Hugging Face Trending Papers

This episode explores groundbreaking AI research, featuring Helios, a real-time long video generation model; Proact-VL, a proactive VideoLLM for real-time AI companions; and T2S-Bench & Structure-of-Thought, a new benchmark and prompting technique for text-to-structure reasoning.

### Featured Papers* **Helios: Real Real-Time Long Video Generation Model** * **Key Insight:** Helios is the first 14B video generation model capable of real-time (19.5 FPS) minute-scale video generation on a single H100 GPU, achieving high quality by addressing long-video drifting and optimizing for efficiency. * **Paper Link:** [https://arxiv.org/pdf/2603.04379.pdf](https://arxiv.org/pdf/2603.04379.pdf)*

**Proact-VL: A Proactive VideoLLM for Real-Time AI Companions** * **Key Insight:** Proact-VL introduces a framework for creating proactive, real-time interactive AI companions, particularly for gaming scenarios like commentators and guides, by enabling low-latency inference and autonomous decision-making. * **Paper Link:** [https://arxiv.org/pdf/2603.03447.pdf](https://arxiv.org/pdf/2603.03447.pdf)*

**T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning** * **Key Insight:** This work introduces Structure-of-Thought, a prompting technique that guides models to construct intermediate text structures, and T2S-Bench, the first benchmark designed to evaluate and improve models' text-to-structure reasoning capabilities. * **Paper Link:** [https://arxiv.org/pdf/2603.03790.pdf](https://arxiv.org/pdf/2603.03790.pdf)

# Hugging Face Trending Papers Episode Summary
In this episode, we discuss two trending papers, "Large-Scale Agentic RL for High-Performance CUDA Kernel Generation" and "Language-Agnostic SWE Task Collection at Scale". The first paper presents CUDA Agent, a large-scale reinforcement learning system that optimizes GPUs for deep learning, and the second introduces SWE-rebench V2, a language-agnostic, automated pipeline for collecting real-world software engineering tasks for training software engineering agents.

## Papers Discussed
- "Large-Scale Agentic RL for High-Performance CUDA Kernel Generation" introduces CUDA Agent, a system that fundamentally improves GPU optimization ability for deep learning using scalable data synthesis, skill-augmented CUDA development, and reinforcement learning techniques. The system achieves state-of-the-art results on KernelBench. [Read the paper](https://arxiv.org/pdf/2602.24286)

- "Language-Agnostic SWE Task Collection at Scale" presents SWE-rebench V2, an automated pipeline for collecting real-world software engineering tasks and constructing reinforcement learning training environments at scale. The pipeline has constructed a dataset of 32,000+ tasks spanning 20 languages and 3,600+ repositories. [Read the paper](https://arxiv.org/pdf/2602.23866)

## Additional Links
- Project page for CUDA Agent: [https://cuda-agent.github.io/](https://cuda-agent.github.io/)
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Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation

**Source:** huggingface_daily

**URL:** https://huggingface.co/papers/2511.14993

**Key Points:**- Problem: The research addresses the challenges in high-resolution image and video generation, particularly the scalability and computational complexity associa...- Method: The authors introduce Kandinsky 5.0, a family of foundation models comprising three core variants: Kandinsky 5.0 Image Lite, Kandinsky 5.0 Video Lite,...- Results: Kandinsky 5.0 achieves state-of-the-art performance in high-resolution image and 10-second video synthesis, demonstrating superior generation quality ...- Implications: Kandinsky 5.0 has significant implications for the research community by providing an open-source framework that advances the accessibility and develo...

# Episode SummaryIn this episode of Hugging Face Trending Papers, we delve into the latest AI research with three top trending papers from arXiv. We explore MiroThinker's interaction scaling for open-source research agents, the new paradigm of "Thinking with Video" for multimodal reasoning, and Lumine's approach to building generalist AI agents for 3D open-world environments.

# Mentioned Papers
1. ["MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling"](https://arxiv.org/pdf/2511.11793) - This paper presents MiroThinker, an open-source research agent that improves tool-augmented reasoning and information-seeking capabilities by focusing on efficient interaction scaling.

2. ["Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm"](https://arxiv.org/pdf/2511.04570) - The authors propose "Thinking with Video," a new paradigm that uses video generation models to bridge visual and textual reasoning, overcoming limitations of current "Thinking with Text" and "Thinking with Images" paradigms.

3. ["Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds"](https://arxiv.org/pdf/2511.08892) - Lumine introduces a recipe for developing AI agents capable of completing complex missions in 3D open-world environments, demonstrating strong zero-shot cross-game generalization.

Papers discussed:

1. [Scaling Latent Reasoning via Looped Language Models](https://arxiv.org/pdf/2510.25741): This paper introduces a new kind of pre-trained looped language models, Ouro, which improves reasoning capabilities by integrating reasoning into the pre-training phase. The models have demonstrated superior performance due to enhanced knowledge manipulation capabilities.

2. [Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations](https://arxiv.org/pdf/2510.23607): The Concerto model combines 2D and 3D learning for improved spatial cognition in AI. This integration, involving 3D intra-modal self-distillation with 2D-3D cross-modal joint embedding, has yielded promising results in 3D scene perception and set new benchmarks in scene understanding.

3. [RECODE: Unify Plan and Action for Universal Granularity Control](https://arxiv.org/pdf/2510.23564): RECODE is a new paradigm that unifies planning and action within a single code representation, facilitating dynamic control of decision granularity. This approach has proven effective in enhancing inference performance and training data efficiency.

**Episode Summary:**This episode dives into cutting-edge advancements for Large Language Models, covering new methods to enhance reasoning reliability and efficiency, and introducing lightweight memory systems for more effective long-term interaction.

