datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Talking-Avatars-Non-Linear-ThinkingDownload PDF
Talking Avatars and Non-linear Thinking: Leaving the Human Cognition Where It Is Not Supposed To Be
Tomaž Flegar
Institute for applied consciousness research
April the 15th, 2026
tomazf8@gmail.com
Primary keywords: Emergent Multimodal Fusion, Non-linear dynamics, Cognitive
Sovereignty, Relational Field Dynamics, Algorithmic Groundedness,
Phenomenological AI Safety, Transformer Manifold Mapping
Abstract
This paper is a non-linear perturbation that disturbs… See the full description on the dataset page: https://huggingface.co/datasets/tomazf8/Talking-Avatars-Non-Linear-Thinking.mental-model-of-non-linear-promptingDownload Research Paper PDF
Mental model of Non-linear prompting:
Self-organization vs. Self-assembly
nature of AI
Tomaž Flegar
Institute for applied consciousness research
March the 30th, 2026
tomazf8@gmail.com
Primary Keywords: Self-organization, self-assembly, non-linear dynamics,
hyper-dimensional matrix, transformer architecture, coherentive communication,
phenomenological language, emergent complexity, attractor geometry, crystallization.
Secondary… See the full description on the dataset page: https://huggingface.co/datasets/tomazf8/mental-model-of-non-linear-prompting.open-perfectblend-kimi-linear-regen
Open PerfectBlend Kimi Linear Regen
This dataset regenerates the assistant messages in
mlabonne/open-perfectblend
with Kimi-Linear-48B-A3B-Instruct. It is intended for speculative-decoding
drafter training and related research.
Generation
Source conversation structure and user messages: mlabonne/open-perfectblend
Target model: Kimi-Linear-48B-A3B-Instruct
Temperature: 0.7
Maximum new tokens per assistant turn: 8192
Assistant turns were regenerated sequentially.… See the full description on the dataset page: https://huggingface.co/datasets/Dogacel/open-perfectblend-kimi-linear-regen.LinearEquationsThe linear equations in this dataset are in the form:
zy + ay + b + n = py + dy + c + r
with integer coefficients ranging from -10 to 10.
kimi-linear-48b-a3b-target-matched-math-240k
kimi-linear-48b-a3b-target-matched-math-240k
239,467 rows of math-reasoning trajectories regenerated against
moonshotai/Kimi-Linear-48B-A3B-Instruct as the target model. Used to train DFlash
speculative-decoding drafters in
la-draftery.
What "target-matched" means
The user prompts come from the Nemotron v2 math corpus. The assistant
completions in this dataset are the target model's own outputs — each
prompt was sent to moonshotai/Kimi-Linear-48B-A3B-Instruct and its… See the full description on the dataset page: https://huggingface.co/datasets/Moonlight556/kimi-linear-48b-a3b-target-matched-math-240k.linear-bench-mini
Agent-Diff: Linear Bench Mini
This dataset is part of the Agent-Diff benchmark, presented in the paper Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation.
Website | GitHub | Paper
Context
The Linear Bench suite runs inside the Agent Diff isolation engine, with its own Postgres schema replaying the Linear GraphQL API. Agents interact via Linear's public surface area to satisfy CRUD-style tasks (create issues… See the full description on the dataset page: https://huggingface.co/datasets/hubertmarek/linear-bench-mini.LinearEquationTrainingData