Google DeepMind detailed a new capability for Private AI Compute: private, server-side persistent memory that lets an AI assistant keep context across devices. Data sits sealed in encrypted storage, and the unlock key stays only on the user's device. When the model needs access, an end-to-end encrypted channel carries it into a secure cloud enclave, where it is briefly decrypted in isolated memory and immediately re-encrypted.

An ICML 2026 paper presents an ISR=1 answer-abstain gate for evidence-grounded QA, and ntkMirror implements it for local open-weight models with multiple evidence orderings, reporting 0.0–0.7% hallucination at about 24% abstention in the held-out audit.

openJiuwen proposed MANGO, a multi-agent flow-network framework that combines reinforcement learning, textual gradients, and a Skip-k mechanism; using GPT-4o-mini, it reports a 12.8% accuracy gain over MaAS on MATH500 and a 5.1% F1 gain over AFlow on DROP.

Alibaba’s RTP team released RTPurboV2, replacing 85% of attention heads with SWA and compressing the remaining 15% retrieval heads using low-rank projection, clustering, and dynamic top-p; the adaptation uses about 600 training steps and roughly 1M label tokens, with reported Prefill speedup up to 9.36x.

Domino reports up to 5.8x throughput speedup on Qwen3 by decoupling causal modeling from autoregressive drafting in speculative decoding. The Reddit snippet links the arXiv paper, GitHub code, and Hugging Face models, but does not disclose hardware, baseline settings, dataset, or acceptance-rate details.

A developer used Qwen2.5-3B to build a five-agent forest economy, and across 15 simulation rounds honey prices fell from 10 to 3, firewood rose from 4 to 7, and the Gini coefficient increased from 0.14 to 0.38.

CMU and the University of Maryland propose Language Models Need Sleep: when each L-token context window fills, the model runs N offline recurrent forward passes and updates SSM fast weights before evicting the KV cache. On GSM-Infinite, Jet-Nemotron 2B with 6 sleep loops improves 6-step arithmetic accuracy from 0.742 to 0.812.

Ali Kayyam and coauthors evaluate three QKV projection-sharing variants across synthetic, vision, and language-modeling settings, including 300M and 1.2B parameter models trained on 10B tokens; Q-K=V halves the KV cache with a 3.1% perplexity degradation, while Q-K=V plus MQA reduces cache use by 96.9%.

Together AI, the University of Sydney, and UIUC introduced OSCAR, a 2-bit KV Cache quantization method that uses about 2.28 effective bits per KV element and scores 71.86 on Qwen3-4B-Thinking, 40.1 points above TurboQuant.

Xpeng presented its world-model stack at CVPR 2026, covering X-World, X-Foresight, and X-Cache; the article says X-Cache cuts about 70% of repeated computation, the second-generation VLA used over 4 trillion training tokens, and the in-car stack reduced inference latency to 80 ms.

Apple’s team published PICO, a perceptual image codec that uses 57%-70% fewer bits than AV1, VVC, and JPEG AI at the same subjective visual quality, while encoding a 12MP photo in 230 ms and decoding it in 150 ms on an iPhone 17 Pro Max.

TogetherAI and collaborators released OSCAR, a 2.28 BPE INT2 KV Cache system integrated with SGLang, reporting up to 3× decode speedup at 100k context and up to 7× job-level throughput under a fixed memory budget.

FaceMind links low-frequency token degradation to two papers: SLoW appeared at EMNLP 2025, Adam's Law was accepted as an ACL 2026 Oral, and high-frequency rewriting raised DeepSeek-V3 math accuracy from 63.55% to 71.54%.

Mila and Google DeepMind proposed Unified Neural Scaling Law, a multivariate scaling-law form that models parameter count, token count, training steps, bottlenecks, overfitting, and adverse hyperparameter effects; UNSL achieved the best extrapolation on 60.87% of vision tasks and 88.89% of language tasks in the reported experiments.

Carnegie Mellon University and the University of Maryland propose a “sleep” mechanism for language models: when the context window is nearly full, the model stops accepting new tokens, runs multiple offline recursive forward passes to compress accumulated context into fast weights, clears the KV cache, and then resumes inference; tests cover cellular automata, multi-hop graph retrieval, and GSM-Infinite reasoning tasks.

AMD and Penn State pretrained Llama 3.1-8B with MXFP4 on MI355X native FP4 hardware, achieving 9-10% end-to-end speedup over an FP8 baseline, while the paper identifies Wgrad quantization as the bottleneck that raises token overhead to 26-27% without deterministic Hadamard stabilization.

Nanjing University LAMDA and Alibaba Intelligent Engine proposed DAR, a timestep-aware cross-layer routing method that replaces fixed residual accumulation in DiT; on ImageNet 256x256, it reduced SiT-XL/2 FID from 9.67 to 7.56 and reached baseline convergence quality with 8.75x fewer training iterations.

RTPurbo converts full-attention LLMs to sparse inference with a few hundred adaptation steps. It keeps the full KV cache only for retrieval heads, uses a 16-dimensional token indexer, and reports up to 9.36x prefill speedup at 1M context plus about 2.01x decode speedup on long-context and reasoning benchmarks.

OpenBMB released BitCPM-CANN, a 1.58-bit QAT training stack on Ascend NPU with 0.5B, 1B, 3B, and 8B models trained from scratch, where the 1B to 8B variants retain 95.7%–97.2% of full-precision MiniCPM4 performance across 11 benchmarks.

