World Wire

Google Research has released Agile Deliberation , a human-in-the-loop framework for building image classifiers around subjective concepts. Think ‘healthy food’, ‘inspiring artwork’ or clickbait in content moderation. Most tooling assumes users already know the concept boundary. Agile Deliberation assumes they do not, and helps them find it. TL;DR Framework : It prompts Gemini 2.5 Pro and Gemini 2.5 Flash through the API, without fine-tuning. Runs on: A Jupyter or Colab notebook. Retrieval uses CLIP embeddings and a FAISS index on CPU or GPU. The demo index starts at about 260 MB. Performance: 10.5 F1 points above zero-shot Gemini 2.5 Flash on average, across 2 concepts and 18 live sessions. Best: Paid to play F1 of 0.59 vs 0.48 zero-shot, with precision up 22 points (0.35 to 0.57). Worst: Recall drops 17 points on paid to play (0.80 to 0.63) and 15 points on healthy food. What is Agile Deliberation? Agile Deliberation is a G...

Ricursive Intelligence just released Ripple-65B Preview . Ripple-65B Preview is a 65B dense open-weight model from Ricursive, pretrained from scratch for agentic coding. It is a general-purpose variant of the proprietary model behind Ricursive’s AI chip design platform. The main claim: 80.52 on Terminal-Bench 2.1 , the top self-reported score among US open-weight models. Every MoE rival it beats on that test carries 501B to 975B total parameters. TL;DR Size: 65B dense (65B active), pretrained on 12T tokens, up to 1M context. Runs on: BF16 on datacenter GPUs (250 tok/s on 2x B200). Experimental NVFP4 build targets NVIDIA DGX Spark. Performance: Frontier-level agentic coding for its size; trails larger MoE models on knowledge and science coding. Best: Terminal-Bench 2.1, 80.5 (self-reported). Worst: SciCode, 39.4, lowest of the 5 models in Ricursive’s own table. What is Ripple-65B Preview? The release includes 3 checkpoints : an instruc...

Tencent’s WeChat Vision team has released WeVisOne-VL , an open vision language model in 2B and 4B sizes. The WeVisOne-VL vision language model handles detection, referring, counting, pointing, OCR, segmentation and alpha matting in one model. Pixel outputs come from a 9.6M-parameter decoder, so no separate SAM is needed. It also answers “None” when a queried object is absent. For developers, that can replace a detector, an OCR engine and a segmenter with one model. TL;DR Size: 2B ( 2.44B params ) and 4B ( 4.83B params ) on Hugging Face, and a 9.6M-parameter pixel decoder. Trained with up to 8,192 tokens of context; deployment context length is not disclosed. Runs on: A single GPU through Transformers or vLLM in BF16. The package also lists NPU and CPU devices. VRAM needs are not disclosed. A community GGUF ran object-level tasks on a 6 GB RTX 2060. Performance: Beats the same-scale VLM baselines in Tencent’s tables on most of 30+ benchmarks...

Anthropic’s Frontier Red Team has released FlashProve , an open-source research prototype for provable inference . FlashProve is a zkSNARK system from Anthropic that proves a reply came from a committed model. The proof reveals nothing about the model weights. On Llama 4 Maverick , a 400B mixture-of-experts model, it proves about 110 tokens/second on 1 H100. For developers, this targets a real gap: today you cannot check which model actually answered your API call. TL;DR Size: Proves a 400B MoE model (~17B active per token); context capped at 1,024 tokens per request. Runs on: Prover needs 1 H100 80 GB, 24 CPU cores and ~290 GB host RAM. Verifier runs on a CPU, no GPU. Performance: Orders of magnitude faster than prior open-source zkML systems on the same 400B model. Best: 110 tokens/second proving on 1 H100, with 18.78 MB proofs checked in 17 seconds. Worst: Proving costs ~70 to 95x the GPU time of plain vLLM inference. What problem does provab...