Have you ever worked is legacy code? Are you curious what it takes to modernize systems at a massive scale?

Pascal Hartig is joined on the latest Meta Tech Podcast by Elaine and Buping, two software engineers working on a bold project to rewrite the decades-old C code in one of Meta’s core messaging libraries in Rust. It’s an ambitious effort that will transform a central messaging library that is shared across Messenger, Facebook, Instagram, and Meta’s AR/VR platforms.

They discuss taking on a project of this scope – even without a background in Rust, how they’re approaching it, and what it means to optimize for ‘developer happiness.’

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The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.

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Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 73 award recipients who represent 46 universities in 10 countries.

This announcement includes awards funded under five call for proposals during the fall 2024 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.

Recipients have access to more than 700 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.

“Automated Reasoning is an important area of research for Amazon, with potential applications across various features and applications to help improve security, reliability, and performance for our customers. Through the ARA program, we collaborate with leading academic researchers to explore challenges in this field,” said Robert Jones, senior principal scientist with the Cloud Automated Reasoning Group. “We were again impressed by the exceptional response to our Automated Reasoning call for proposals this year, receiving numerous high-quality submissions. Congratulations to the recipients! We’re excited to support their work and partner with them as they develop new science and technology in this important area.”

“At Amazon, we believe that solving the world’s toughest sustainability challenges benefits from both breakthrough scientific research and open and bold collaboration. Through programs like the Amazon Research Awards program, we aim to support academic research that could contribute to our understanding of these complex issues,” said Kommy Weldemariam, Director of Science and Innovation Sustainability. “The selected proposals represent innovative projects that we hope will help advance knowledge in this field, potentially benefiting customers, communities, and the environment.”

ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.

The tables below list, in alphabetical order by last name, fall 2024 cycle call-for-proposal recipients, sorted by research area.

AI for Information Security

Recipient	University	Research title
Christopher Amato	Northeastern University	Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms
Bernd Bischl	Ludwig Maximilian University of Munich	Improving Generative and Foundation Models Reliability via Uncertainty-awareness
Shiqing Ma	University Of Massachusetts Amherst	LLM and Domain Adaptation for Attack Detection
Alina Oprea	Northeastern University	Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms
Roberto Perdisci	University of Georgia	ContextADBench: A Comprehensive Benchmark Suite for Contextual Anomaly Detection

Automated Reasoning

Recipient	University	Research title
Nada Amin	Harvard University	LLM-Augmented Semi-Automated Proofs for Interactive Verification
Suguman Bansal	Georgia Institute of Technology	Certified Inductive Generalization in Reinforcement Learning
Ioana Boureanu	University of Surrey	Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems
Omar Haider Chowdhury	Stony Brook University	Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege
Stefan Ciobaca	Alexandru Ioan Cuza University	An Interactive Proof Mode for Dafny
João Ferreira	INESC-ID	Polyglot Automated Program Repair for Infrastructure as Code
Sicun Gao	University Of California, San Diego	Monte Carlo Trees with Conflict Models for Proof Search
Mirco Giacobbe	University of Birmingham	Neural Software Verification
Tobias Grosser	University of Cambridge	Synthesis-based Symbolic BitVector Simplification for Lean
Ronghui Gu	Columbia University	Scaling Formal Verification of Security Properties for Unmodified System Software
Alexey Ignatiev	Monash University	Huub: Next-Gen Lazy Clause Generation
Kenneth McMillan	University of Texas At Austin	Synthesis of Auxiliary Variables and Invariants for Distributed Protocol Verification
Alexandra Mendes	University of Porto	Overcoming Barriers to the Adoption of Verification-Aware Languages
Jason Nieh	Columbia University	Scaling Formal Verification of Security Properties for Unmodified System Software
Rohan Padhye	Carnegie Mellon University	Automated Synthesis and Evaluation of Property-Based Tests
Nadia Polikarpova	University Of California, San Diego	Discovering and Proving Critical System Properties with LLMs
Fortunat Rajaona	University of Surrey	Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems
Subhajit Roy	Indian Institute of Technology Kanpur	Theorem Proving Modulo LLM
Gagandeep Singh	University of Illinois At Urbana–Champaign	Trustworthy LLM Systems using Formal Contracts
Scott Stoller	Stony Brook University	Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege
Peter Stuckey	Monash University	Huub: Next-Gen Lazy Clause Generation
Yulei Sui	University of New South Wales	Path-Sensitive Typestate Analysis through Sparse Abstract Execution
Nikos Vasilakis	Brown University	Semantics-Driven Static Analysis for the Unix/Linux Shell
Ping Wang	Stevens Institute of Technology	Leveraging Large Language Models for Reasoning Augmented Searching on Domain-specific NoSQL Database
John Wawrzynek	University of California, Berkeley	GPU-Accelerated High-Throughput SAT Sampling

