Tag: LLM Optimization
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RoboPhD: Efficient Evolution of Complex AI Agents
Discover RoboPhD, a toolkit for evolving diverse AI agents efficiently under tight evaluation budgets, outperforming leading optimization methods.
MAC-Attention: Fast, Accurate Attention for Long-Context LLMs
Discover MAC-Attention, a novel scheme boosting large language models with fast, accurate long-context attention and up to 14x speed improvements.
Signal-Based Trajectory Sampling for Efficient Agent Interaction
Optimize agentic interactions with signal-based trajectory sampling and triage for improved efficiency and post-deployment performance.
Adaptive Context Compression for Large Language Models
Boost LLM performance in long interactions with adaptive context compression using memory selection, coherence filtering, and dynamic resource allocation.
Efficient Batch Query Routing for Large Language Models
Optimize large language model queries with batch-level routing under cost, GPU, and capacity constraints for improved performance and resource use.