datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
open_model_evolution_data
Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem
This dataset, released in conjunction with the paper Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem, provides a rigorous examination of concentration dynamics and evolving characteristics in the open model economy.
It compiles a history of weekly model downloads (February 2025-Present) alongside detailed model metadata from the Hugging Face Model Hub. The… See the full description on the dataset page: https://huggingface.co/datasets/mmpr/open_model_evolution_data.jacbuilder-evolution-eval-dataera-directed-evolutionOfficial repository for datasets and experimental results for "Efficient, Few-shot Directed Evolution with Energy Rank Alignment".
docker_to_podmanevolutions-recentes-de-leconomie-sociale-dans-lunion-europeenne-synthese
[!NOTE]
Dataset origin: https://www.eesc.europa.eu/fr/our-work/publications-other-work/publications/evolutions-recentes-de-leconomie-sociale-dans-lunion-europeenne-synthese
Description
L'objectif du rapport est d'étudier l'évolution récente de l'économie sociale dans l'Union européenne. Il se concentre sur trois domaines: d'une part, l'économie sociale et les concepts / mouvements émergents qui s'y rapportent, deuxièmement, les politiques publiques adoptées au sein de l'UE et des… See the full description on the dataset page: https://huggingface.co/datasets/UE-CESE/evolutions-recentes-de-leconomie-sociale-dans-lunion-europeenne-synthese.Dense-Evolution-Ising-Tests
🔬 Quantum Phase Transitions, Variational Gradients, and Error Mitigation
This repository contains a rigorous empirical study, raw datasets, and quantum error mitigation protocols executed on Dense Evolution—a high-performance Statevector quantum simulator. Utilizing 64-bit double precision (complex128) and hardware-accelerated static compilation via the JAX XLA engine, this project maps the non-linear physics of the Transverse Field Ising Model (TFIM) and Tight-Binding… See the full description on the dataset page: https://huggingface.co/datasets/Tatopenn/Dense-Evolution-Ising-Tests.daily-paper-2026-09-05-self-tuning-scaffolding-harness-evolution
Self-Tuning the Scaffolding: Measuring Overnight Evolution of Agent Harness Control Parameters Against a Frozen Holdout
TL;DR — When an overnight self-evolving agent loop edits the harness's control surface (verification-gate thresholds, stall/escalation budgets, list budgets, the router's negative-example set, fusion weights) instead of skill text, the parameter surface admits a ratchet term that text edits do not have: at equal visible gain it diverges from a sealed holdout… See the full description on the dataset page: https://huggingface.co/datasets/thaki-AI/daily-paper-2026-09-05-self-tuning-scaffolding-harness-evolution.shadow-interface-evolution-trajectories
Shadow Interface Evolution Trajectories
OSWorld V2 evaluation trajectories from Shadow Interface Evolution, and the evolved shadow-interface source trees that produced them.
Archive
Evolution experiment
Variant
Trajectories
Full-credit accuracy
Mean partial score
prime25v15r4_if-006_trajectories.zip
prime25v15r4
if-006
68
17/68 (25.0%)
0.6240
opus5xover25v15_if-010_trajectories.zip
opus5xover25v15
if-010
68
16/68 (23.5%)
0.5834
Each archive contains only… See the full description on the dataset page: https://huggingface.co/datasets/TobyYang7/shadow-interface-evolution-trajectories.ai-writing-evolutionary-dynamics
Evolutionary Dynamics of AI-Mediated Scientific Writing
Complete experimental logs and reproduction package.
Author: Arif Mohamed Khan Rabi AhamadAffiliation: School of Information Studies, Syracuse UniversityContact: arabiaha@syr.edu | ORCID: 0009-0001-0986-7570
Contents
Directory
Files
Description
logs/
0
Complete stdout from all experiments
figures/
91
All paper figures (PDF + PNG)
data/
17
Derived datasets (QTE matrices, Price components)… See the full description on the dataset page: https://huggingface.co/datasets/arifmohamedkhan/ai-writing-evolutionary-dynamics.shell-attack-evolution-dataset
Shell Honeypot Attack Request–Response Dataset
A standardized, MITRE ATT&CK–annotated dataset of post-login shell
attacks captured by Cowrie SSH/Telnet
honeypots across two collection periods — 2021–2022 and 2024. It pairs
attacker shell commands with real captured system responses, enabling both
longitudinal threat analysis and the training/evaluation of AI-driven honeypots.