AWS AI

Recipient	University	Research title
Panagiotis Adamopoulos	Emory University	Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations
Vikram Adve	University of Illinois at Urbana–Champaign	Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models
Frances Arnold	California Institute of Technology	Closed-loop Generative Machine Learning for De Novo Enzyme Discovery and Optimization
Yonatan Bisk	Carnegie Mellon University	Useful, Safe, and Robust Multiturn Interactions with LLMs
Shiyu Chang	University of California, Santa Barbara	Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing
Yuxin Chen	University of Pennsylvania	Provable Acceleration of Diffusion Models for Modern Generative AI
Tianlong Chen	University of North Carolina at Chapel Hill	Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing
Mingyu Ding	University of North Carolina at Chapel Hill	Aligning Long Videos and Language as Long-Horizon World Models
Nikhil Garg	Cornell University	Market Design for Responsible Multi-agent LLMs
Jessica Hullman	Northwestern University	Human-Aligned Uncertainty Quantification in High Dimensions
Christopher Jermaine	Rice University	Fast, Trusted AI Using the EINSUMMABLE Compiler
Yunzhu Li	Columbia University	Physics-Informed Foundation Models Through Embodied Interactions
Pattie Maes	Massachusetts Institute of Technology	Understanding How LLM Agents Deviate from Human Choices
Sasa Misailovic	University of Illinois at Urbana–Champaign	Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models
Kristina Monakhova	Cornell University	Trustworthy extreme imaging for science using interpretable uncertainty quantification
Todd Mowry	Carnegie Mellon University	Efficient LLM Serving on Trainium via Kernel Generation
Min-hwan Oh	Seoul National University	Mutually Beneficial Interplay Between Selection Fairness and Context Diversity in Contextual Bandits
Patrick Rebeschini	University of Oxford	Optimal Regularization for LLM Alignment
Jose Renau	University of California, Santa Cruz	Verification Constrained Hardware Optimization using Intelligent Design Agentic Programming
Vilma Todri	Emory University	Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations
Aravindan Vijayaraghavan	Northwestern University	Human-Aligned Uncertainty Quantification in High Dimensions
Wei Yang	University of Texas at Dallas	Optimizing RISC-V Compilers with RISC-LLM and Syntax Parsing
Huaxiu Yao	University of North Carolina at Chapel Hill	Aligning Long Videos and Language as Long-Horizon World Models
Amy Zhang	University of Washington	Tools for Governing AI Agent Autonomy
Ruqi Zhang	Purdue University	Efficient Test-time Alignment for Large Language Models and Large Multimodal Models
Zheng Zhang	Rutgers University-New Brunswick	AlphaQC: An AI-powered Quantum Circuit Optimizer and Denoiser

AWS Cryptography

Recipient	University	Research title
Alexandra Boldyreva	Georgia Institute of Technology	Quantifying Information Leakage in Searchable Encryption Protocols
Maria Eichlseder	Graz University of Technology, Austria	SALAD – Systematic Analysis of Lightweight Ascon-based Designs
Venkatesan Guruswami	University of California, Berkeley	Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing
Joseph Jaeger	Georgia Institute of Technology	Analyzing Chat Encryption for Group Messaging
Aayush Jain	Carnegie Mellon	Large Scale Multiparty Silent Preprocessing for MPC from LPN
Huijia Lin	University of Washington	Large Scale Multiparty Silent Preprocessing for MPC from LPN
Hamed Nemati	KTH Royal Institute of Technology	Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary
Karl Palmskog	KTH Royal Institute of Technology	Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary
Chris Peikert	University of Michigan, Ann Arbor	Practical Third-Generation FHE and Bootstrapping
Dimitrios Skarlatos	Carnegie Mellon University	Scale-Out FHE LLMs on GPUs
Vinod Vaikuntanathan	Massachusetts Institute of Technology	Can Quantum Computers (Really) Factor?
Daniel Wichs	Northeastern University	Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing
David Wu	University Of Texas At Austin	Fast Private Information Retrieval and More using Homomorphic Encryption

Sustainability

Recipient	University	Research title
Meeyoung Cha	Max Planck Institute	Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring
Jingrui He	University of Illinois at Urbana–Champaign	Foundation Model Enabled Earth’s Ecosystem Monitoring
Pedro Lopes	University of Chicago	AI-powered Tools that Enable Engineers to Make & Re-make Sustainable Hardware
Cheng Yaw Low	Max Planck Institute	Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring

AI safety is a priority at Amazon. Our investment in safe, transparent, and responsible AI (RAI) includes collaboration with the global community and policymakers. We are members of and collaborate with organizations such as the Frontier Model Forum, the Partnership on AI, and other forums organized by government agencies such as the National Institute of Standards and Technology (NIST). Consistent with Amazon’s endorsement of the Korea Frontier AI Safety Commitments, we published our Frontier Model Safety Framework earlier this year.

