Madhu Shantan

Introduction Every team building on top of LLMs has a version of the same mental model: pretraining teaches the model what it knows, and post-training teaches it how to behave. Don't want it to say harmful things? Train that out. Want it to be more helpful? Push that

Introduction Voice AI hit a genuine inflection point when full-duplex models arrived. Systems like Moshi finally cracked the core problem with conversational speech: the awkward cascade of listen, then transcribe, then think, then speak. Full-duplex models [models that listen and speak simultaneously over a continuous audio stream, the same way

Introduction Every mainstream large language model today generates text the same way: one token at a time, left to right, no looking back. It works remarkably well, but it has a structural flaw that's easy to overlook until you care about speed at scale. The model can never

Introduction If you've spent time working with LLM agents for web research, coding assistance in cursor or even extended conversations in ChatGPT, you've probably noticed something: as tasks or multi turn conversations grow longer and more complex, the quality of responses deteriorates - essentially because of

Model's Replication Scores Average Replication Scores on PaperBench

Picture this scenario: You’re very new to AI, exploring chatgpt by testing its capabilities on various topics, expecting honest answers unaware that behind the scenes, it already figured out that it’s being tested and is subtly changing its behaviour to ace your tests. This feels like a subtle