Multitask training objectives fight each other at every gradient step. What if you stopped forcing them to share? ControlG reframes multitask coordination as a temporal allocation problem, dedicating computational capacity to one objective at a time instead of blending conflicting gradients per step. A proportional-integral-derivative (PID) controller decides which objective needs attention next, operating across three time scales: estimating per-objective difficulty, optimizing per-epoch allocation via log-hypervolume sensitivity, and tracking the plan with feedback loops. Amazon researchers applied ControlG to the problem of graph self-supervised learning. Across nine graph datasets, ControlG achieves average ranks of 1.4, 1.9, and 1.8 for node classification, link prediction, and node clustering, exceeding all baselines.
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Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. Follow us on LinkedIn and visit our website to get a deep dive on innovation at Amazon, and explore the many ways you can engage with our scientific community. #AmazonScience
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Most healthcare AI benchmarks test medical knowledge in static form or evaluate agentic, tool-using agents on technical tasks done for providers. Neither captures what a patient-facing agent has to do: reason about a patient's health record over a multiturn conversation and decide what actions to take. To capture such complex interactions, PatientAgentBench generates a synthetic patient health record, a realistic clinical vignette, and a patient agent that converses with the AI system under evaluation. An LLM-as-a-jury panel scores each conversation against over 100 clinician-vetted criteria across six dimensions — clinical safety, triage quality, workflow accuracy, task completion, clinical helpfulness, and conversational quality. The framework generates fresh scenarios on demand, preventing "training contamination", where pretrained models learn about common benchmarks from published results. Results across frontier models reveal a severity paradox: agents score higher on obvious emergencies than on routine cases that hide real risk. The dominant safety failure was crisis resource omission — recognizing suicidal ideation but failing to provide hotline information. Model capability alone narrows clinical gaps but does not close them.
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As AI agents take on higher-stakes decisions, Amazon is further investing in mathematical proof to enable verified, trustworthy AI agents. We're providing substantial, long-term financial support to the Lean Focused Research Organization, the single largest donation in the FRO's history, to make proof accessible to every developer in the world. Mathematical proof shows with certainty that a system cannot behave incorrectly, no matter what inputs it gets. Amazon already uses Lean to help prove AI agent boundaries are correct in Bedrock AgentCore, guarantee differential-privacy protections in AWS Clean Rooms are sound, and verify compilation to Amazon's AI acceleration chips.
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A faithful conversation transcript is not a faithful token record. Agent harnesses do useful things that make RL bookkeeping harder: compacting older messages, retrying malformed tool calls, branching into subagents, merging results back. Each rewrite is a chance for the next request's token sequence to drift from what the model generated. Turnstile is an open-source Rust proxy that records the exact token-level history at the only point where it's correct: the moment of generation. It speaks the Chat Completions API, requires no harness changes, and exports generic trajectories with token IDs, log probabilities, loss masks, and weight-version boundaries. It also captures mixture-of-experts routing decisions and processed multimodal inputs, splitting the trajectory rather than training under incorrect state.
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The Chronos family of models has reached 1 billion downloads on Hugging Face! 🤗 Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training — but existing approaches largely focus on univariate forecasting, limiting their use in real-world scenarios where multivariate data and covariates matter. Chronos-2 addresses this. Our foundation model handles univariate, multivariate, and covariate-informed forecasting in a zero-shot manner, outperforming existing time series foundation models by a substantial margin across multiple benchmarks.
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What if a robot could learn to feel without ever touching anything real? HydroShear is a physics-based tactile simulator that accurately models how forces build up and change during contact, using path-dependent force tracking in hydroelastic contact models. It remembers the motion history of objects as they move across a soft sensor, capturing friction, slipping, and elastomer deformation. Trained entirely in simulation and deployed on a real Franka robot with GelSight Mini sensors, HydroShear achieved a 93% average success rate across four contact-rich tasks with no modification or fine tuning. Baselines TacSL (34%) and FOTS (58-61%) fall far short.
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Only 0.7% of submissions to ICML 2026 were selected for oral presentation — and Amazon Scholar Usman Khan's research on scalable multi-agent path finding is one of them. Check out the paper: https://amzn.to/4vSipl0 #ICML2026
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Amazon researchers have accepted publications at [ICML] Int'l Conference on Machine Learning spanning machine learning, causal reasoning, LLM inference, agentic systems, vision-language models, graph learning, robotics, and more. Explore the full list of papers: https://amzn.to/4veenlQ