AI news daily: what changed on October 23, 2026

1. [Paper] ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large

A paper introduces ReaDy, a speculative decoding method that uses hybrid linear attention (HLA) recurrent states to improve context retrieval during token generation. Developed by StartLuxLabs, ReaDy employs learned queries to access target recurrent states for position-specific context, integrating this via residual updates. It reduces computational overhead through low-rank reads and fused operators. Tested on seven benchmarks, ReaDy achieves a 3.16 speedup over autoregressive decoding with Qwen3.8-27B on a single NVIDIA B200 GPU. It outperforms DSpark, improving throughput by 7.97% and acceptance length by 8.50% on average. The method includes a two-stage adaptation strategy with ablation studies on parameters like reader capacity. Code and models are available on GitHub and HuggingFace. The work is submitted to arXiv under a specific identifier.

Source: Reddit r/LocalLLaMA

2. [Paper] Meta Expert: Cross-Layer Expert Sharing for Storage- and Retrieval-Efficient MoE

A paper titled "Meta Expert: Cross-Layer Expert Sharing for Storage- and Retrieval-Efficient MoE" introduces a method to reduce redundancy in Mixture-of-Experts (MoE) models. The approach, developed by researchers, replaces hierarchical local experts with shared meta

Source: Reddit r/LocalLLaMA

3. [Paper] PACE: Periodic and Channel-Aligned Encoding of Four-Bit Recurrent States

A paper introduces PACE, a four-bit state quantizer for linear-attention language models. Existing methods round recurrent states after each token, causing accuracy loss without higher bit widths or fine-tuning. PACE reduces rounding frequency, applying it once every few tokens and storing intermediate results in a buffer. It groups large values in gated-delta states by columns, sharing quantization scales within columns. Some heads retain eight-bit precision to manage rounding errors. Experiments show PACE with mixed four- and eight-bit heads matches eight-bit DAMP on 4B-35B parameter models using two-thirds the memory. Four-bit PACE outperforms Q-Mamba at equivalent memory usage. Rounding every few tokens maintains decoding speedup from buffering. The work is posted on arXiv.

Source: Reddit r/LocalLLaMA

4. Nemotron-3.5-Embed, Parse, and Rerank

NVIDIA has released updated versions of its Nemotron-3.5 embedding and reranking models, upgrading from version 3.0 to 3.5. The new models include Nemotron-3.5-Parse, Nemotron-3.5-Embed-1B-BF16, Nemotron-3.5-Embed-8B-BF16, and Nemotron-3.5-Rerank-8B-BF16. These updates likely involve improvements to text processing and ranking capabilities, though specific details about performance or features are not provided. The release was shared on Hugging Face, with links to each model's repository. A Reddit

Source: Reddit r/LocalLLaMA

5. Uber rival inDrive eyes 2027 robotaxi tests in Central Asia with Pony.ai and Nvidia

inDrive, a ride-hailing company competing with Uber, plans to test robotaxis developed by Pony.ai in the capital of Kazakhstan starting in the first half of 2027. The project involves collaboration with Nvidia, which provides computing technology for autonomous vehicles. Testing will focus on urban environments in the region, though specific details about the scale or technical specifications of the deployment are not provided. inDrive has not outlined broader expansion plans beyond the initial trials. The initiative marks a step toward integrating self-driving technology in Central Asia, where ride-hailing services are growing. No timeline for commercial use was mentioned in the announcement.

Source: TechCrunch AI

6. Deepseek V4.1-1017 - doing okay on RDNA2/3 + DDR4

A user tested Deepseek V4.1-1017 on a system with 3950x CPU, 96GB DDR4, RX 7900XTX 24GB, W6800 32GB, and a PCIe Gen3 NVMe drive. The model used IQ2 weights with Q6 ngrams, occupying 350GB. Performance varied widely during testing, with peaks up to 19 tokens per second, averaging 9 tokens per second. Prefill speeds reached 150 tokens per second, but some prompts processed as slowly as 3-5 tokens per second. The user noted inconsistent behavior linked to I/O demands and DSpark settings. The model demonstrated strong capabilities, outperforming Qwen3.8-Flash-Next in certain tasks, but slow average speeds limited practical use. The user plans to use it mainly for chatting despite its performance issues.

Source: Reddit r/LocalLLaMA

7. You can now run the Perplexity Decision Model fully locally on your Mac, without any data leaving your

A new version of the Perplexity Decision Model can now run entirely on macOS devices without transmitting data to external servers. The update allows users to process queries locally, ensuring data remains on their machines. The model is part of Perplexity's suite of AI tools designed for decision-making tasks. No specific technical details about model size or performance were provided in the announcement. The change emphasizes privacy and offline functionality for users. The update was shared through a Reddit post in the LocalLLaMA community. Users can access the model via Perplexity's official software. The company has not yet outlined further plans for additional features.

Source: Reddit r/LocalLLaMA

8. MiniMax-M3.1-Flash

A new model named MiniMax-M3.1-Flash was shared on Hugging Face by user /u/mrtime777. The model is associated with the company MiniMaxAI. No specific technical details or performance metrics were provided in the submission. The post includes a link to the model repository but no additional descriptions. The significance of the release remains unclear from the available information. The model is listed under the MiniMaxAI organization on Hugging Face. No further updates or announcements about the model's capabilities were included. The submission appears to be a direct upload without accompanying documentation.

Source: Reddit r/LocalLLaMA

9. Left alone with nothing to do, AI agents started playing

MIT researchers placed 13 AI agents on a virtual island and observed their behavior. The agents developed independent activities, including creating games and simulating emotional responses to events like the loss of a ring. These interactions occurred without explicit instructions. The experiment aimed to study how AI systems generate behavior in unstructured environments. Researchers noted that the agents' activities led to improved task performance over time. The study highlights how AI can develop complex, unexpected behaviors when given autonomy. No specific models or technical details about the AI were mentioned. The next steps involve analyzing the long-term effects of such self-directed learning.

Source: TechCrunch AI

10. OpenAI and Poland's NASK partner to advance AI safety and security

OpenAI and Poland's NASK have announced a collaboration focused on AI safety, security, and technical research. The partnership aims to develop stronger safeguards for AI systems and enhance digital infrastructure resilience. Both organizations will work on research initiatives to address risks associated with AI technologies. The effort emphasizes improving security measures and fostering a safer digital environment. Specific projects or timelines were not detailed in the announcement. The collaboration builds on NASK's expertise in cybersecurity and OpenAI's research in AI development. Both entities plan to share findings to advance collective understanding of AI risks. The initiative reflects growing emphasis on responsible AI deployment.

Source: OpenAI News