datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
EverMemBench-Dynamic
EverMemBench-Dynamic
A benchmark dataset for evaluating long-term memory capabilities in conversational AI systems. It is part of EverMemBench, the first benchmark designed for long-horizon collaborative memory, introduced in the paper Evaluating Long-Horizon Memory for Multi-Party Collaborative Dialogues — accepted at KDD 2026 (Oral).
Configurations
This dataset has three configurations (subsets):
dialogues
Multi-turn group dialogues spanning ~250… See the full description on the dataset page: https://huggingface.co/datasets/EverMind-AI/EverMemBench-Dynamic.live-facts-snapshot
Live Facts Snapshot
A daily snapshot of verifiable, post-training-cutoff world-state facts — the kind of
ground truth language models cannot know from training data — exported through
Dynamic Feed, a live, verifiable data API whose every response
is Ed25519-signed. One file per day (data/YYYY-MM-DD.jsonl), one fact per line, and
every row carries its own source, source_url and measured_at.
Facts covered per day:
tool
facts
upstream source
licence
software_version… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/live-facts-snapshot.groundtruth-dynamic-benchmarking
Groundtruth Dynamic Benchmarking — Geology
Question sets and grading rubrics for evaluating LLMs on real-world geological
reasoning. Every question is authored from a real source corpus, and every
claim in the grading key carries an evidence locator back to that corpus —
nothing is synthetic. Licensing/redistribution status varies by corpus — see
License.
This dataset holds the questions, grading rubrics, and source corpora.
Running an evaluation (generating answers from a model… See the full description on the dataset page: https://huggingface.co/datasets/EigenformAI/groundtruth-dynamic-benchmarking.generalization-dynamics-evals
Generalization Dynamics — Main Eval Suite
Prepared test sets for the 6 main evaluation families from
Generalization dynamics across fine-tuning
(Table 1).
Use with the unified runner:
https://github.com/jiaxin-wen/FT-generalization/tree/main/release
from huggingface_hub import snapshot_download
root = snapshot_download(
repo_id="jiaxin-wen/generalization-dynamics-evals", repo_type="dataset")
Or browse a single task (the dataset viewer shows all configs):
from datasets… See the full description on the dataset page: https://huggingface.co/datasets/jiaxin-wen/generalization-dynamics-evals.update-interrupt-benchmark
Update-Driven Math & Code Interrupt Datasets
Paper: Are Large Reasoning Models Interruptible?
Authors: Tsung-Han Wu*, Mihran Miroyan*, David Chan, Trevor Darrell, Narges Norouzi, Joseph Gonzalez
Project page: https://dynamic-lm.github.io/
Github: https://github.com/dynamic-lm/interrupt-lrm
This dataset page contains the update-driven interrupt subsets for math (GSM8K, MATH500, AIME) and coding (LiveCodeBench) problems. For both splits, we revise the source problems and… See the full description on the dataset page: https://huggingface.co/datasets/dynamic-lm/update-interrupt-benchmark.dynamicqa
DYNAMICQA
This is a repository for the paper DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models accepted at Findings of EMNLP 2024.
Our paper investigates the Language Model's behaviour when the conflicting knowledge exist within the LM's parameters. We present a novel dataset containing inherently conflicting data, DYNAMICQA. Our dataset consists of three partitions, Static, Disputable 🤷♀️, and Temporal 🕰️.
We also evaluate several measures on their ability… See the full description on the dataset page: https://huggingface.co/datasets/copenlu/dynamicqa.dynamic_sonnet_llama3
Dynamic Sonnet - Llama3
Curated dataset for benchmarking LLM serving systems
In real-world service scenarios, each request comes with varying input token lengths.
Some requests generate only a few tokens, while others produce a significant number.
Traditional fixed-length benchmarks fail to capture this variability, making it difficult to accurately assess real-world throughput performance.
This dynamic nature of input token lengths is crucial as it directly affects key features of… See the full description on the dataset page: https://huggingface.co/datasets/squeezebits/dynamic_sonnet_llama3.dynamic_sonnet_llama2
Dynamic Sonnet - Llama2
Curated dataset for benchmarking LLM serving systems
In real-world service scenarios, each request comes with varying input token lengths.
Some requests generate only a few tokens, while others produce a significant number.
Traditional fixed-length benchmarks fail to capture this variability, making it difficult to accurately assess real-world throughput performance.
This dynamic nature of input token lengths is crucial as it directly affects key features of… See the full description on the dataset page: https://huggingface.co/datasets/squeezebits/dynamic_sonnet_llama2.dynamics-reasoning-traces-sample
DYNAMICS-8 Behavioural Reasoning Traces
Personality-conditioned chain-of-thought reasoning data for LLM alignment and persona fine-tuning.
What This Dataset Contains
Each record is a first-person behavioural response from a synthetic persona with a validated 8-dimension personality profile (DYNAMICS-8), accompanied by a structured reasoning trace showing which personality dimensions drove the decision.
This is not survey data. It is not statistical synthetic data. Each… See the full description on the dataset page: https://huggingface.co/datasets/Kronaxis/dynamics-reasoning-traces-sample.DynamicRAG_Training_Data_150kDynamicMem
DynamicMem
This dataset contains anonymized app logs and corresponding evaluation task files for 10 users.
Dataset Structure
app_logs/: app interaction logs for each user.
eval_tasks/: corresponding evaluation task data files.
metadata.csv: mapping between users, app log files, and evaluation task files.
License
CC BY 4.0