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Kheeran Naidu

Kheeran Naidu

Research Scientist

Posts

October Papers: Fast and Smart Language Models

October was packed with insights into making language models faster and smarter. We reviewed four of our favorite papers for you in detail:

  • First up, Grouped Lattice Vector Quantisation introduces a novel technique for a fine-grained post-training quantisation of LLMs, retaining good performance even at low bit widths.
  • Planned Diffusion combines autoregressive planning with text diffusion, achieving low-latency text generation.
  • Rethinking Thinking addresses the problem of long reasoning chains by distilling intermediate results into a bounded workspace for faster answers.
  • Finally, When Structure Doesn’t Help compares techniques for encoding graphs for consumption by LLMs with surprising results.

August Papers: Optimal Dataset Mixtures, Stable Molecule Generation, and Agentic Hypergraph RAG

August, even with its heat waves and holidays, left no shortage of exciting research. Our top papers for this month are the following: - ADMIRE-BayesOpt that investigates how to weight different data sources when they are mixed to make a single training dataset where, using multi-Fidelity Bayesian Optimization, the search for the optimal mixture can be automated; - Stable Molecule Generation that uses a force-field based reward function to fine-tune pre-trained 3D molecule generation diffusion models with the goal of sampling physically stable and valid molecules; and - Graph-R1 that takes an agentic RAG approach with a knowledge hypergraph to effectively represent and retrieve information from a corpus of documents.

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.