Kled • HADES
Kled • HADES
The Human Aligned Data Evaluation Standard.
The Human Aligned Data Evaluation Standard.
We research the science of data utility - developing the training sets and rigorous evaluations necessary to align AI with the real world.
We research data utility - developing training sets and evaluations to align AI with the world.
Featured Research
From the Research Team
From the Research Team
Insights and findings from our ongoing work in data quality and model behavior.
Insights and findings from our ongoing work in data quality and model behavior.
The Uncanny Valley of Perfection
5 MIN READ
COMPUTER VISION
RESEARCH
State-of-the-art models like Flux and SDXL suffer from "aesthetic mode collapse," producing plastic, studio-like outputs. By fine-tuning on a curated Kled coreset of high-utility images, we successfully injected realistic optical factors—such as photon noise and lens aberrations—back into the generation process, consistently outperforming prompt engineering in texture fidelity.
3.1K
Coreset Images
3
Training Epochs
10
Metrics Evaluated
The Uncanny Valley of Perfection
5 MIN READ
COMPUTER VISION
RESEARCH
State-of-the-art models like Flux and SDXL suffer from "aesthetic mode collapse," producing plastic, studio-like outputs. By fine-tuning on a curated Kled coreset of high-utility images, we successfully injected realistic optical factors—such as photon noise and lens aberrations—back into the generation process, consistently outperforming prompt engineering in texture fidelity.
3.1K
Coreset Images
3
Training Epochs
10
Metrics Evaluated
The Uncanny Valley of Perfection
5 MIN READ
COMPUTER VISION
RESEARCH
State-of-the-art models like Flux and SDXL suffer from "aesthetic mode collapse," producing plastic, studio-like outputs. By fine-tuning on a curated Kled coreset of high-utility images, we successfully injected realistic optical factors—such as photon noise and lens aberrations—back into the generation process, consistently outperforming prompt engineering in texture fidelity.
Our Approach
How We Study Data and Models
How We Study Data and Models
We study how real-world data behaves and how it shapes modern AI systems.
We study how real-world data behaves and how it shapes modern AI systems.
Dataset Structure
We analyze entropy, texture diversity, compression patterns, and optical artifacts to reveal the subtle real-world signals that models often miss. This helps us understand how data complexity influences downstream representations.
Annotation Quality
We evaluate contributor consistency, label variance, and trust metrics across large-scale pipelines. By examining noise patterns and human error modes, we identify where annotation quality meaningfully impacts model behavior.
Training Dynamics
We compare zero-shot, prompt-engineered, and fine-tuned training setups to understand how different strategies affect realism, texture retention, and robustness. These controlled experiments highlight the tradeoffs in various training recipes.
Generalization
We benchmark models across in-distribution and out-of-distribution scenarios to measure transfer, stability, and failure modes. This helps us map how well models adapt to data beyond their training core.
Dataset Structure
We analyze entropy, texture diversity, compression patterns, and optical artifacts to reveal the subtle real-world signals that models often miss. This helps us understand how data complexity influences downstream representations.
Annotation Quality
We evaluate contributor consistency, label variance, and trust metrics across large-scale pipelines. By examining noise patterns and human error modes, we identify where annotation quality meaningfully impacts model behavior.
Training Dynamics
We compare zero-shot, prompt-engineered, and fine-tuned training setups to understand how different strategies affect realism, texture retention, and robustness. These controlled experiments highlight the tradeoffs in various training recipes.
Generalization
We benchmark models across in-distribution and out-of-distribution scenarios to measure transfer, stability, and failure modes. This helps us map how well models adapt to data beyond their training core.
Dataset Structure
We analyze entropy, texture diversity, compression patterns, and optical artifacts to reveal the subtle real-world signals that models often miss. This helps us understand how data complexity influences downstream representations.
Annotation Quality
We evaluate contributor consistency, label variance, and trust metrics across large-scale pipelines. By examining noise patterns and human error modes, we identify where annotation quality meaningfully impacts model behavior.
Training Dynamics
We compare zero-shot, prompt-engineered, and fine-tuned training setups to understand how different strategies affect realism, texture retention, and robustness. These controlled experiments highlight the tradeoffs in various training recipes.
Generalization
We benchmark models across in-distribution and out-of-distribution scenarios to measure transfer, stability, and failure modes. This helps us map how well models adapt to data beyond their training core.
The Leading Data Marketplace.
Support
A Nitrility Inc. Company
Kled AI © 2025
The Leading Data Marketplace.
Support
A Nitrility Inc. Company
Kled AI © 2025
The Leading Data Marketplace.
Support
A Nitrility Inc. Company
Kled AI © 2025