AI News Archive: May 7, 2026 — Part 20
Sourced from 500+ daily AI sources, scored by relevance.
- Velasong — AI Song Gifts
Turn your words into a personalized song
- ParaPulse
See which AI models are rising — before the crowd catches on
- thefuneral.ai
Clarity and guidance for life’s hardest decisions
- elsai Foundry
One platform to design, deploy, and govern AI agents
- Miraja Ai
The Future of Empathic Cinematic Interfaces.
- IdeaLoop
Daily startup ideas discovered & validated by multi-agent AI
- Renovato AI
Renders, renovated.
- AI Faction Quiz
Claude, GPT, Gemini or Grok — find your AI faction in 8 Qs
- Gpt Image 2 Api
Generate AI Images with GPT Image 2 Online
- EvrySign
Modern e-sign platform with AI templates & tracking
- EcoActive
AI-native disclosure management for modern reporting
- Find Prospects While You Sleep
1stContact.ai is the AI CRM That Sells For You
- Bloox - an on-device-AI audiobook player
Privacy focused, offline AI powered audiobook player
- Let AI find you NYC street parking
Get an email when AI see's open parking in the live DOT feed
- TELMO eSIM
AI-Powered Global eSIM & Travel Fintech
- LZCLIP (เลซี่คลิป)
ระบบสร้างวิดีโอปิดการขายด้วย Ai ภาษาไทย ใน 1 นาที
- Spark3R: Asymmetric Token Reduction Makes Fast Feed-Forward 3D Reconstruction
Feed-forward 3D reconstruction models based on Vision Transformers can directly estimate scene geometry and camera poses from a small set of input images, but scaling them to video inputs with hundreds or thousands of frames remains challenging due to the quadratic cost of global attention layers. R...
- Look Beyond Saliency: Low-Attention Guided Dual Encoding for Video Semantic Search
Video semantic search in densely crowded scenes remains a challenging task due to visual encoders tendency to prioritize salient foreground regions while neglecting contextually important, background areas. We propose an Inverse Attention Embedding mechanism that explicitly captures and highlights t...
- Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance
We present a differentiable framework to adaptively compute 4D illumination conditions with respect to an object, for efficient, high-quality simultaneous acquisition of its shape and reflectance, with a unified spatial-angular structured light and a single camera. Using a simple histogram-based pix...
- Playing the network backward: A Game Theoretic Attribution Framework
Attribution methods explain which input features drive a model's prediction, making them central to model debugging and mechanistic interpretability. Yet backward attribution methods, including gradients, LRP, and transformer-specific rules, lack a shared framework in which to compare the underlying...
- Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation
Most discrete visual tokenizers rely on a default design: every position in the sequence shares the same codebook. Researchers try to scale the codebook size $K$ to get better reconstruction performance. Such a constant-codebook design hits a fundamental information-theoretic limit. We observe that ...
- EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
Pretrained video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models. While recent world-action models jointly optimize future videos and actions, they predominantly treat video generation as an auxiliary representation for po...
- SuperFace: Preference-Aligned Facial Expression Estimation Beyond Pseudo Supervision
Accurate facial estimation is crucial for realistic digital human animation, and ARKit blendshape coefficients offer an interpretable representation by mapping facial motions to semantic animation controls. However, learning high-quality ARKit coefficient prediction remains limited by the absence of...
- Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation
Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are limited to image-level classification and lack clinically structured reporting. We propose Retina-RAG, a low-cost modular framework that jointly performs...
- DynT2I-Eval: A Dynamic Evaluation Framework for Text-to-Image Models
Existing text-to-image (T2I) benchmarks largely rely on fixed prompt sets, leaving them vulnerable to overfitting and benchmark contamination once publicly released and repeatedly reused. In this work, we propose DynT2I-Eval, a fully automated dynamic evaluation framework for T2I models. It construc...
- Secure Seed-Based Multi-bit Watermarking for Diffusion Models from First Principles
The rapid emergence of generative image models has led to the development of specialized watermarking techniques, particularly in-generation methods such as seed-based embedding. However, current evaluations in this area remain largely empirical, making them heavily reliant on the specific model arc...
- Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
Ranking LLMs via pairwise human feedback underpins current leaderboards for open-ended tasks, such as creative writing and problem-solving. We analyze ~89K comparisons in 116 languages from 52 LLMs from Arena, and show that the best-fit global Bradley-Terry (BT) ranking is misleading. Nearly 2/3 of ...
- Crafting Reversible SFT Behaviors in Large Language Models
Supervised fine-tuning (SFT) induces new behaviors in large language models, yet imposes no structural constraint on how these behaviors are distributed within the model. Existing behavior interpretation methods, such as circuit attribution approaches, identify sparse subnetworks correlated with SFT...
- Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows
Classical generative adversarial networks (GANs) have been applied to generate adversarial network traffic capable of attacking intrusion detection systems, but they suffer from shortcomings such as the need for large amounts of high-dimensional datasets, mode collapse, and high computational overhe...
- Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent
Transformers have demonstrated remarkable in-context learning (ICL) capabilities. The strong ICL performance of transformers is commonly believed to arise from their ability to implicitly execute certain algorithms on the context, thereby enhancing prediction and generation. In this work, we investi...
- How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation
Evaluating and predicting the performance of large language models (LLMs) in multi-turn conversational settings is critical yet computationally expensive; key events -- e.g., jailbreaks or successful task completion by an agent -- often emerge only after repeated interactions. These events might be ...
- imgn.ai
AI production platform for film and TV teams
- FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
Watermark radioactivity testing type of methods can detect whether a model was trained on watermarked documents, and have become key tools for protecting data ownership in the fine-tuning of large language models (LLMs). Existing works have proved their effectiveness in centralized LLM fine-tuning. ...
- ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting
Retargeting human kinematic reference motion onto a robot's morphology remains a formidable challenge. Existing methods often produce physical inconsistencies, such as foot sliding, self-collisions, or dynamically infeasible motions, which hinder downstream imitation learning. We propose a bilevel o...
- On the Safety of Graph Representation Learning
Graph representation learning (GRL) has evolved from topology-only graph embeddings to task-specific supervised GNNs, and more recently to reusable representations and graph foundation models (GFMs). However, existing evaluations mainly measure clean transfer, adaptation, and task coverage. It remai...
- CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security mechanisms. While Federated Learning (FL) offers a privacy-preserving alternative to centralized Intrusion Detection Systems ...
- Criticality and Saturation in Orthogonal Neural Networks
It has been known for a long time that initializing weight matrices to be orthogonal instead of having i.i.d. Gaussian components can improve training performance. This phenomenon can be analyzed using finite-width corrections, where the infinite-width statistics are supplemented by a power series i...
- Feature Dimensionality Outweighs Model Complexity in Breast Cancer Subtype Classification Using TCGA-BRCA Gene Expression Data
Accurate classification of breast cancer subtypes from gene expression data is critical for diagnosis and treatment selection. However, such datasets are characterized by high dimensionality and limited sample size, posing challenges for machine learning models. In this study, we evaluate the impa...
- Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
The design of biological systems is hindered by uncertainty arising from both intrinsic stochasticity of biomolecular reactions and variability across laboratory or experimental conditions. In this work, we present a sequential framework to optimize genetic circuits under both forms of uncertainty. ...
- LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation
Modern sensors generate rich, high-fidelity data, yet applications operating on wearable or remote sensing devices remain constrained by bandwidth and power budgets. Standardized codecs such as JPEG and MPEG achieve efficient trade-offs between bitrate and perceptual quality but are designed for hum...
- Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization
Weight decay is widely used as a regularizer in large language models, yet its precise role in shaping Transformer loss landscapes remains theoretically underexplored. This paper provides the first rigorous functional-analytic characterization of the standard Transformer objective--cross-entropy los...
- BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics through nuclear and space engineering to medical physics. Exploiting the locality and the Markov nature of particle interactions, we create a \emph{nex...
- SNAPO: Smooth Neural Adjoint Policy Optimization for Optimal Control via Differentiable Simulation
Many real-world problems require sequential decisions under uncertainty: when to inject or withdraw gas from storage, how to rebalance a pension portfolio each month, what temperature profile to run through a pharmaceutical reactor chain. Dynamic programming solves small instances exactly but scales...
- Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance
We present EDDY (Exact-marginal Diversification via Divergence-free dYnamics), a guidance mechanism for diffusion and flow matching models that promotes diversity among samples generated while maintaining quality. EDDY exploits symmetries of the Fokker-Planck equation, using drift perturbations that...
- Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
Diffusion-based posterior samplers use pretrained diffusion priors to sample from measurement- or reward-conditioned posteriors, and are widely used for inverse problems. Yet their theoretical behavior remains poorly understood: even with exact prior scores, their outputs are biased, and in low-temp...
- A Geometry-Aware Residual Correction of Hagan's SABR Implied Volatility Formula
This paper proposes a hybrid methodology to improve the approximation of SABR (Stochastic Alpha Beta Rho) implied volatility by combining analytical structure with machine learning. The approach augments the neural-network input representation with geometric features derived from the stochastic diff...
- Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
This position paper argues that, in debiased machine learning, balancing functions should be derived from the Neyman orthogonal score, not chosen only as functions of covariates. Covariate balancing is effective when the regression error entering the score can be represented by functions of covariat...
- Verfi
AI-powered accounting, simplified
- Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance
When modeling class-imbalanced data, it is crucial to address the imbalance, as models trained on such data tend to be biased towards the majority classes. This problem is amplified under partial supervision, where pseudo-labels for unlabeled data are predicted based on imbalanced labeled data, prop...
- ConquerNet: Convolution-Smoothed Quantile ReLU Neural Networks with Minimax Guarantees
Quantile regression is a fundamental tool for distributional learning but poses significant optimization challenges for deep models due to the non-smoothness of the pinball loss. We propose ConquerNet, a class of \textbf{con}volution-smoothed \textbf{qu}antil\textbf{e} \textbf{R}eLU neural \textbf{n...