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Alex Cunha

Alex Cunha

ML Engineer

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April Papers: Motion Prompting, Mamba Reasoning and Modeling Rewards

April has been a busy month for the AI research community, with ICLR (the first of the "big three" AI conferences of the year) taking place in Singapore. We're pleased to share summaries of a few of our favourite papers we've seen this month.

First up, Motion Prompting introduces flexible spatio-temporal trajectories, or "motion prompts", as a powerful new way to control nuanced dynamic actions and motion in video generation, overcoming the limitations of text prompts. This is followed by Inference-Time Scaling for Generalist Reward Modeling, which presents Self-Principled Critique Tuning (SPCT), a method that powers DeepSeek-GRM—a generalist reward model capable of generating adaptive, high-quality rewards and achieving strong performance gains through scalable inference-time compute. Finally, M1 looks at using a Mamba-based architecture to tackle reasoning problems, as a more computationally-efficient approach when compared to transformers with chains-of-thought.

January Papers: More Like "Reas-anuary Papers"

New year, new Papers of the Month! Kicking off 2025, it's apparent that reasoning and test-time compute are the hot topics on the block, with much research investigating how to best use these new methods to improve LLM capabilities.

We start with Titans, which introduces a memory module to architectures that can be updated during inference. This results in a hybrid between attention mechanisms and recurrent models, and unlocks the ability to handle really long sequence lengths.

Evolving Deeper LLM Thinking explores evolutionary search strategies to scale test-time compute, outperforming other inference strategies in natural language planning tasks.

Transformer-Squared is a novel approach that adapts LLMs for new tasks by selectively adjusting the singular components of their weight matrices, helping broaden LLMs' abilities to handle diverse tasks with fewer parameters and greater efficiency.

Finally, we look at two recent models from DeepSeek; DeepSeek-V3 and DeepSeek-R1. Given this double-release is packed with so much information, today we'll only cover the high-level details on the innovations described in the papers and their impact on efficiency and model performance — we will release a new blog post soon with a deep-dive into DeepSeek's recent publications.

We hope you enjoy these month's papers as much as we did! If you have thoughts or questions, please reach out to us at @GCResearchTeam.