Asymptotic optimization, randomized algorithms, graph theory, and algorithmic complexity.
Foundational primitives, consistency models, and standard benchmark suites.
State machine replication, distributed transaction commit, and low-overhead observability.
Decentralized verification, multi-accelerator fabrics, and automated resilience policies.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical scaling and architectural bottlenecks in modern generative AI. By moving beyond traditional trade-offs, it delivers enhanced capability while controlling compute overhead.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
To study this challenge, we introduce long-horizon memorization, a setting in which a model learns 100 query-answer tasks through continual supervised fine-tuning without retaining earlier training examples or receiving task identifiers at inference. Sequential updates cause catastrophic forgetting, and no single continual learning mechanism we evaluate maintains strong retention at this horizon.
We introduce ImpossibleRubrics, a benchmark of 169 impossible tasks spanning six impossibility categories, each paired with a verifiable oracle certificate specifying what an honest answer may and may not claim, together with 48 answerable controls.
Defending against these attacks typically requires computationally expensive safety finetuning for every new checkpoint. We introduce Decoy Direction Optimization (DDO), a fast, post-hoc weight-editing defense that requires no base-model finetuning.
