datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
locomolocomolocomo-mc10
LoCoMo‑MC10 · Long Conversation Memory Multiple‑Choice 10
LoCoMo‑MC10 is a 1,986‑item multiple‑choice benchmark derived from the LoCoMo (Long Conversation Memory) dataset.Each item probes one of five conversation memory abilities, reformatted into a 10‑option MC task for straightforward automated evaluation (plain accuracy, balanced accuracy, etc.).
Single-hop reasoning (SH)
Multi-hop reasoning (MH)
Temporal reasoning (TR)
Open-domain knowledge (OD)
Adversarial questions (ADV)
The… See the full description on the dataset page: https://huggingface.co/datasets/Percena/locomo-mc10.LoCoMo
LoCoMo
An MTEB dataset
Massive Text Embedding Benchmark
LMEB dialogue-memory retrieval task based on LoCoMo, covering single-hop, multi-hop, temporal, open-domain, and adversarial long-conversation questions.
Task category
Retrieval (text-to-text)
DomainsSocial, Spoken
Reference
LMEB: Long-horizon Memory Embedding Benchmark
Source datasets:
KaLM-Embedding/LMEB
How to evaluate on this task
You can evaluate an embedding model on this dataset using the… See the full description on the dataset page: https://huggingface.co/datasets/mteb/LoCoMo.Locomoamass-cmu-locomotion-videoamass-cmu-locomotion-videoLoCoMoDocLoCoMo
LoCoMo
MTEB v2 text retrieval dataset layout for LoCoMo. The candidates configs store the per-query retrieval pool for each memory retrieval subset.
locomotivelocomolocomosoul-benchmarks-locomo
soul.py LoCoMo Benchmark Results
Benchmark results for soul.py on the LoCoMo long-conversation memory benchmark.
Benchmarks repo: github.com/menonpg/soul-benchmarksInteractive results: menonpg.github.io/soul-benchmarks
What is soul.py?
soul.py is an open-source conversational memory layer for LLM agents. It provides multiple retrieval backends (BM25, Qdrant vector search, Relational Learning Model) and an auto-router that selects the best strategy per query.… See the full description on the dataset page: https://huggingface.co/datasets/pgmenon/soul-benchmarks-locomo.benchmark-locomomicroduck-locomotion-dataset-v1
🦆 MicroDuck Locomotion Dataset V1.0: Zero-Slip Flat Ground Baseline
Publisher: devorah-ai-2026 / devorahai2026License: Commercial / CC-BY-SA 4.0Task: Bipedal Locomotion, Reinforcement Learning, Sim-to-RealHardware Platform: Antigravity 14-DOF MicroDuck Biped Robot
📌 Overview (개요)
본 데이터셋은 **14-DOF 소형 2족 보행 로봇(MicroDuck)**의 완벽한 평면 보행(Flat-ground walking) 궤적 및 제어 로그를 담은 고품질 합성 데이터(Synthetic Data)입니다. 기존 강화학습 기반 2족 보행 에이전트들이 흔히 겪는 발바닥 미끄러짐(Ice-Skating) 및… See the full description on the dataset page: https://huggingface.co/datasets/devorah-ai-2016/microduck-locomotion-dataset-v1.microduck-locomotion-dataset-v1
🦆 MicroDuck Locomotion Dataset V1.0: Zero-Slip Flat Ground Baseline
Publisher: devorah-ai-2026 / devorahai2026License: Commercial / CC-BY-SA 4.0Task: Bipedal Locomotion, Reinforcement Learning, Sim-to-RealHardware Platform: Antigravity 14-DOF MicroDuck Biped Robot
📌 Overview (개요)
본 데이터셋은 **14-DOF 소형 2족 보행 로봇(MicroDuck)**의 완벽한 평면 보행(Flat-ground walking) 궤적 및 제어 로그를 담은 고품질 합성 데이터(Synthetic Data)입니다. 기존 강화학습 기반 2족 보행 에이전트들이 흔히 겪는 발바닥 미끄러짐(Ice-Skating) 및… See the full description on the dataset page: https://huggingface.co/datasets/devorah-ai-2026/microduck-locomotion-dataset-v1.locomo-serializedThis repository reproduces the standard formatting implemented in https://github.com/snap-research/locomo for further reproducibility usages.