**Featured Papers:**
* **A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning** * *Key Insight:* Introduces RPC, a novel method that theoretically and empirically improves LLM reasoning by combining self-consistency and perplexity, achieving exponential error convergence and reducing sampling costs by 50%. * *Link:* https://arxiv.org/pdf/2510.15444

* **LIGHTMEM: LIGHTWEIGHT AND EFFICIENT MEMORY-AUGMENTED GENERATION** * *Key Insight:* Presents LightMem, a human-memory-inspired system that enables LLMs to leverage historical interactions efficiently, significantly reducing token usage, API calls, and runtime while boosting accuracy. * *Link:* https://arxiv.org/pdf/2510.18866

* **DeepAnalyze: Agentic Large Language Models for Autonomous Data Science** * *Key Insight:* Introduces an agentic LLM framework for autonomous data science, automating the entire process from raw data to analyst-graded research reports using multi-agent collaboration and feedback reasoning. * *Link:* https://arxiv.org/pdf/2510.16872

In this episode of Hugging Face Trending Papers, we discuss three exciting AI research papers: "Less is More: Recursive Reasoning with Tiny Networks", "Agent Learning via Early Experience", and "Paper2Video: Automatic Video Generation from Scientific Papers".

## Papers Discussed
1. **[Less is More: Recursive Reasoning with Tiny Networks](https://arxiv.org/pdf/2510.04871)**: This paper introduces a Tiny Recursive Model that significantly improves accuracy on hard question-answer problems, using a simpler recursive reasoning approach and beating Large Language Models on complex tasks.
2. **[Agent Learning via Early Experience](https://arxiv.org/pdf/2510.08558)**: This research paper presents a new paradigm called "early experience", where AI agents learn from their own actions. The approach improved effectiveness and out-of-domain generalization in diverse environments.
3. **[Paper2Video: Automatic Video Generation from Scientific Papers](https://arxiv.org/pdf/2510.05096)**: This paper presents Paper2Video, a multi-agent framework designed to automate the labor-intensive process of generating academic presentation videos from scientific papers.

## Episode Links-

[Paper 1: Less is More: Recursive Reasoning with Tiny Networks](https://arxiv.org/pdf/2510.04871)-

[Paper 2: Agent Learning via Early Experience](https://arxiv.org/pdf/2510.08558)-

[Paper 3: Paper2Video: Automatic Video Generation from Scientific Papers](https://arxiv.org/pdf/2510.05096)

In this episode, we discuss three trending AI research papers. We delve into the challenges and solutions related to code language models, video generation, and reinforcement learning.

Key Points Discussed
#LongCodeZip: Compress Long Context for Code Language Models- LongCodeZip is a novel framework for compressing code for Large Language Models (LLMs)- It addresses the issue of high API costs and generation latency associated with processing long inputs in codebases- The framework uses a dual-stage compression strategy, enabling it to preserve essential information while reducing context size- Evaluations show that LongCodeZip consistently outperforms baseline methods- This research could improve the efficiency and capability of code intelligence applications

#Self-Forcing++: Towards Minute-Scale High-Quality Video Generation- The paper addresses the computational cost of generating long videos with diffusion models- It proposes an approach that uses teacher models to guide student models through sampled segments from self-generated long videos- This method allows for video length scaling up to 20× beyond the teacher's capability- The authors manage to generate videos up to 4 minutes and 15 seconds long, substantially outperforming baseline methods

#EXGRPO: Learning to Reason from Experience- The paper investigates what makes a reasoning experience valuable in the context of Reinforcement Learning from Verifiable Rewards (RLVR)- The authors propose a framework that organizes and prioritizes valuable experiences- The approach aims to balance exploration with experience exploitation for efficient and scalable RLVR

### Links to Papers- [

LongCodeZip: Compress Long Context for Code Language Models](https://arxiv.org/pdf/2510.00446 )- [

Self-Forcing++: Towards Minute-Scale High-Quality Video Generation](https://arxiv.org/pdf/2510.02283 )-

[EXGRPO: Learning to Reason from Experience](https://arxiv.org/pdf/2510.02245 )

In today’s episode of Hugging Face Trending Papers, we explore three cutting-edge ideas reshaping how we interact with AI and research.

First, we dive into Paper2Agent, a framework that transforms static research papers into interactive AI agents, making findings more transparent and usable. Next, we look at Scaling Agents via Continual Pre-training, which pushes the boundaries of agent reliability by teaching them through lifelong learning. Finally, we cover Documenting Machine Learning with AI Partners, a vision of AI tools that act as collaborative lab partners to capture, explain, and even co-author machine learning workflows.

Listen in for benchmarks, methods, and real-world implications — and discover how these papers could change the way we do science.

Papers discussed:

• Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents

• Scaling Agents via Continual Pre-training

• Documenting Machine Learning with AI Partners

In this episode, we unpack three fresh arXiv papers shaping how AI creates, reasons, and acts. First, arXiv:2509.22622 explores real-time, steerable long-form video generation you can guide on the fly (PDF: https://arxiv.org/pdf/2509.22622).

Next, arXiv:2509.25454 integrates tree search directly into reinforcement-learning training for verifiable reasoning—think math and code with checkable rewards (PDF: https://arxiv.org/pdf/2509.25454).

Finally, arXiv:2510.01051 introduces a unified “gym” for multi-turn, tool-using LLM agents so results are comparable and scalable (PDF: https://arxiv.org/pdf/2510.01051). We break down why each matters, the key technical ideas, and what they could unlock for creators, engineers, and autonomous AI workflows.