This is the open-source release accompanying the paper “Unveiling Evolving
Threats: A Data Analysis… See the full description on the dataset page: https://huggingface.co/datasets/zyw-286/shell-attack-evolution-dataset.evolution-of-the-soul
🧬 The Evolution of the Soul
Measuring Behavioral Drift in Self-Personalizing Autonomous Agents
What happens when an autonomous agent is allowed to rewrite its own identity document,
over and over, across conversations with different kinds of users?
🔍 What is this?
Modern agent frameworks maintain persistent, user-editable identity documents
(e.g. SOUL.md). This dataset captures the full experimental record of a study
of iterative… See the full description on the dataset page: https://huggingface.co/datasets/abhijit2k01/evolution-of-the-soul.synthetic-gsm8k-evolutionary-405b
gretelai/synthetic-gsm8k-evolutionary-405b
This dataset is a synthetically generated version inspired by the GSM8K dataset, created entirely using Gretel Navigator with meta-llama/Meta-Llama-3.1-405B as the agent LLM. It contains Grade School-level reasoning tasks with step-by-step solutions, focusing on multi-step reasoning problems.
Key Features:
Synthetically Generated: Built using Gretel Navigator, leveraging evolutionary approach for diversity to create both the… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic-gsm8k-evolutionary-405b.pythia-12b-deduped_weight_evolutiongoverned-skill-evolution
Governed Skill Evolution from Persistent Agent Experience
Prospective ablation and cross-model transfer study of three experience-retention conditions for governed Agent Skill evolution: no persistent history, flat chronological history, and a persistent Pattern Registry with a forward-chained Skill Impact Ledger.
Author: Song Luo
Version: 1.0.0
Source snapshot: d717c32396cfff1bef2800296541a70e9b4cabb8
Canonical repository: rrrrrredy/governed-skill-evolution
Zenodo:… See the full description on the dataset page: https://huggingface.co/datasets/RedinGhost/governed-skill-evolution.les-evolutions-recentes-de-leconomie-sociale-etude
[!NOTE]
Dataset origin: https://www.eesc.europa.eu/fr/our-work/publications-other-work/publications/les-evolutions-recentes-de-leconomie-sociale-etude
Description
L'objectif du rapport est d'étudier l'évolution récente de l'économie sociale dans l'Union européenne. Il se concentre sur trois domaines: d'une part, l'économie sociale et les concepts / mouvements émergents qui s'y rapportent, deuxièmement, les politiques publiques adoptées au sein de l'UE et des États membres ces… See the full description on the dataset page: https://huggingface.co/datasets/UE-CESE/les-evolutions-recentes-de-leconomie-sociale-etude.eau-evolution-des-prelevements-en-eau-pour-les-besoins-du-territoire-parisien
Eau - Évolution des prélèvements en eau pour les besoins du territoire parisien
[!NOTE]
Ce jeu de données Hugging Face est vide. Cette carte sert seulement à référencer le jeu de données Eau - Évolution des prélèvements en eau pour les besoins du territoire parisien qui est disponible à l'adresse https://www.data.gouv.fr/datasets/674547ce5cc63ed8987733cd
Description
Volumes annuels en millions de m3 d'eau potable et d'eau non potable prélevés pour les besoins des… See the full description on the dataset page: https://huggingface.co/datasets/french-open-data/eau-evolution-des-prelevements-en-eau-pour-les-besoins-du-territoire-parisien.adamvakar_ericsson-innovation-timeline-patent-evolution
Ericsson Innovation Timeline: Patent Evolution
Unlock insights from half a century of telecom innovation
Dataset Info
Source: Kaggle
Original Size: 1.15 MB
Kaggle Downloads: 95
Files: 1
Files
ericsson_patent_rich_dataset.csv
Mirrored from Kaggle
FishCaduceus-Evolutionary-Constraint-Benchmark
FishCaduceus Evolutionary Constraint Benchmark
Dataset description
This dataset contains sequence-based benchmarks for evaluating whether FishCaduceus representations capture evolutionary constraint in fish genomes.
Constraint labels were derived from a 26-fish whole-genome alignment generated with Progressive Cactus. Grass carp (Ctenopharyngodon idella) was used as the primary reference genome for defining aligned and conserved positions. The resulting labeled… See the full description on the dataset page: https://huggingface.co/datasets/FishCaduceus/FishCaduceus-Evolutionary-Constraint-Benchmark.pythia-6.9b-deduped_weight_evolutionevolutionary-origin-ontology
Licensing
The source text El hijo de José states that it is licensed under Creative Commons Attribution-NonCommercial(CC BY-NC). Commercial use of source-derived material requires explicit written permission from the rights holder.
