ALTO: Efficient Network Orchestrator for Compound AI Systems
ALTO optimizes compound AI systems by streaming partial outputs, improving throughput and reducing latency.
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
언어 모델을 활용하여 코드 생성을 재귀적으로 자체 향상시키는 STOP 프레임워크 소개
Achieving Better Adoption of AI Systems in Natural Science Research
Effective collaboration and support are crucial for improving AI adoption among domain experts in natural science research.
ALTO: Efficient Network Orchestrator for Compound AI Systems
ALTO is a network orchestrator that efficiently serves compound AI systems, optimizing throughput and latency by streaming intermediate outputs between stages.
PROC2PDDL: Open-Domain Planning Representations from Texts
LMs struggle with open-domain planning due to syntactic and semantic errors.
Verifiable Training Method for AI Models with Hardware Control
Proposing a method to achieve verifiable training by controlling hardware nondeterminism, ensuring correctness and guarding against attacks.
ALTO: Efficient Network Orchestrator for Compound AI Systems
ALTO optimizes compound AI systems by streaming intermediate outputs, addressing correctness and load balancing challenges, resulting in increased throughput and reduced latency.
