Model Detail
transformation_lora
—transformation_lora is an image generation model released by Ashmotv. The model is registered under the image-to-video pipeline tag on Hugging Face, distributed under a other license.
Distribution is governed by the other license — review the exact terms before commercial deployment.
transformation_lora is best fit for text-to-image generation and creative iteration. It is a less obvious choice for production photography pipelines that need exact reproducibility. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Natural Code Transformation Confuses Agents in Vulnerability Analysis
arXiv:2610.14350v2 Announce Type: cross Abstract: Having demonstrated a strong potential for automating vulnerability discovery, AI agents can also be used to attack software systems at scale. Today's software systems need mechanisms to level the playing field by slowing down potential attacks, givi
Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction
arXiv:2610.19640v1 Announce Type: new Abstract: The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that generates and curates synthetic reaction data, uses these data to pretrain a graph-to-sequence predictor
An Investigation of Robustness of LLMs in Mathematical Reasoning: Benchmarking with Mathematically-Equivalent Transformation of Advanced Mathematical Problems
arXiv:2508.08833v4 Announce Type: replace-cross Abstract: Frontier large language models (LLMs) now reach near-ceiling accuracy on standard mathematical-reasoning benchmarks and gold-medal-level performance at the International Mathematical Olympiad. As these benchmarks saturate and their items leak
Cylindrical Geodesic Flow Matching for Quasiperiodic Physiological Signal Transformation
arXiv:2610.08510v1 Announce Type: new Abstract: Paired translation between quasiperiodic physiological waveforms (i.e., recovering a target oscillatory signal from the source) is central to the interpretation of cardiovascular signals derived from wearables placed at different body locations. This s
Building Transformation Layers for Riemannian Neural Networks
arXiv:2609.35436v2 Announce Type: replace-cross Abstract: Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. One recent focus is the generalization of Euclidean fully connected (FC) and convolutional lay
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
arXiv:2407.16139v2 Announce Type: replace Abstract: Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions. Personalized Federated Learning (PFL) addresses this by enabling each client to possess an individu
