Overview
The papers collectively explore advancements in the capabilities and applications of state-of-the-art models and systems across various fields. A significant focus is on enhancing model scalability and contextual processing, exemplified by MiniMax-01’s implementation of lightning attention and MoE techniques enabling extensive token context handling. Another common theme is the integration of multimodal data, as demonstrated in VideoRAG’s retrieval-augmented generation framework utilizing both video and textual inputs. Meanwhile, the application of advanced reasoning is highlighted in robotics and medical domains, with OmniManip using vision-language models for robotic manipulation, and inference-time scaling improving LLM performance in medical diagnostics. Reinforcement learning’s role in improving reasoning abilities of large language models is also emphasized, showcasing the continuous development of strategies to better emulate human-like reasoning processes.
Spotlight 
MiniMax-01: Scaling Foundation Models with Lightning Attention
Minimax
This paper presents the MiniMax-01 models, which push boundaries with their impressive ability to handle longer contexts in text and vision-language tasks. By incorporating lightning attention and a Mixture of Experts strategy, the models achieve efficient scalability with a whopping 456 billion parameters. It’s fascinating to see how these models manage to match the performance of established ones like GPT-4o but go beyond them in terms of context length, dealing with up to 4 million tokens during inference. The authors also highlight the models’ balanced cost-effectiveness, which might make them quite attractive for large-scale tasks. I’m impressed by how these innovations could pave the way for more efficient and scalable model architectures in the industry.
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Other papers
VideoRAG: Retrieval-Augmented Generation over Video Corpus
KAIST; DeepAuto.ai
This paper presents an innovative framework called VideoRAG, which enhances traditional Retrieval-Augmented Generation by incorporating video content into the generation process. By integrating both visual and textual data, VideoRAG achieves superior results compared to existing methods, effectively utilizing multimodal knowledge to generate more accurate responses. The research showcases the value of dynamically retrieved videos in enriching the context and improving the overall quality of generated content.
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CFCS, School of CS, Peking University; PKU-AgiBot Lab; AgiBot
This paper introduces OmniManip, which bridges high-level reasoning and low-level precision in robotic manipulation by integrating Vision-Language Models with object-centric interaction primitives. By employing a dual closed-loop system, it achieves impressive zero-shot generalization across diverse tasks without extensive model fine-tuning. I find the approach particularly compelling for its potential to tackle manipulation challenges in unstructured environments efficiently, making it a valuable contribution to the field.
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O1 Replication Journey – Part 3: Inference-time Scaling for Medical Reasoning
Shanghai Jiao Tong University; SII; SPIRAL Lab; Generative AI Research Lab (GAIR)
This paper delves into how inference-time scaling can boost the performance of large language models in medical reasoning tasks. It highlights that extending the reasoning time leads to a 6%-11% improvement in diagnosing and treatment functions, even with limited training data. The findings underscore the potential of integrating inference-time scaling with traditional clinical reasoning methods like the hypothetico-deductive approach, making it a noteworthy contribution to the field of AI in healthcare.
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Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Tsinghua University, Beijing, China; HKUST (GZ), Guangzhou, China; Emory University, Atlanta GA, USA
This paper provides a comprehensive survey on the intersection of large language models (LLMs) and reinforcement learning (RL), particularly focusing on how RL can be leveraged to improve the reasoning abilities of LLMs. It explores the significance of structured “thought” sequences in replicating human reasoning and reviews innovative strategies to boost reasoning accuracy, such as test-time scaling. The survey also delves into key foundational concepts, technical elements, and open-source projects while pinpointing future avenues for research and challenges in this rapidly evolving area.
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Acknowledgements
Papers are retrieved from Hugging Face.