datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3953125
Action score: 0.446875
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4
total-300-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4046875
Action score: 0.4140625
Valid samples: 320/320
total-300-lambda00-s_signal_type6-jh-epoch4
total-300-lambda00-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3875
Action score: 0.43125
Valid samples: 320/320
total-300-lambda05-s_signal_type6-jh-epoch4
total-300-lambda05-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.35703125
Action score: 0.4375
Valid samples: 320/320
total-300-lambda08-s_signal_type6-jh-epoch4
total-300-lambda08-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38046875
Action score: 0.4078125
Valid samples: 320/320
total-300-lambda10-s_signal_type6-jh-epoch4
total-300-lambda10-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.41875
Valid samples: 320/320
total-300noapp-lambda02-s_signal_type6-jh-epoch4
total-300noapp-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36640625
Action score: 0.409375
Valid samples: 320/320
total-300app-lambda02-s_signal_type6-jh-epoch4
total-300app-lambda02-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3625
Action score: 0.4015625
Valid samples: 320/320
total-131-lambda02-residual-s_signal_type6-jh-epoch4
total-131-lambda02-residual-s_signal_type6-jh-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3765625
Action score: 0.4171875
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-retry-epoch4
total-300-lambda02-s_signal_type6-jh-retry-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.36953125
Action score: 0.3984375
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4-reeval2
total-300-lambda02-s_signal_type6-jh-epoch4-reeval2
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4125
Action score: 0.4265625
Valid samples: 320/320
total-300-lambda02-s_signal_type6-jh-epoch4-reeval1
total-300-lambda02-s_signal_type6-jh-epoch4-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38828125
Action score: 0.4234375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41328125
Action score: 0.4359375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41953125
Action score: 0.4515625
Valid samples: 320/320
qwen35-4b-filter-s_signal5-200-qwen38-27b-newprompt-4k-epoch4
qwen35-4b-filter-s_signal5-200-qwen38-27b-newprompt-4k-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3890625
Action score: 0.4359375
Valid samples: 320/320
fi-etf-macro-signal-master-databit-signal-store
Bit Signal Store
A shared, append-only cache of raw time-series model outputs and OHLCV
prices, used by the Backtest Lab
Space.
The one rule
This store holds raw model outputs and prices. It never holds trade
decisions. No entries, no exits, no position sizes, no P&L. Trading rules,
costs, slippage and sizing are applied live, per request, by the Backtest
Lab's engine. That separation is what lets many different strategies be
compared against the same model… See the full description on the dataset page: https://huggingface.co/datasets/The-Bit-Trading-Company/bit-signal-store.traffic_signal_imagesThis dataset contains traffic images from traffic signal cameras of singapore. The images are captured at 1.5 minute interval from 6 pm to 7 pm everyday for the month of January 2024.world-signals
World Signals — a daily cross-country snapshot of attention
One folder per day under data/YYYY-MM-DD/, and the same files copied to latest/.
Built every morning (JST) by the EmpireOS world model. Nothing is generated by a model; every row is a measurement from a public source.
file
what
source
search_trends.csv
rising searches, 30 countries, with approximate traffic and the headline that drove them
Google Trends daily RSS
podcast_charts.csv
top-100 podcasts, 30… See the full description on the dataset page: https://huggingface.co/datasets/Lilambd/world-signals.msp-raw-multimodal-signal-corpus
Dataset Card for MSP Raw Multimodal Signal Corpus
Dataset Summary
This repository hosts the raw, uncompressed iterations of the Multimodal Signal Corpus (Epoch 2). The objective of this dataset is to provide researchers with high-density, completely unstructured multimodal vectors—including raw acoustic captures, spatial matrices, and continuous signal representations.
Because the focus of this research phase is on handling unformatted, noisy, and uncompressed latent… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-signal-group/msp-raw-multimodal-signal-corpus.msp-spectral-interference-dumps
Dataset Card for MSP Spectral Interference and Noise Dumps
Dataset Summary
This is the sister repository to the Raw Multimodal Signal Corpus, specifically dedicated to archiving high-density background noise, structural interference patterns, and synthesized degradation artifacts.
Evaluating multimodal architectures requires robust stress-testing against corrupted or saturated latent states. Therefore, the files in this repository consist of massive, uncompressed binary… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-signal-group/msp-spectral-interference-dumps.malnet_signal
Malnet Signal
MalNet dataset where the raw binaries have been downloaded from Androzoo and processed into 1D signal representations instead of byteplot images as described in this work.
A full code base for generating and modelling the data can be found here.