Zhejiang University and the University of Adelaide introduced FlashAR, using 0.05% of the original training data to reduce Emu3.5-Image-34B 512×512 generation latency from 130.10 seconds to 5.68 seconds, while GenEval changed from 80.48 to 80.29.

Yoshua Bengio’s team introduced GRAM, a generative recursive reasoning model that samples multiple latent trajectories; on Sudoku-Extreme, GRAM reached 97.0% accuracy with 16 recursive steps and 20 parallel samples, exceeding TRM’s 90.5% result at 320 serial recursive steps.

ModelBest, Tsinghua University, and OpenBMB released BitCPM-CANN, a 0.5B-8B open model family trained natively on Huawei Ascend 910B NPUs with 1.58-bit ternary weights, cutting memory use by about 6x versus BF16 while retaining 95-97% of full-precision benchmark performance.

arXiv 2605.20315 argues for W4A4 quantization during prefilling to target a theoretical 4x gain, while keeping decoding on the original high-precision path because activation errors can perturb sampled tokens and accumulate across autoregressive generation.

OpenAI said an internal general-purpose reasoning model disproved the 1946 Erdős planar unit distance problem by finding a new family of constructions; the reasoning summary reportedly spans about 125 pages, while outside observers speculate the run used under 32 hours or under $1,000.

VAST and Tsinghua propose DeG, a 3D Gaussian generation method that samples Gaussian centers from a learned density distribution and trains density control with a render loss contribution gradient; in some settings, it reaches TRELLIS-like visual quality with less than half the Gaussian count.

NVIDIA released the Nemotron-Labs-Diffusion 3B, 8B, and 14B dense model family with AR decoding, diffusion parallel decoding, and self-speculation; the 8B model reaches 850 tok/s on GB200 at concurrency 1, compared with 253 tok/s for AR and 360 tok/s for Eagle3.

Jiqizhixin translated Sebastian Raschka’s blog on recent LLM architecture changes, covering long-context cost reductions in Gemma 4, Laguna XS.2, and ZAYA1-8B; the article states that Gemma 4 E2B saves about 2.7GB of KV cache at 128K context with bfloat16 precision.

Import AI 457 covers fast16, Aurora, and positive alignment: SentinelOne found fewer than 10 matching files for fast16 signatures, while Tilde Research reports Aurora reached 2.26 loss on 1.1B-parameter transformers versus Muon’s 2.31 under a ~100B-token setup.

Huawei GTS proposed EDCO, a dynamic curriculum method that selects fine-tuning samples by inference entropy; prefix entropy estimation cuts per-sample scoring time from 2.24 seconds to 0.37 seconds.

Orthrus-Qwen3-8B adds a trainable diffusion attention head to a frozen Qwen3-8B backbone, reaches up to 7.8× tokens per forward and about 6× wall-clock speedup on MATH-500, while training 16% of parameters with under 1B tokens over 24 hours on 8×H200 GPUs.

ByteDance’s commercial technology team proposed GRN, a visual generation architecture using HBQ, global refinement, and complexity-aware sampling to address quantization loss, error accumulation, and fixed-step inference; on a 130M model, adaptive sampling reduced inference from 50 steps to an average of 24, while gFID changed from 3.56 to 3.79.

OpenAI’s Parameter Golf brought together over 1,000 participants and more than 2,000 submissions to test AI-assisted machine learning research, coding agents, model quantization, and model design under strict parameter constraints.

The author proposes prompt caching for RL training. On Qwen3.5-4B, it reports a 7.5x speedup with 16k-token prompts and 64-token outputs, and the G=8 example with 1000-token prompts and 100-token responses reduces 8800 processed tokens to 1800 unique tokens.

PRISM raises LLaDA-8B-Instruct on GSM8K from 67.58% to 85.30% by combining hierarchical trajectory search, partial remasking, and self-verified feedback, reducing dLLM test-time scaling cost from O(NT) toward O(N+KT) under a final candidate width K.

NVIDIA AI released Star Elastic, a single checkpoint that can zero-shot slice 30B, 23B, and 12B reasoning models in BF16, FP8, and NVFP4; when the 23B submodel handles thinking and the 30B model handles final answers, reported accuracy rises 16% and latency drops 1.9× on AIME-2025, GPQA, LiveCodeBench v5, and MMLU-Pro.

AllenAI introduced EMO, a mixture-of-experts model with 14B total parameters and 1B active parameters, trained on 1 trillion tokens and able to use only 12.5% of its experts for specific tasks while retaining near-full-model performance.

UC Santa Barbara and Apple researchers introduced LenVM, which models remaining generation length as a token-level value function; Qwen2.5-3B with a 1.5B LenVM scored 62.6 on LIFEBench length control, above GPT-5.4 at 37.4 and Claude-Opus-4-6 at 35.5.

HIT Shenzhen, Huawei, and Shenzhen Hetao College proposed Dynamic-dLLM, a training-free dLLM acceleration framework that raises LLaDA-8B-Instruct throughput on GSM8k from 8.32 TPS to 37.29 TPS with almost no accuracy loss.

Proprioceptive AI says Cygnus adds adapters to frozen LLMs and raises Qwen-32B on ARC-Challenge from 82.2% to 94.97%. It projects hidden states into a gl(4,R) Lie-algebra space to isolate “dark modes.” Watch replication; the post does not disclose full eval sets or controls.

Zhejiang University and Alibaba proposed MetaCompress, a learned token-compression framework that generates a compression mapping from the input image alone for multi-turn VQA. The article says it can remove 90% of visual tokens while preserving accuracy, and reports only 1.71% overlap between optimally retained tokens and high-attention tokens.