This paper investigates whether diffusion language models can continue reasoning across generation chunks without keeping earlier text in context, and whether using a fixed-size "register" can improve performance. Practitioners might care about this because it could lead to more efficient and flexible language generation models.
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In this episode, Philip Kiely and Ali Taha from Baseten discuss the complexities and innovations in inference engineering for large AI models. They cover topics including model deployment, speculative decoding, quantization, hardware optimi…
Inference engineeringSpeculative decodingQuantizationModel deploymentTool callingKV cacheTensor parallelismExpert parallelismGPU hardwareRubin GPUVideo diffusionAutoregressive modelsDiffusion modelsTraining-inference convergenceContinual learningOpen source modelsInference infrastructureModel optimizationLatency vs throughputMulti-modal models
This episode features Bo Wang and Si Chu from Xaira Therapeutics, discussing their AI drug discovery platform, X-Cell, which uses high-throughput experimentation to generate causal data for predicting cellular responses to genetic perturbat…
AI drug discoveryCausal modelsGene expression predictionHigh-throughput experimentationVirtual cell modelsProtein designPatient stratificationSingle-cell genomicsCRISPR perturbationDiffusion modelsBiological prior knowledgeModel generalizationOpen scienceSpatial transcriptomicsTemporal cell dynamics
🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI
In this episode of Latent Space, Evan Feinberg and Sergey Edunov of Genesis Molecular AI discuss their pioneering work in applying diffusion models to protein-small molecule interactions for drug discovery. They explain how their foundation…
In this episode, Thomas Ahle discusses the development of thermodynamic computing chips and the challenges of chip design automation using AI agents. He explains how his team built an open-source Verilog simulator with AI collaboration to o…
thermodynamic computingchip design automationformal verificationVerilog simulationAI agentsopen source EDAcontinual learningprobabilistic machine learninghardware-software co-designauto formalizationsoftware complexityAI-generated codeBayesian inferencestochastic differential equationsMarkov chain Monte Carlodiffusion modelsneural network accelerationrecursive self-improvementAI safetyagentic coding
This episode features John Jumper, co-creator of AlphaFold, discussing the breakthrough in protein structure prediction that won the 2024 Nobel Prize in Chemistry. Jumper explains the technical innovations behind AlphaFold, its impact on bi…
The Model Eats the Scaffolding: DeepMind's Logan Kilpatrick & Tulsee Doshi on 3.5 Flash, Omni & More
This episode features Logan Kilpatrick and Tulsee Doshi of Google DeepMind discussing Google's AI strategy and new launches at Google I/O, including Gemini 3.5 Flash, the Omni video generation model, and the Gemini Spark agentic product. Th…
Google I/O launchesGemini 3.5 FlashAI agent infrastructureMultimodal AICost-adjusted efficiencyModel scaling strategyAgent harness developmentProduct integrationRecursive self-improvementAI research collaborationAudio input modalitiesContext window limitsDiffusion modelsModel psychologySearch grounding