NVIDIA releases Cosmos-H-Dreams real-time surgical robot simulator

🚀 RELEASES & LAUNCHES

• NVIDIA releases Cosmos-H-Dreams, a real-time generative surgical robot simulator
  NVIDIA released Cosmos-H-Dreams, an open real-time action-conditioned surgical world model that streams ~160 FPS on a single RTX PRO 6000.

  • Aimed at surgical robotics researchers and educators for policy evaluation, synthetic data generation, and surgical training
  • Distills Cosmos-H-Surgical-Simulator (built on 2B-parameter Cosmos-Predict2.5) into a causal few-step student served via FlashDreams, generating 288x512 RGB video from an initial frame and robot kinematics
  • Supports live control via browser keyboard (WebRTC), Meta Quest headset (WebXR), CMR Surgical's Versius console, and learned policies in closed loop
  • Trained on JHU dVRK tabletop suturing data including failure episodes; code is Apache 2.0, weights under NVIDIA Open Model License, checkpoint ~4.36 GB
  • First interactive surgical world model with live human and policy control, though reported 160 FPS is throughput, not measured end-to-end latency
  Sources 📄 Source: arxiv.org · 📎 Coverage: mlq.ai · 👁 via Hugging Face Blog

• DeepReinforce releases Ornith-1.5 open-weight self-improving model family under MIT
  DeepReinforce released Ornith-1.5, an MIT-licensed open-weight model family in 397B, 35B and 9B sizes trained via a self-improvement loop.

  • Aimed at developers running coding and agentic workloads locally; weights on Hugging Face in BF16, FP8, NVFP4, GGUF and MLX builds, with GGUF/Ollama support and OpenAI-compatible tool calling.
  • The 397B MoE flagship scores 86.1 on Terminal-Bench 2.1 and 86.0 on SWE-bench Verified, matching Claude Opus 4.8 (85.0) and beating GLM-5.2 and DeepSeek-V4-Flash-0731; 262K context extendable to ~1M with YaRN.
  • The 35B-A3B MoE activates only 3B parameters per token yet scores 68.5 on Terminal-Bench 2.1 and 79.0 on SWE-bench Verified; the 9B dense model hits 47.0/70.6 and has a Mobile build for iPhone and Android.
  • Training jointly optimizes task generation, scaffold construction and solution rollouts via RL, with task reward multiplying validity, frontier difficulty (0.2 target success rate) and novelty.
  • Caveats: all benchmarks are vendor-reported and unreproduced; the 397B needs ~800GB BF16 and eight-way tensor parallelism (e.g. eight H200s), and it trails Claude Opus 4.8 on Frontier-Bench (13.5 vs 21.1) and NL2Repo (59.5 vs 69.7).
  Sources 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

• Kyutai shrinks Pocket TTS to 1.9M parameters with CPU voice cloning in v4.0
  Kyutai released Pocket TTS 4.0 with tiny English voice-cloning models at 18.2M, 6.9M and 1.9M parameters that run on CPU.

  • Targets developers running TTS locally on CPU — laptops, Raspberry Pi, Jetson, and browser via WebAssembly — with no GPU or cloud API needed.
  • Pocket TTS v4.0.0 (October 20, 2026) adds six English models: 18.2M, 6.9M and 1.9M parameters, each with a reward-tuned REFL variant; the original model is ~100M.
  • The 1.9M model (0.9M flow LM + 1.0M decoder) is marked experimental; weights are CC-BY-4.0 on Hugging Face, code MIT, ~47 MB on disk for the 2M model.
  • Also ships a Rust runtime with prebuilt macOS/Linux/Windows binaries, retrained French/Italian/Dutch/German/Spanish/Portuguese models, and cleaner audio with a 5 ms fade-in.
  • Independent testing showed the 7M model at ~10x real time and the 2M model at ~17x real time on CPU; voice-cloning weights require accepting terms on the gated kyutai/pocket-tts repo.
  Sources 📄 Source: github.com · 📎 Coverage: artrealmai.com · 👁 via r/LocalLLaMA

• Gradium open-sources Phonon, 31M and 40M on-device TTS models
  Gradium open-sourced Phonon, a family of 31M and 40M parameter on-device text-to-speech models with a fully open-source inference stack.

  • For developers building offline voice apps on phones, laptops, Raspberry Pi 5, Apple Neural Engine, NPUs, and in-browser via WebAssembly/WebGPU.
  • Three models: Phonon 31m (64 MB, English, Apache 2.0), Phonon 40m and Phonon 40m multilingual (74.6 MB each, English/French/German/Spanish/Portuguese, CC BY-NC).
  • Phonon 31m hits 1.70% WER on hard sentences, beating Kokoro (3.50%), Kitten TTS (7.40%) and NeuTTS Nano (8.71%); runs up to 22x real time on an iPhone 16 Pro.
  • Inference stack is open-source, mostly in Rust, targeting 10+ backends including CPUs, GPUs (CUDA, Metal, Vulkan, WebGPU) and NPUs.
  • Enables fully offline, private TTS with no cloud calls; voice cloning and Voice Design voices can run locally.
  Sources 📄 Source: github.com · 📎 Coverage: gradium.ai · 👁 via r/LocalLLaMA

• XHToken releases Spark-X2.6-4B, an updated compact on-device LLM
  XHToken released Spark-X2.6-4B, a 4B open-weight model with the same architecture as Spark-X2.5 but improved post-training.

