Nokia Open-Sources AnyJev: A Training-Free Layer That Turns Any Open LLM Into a Calibrated Decision Model
Nokia’s applied research team has open-sourced AnyJev, a Python library that turns an open LLM into a decision model. It needs no training. It targets a common production job: picking one answer from a fixed set instead of writing a sentence. Is it deployable? Yes, it installs from PyPI, ships under Apache-2.0, and has transformers and vLLM backends with shared-prefix scoring. What is AnyJev? AnyJev borrows its interface from Jev. Jev is the System One decision model that TypeS
Get smart on it
AnyJev is an open-source Python library that converts open language models into decision models without requiring training. Instead of generating text, it picks one answer from a fixed set of options and provides a probability score based on the model's token distribution. The library addresses two key problems with directly reading model scores: answers can change when options are reordered, and probabilities are often poorly calibrated. AnyJev solves these issues through two levels of correction: L0 uses cyclic shifts of option order and batch prior correction to remove position and label bias, while L1 adds temperature scaling for better calibration.
Relevant courses
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on MavenRelated news
Tencent releases Hy4, powerful frontier LLM introducing visual understanding and computer use
Tencent Holdings Ltd. today introduced Hy4, a new large language model that adds computer-use capabilities for the first time, using advanced visual understanding, reasoning and tool-calling to deliver long-horizon agentic performance. The company said the model marks a significant leap in performance over its Hy4 Preview, which arrived August 28, weighing in at 770 billion parameters with 49 […] The post Tencent releases Hy4, powerful frontier LLM introducing visual understanding and comp
Daybreak Red is now available in AWS GovCloud (US-East)
Daybreak Red brings GPT-5.6 Cyber to approved customers in AWS GovCloud (US-East) through Amazon Bedrock for authorized cybersecurity work.
Tencent WeVisOne-VL Unifies Detection, OCR, Segmentation and Matting
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
42AI comes out of stealth with $62.5M and a world model that can ‘predict human behavior’
Here's a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?
Also reported by SiliconANGLE
Anthropic Releases FlashProve: Cryptographic Proofs That a Reply Came From Committed Weights
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
Deep Principle Releases Hydrogen-1.0: A 753B Open-Weight Model for Scientific Discovery
Deep Principle has released Hydrogen-1.0, an open-weight scientific model for chemistry, materials science and life sciences. It is built by post-training Z.ai’s GLM-5.3 on domain data, laboratory records and agent discovery trajectories. For developers, it offers open weights that report a FrontierScience-Research win over GPT-6-Astra. The weights ship under the MIT License. TL;DR Size: 753B parameters (MoE, ~40B active per the GLM-5.3 base), 1M-token context, 128K max output.