Super thorny problem to agentify. Excited to follow your lead here.

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7. InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning 🔑 Keywords: Multimodal Contextual Reasoning, Multimodal Multi-head Latent Attention, Video Agent, Long-Horizon Tasks 💡 Category: Multi-Modal Learning 🌟 Research Objective: - The study aims to enhance long-horizon multimodal tasks through Multimodal Contextual Reasoning and efficient attention mechanisms, particularly focusing on video understanding challenges. 🛠️ Research Methods: - The research employs a novel framework named InternVideo3, which utilizes Multimodal Contextual Reasoning and introduces Multimodal Multi-head Latent Attention to improve efficiency in video task processing. The training process involves staged training with components like continued pretraining, short-to-long supervised fine-tuning, rule-based reinforcement learning, and on-policy distillation. 💬 Research Conclusions: - InternVideo3 exhibits strong performance on video understanding benchmarks such as Video-MME, MLVU, and EgoSchema. It also demonstrates robust and evidence-grounded behavior as a video agent, suggesting that efficient context handling and closed-loop reasoning are critical for long-horizon visually grounded agency. 👉 Paper link: huggingface.co/papers/2606.1…

📚 AI Native Daily Paper Digest - 2026-06-11🌟 Follow @AINativeF for the latest insights on AI Native. Covering AI research papers from Hugging Face, featured in the image. 💡 Stay updated with the latest research trends and dive deep into the future of AI! 🚀 #AI #HuggingFace #AIPaper #AINative #AINF — Appendix: Today's AI research papers — 1. Redesign Mixture-of-Experts Routers with Manifold Power Iteration 2. Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application 3. Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions 4. Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning 5. World Pilot: Steering Vision-Language-Action Models with World-Action Priors 6. ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics 7. InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning 8. TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning 9. ICA Lens: Interpreting Language Models Without Training Another Dictionary 10. Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models 11. World Model Self-Distillation: Training World Models to Solve General Tasks 12. ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction 13. i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models

7. InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning     InternVideo3: 使用多模态上下文推理赋能基础模型的智能化 🔑 关键词: 多模态上下文推理, 多模态多头潜在注意力, 视频代理, 长视距任务 💡 类别: 多模态学习 🌟 研究目标: - 本研究旨在通过多模态上下文推理和高效注意力机制改善长视距的多模态任务, 特别是聚焦于视频理解的挑战。 🛠️ 研究方法: - 该研究采用了一种名为 InternVideo3 的新框架, 利用多模态上下文推理并引入多模态多头潜在注意力来提高视频任务处理的效率。训练过程包含分阶段训练, 其中包括持续预训练、由短到长的监督微调、基于规则的强化学习以及策略内蒸馏。 💬 研究结论: - InternVideo3 在诸如 Video-MME、MLVU 和 EgoSchema 的视频理解基准上表现出色。它还展示了作为视频代理的稳健和有据可依的行为, 这表明高效的上下文处理和闭环推理对于长视距视觉基础的代理至关重要。 👉论文地址: huggingface.co/papers/2606.1…

