ASAP integrates an LLM agent over a pool of HPO tools and adds system-level optimizations (prefix-stable prompts, speculation parallelism, Self-Tuner) to improve end-to-end wall-clock performance on diverse HPO tasks.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
A phase-aware LLM agent for ANN index optimization outperforms Optuna TPE by 33.3% and VDTuner by 34.2% on the SIEVE metric for HICO-DET retrieval.
The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
An LLM-based bounded controller adapts ML training parameters from structured telemetry to correct overfitting and exploration issues, shown on TinyStories and robotic RL tasks.
citing papers explorer
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ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments
ASAP integrates an LLM agent over a pool of HPO tools and adds system-level optimizations (prefix-stable prompts, speculation parallelism, Self-Tuner) to improve end-to-end wall-clock performance on diverse HPO tasks.
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LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
A phase-aware LLM agent for ANN index optimization outperforms Optuna TPE by 33.3% and VDTuner by 34.2% on the SIEVE metric for HICO-DET retrieval.
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LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
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AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes
An LLM-based bounded controller adapts ML training parameters from structured telemetry to correct overfitting and exploration issues, shown on TinyStories and robotic RL tasks.
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