Malware Signals
Malware signals are 1D representations of the bytecode of an executable which act as an alternative to byteplot images as input to machine learning models. These signals can be statically… See the full description on the dataset page: https://huggingface.co/datasets/jackwilkie/malnet_signal.Mixed-Signals-V2X
Mixed Signals V2X: Collaborative 3D Object Detection Dataset
Point clouds and 3D bounding-box labels for the Mixed Signals dataset, a
diverse, real-world dataset for heterogeneous LiDAR V2X collaboration
(ICCV 2025). Collected at a busy intersection with 3 connected vehicles and
a roadside unit (RSU) carrying two LiDARs, for 5 LiDAR sensors per synchronized
frame.
This repository accompanies a collaborative 3D object detection competition on
Codabench, built on the
Mixed Signals… See the full description on the dataset page: https://huggingface.co/datasets/sberrio/Mixed-Signals-V2X.telegram-financial-signalsv2
Financial Trading Signals Sentiment Dataset
Overview
This dataset contains 4,664 trading signals extracted from Telegram group chats, focused on financial instruments such as Forex pairs, commodities (Gold, Silver), stocks, and indices. Each signal is labeled with a sentiment value for use in financial sentiment analysis and machine learning applications.
Data Description
Each record represents a trading signal and includes fields for symbol, sentiment (both… See the full description on the dataset page: https://huggingface.co/datasets/ZombitX64/telegram-financial-signalsv2.rf-signal-dataset
RF Signal Classification Dataset
This dataset contains raw IQ samples across four signal classes used to train and evaluate the RF Signal Classification models.
Classes
Class
Description
ADS_B
Aircraft transponder signals at 1090 MHz
FM_broadcast
Commercial FM radio signals (91.1, 93.5, 95.0, 98.3, 104.8, 106.4 MHz)
ISM_sensors
ISM-band device signals at 433 MHz
noise
Background RF noise captured across 50, 300, 470, 800, 1200 MHz
160… See the full description on the dataset page: https://huggingface.co/datasets/ishisan28/rf-signal-dataset.clawhub-security-signals
ClawHub Security Signals
🦀 ClawHub | 📝 OpenClaw Blog | 🤗 Hugging Face Blog | 📄 Paper | 📄 Pre-Print
ClawHub Security Signals is a sanitized, MIT-licensed security-signals dataset for public OpenClaw agent skills. It captures how an agent-skill registry evaluates trust, provenance, bundled code, and scanner evidence at scale.
This dataset was presented in the paper ClawHub Security Signals: When VirusTotal, Static Analysis, and SkillSpector Disagree.
Paper snapshot: this… See the full description on the dataset page: https://huggingface.co/datasets/OpenClaw/clawhub-security-signals.mat-01-lr-versus-learning-signal
01 Learning rate versus the learning signal
When the round-best score on a CPU-only Kaggle task stops rising during GRPO training, is the binding constraint the learning rate (too small to move the policy, or too large to keep it stable), or the learning signal itself (what the search samples and how the reward separates it)? This repository is the data root of that question: every training run's tree-search rollout archive it produced between 2026-06-29 and 2026-07-03, minus… See the full description on the dataset page: https://huggingface.co/datasets/t2ance/mat-01-lr-versus-learning-signal.SIGNAL
SIGNAL
Dataset for Semantic and Inferred Grammar Neurological Analysis of Language
License: CC BY 4.0
Authors
Anna Komissarenko1,2,*,
Ekaterina Voloshina1,
Anastasia Cheveleva2,
Ilia Semenkov1,2,
Oleg Serikov3,
Alex Ossadtchi1,2,4,*
1 AIRI, Moscow, Russia2 Higher School of Economics, Moscow, Russia3 Center of Excellence for Generative AI, KAUST, KSA4 LIFT, Life Improvement by Future Technologies Institute, Moscow, Russia
*Corresponding authors… See the full description on the dataset page: https://huggingface.co/datasets/ContributorsSIGNAL/SIGNAL.persistence-signal-detector
UCIP Phase I Reproducibility Dataset
This repository contains the frozen Phase I reproducibility artifacts for the Unified Continuation-Interest Protocol (UCIP).
UCIP is a bounded measurement framework for distinguishing two objective regimes that can appear behaviorally similar in autonomous agents:
Type A: continuation is intrinsic to the objective itself
Type B: continuation is instrumentally useful for maximizing some other reward
Figure 1. Entanglement entropy separates… See the full description on the dataset page: https://huggingface.co/datasets/Cohaerence/persistence-signal-detector.signalign-corpus-staging