Hugging Face Daily Papers — 2026-06-11 40 papers worth scanning today, spanning agentic RL, multimodal reasoning, efficient architectures, security, world models, and scientific discovery. 1. Redesign Mixture-of-Experts Routers with Manifold Power Iteration Highlight: Router is the cornerstone component to the Mixture-of-Experts models. Serving as expert proxies, the rows of the router matrix compute their simila. arXiv: arxiv.org/abs/2606.12397 2. Toward Generalist Autonomous Research via Hypothesis-Tree Refinement Highlight: Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret t. arXiv: arxiv.org/abs/2606.11926 3. Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application Highlight: Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving t. arXiv: arxiv.org/abs/2606.12191 4. Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks Highlight: General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-. arXiv: arxiv.org/abs/2606.12344 5. Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions Highlight: Reward models are central to text-to-image post-training, but visual preference is subjective and better represented as a distribution over rubric. arXiv: arxiv.org/abs/2606.09076 6. TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders Highlight: Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to comp. arXiv: arxiv.org/abs/2606.09323 7. Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning Highlight: Spatial reasoning from egocentric videos is inherently challenging because the observable evidence is constrained by the camera trajectory. Existin. arXiv: arxiv.org/abs/2606.11683 8. DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch Highlight: As the capabilities of LLM-based code agents continue to advance, their expected role is expanding beyond localized bug fixing in existing codebase. arXiv: arxiv.org/abs/2606.10728 9. World Pilot: Steering Vision-Language-Action Models with World-Action Priors Highlight: Vision-Language-Action (VLA) models inherit semantic grounding from large-scale pretraining and perform competently across in-distribution manipula. arXiv: arxiv.org/abs/2606.12403 10. On Subquadratic Architectures: From Applications to Principles Highlight: Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures off. arXiv: arxiv.org/abs/2606.12364 11. ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics Highlight: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous st. arXiv: arxiv.org/abs/2606.10479 12. Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code Highlight: Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwh. arXiv: arxiv.org/abs/2606.11817 13. InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning Highlight: Recent progress in foundation models has shifted toward agentic behavior involving multi-step reasoning and tool use. However, open-source efforts. arXiv: arxiv.org/abs/2606.12195 14. Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling Highlight: Reinforcement learning (RL) has become a key component in modern large language models, yet the rollout stage remains the key bottleneck in RL trai. arXiv: arxiv.org/abs/2606.12370 15. Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models Highlight: Vision-language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention comp. arXiv: arxiv.org/abs/2606.12412 16. TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning Highlight: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. arXiv: arxiv.org/abs/2606.11119 17. ICA Lens: Interpreting Language Models Without Training Another Dictionary Highlight: Finding interpretable directions in language-model representations is critical for understanding and controlling model behavior. Sparse autoencoder. arXiv: arxiv.org/abs/2606.11722 18. EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Highlight: Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static. This limitation sharpens in agentic RL,. arXiv: arxiv.org/abs/2606.03108 19. Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models Highlight: We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embod. arXiv: arxiv.org/abs/2606.11324 20. Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization Highlight: Reinforcement Learning (RL) with verifiable environments has emerged as a powerful approach for enhancing the reasoning capabilities of Large Langu. arXiv: arxiv.org/abs/2606.12373 21. World Model Self-Distillation: Training World Models to Solve General Tasks Highlight: Pretrained video generators are promising visual world models that exhibit emergent task-solving abilities; however, their reliance on detailed tex. arXiv: arxiv.org/abs/2606.12072 22. Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency Highlight: Pipeline parallelism is essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consist. arXiv: arxiv.org/abs/2606.07881 23. i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models Highlight: Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific. arXiv: arxiv.org/abs/2606.11289 24. POISE: Position-Aware Undetectable Skill Injection on LLM Agents Highlight: Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A. arXiv: arxiv.org/abs/2606.07943 25. ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction Highlight: Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual. arXiv: arxiv.org/abs/2605.11212 26. Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training Highlight: There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs). While Low-Rank Adaptation (LoRA) introduces. arXiv: arxiv.org/abs/2606.11854 27. Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation Highlight: Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive fea. arXiv: arxiv.org/abs/2606.11990 28. DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models Highlight: Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable p. arXiv: arxiv.org/abs/2606.05758 29. Large Language Models Are Overconfident in Their Own Responses Highlight: Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However. arXiv: arxiv.org/abs/2606.03437 30. Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models Highlight: Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have eme. arXiv: arxiv.org/abs/2606.12203 31. Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay Highlight: Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource lang. arXiv: arxiv.org/abs/2606.11786 32. APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations Highlight: Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across pro. arXiv: arxiv.org/abs/2606.11553 33. Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs Highlight: Modern LLM training pipelines increasingly rely on other models to generate data, filter corpora, judge outputs, and guide development decisions. T. arXiv: arxiv.org/abs/2606.12385 34. Building Social World Models with Large Language Models Highlight: Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundam. arXiv: arxiv.org/abs/2606.11482 35. Towards Diverse Scientific Hypothesis Search with Large Language Models Highlight: Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scient. arXiv: arxiv.org/abs/2606.10587 36. $τ$-Rec: A Verifiable Benchmark for Agentic Recommender Systems Highlight: As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace. Current b. arXiv: arxiv.org/abs/2606.10156 37. FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching Highlight: Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain. arXiv: arxiv.org/abs/2601.05212 38. SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference Highlight: Sparse attention reduces compute and memory bandwidth for long-context LLM inference. However, two key challenges remain: (1) KV cache capacity sti. arXiv: arxiv.org/abs/2606.04511 39. Can Generalist Agents Automate Data Curation? Highlight: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, imple. arXiv: arxiv.org/abs/2606.04261 40. Distilling LLM Feedback for Lean Theorem Proving Highlight: Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly wit. arXiv: arxiv.org/abs/2605.30861 Trend summary: ML/LLM training 11, NLP & language agents 9, Vision/multimodal 8, AI reasoning/evaluation 5, Robotics/embodied AI 2, AI security 2, Software agents 1, Social world models 1, Recommender agents 1.

Do not try to agentify the whole agency at once. Pick the single most painful, most repetitive workflow you have, usually research or versioning, and build one small crew for it. Get it genuinely useful, learn what your team trusts and what they do not, and let the reusable pieces accumulate from there. acalytica.com/blog/your-next…

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it would also increase return requests, i don’t think Amazon or others are ready for handling that volume yet. the minute you agentify ecommerce it becomes a stock market.

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Excellent point. I have been reworking the entire work flow of our agency...but constantly catching myself starting to agentify accepted (but not first principles) practices. It's not easy, but essential.

Last week in London, Automat-it and AWS combined AI theory with building at our Agentify Your Own Data workshop. Thank you to everyone who attended and got into the weeds with Amazon Bedrock AgentCore. The working MVPs from the day were amazing to see come to life. A special congratulations to our prize winners. We love these kind of events because they combine learning and expertise, with hands-on implementation. That is basically what we stand for at Automat-it! We will be hosting more deep-dive sessions like this to help you move from a basic AI implementation to production-ready systems. Keep an eye out for your region. We would love to see you at the next one. Missed the session but want to agentify your own data? Get in touch: eu1.hubs.ly/H0v-M2C0

You would build data centers right? As long as there enough land for that? And agentify the agents, it’s a recurring loop, it will take a while to hit that limit, you can go on.