Generative AI

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The Real Reason OpenAI Shut Sora Down Is a Warning to Every AI Startup

Really, you made this without AI? Prove it | The Verge

Creatives want to start labeling human-made text, images, audio, and video with AI-free logos. Now they just have to pick one.

General Catalyst commits $5B to India over five years | TechCrunch

General Catalyst has announced a $5 billion investment in India's startup ecosystem over the next five years, significantly increasing it...

All the important news from the ongoing India AI Impact Summit | TechCrunch

India's AI Impact Summit gathers leaders from major tech firms and government to discuss AI investments, innovations, and the future of t...

[2510.12915] Toward LLM-Supported Automated Assessment of Critical Thinking Subskills

This paper explores the feasibility of using large language models (LLMs) to automate the assessment of critical thinking subskills in ed...

[2510.12581] LayerSync: Self-aligning Intermediate Layers

LayerSync introduces a novel approach to enhance diffusion models by self-aligning intermediate layers, improving training efficiency and...

[2502.10361] Enhancing Multilingual LLM Pretraining with Model-Based Data Selection

This article presents a model-based data selection framework for enhancing multilingual LLM pretraining, demonstrating significant effici...

[2412.06106] Efficient Context Propagating Perceiver Architectures for Auto-Regressive Language Modeling

This article presents the Efficient Context Propagating Perceiver (ECP) architecture, which enhances auto-regressive language modeling by...

[2602.11893] Universal Diffusion-Based Probabilistic Downscaling

The paper presents a universal diffusion-based framework for downscaling weather forecasts, enhancing low-resolution predictions into hig...

[2510.14190] Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation

The paper presents Contrastive Diffusion Alignment (ConDA), a method that enhances the interpretability and control of diffusion models b...

[2506.16404] Generating Directed Graphs with Dual Attention and Asymmetric Encoding

This article presents a novel generative model for directed graphs called Directo, which utilizes dual attention and asymmetric encoding ...

[2506.07198] GGBall: Graph Generative Model on Poincaré Ball

The paper introduces GGBall, a novel graph generative model utilizing hyperbolic geometry to enhance the generation of hierarchical struc...

[2602.17211] MGD: Moment Guided Diffusion for Maximum Entropy Generation

The paper introduces Moment Guided Diffusion (MGD), a novel method for generating maximum entropy distributions by guiding moments toward...

[2602.16961] Greedy Multi-Path Block Verification for Faster Decoding in Speculative Sampling

This paper presents Greedy Multi-Path Block Verification (GBV), a method that enhances the efficiency of speculative decoding in machine ...

[2602.16721] Speech to Speech Synthesis for Voice Impersonation

The paper presents the Speech to Speech Synthesis Network (STSSN), a novel model that combines speech recognition and synthesis for effec...

[2602.17646] Multi-Round Human-AI Collaboration with User-Specified Requirements

The paper discusses a framework for multi-round human-AI collaboration, emphasizing user-specified requirements to enhance decision quali...

[2602.17554] A Theoretical Framework for Modular Learning of Robust Generative Models

This article presents a theoretical framework for modular learning in robust generative models, exploring the combination of domain-speci...

[2602.17477] Variational Grey-Box Dynamics Matching

The paper presents a novel grey-box method that integrates incomplete physics models into deep generative models, enabling the learning o...

[2602.17270] Unified Latents (UL): How to train your latents

The paper introduces Unified Latents (UL), a framework for training latent representations using a diffusion prior, achieving competitive...

[2602.17089] Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling

This article presents a novel approach to stochastic closure modeling by integrating transport-based generative models with latent geomet...

[2602.16994] Dynamic Delayed Tree Expansion For Improved Multi-Path Speculative Decoding

This article presents a novel approach to multi-path speculative decoding in machine learning, introducing dynamic delayed tree expansion...

[2602.16954] Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints

The paper presents Neuro-Symbolic Graph Generative Modeling (NSGGM), a framework that enhances molecule generation by integrating symboli...