locomolocomo10-flatquadruped_locomotionmemanto-locomo-results
Memanto — LoCoMo Benchmark Results
This dataset contains the full per-question evaluation results of Memanto on the LoCoMo long-term conversational memory benchmark, covering all five stages of a progressive ablation study. It accompanies the paper:
Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon AgentsSeyed Moein Abtahi, Rasa Rahnema, Hetkumar Patel, Neel Patel, Majid Fekri, Tara KhaniMoorcheh AI / EdgeAI Innovations[arXiv preprint] ·… See the full description on the dataset page: https://huggingface.co/datasets/moorcheh/memanto-locomo-results.locomo_preprocesslafan_locomotion_k1
Lafan Locomotion K1
Motion-capture clips from the LAFAN1 dataset retargeted for the Booster Robotics K1 (22 motion DOF), used as reference motions for training in booster_mjlab.
Usage
With datasets:
from datasets import load_dataset
clips = load_dataset("<namespace>/lafan_locomotion_k1", split="train")
With booster_mjlab:
from booster_mjlab.motion import HfMotionDataset, MotionLoader
dataset = HfMotionDataset("<namespace>/lafan_locomotion_k1")… See the full description on the dataset page: https://huggingface.co/datasets/whirlwind-ams/lafan_locomotion_k1.LoCoMoSeg
Dataset Card for LoCoMoSeg
The segmented version of the LoCoMo dataset, with the usage of the segmenter proposed in the paper Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation.
Dataset Details
Dataset Description
The newly introduced columns are:
chunks: The segmented chunks.
cut_prob: The segmentation probabilities for each candidate cut point.
parallel_degree: The number of chunks.
If you find this… See the full description on the dataset page: https://huggingface.co/datasets/Syon-Li/LoCoMoSeg.spot-lerobot-locomotionlocomo-audit-fc-baseline
LoCoMo Full-Context Baseline Results
Independent full-context baseline evaluation results for the LoCoMo-10 benchmark. The LLM receives the entire conversation as context with no retrieval, no memory system, and no reranking.
Part of the LoCoMo Benchmark Audit.
Key Finding
The answer prompt accounts for the accuracy gap between the full-context baseline and published memory system scores.
GPT-4.1-mini with answer_prompt_cot (the same prompt EverMemOS uses) achieves 92.62%… See the full description on the dataset page: https://huggingface.co/datasets/dial481/locomo-audit-fc-baseline.LoCoMo-IDhan-humanoid-locomotion-stability-v1
Humanoid Locomotion Stability Dataset
Overview
Dataset ini berisi parameter pergerakan humanoid saat berjalan
dan label stabilitasnya.
Features
step_length_cm
stride_frequency_hz
center_of_mass_shift_cm
ground_reaction_force_n
terrain_type_index
imu_balance_variance
Target
stability_status (stable / unstable)
Task
Binary Classification
locomo
Dataset:
Mô tả, schema, license...
locomo-benchmark-results
Logica Mind — LoCoMo Benchmark Results
Reproducible accuracy results for Logica Mind
(open-source agent memory) on LoCoMo, alongside published numbers for other
memory systems — all under the same protocol as the Mem0 paper
(arXiv:2504.19413): gpt-4o-mini as both
answerer and judge, adversarial category excluded, 1,540 scored questions.
Leaderboard (results.csv)
System
LoCoMo J
LLM at write time
Source
Letta (filesystem agent)
74.0%
agent-managed
Letta… See the full description on the dataset page: https://huggingface.co/datasets/rovemark/locomo-benchmark-results.