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From shrimp Jesus to erotic tractors: how viral AI slop took over the internet
Clockwise from top left: Shrimp Jesus, Nayib Bukele, Justin Bieber and Super Cat League. Clockwise from top left: Shrimp Jesus, Nayib Bukele, Justin Bieber and Super Cat League. In the algorithm-driven economy of 2025, one man's shrimp Jesus is another man's side hustle. AI slop - the low-quality, surreal content flooding social media platforms, designed to farm views - is a phenomenon, some would say the phenomenon of the 2024 and 2025 internet. Merriam-Webster's word of the year this year is "slop", referring exclusively to the internet variety.
Smoothing the Landscape Boosts the Signal for SGD Optimal Sample Complexity for Learning Single Index Models
The outline of our paper is as follows.
Causal Contrastive Learning for Counterfactual Regression Over Time
Estimating treatment effects over time holds significance in various domains, including precision medicine, epidemiology, economy, and marketing.
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
The proposed method is dubbed as UniHOI .
DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion Weicai Y e 1,3, Chenhao Ji2, Zheng Chen 4 Junyao Gao 2
We further design a spherical epipolar-aware multi-view diffusion model to ensure the multi-view consistency of the generated panoramic images.
Batchnorm Allows Unsupervised Radial Attacks Amur Ghose
Our losses expand intermediate latent based attacks that usually require labels.
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
We individually consider the gap-independent vs. gap-dependent
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
We individually consider the gap-independent vs. gap-dependent
VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation
Benefiting from language flexibility and compositionality, humans naturally intend to use language to command an embodied agent for complex tasks such as navigation and object manipulation. In this work, we aim to fill the blank of the last mile of embodied agents--object manipulation by following human guidance, e.g., "move the red mug next to the box while keeping it upright." To this end, we introduce an Automatic Manipulation Solver (AMSolver) system and build a Vision-and-Language Manipulation benchmark (VLMbench) based on it, containing various language instructions on categorized robotic manipulation tasks. Specifically, modular rule-based task templates are created to automatically generate robot demonstrations with language instructions, consisting of diverse object shapes and appearances, action types, and motion constraints. We also develop a keypoint-based model 6D-CLIPort to deal with multi-view observations and language input and output a sequence of 6 degrees of freedom (DoF) actions. We hope the new simulator and benchmark will facilitate future research on language-guided robotic manipulation.