OpenDS-World A new benchmark for deep search agents in the live, open Web: 200 tasks across eight domains, with multimodal evidence and interactive access. Best of 12 frontier models reaches 39.0% accuracy.

MiniMax just released their M3.1 Flash model on Hugging Face A native multimodal MoE for coding, agentic workflows, and computer use with 1M token context and sparse attention throughout.

Tencent releases Looking Ahead A new training method for autoregressive video models that predicts future chunks, improving physical plausibility by 11.2% (I2V) and 9.4% (V2V) on Physics-IQ, while using 57.9% less compute and 63.4% less data.

DONIC: one policy, two hands A single reference-conditioned policy controls two dexterous robot hands for bimanual tracking. Trained on diverse human demonstrations, it generalizes to unseen trajectories and objects without any fine-tuning.

SpatialScout: Can VLM agents actively explore and map 3D scenes? A new benchmark for evaluating vision-language models on active object-level 3D mapping across 80 scenes and 3,140 objects.

StreamEval: The first benchmark for interactive streaming video generation It evaluates 37 models across 5 tasks with 300 scripted test cases, revealing that reliable request execution remains a central challenge.

DeepSeek just released an upgraded Flash model on Hugging Face Better reasoning, coding, agentic tasks and visuals, with faster execution and shorter outputs at lower cost.

AKV: Cache Engineering for Agents A new serving system that lets LLM agents directly manage their KV cache — dropping unused history and repositioning retained keys without rewriting or re-prefilling. Reduces average cost by 27.7% while improving task accuracy by 1.7 points.

Microsoft Research's WildAWM A scalable pipeline that synthesizes executable computer-use agent environments and tasks using only open-source models, yielding 341 long-horizon env-task pairs and Qwen agents that generalize to 8 unseen benchmarks.

Meta just released MemProBench on Hugging Face 800 frozen memory histories from real-world Ego4D activity, built to study memory-driven proactive assistance: when should an assistant speak up, not just what to say?