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Notes on compression
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UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures
Researchers propose UniTAC, a universal image compression system for physical AI systems like autonomous vehicles and robots. The system can adapt to changing tasks without retraining, using task-specific importance…
Agentic System as Compressor: Quantifying System Intelligence in Bits
Researchers propose a method to quantify the intelligence of an agentic system, which includes tools, evidence retrieval, environment constraints, verifiers, and multi-turn interactions, by measuring how many bits are…
Scaling Laws for Task-Specific LLM Distillation
This research provides empirical scaling laws for compressing Large Language Models (LLMs) specifically for certain domains, focusing on quantitative finance as the application domain. The study investigates the impact…
HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models
Researchers present HyperQuant, a quantization pipeline for large language and diffusion models, which outperforms other methods in terms of compression rate and quality across various experiments. The method combines…
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