distills that behavior into a small diffusion model that produces all retr
Search and recommendation systems need to return diverse sets of results rather than near-identical matches, a challenge addressed by a new framework that uses reinforcement learning to improve how queries are split into multiple sub-queries. The framework trains a language model offline to generate better sub-queries, then distills this behavior into a small diffusion model that produces all retrieval directions in a single pass rather than sequentially. This approach prevents "paraphrastic collapse," where an AI generates near-synonym queries that retrieve homogeneous results, and runs 12 to 20 times faster than traditional methods. Testing on fashion outfit and music datasets showed the approach improved quality scores and maintained diversity while significantly reducing latency.
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Anthropic releases new Mythos and Fable models amid RSI concerns
Anthropic says the new models "show improvements in areas like coding, reasoning, and vision," as well as in communicating more clearly with users.
Also reported by The Verge, SiliconANGLE and MarkTechPost
Google Launches Gemini Decisions API With Gemini Decision 1 at $0.10/M Tokens
Google has launched the Gemini Decisions API, powered by a new model, Gemini Decision 1 (gemini-decision-1). The Gemini Decisions API does not generate text. It scores up to 1,000 typed questions against one shared input and returns calibrated probabilities. For developers, this targets the small branching calls inside agents: routing, policy checks, tool selection and grading. Google prices it at $0.10 per 1M input tokens, with zero output tokens billed. TL;DR Size: Context is 262,144 t
Also reported by SiliconANGLE
Google releases Gemini 4 Carbon with agent ‘swarm’ feature
Google is rolling out its latest AI model, Gemini 4 Carbon, to the general public after releasing it to a select group of "cybersecurity defenders" two weeks ago. Google claims Carbon is its "strongest model" yet, and capable of competing with the very best systems from Anthropic and OpenAI. Google says Gemini 4 Carbon "pushes the frontier even further" than Argon, referring to the company's other frontier model that launched only last month. The company said Argon will likely
Gemini 4 Carbon is now available
The latest version aims to deliver improved performance while reducing the computational resources and energy required for AI operations.
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
AlphaProof Code agents wrote three million lines of proofs in Lean to verify Signal’s core encryption.
NVIDIA launches GR00T 2 and Isaac Lab 3.0 at CoRL
A range of developers, including RLWRLD, Skild AI, and Flexion, are already using NVIDIA’s latest technology. | Source: NVIDIA NVIDIA Corp. today introduced GR00T 2, its updated open frontier world action model, and Isaac Lab 3.0, the latest version of its open-source robot learning framework. The company made these announcements at the Conference on Robot Learning, or CoRL, in Austin, Texas. NVIDIA also gave a look at how its partners, like Ambi Robotics, Bosch Research, Dexmate, Dyna Rob