datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
VeriLoop-Coder-E1-Evaluation-Evidence
VeriLoop Coder-E1 Evaluation Evidence
This repository contains the public evaluation-evidence packages
referenced by the official VeriLoop Coder-E1 benchmark result files.
Model repository:
tsinghua-sigs-robot-lab/veriloop-coder-e1
Evidence packages
Benchmark
Evidence directory
DeepSWE
veriloop-coder-e1-deepswe-evaluation-evidence-v1.0.0
SWE-bench Pro
veriloop-coder-e1-swe-bench-pro-evaluation-evidence-v1.0.0
SWE-bench Verified… See the full description on the dataset page: https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-Coder-E1-Evaluation-Evidence.VeriLoop-E2-Evaluation-Evidence
VeriLoop E2 Evaluation Evidence
Public evaluation evidence for VeriLoop E2 across nine code, agentic, mathematical, and scientific reasoning benchmarks.
This dataset repository is the canonical public evidence layer for the reported benchmark results of VeriLoop E2, a post-trained model based on Qwen 3.8-27B. It is designed to separate headline benchmark reporting from the underlying auditable artifacts required to inspect, reproduce, and verify those results.
The repository… See the full description on the dataset page: https://huggingface.co/datasets/tsinghua-sigs-robot-lab/VeriLoop-E2-Evaluation-Evidence.qwen3.8-conv1d-sigscalesync
The conv1d sigma outliers in Qwen3.8-27B are real. The quantization story attached to them is not — and the rescale variant I tested cost perplexity while fixing nothing measurable.
Scope revision (2026-08-24). An earlier version of this README said the rescale was applied
"exactly as published" and that the mechanism "cannot work". Both were too strong. What I tested
is a 7-tensor median-normalization variant (σ > 1.5× median → α = median/σ); the intervention
actually… See the full description on the dataset page: https://huggingface.co/datasets/windowsxp811203/qwen3.8-conv1d-sigscalesync.MapReader_Data_SIGSPATIAL_2022TODOAgenticVLA_peach_into_drawerThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/AgenticVLA_peach_into_drawer.bimanual_stack_cup_bowlThis dataset was created using LeRobot.
Dataset Structure
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"left_joint_6.pos",
"left_gripper.pos"… See the full description on the dataset page: https://huggingface.co/datasets/THU-SIGS-EILAB/bimanual_stack_cup_bowl.AgenticVLA_open_drawerThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/AgenticVLA_open_drawer.AgenticVLA_banana_onto_plateThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/AgenticVLA_banana_onto_plate.cup_pickThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/cup_pick.AgenticVLA_place_plateThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/AgenticVLA_place_plate.SIGS-symbolic-expressions
SIGS symbolic expression latent corpus
This dataset contains 23,695 grammar-generated symbolic expressions used by the SIGS Grammar-VAE, together with their 32-dimensional latent statistics and variable-based mathematical classes.
Dataset structure
Each row contains:
id: stable row index;
expression: symbolic expression generated by the SIGS grammar;
math_class: one of CONSTANT, TEMPORAL_1D, SPATIAL_1D, SPATIAL_2D, SPATIOTEMPORAL_2D, or SPATIOTEMPORAL_3D;
has_x… See the full description on the dataset page: https://huggingface.co/datasets/oroikono/SIGS-symbolic-expressions.kiwiThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/kiwi.cup_place_middleThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/cup_place_middle.sigs-symbolic-pde-corpus
SIGS Grammar Production Corpus
This dataset contains the one-hot grammar-production sequences used to train the
Grammar-VAE in SIGS: Neuro-Symbolic AI for Analytical Solutions of
Differential Equations (Oikonomou et al., ICML 2026).
Structure
Each row contains:
inputs: a float32 tensor shaped [grammar productions, sequence length];
labels: the corresponding integer production indices shaped
[sequence length].
The deterministic default split uses seed 42 with 70%… See the full description on the dataset page: https://huggingface.co/datasets/oroikono/sigs-symbolic-pde-corpus.AgenticVLA_close_drawerThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/AgenticVLA_close_drawer.stack_cupsThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/stack_cups.cup_place_bottomThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/cup_place_bottom.orangeThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/orange.fruitsThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/fruits.cup_place_topThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/cup_place_top.peachmuat-sigs-with-input-correlations
Subject Models for Interpretability Training
These examples are intended for training an interpreter to:
Identify what patterns a model classifies as positive based on an activation signature, with examples of: trained model + signature → pattern identification.
Signature Extraction
Neuron Profile Methods
mean, std, fourier, input_correlations, pre_activation_mean, pre_activation_std
Prompt Format
separate
Signature Dataset… See the full description on the dataset page: https://huggingface.co/datasets/maximuspowers/muat-sigs-with-input-correlations.lemonThis dataset was created using LeRobot.
Dataset Structure
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"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/sigs-eilab/lemon.SIGS_UR_Datasetssigsparse-assets