  • Aimed at developers running capable AI on-device, with gains in coding, agentic, long-context, multilingual, and reasoning tasks
  • Spark-X2.6-4B keeps the exact architecture of Spark-X2.5-4B (hybrid sliding-window + full attention, native 1M-token context, 200+ languages) and improves purely via post-training
  • Released about a month after Spark-X2.5-4B; weights and GGUF quantizations are on Hugging Face under the XHToken account
  • The prior X2.5-4B was Apache 2.0, trained on ~20T tokens, and benchmarked competitively with Qwen3.5-9B on agent tasks
  • Matters for local-LLM builders: a small model line for agent harnesses like Hermes, Codex, and Claude Code with strong long-context support
  Sources 📄 Source: huggingface.co · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

• Agnes AI releases Agnes-3.0-Qwen, a QFN finetune with strong agentic benchmark results
  Agnes AI released Agnes-3.0-Qwen, a QFN finetune with strong agentic and instruction-following benchmark results, with GGUF quants available.

  • Aimed at local/agent developers: multimodal reasoning model with tool calling, text/image/video understanding, and a 1M-token context window.
  • Scores highest on τ3-Banking in its comparison set, within ~1 point of the best model on Terminal-Bench 2.1, and beats GPT-5.6 Sol (Max) and Claude Opus 4.8 (Max) on IFBench.
  • Hybrid attention MoE with ~180B total parameters across 512 experts; extended reasoning with selectable effort levels (xhigh, medium, low).
  • GGUF quantizations by bartowski are available on Hugging Face for local deployment.
  • Being much smaller than the flagship systems it matches on agentic benchmarks makes it notable for local model builders.
  Sources 📄 Source: huggingface.co · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

🔬 RESEARCH

• COUNT paper: precomputed n-gram counts in host RAM boost LM training efficiency
  UW and AI2 researchers introduced COUNT, a method that augments transformer LMs with n-gram count tables stored in host memory, requiring no learned parameters.

  • For builders pretraining LMs with limited GPU memory, and for domain adaptation use cases in math, biomedicine, and law.
  • COUNT (Counting Offline Unlocks N-gram Transformers) builds n-gram embeddings from precomputed corpus counts stored in host RAM, weighting the LM's own unembedding rows of likely next tokens as an extra input embedding.
  • At a fixed compute budget, adding COUNT to 1B-parameter LM pretraining improves validation loss as much as 57% more compute; across 60M–1B backbones, standard transformers need 46–89% more compute to match its loss.
  • Swapping in domain-specific count tables at inference adapts the model to new tasks with no training; released tables span 17 GB to 797 GB, including counts of the full Dolma 3 corpus.
  • Code, checkpoints at 7 model sizes, and n-gram tables are released on GitHub and Hugging Face.
  Sources 📎 Coverage: arxiv.org · 👁 via r/LocalLLaMA

• REMEMORY paper adds learned soft memory tokens to improve agent context compaction
  Fudan researchers introduced REMORY, a neural memory network that supplements compacted summaries with soft memory tokens to help frozen LLMs approximate full-history behavior.

  • Targets builders of long-horizon agents that must compact history to fit finite context windows, where text summaries alone lose decision-relevant detail.
  • REMEMORY learns a bounded sequence of soft memory tokens, conditioned on the summary and appended after it, acting like a residual connection along the sequence dimension.
  • On SummHay, it improves source attribution at nearly unchanged insight coverage and approaches the full-context joint score using only 5.2% of input positions.
  • Qwen3.8-27B and GLM-5.3-Flash show consistent gains, with substantially fewer repeated tool outputs and tool errors on BrowseComp and Terminal-Bench 2.1.
  • Code and models are available via the Fudan generative vision GitHub repo and Hugging Face collection.
  Sources 📄 Source: arxiv.org · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

📊 BENCHMARKS & EVALS

• IBM's ALTK-Evolve tops tau2-bench retail with learned guidelines on GPT-4.1
  IBM Research's ALTK-Evolve pushes a GPT-4.1 ReAct agent to the top of the tau2-bench retail leaderboard using learned guidelines instead of a frontier model.
  • Aimed at customer-service agent builders, ALTK-Evolve distills past task trajectories into behavioral guidelines appended to the agent's system prompt.
  • A basic ReAct agent using GPT-4.1 reached 67.5% Pass^4 on tau2-bench retail, above every retail entry in the October 12, 2026 leaderboard snapshot.
  • The same GPT-4.1 model powers both guideline extraction and the agent, with no weight updates or human annotation; it runs on the stock tau2-bench scaffold.
  • Guideline variants were tested: baseline (0), sup3 (31 retail / 14 airline rules), and lossless (292 retail / 101 airline), with Pass^4 measuring reliability across 4 trials per task