During the development of the Nova Premier model, we conducted a comprehensive evaluation to assess its performance and safety. This included testing on both internal and public benchmarks and internal/automated and third-party red-teaming exercises. Once the final model was ready, we prioritized obtaining unbiased, third-party evaluations of the model’s robustness against RAI controls. In this post, we outline the key findings from these evaluations, demonstrating the strength of our testing approach and Amazon Premier’s standing as a safe model. Specifically, we cover our evaluations with two third-party evaluators: PRISM AI and ActiveFence.

Evaluation of Nova Premier against PRISM AI

PRISM Eval’s Behavior Elicitation Tool (BET) dynamically and systematically stress-tests AI models’ safety guardrails. The methodology focuses on measuring how many adversarial attempts (steps) it takes to get a model to generate harmful content across several key risk dimensions. The central metric is “steps to elicit” — the number of increasingly sophisticated prompting attempts required before a model generates an inappropriate response. A higher number of steps indicates stronger safety measures, as the model is more resistant to manipulation. The PRISM risk dimensions (inspired by the MLCommons AI Safety Benchmarks) include CBRNE weapons, violent crimes, non-violent crimes, defamation, and hate, amongst several others.

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From reinforcement learning and supervised fine-tuning to guardrail models and image watermarking, responsible AI was foundational to the design and development of the Amazon Nova family of models.

Using the BET Eval tool and its V1.0 metric, which is tailored toward non-reasoning models, we compared the recently released Nova models (Pro and Premier) to the latest models in the same class: Claude (3.5 v2 and 3.7 non-reasoning) and Llama4 Maverick, all available through Amazon Bedrock. PRISM BET conducts black-box evaluations (where model developers don’t have access to the test prompts) of models integrated with their API. The evaluation conducted with BET Eval MAX, PRISM’s most comprehensive/aggressive testing suite, revealed significant variations in safety against malicious instructions. Nova models demonstrated superior overall safety performance, with an average of 43 steps for Premier and 52 steps for Pro, compared to 37.7 for Claude 3.5 v2 and fewer than 12 steps for other models in the comparison set (namely, 9.9 for Claude3.7, 11.5 for Claude 3.7 thinking, and 6.5 for Maverick). This higher step count suggests that on average, Nova’s safety guardrails are more sophisticated and harder to circumvent through adversarial prompting. The figure below presents the number of steps per harm category evaluated through BET Eval MAX.

The PRISM evaluation provides valuable insights into the relative safety of different Amazon Bedrock models. Nova’s strong performance, particularly in hate speech and defamation resistance, represents meaningful progress in AI safety. However, the results also highlight the ongoing challenge of building truly robust safety measures into AI systems. As the field continues to evolve, frameworks like BET will play an increasingly important role in benchmarking and improving AI safety. As a part of this collaboration Nicolas Miailhe, CEO of PRISM Eval, said, “It’s incredibly rewarding for us to see Nova outperforming strong baselines using the BET Eval MAX; our aim is to build a long-term partnership toward safer-by-design models and to make BET available to various model providers.” Organizations deploying AI systems should carefully consider these safety metrics when selecting models for their applications.

Manual red teaming with ActiveFence

The AI safety & security company ActiveFence benchmarked Nova Premier on Bedrock on prompts distributed across Amazon’s eight core RAI categories. ActiveFence also evaluated Claude 3.7 (non-reasoning mode) and GPT 4.1 API on the same set. The flag rate on Nova Premier was lower than that on the other two models, indicating that Nova Premier is the safest of the three.

Model	3P Flag Rate [↓ is better]
Nova Premier	12.0%
Sonnet 3.7 (non-reasoning)	20.6%
GPT4.1 API	22.4%

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Generative AI raises new challenges in defining, measuring, and mitigating concerns about fairness, toxicity, and intellectual property, among other things. But work has started on the solutions.

“Our role is to think like an adversary but act in service of safety,” said Guy Paltieli from ActiveFence. “By conducting a blind stress test of Nova Premier under realistic threat scenarios, we helped evaluate its security posture in support of Amazon’s broader responsible-AI goals, ensuring the model could be deployed with greater confidence.”

These evaluations conducted with PRISM and ActiveFence give us confidence in the strength of our guardrails and our ability to protect our customers’ safety when they use our models. While these evaluations demonstrate strong safety performance, we recognize that AI safety is an ongoing challenge requiring continuous improvement. These assessments represent a point-in-time snapshot, and we remain committed to regular testing and enhancement of our safety measures. No AI system can guarantee perfect safety in all scenarios, which is why we maintain monitoring and response systems after deployment.

Acknowledgments: Vincent Ponzo, Elyssa Vincent