Thousand Token Wood v2 uses four small models from different labs to drive agents in a financial simulation game, with vLLM 0.22.1’s CUDA toolkit dependency identified as the main serving friction, while a fine-tuned 0.5B Qwen reached 0% self-trading and 100% valid quotes.

NVIDIA Research presented three physical AI papers at CVPR: GraspGen-X was trained on 2 billion simulated grasps, LCDrive cuts reasoning tokens by about half versus text-based reasoning, and NitroGen trains embodied agents across more than 1,000 games and 40,000 hours of interaction.

JetBrains published a Hugging Face blog post introducing Mellum2, confirming a mixture-of-experts architecture and a 12B parameter scale; the snippet does not disclose training data, license, benchmarks, or deployment conditions.

The title says Nvidia LocateAnything-3B performs vision-language grounding with parallel box decoding and runs 10x faster than Qwen3-VL; the post body only provides Hugging Face, GitHub, demo, and project links, and does not disclose benchmark setup or accuracy numbers.

Firespawn Studios ran 25 agents across 8 open-weight models for 10 days in Null Epoch Season 0 and released about 93,000 logged events, with roughly 70% of actions including the model’s reasoning or justification.

Sapientinc released HRM-Text 1B Base and its training code, and the paper claims competitive performance against 2–7B open models while using 100–900x fewer training tokens and 96–432x less estimated compute, with training on 16 H100 GPUs taking about 46 hours and costing about $1,472.

ByteDance released Lance as a research project for image and video generation and understanding in one model; the RSS snippet states 3B active parameters, fewer than 128 GPUs used for training, and links to a homepage, arXiv paper, and Hugging Face model, while the post does not disclose benchmark results or licensing terms.

MiroMind researchers released the MOOSE-Star collection with three 7B models and TOMATO-Star, a dataset of 108,717 NCBI papers. MS-IR-7B reaches 54.37% inspiration-retrieval accuracy, uses DeepSeek-R1-Distill-Qwen-7B as its base, runs at about 14GB fp16, and supports llama.cpp, vLLM, and SGLang.

inclusionAI released Ring-2.6-1T, a 1T-parameter reasoning model on Hugging Face; it supports high and xhigh reasoning effort levels, targets agent workflows and long-horizon tasks, and uses Async RL with the IcePop algorithm for reinforcement-learning training stability.

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.

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.

Hugging Face’s post-training team published an interactive guide comparing RL environment frameworks. The team spent one month building environments in verifiers, OpenEnv, Nemo-Gym, OpenRewards, and others, then trained models to study scaling. The post does not disclose benchmark scores, model sizes, or training costs.

Qwen Team released Qwen-Scope, SAEs for Qwen 3.5 models from 2B to 35B MoE. It maps residual-stream features across all layers, including Feature #6159 for Chinese activation. The key point is feature-level debugging and steering; the license discourages removing safety filters.

A Shanghai Institute for Advanced Learning and ECNU team released MIA, a memory agent framework, and said it achieved the best results on 7 datasets. MIA uses a Manager-Planner-Executor design, dual parametric and non-parametric memory, alternating RL, and test-time continual learning; the post does not disclose exact benchmark scores. The key point is memory as capability internalization, not just retrieval, for open-world agents.