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Andrew M. Dai 引用介绍了 Glimpse——首个视觉思考模型，能够理解图像与视频中的物体关系及随时间变化的动作，性能可与前沿模型 Astra 相媲美。
Palo Alto startup Elorian, which raised a $55M seed round, unveiled Glimpse, its first AI model built around a 'visual scratch space' that lets it crop, zoom, pull video frames, and run code mid-reasoning to actually see images rather than just reason about tokens. Glimpse launches as a research preview with API access, priced below mid-tier models while claiming wins over GPT-6 Astra on scientific chart reasoning and video captioning.
OrcaRouter 发布 OrcaCyber Nano 1.0，一个 27B 视觉语言推理模型，以 GGUF 格式打包，可在本地离线运行，面向逆向工程、漏洞分析、安全代码审查等防御性安全工作，并公布多项基准测试成绩。
该论文提出 LILIE（Language-Informed and Lossless Image Encoder），利用可逆归一化流编码器为文本到图像生成提供语言监督损失，在 GPIC 数据集上将 JiT 模型的 gFD 从 66.84 降至 53.01，相比纯像素损失显著提升保真度。
NVIDIA 发布 Nemotron 3.5 Bolt 74B-A9B 24GB，这是一款采用原生 2-bit 量化、仅需 24GB 显存即可在本地运行日常智能体任务的视觉-语言模型，兼具速度与智能。
Urbex 提出一种智能体视觉-语言框架，将城市级 3D 场景定位重构为主动空间搜索，利用强化学习优化工具使用策略，在 CityRefer 上取得最佳 Hit@1 和 IoU，并显著优于排名式基线。
OpenBMB 开源发布了 MiniCPM-V 4.7 35B-A3B 视觉语言模型，可在 iPhone/iPad 上以 13-17 tok/s 运行，并引入 LES（前瞻专家调度）技术加速 MoE 推理，同时更新 34 项多模态基准表现。
MiniCPM-V 4.7 1B 是 MiniCPM-V 系列最新模型，主打性能与效率的新帕累托前沿，采用早期视觉 token 压缩架构，支持 iOS/Android/HarmonyOS 及多家芯片平台的端侧部署，并提供 FP8 量化与 Self-Replay 微调方法。
CameraVLM 通过联合学习屏幕外相机运动与屏幕内光流动态，并配合语言对齐的两阶段训练，显著提升视觉语言模型判断相机运动方向的能力，在多项基准上超越 GPT-6 Astra 与人类受试者。
X-Ragent 是一个面向胸片（CXR）证据驱动推理的后训练框架，通过工具增强强化学习解耦辅助证据获取与有效性门控的量化测量；在 Chest AgentBench 等三个基准上取得最佳结果，其中完全未见基准提升 8.6 分。
Users hype Moondream 3.1, a small vision-language model with 9B total params and 2B active params, praising it for outperforming Rex-Omni and LocateAnything on image tasks while being cheaper, fast, and locally runnable.
A pull request to llama.cpp adds inference support for the MiniCPM-V 4.7 multimodal vision-language model, extending the C/C++ LLM inference framework's model coverage.
NandakishorM 开源了基于物理的类型化决策模型 Vega（800M 及 4B 参数版本），采用 Test-Time Training 引擎+适配器架构，支持 73k 上下文和图像输入，通过在隐藏层构建"结果山谷"并用带摩擦的小球物理模拟来判定输出意图，单次推理超越多项 JEV 基准。
OuroWorld 是一个无掩码框架，可将任意静态 3D Gaussian Splatting 场景转化为从任意视角无缝循环的 3D 动态摄影（cinemagraph），利用视觉语言模型引导视频模型生成参考视频，并提出 Inconsistency-Robust Periodic 4DGS 与 Grounded Drift Field 来保证循环一致性并吸收跨视角不一致，在用户研究中以 70.8%-99.0% 的胜率超越所有基线。
MiniCPM-V 4.7 35B-A3B is the first MoE model in the MiniCPM-V series, released in GGUF-quantized form for llama.cpp, enabling lossless on-device multimodal inference at 13+ tokens/s with only 3GB of memory on iPhone 17 Pro Max while surpassing Qwen3.6 35B-A3B in performance-efficiency.
DEPICT is a training-free metric for scoring text-to-image alignment that replaces fixed reference answers with expected agreement between image-based and caption-only answers to decomposed verification questions, boosting negation accuracy from 19% to 88% and outperforming other training-free metrics across five benchmarks and eleven backbones.
The paper introduces PyRUA-Lean, an interactive code-execution framework for VLM robot agents that reduces token overhead by composing robot primitives into Python cells with selective observation. Using the GPT-6 Astra planner across 700 simulated tasks, it raises success from 63.1% to 71.7% while cutting LLM calls by 49% and input tokens by 65%.
autotrust releases JEV-27B-VL, a vision-enabled version of the JEV-27B model built on Qwen3.8-27B that provides fast calibrated System 1 decisions (via POST /v1/decide) alongside full System 2 reasoning, demonstrated on robot-arm pick-and-place (75% success) and computer-use browser tasks (95% completion).
Xiaomi-OCR-0 is a unified 0.8B vision-language model for document parsing and OCR-centric understanding, built from Qwen3.5-0.8B-Base, achieving #1 on Real5-OmniDocBench and outperforming larger models like Qwen3.5-2B on OCR VQA benchmarks.
MotorMind introduces a harness that lets a general-purpose VLM control a robot directly by reasoning over observations and issuing mid-level actions with asynchronous feedback, achieving 66.7% success on LIBERO-PRO zero-shot (vs. up to 13.3% prior methods) and 95% on a real xArm6 robot without task-specific training, coding agents, or grounding tools.