AI Research Today
AI Research Today unpacks the latest advancements in artificial intelligence, one paper at a time. We go beyond abstracts and headlines, walking through architectures, experiments, training details, ablations, failure modes, and the implications for future work. Each episode will choose between one and three new, impactful research papers and go through them in depth. We will discuss the papers at the level of an industry practitioner or AI researcher. If you want to understand the newest topics in AI research but don't have the time to dig through the papers yourself, this is your solution.
AI Research Today
Latest Episodes
GradMem: Teaching LLMs to Remember (Without Retraining)
In this episode, we break down GradMem, a new approach to memory in large language models: https://arxiv.org/pdf/2603.13875v1Instead of relying on the transformer KV cac...
Language Models are Injective and Hence Invertible
In this episode, we break down a fascinating new result from recent research: that modern Transformer language models are almost surely injective—meaning different prompts map to unique internal representations, with no information loss....
Learning to Reason in 13 Parameters
Link to arxiv: https://arxiv.org/pdf/2602.04118Large language models have recently shown impressive reasoning abilities, often learned through reinforcement learning and low-rank adapta...
SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search
Large Language Models often struggle with complex planning tasks that require exploration, backtracking, and self-correction. Once an LLM commits to an early mistake, its linear chain-of-thought reasoning makes recovery difficult. While search ...