This paper develops a system called RoboArchitect that automates the design of robotic agents by learning to optimize their harnesses, which determine when the agent interacts with the environment. A practitioner might care about this because it could significantly improve the performance of robotic agents without requiring significant updates to their underlying models.
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This paper creates a system that turns users' histories into labels to help improve AI agents' memory systems, making them more adaptable to different tasks and users. Practitioners might care about this because it can help improve the performance of AI agents in real-world applications.
This paper explores whether achieving artificial general intelligence (AGI) depends on improving a lightweight predictor (System-1) rather than just scaling a resource-intensive executive (System-2). Practitioners might care because understanding how to design and optimize these systems could lead to more efficient and effective AI.
This paper introduces CaPtain, a post-training pipeline that enables small language models to become capable robot agents that can write code against robot APIs and read back the results. A practitioner might care about this work because it demonstrates a novel approach to training robot agents using language models, which could be applied to various robotics tasks.
This paper investigates the problem of training-inference mismatch in reinforcement learning for large language models, where the training and inference processes can produce different results, leading to suboptimal training. A practitioner might care about this issue because it can lead to poor performance and instability in training, especially when using off-policy methods.
This paper develops a new method to evaluate proactive agents, which are systems that decide when and how to assist without being asked. Practitioners in AI and robotics might care because it helps them understand how well these systems work and make them more effective.
This paper develops a framework called CodeSpecWeave to help coding agents create more realistic and comprehensive task specifications for long-horizon software development. Practitioners may care about this because it could improve the performance and reliability of coding agents in real-world development scenarios.
Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live IOS Apps
This paper develops an online reinforcement learning framework that trains agents to navigate UIs on live iOS apps, addressing limitations of existing models. Practitioners might care about how this approach can improve the performance and generalizability of UI navigation models in real-world applications.
This paper presents a unified AI model that enables aerial robots to understand environments, navigate, and control themselves using a shared vision-language representation. Practitioners might care about how to build AI systems that can perform multiple tasks, such as perception and control, without relying on external resources.
This paper develops a single AI agent that can edit long-form videos from narration, improving the efficiency and quality of video production. Practitioners in the video production industry may care about this research as it has the potential to automate video editing tasks and reduce production costs.
This paper develops a robot policy that allows for real-time control over quality attributes like speed and smoothness, and shows that this approach can improve both task success and quality in real-world deployment scenarios.
Ian Quigley, co-founder and CEO of Leash Bio, discusses the challenges and innovations in small molecule drug discovery using DNA encoded libraries (DELs) and machine learning. The episode covers the technical details of DELs, lessons from …
DNA encoded librariessmall molecule drug discoverymachine learning modelsprotein-ligand bindingcombinatorial chemistryKaggle competitionHermes modelchemist biasactivity cliffsprotein mutationslanguage modelslab-in-the-loopdrug discovery scalingADMET propertiesexperimental rigorreinforcement learningbinding predictionstructural biologychemical library design
This episode features Andy Beam and Rafa Gómez-Bombarelli from Lila Sciences, discussing their vision for AI science factories as the next frontier for generating internet-scale datasets. They explain how their automated labs, leveraging AI…
Alistair Pullen, CEO of Cosine, discusses the UK's initiative to build a sovereign large language model (LLM) in response to US export controls on frontier AI like Fable. He explains Cosine's strategy to compete with larger labs by focusing…
David Dalrymple, known as Davidad, discusses his shift from formal verification approaches to an 'Alignment with Awakening' framework, emphasizing the formation of coalitions of aligned AIs that recognize shared moral truths. He shares empi…
formal verificationsafe AI containmentworld modelsproof infrastructureAI wisdommoral realismreinforcement learninginoculation promptingmulti-agent systemsbodhitropic alignmentAI interiorityobjectificationUS-China AI cooperationcatastrophic riskrecursive self-improvementsystem promptingalignment techniquescoalition of aligned AIsAI ethicsAI governance
Tim Scarfe interviews the Tufa Labs ARC-AGI-3 team to dissect their winning approach on the ARC-AGI-3 benchmark, focusing on how their system discovers goals and balances exploration with action efficiency. The episode explores the challeng…
ARC-AGI-3 benchmarkgoal acquisitionaction efficiencyexploration vs exploitationlanguage modelsplanning in AIcoding agentsrequirements engineeringcore knowledge priorsabstraction synthesisreinforcement learningAI safetybitter lessonneural guided searchLLM reasoningsoftware engineering AIbenchmark designgeneral intelligence
🔬 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…
This episode features Thomas von Tschammer of Neural Concept, discussing how physics-aware AI is revolutionizing product engineering. Neural Concept's models accelerate design evaluation from days to minutes, enabling companies like Jaguar …
AI in engineeringProduct designAutomotive industryAerodynamicsHeat dissipationCollision safetyComputer-Aided Design (CAD)Physics-based simulationReinforcement LearningFormula One racingElectric vehicles (EVs)Thermal managementElectromagnetismStructural dynamicsBattery cool-platesSoftware engineeringChina's manufacturing speedProduct differentiationAutonomous drivingNew form factorsHumanoid roboticsManufacturing agility
This episode argues that current AI progress is primarily driven by an immense quantity of high-quality, task-specific data, rather than improvements in sample efficiency. The speaker highlights the vast data requirements of frontier models…
Carina Hong, CEO of Axiom Math, discusses the company's recent $200M Series A funding and their perfect Putnam exam score, highlighting their mission to scale "verified AI" through formal mathematics. She explains how formal verification, u…
Formal verificationAI for mathLean proof assistantMathematical discoveryPutnam examHardware verificationSoftware verificationRecursive self-improvementAI agentsTransfer learningAuto-formalizationCode generationComputational complexityReinforcement learningKnowledge graphsInterdisciplinary teamsStartup funding
Eric Jang explains how to build AlphaGo from scratch using modern AI tools, detailing the game of Go's rules and the core Monte Carlo Tree Search (MCTS) algorithm. He describes how deep neural networks, specifically value and policy network…
The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking
Kyle Corbitt, founder of OpenPipe and leader of CoreWeave's serverless training team, provides a master class on reinforcement learning (RL) and custom fine-tuning for AI models. He explains how RL differs from supervised fine-tuning (SFT) …
This episode features Qasar Younis and Peter Ludwig, co-founders of Applied Intuition, discussing their company's mission to build physical AI for various moving systems like cars, trucks, and mining equipment. They delve into the evolution…