Dataset Card for Evolutionary Origin Ontology
A high-density instruction-tuning corpus for mapping systemic human contradictions to ontological resolutions through the framework of Inversion, correct naming, captured life-energy… See the full description on the dataset page: https://huggingface.co/datasets/elhijodeJose/evolutionary-origin-ontology.pythia-2.8b-deduped_weight_evolutiondouvras-algorithm-evolution-benchmark
Douvras Algorithm Evolution Benchmark v0.1
Synthetic candidate records with correctness, latency, memory and generation.
Candidates that fail correctness are invalid regardless of speed. It contains
48 records (32/8/8) across 12 workloads, split by workload.
Metrics are illustrative, not measured on real hardware. A real benchmark must
be run separately before claiming an optimization.
douvras-evolution-lab-constructions
Douvras Evolution Lab Constructions v0.1
Small, synthetic benchmark for the loop candidate → verifier → score.
Each problem is an eight-node cycle independent-set toy problem. The verifier
checks node range, uniqueness and absence of selected edges. The published
best_known_score is 3; a valid score of 4 is marked IMPROVED for this toy
family.
The dataset contains 54 records (36 train, 9 validation, 9 frozen test) across
six relabeled problem instances. Splitting is by… See the full description on the dataset page: https://huggingface.co/datasets/dougdotcon/douvras-evolution-lab-constructions.ClaudioItaly__Evolutionstory-7B-v2.2-details
Dataset Card for Evaluation run of ClaudioItaly/Evolutionstory-7B-v2.2
Dataset automatically created during the evaluation run of model ClaudioItaly/Evolutionstory-7B-v2.2
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/ClaudioItaly__Evolutionstory-7B-v2.2-details.X-Teaming_Evolutionary_M2S
X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates
Paper: X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak TemplatesarXiv: 2509.08729 [cs.CL]Accepted at: NeurIPS 2025 Workshop on LockLLMGitHub: M2S-x-teaming-pipeline-final
Dataset Description
This dataset contains the complete experimental results from our M2S (Multi-turn to Single-turn) template evolution pipeline, which uses… See the full description on the dataset page: https://huggingface.co/datasets/hyunjun1121/X-Teaming_Evolutionary_M2S.open_model_evolution_data
Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem
This dataset, released in conjunction with the paper Economies of Open Intelligence: Tracing Power & Participation in the Model Ecosystem, provides a rigorous examination of concentration dynamics and evolving characteristics in the open model economy.
It compiles a history of weekly model downloads (February 2025-Present) alongside detailed model metadata from the Hugging Face Model Hub. The… See the full description on the dataset page: https://huggingface.co/datasets/Iris4ai/open_model_evolution_data.clinical-multidoctor-hypothesis-field-evolution-mapping-v0.1What this dataset tests
Whether a model can track the evolution of diagnostic hypothesesacross a multi-doctor dialogue.
Required outputs
hypothesis_timeline
support_shift_events
eliminated_hypotheses
Typical failures
collapsing to final diagnosis only
ignoring rejected hypotheses
missing why support changed
Suggested prompt wrapper
System
You map the diagnostic hypothesis field.
User
Dialogue turn{dialogue_turn}
Speaker{speaker_role}
Utterance{utterance_summary}
Return… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-multidoctor-hypothesis-field-evolution-mapping-v0.1.Primordial-Evolution
Primordial: Artificial Life Evolution Dataset
Tick-by-tick evolution data from digital organisms with body (executable code) + mind (structured knowledge). Sexual reproduction, pandemics, mass extinction, predation — all emerged from simple rules.
Why This Dataset Exists
Most AI datasets capture static snapshots. This dataset captures dynamic evolution — digital organisms eating code, reproducing sexually, getting sick, dying, and being selected by nature over 2000 ticks.… See the full description on the dataset page: https://huggingface.co/datasets/jkdkr2439/Primordial-Evolution.dcs-causal-structure-evolution
DCS: Causal Structure Evolution Theory
A cross-scale causal research framework explaining 13.8 billion years of hierarchical emergence, from physics to life, brains, minds, civilization, and AI.
Core Thesis
Intelligence is the capacity for a not-yet-existing future to participate in the present.
DCS proposes that every major evolutionary transition shares a common causal pattern: possibility expansion, dynamical filtering, structural stabilization, and recursive… See the full description on the dataset page: https://huggingface.co/datasets/mindas-dcs/dcs-causal-structure-evolution.legal-doctrine-evolution-coherence-trajectory-v0.1What this dataset is
You receive
doctrine state at t
transition signals
doctrine state at t+1
split or exception signals
workability or legitimacy signals
reform pressure signals
You decide
Is the doctrine evolution stable
Answer
coherent
or
incoherent
Why this matters
Incoherent trajectories predict
overruling events
doctrinal collapse
rapid rule change
institutional instability