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What AI disruption means for experimental ad budgets

What AI disruption means for experimental ad budgets

The 2026 ad budget is now a lab experiment as marketers boost experimental budgets for AI and emerging channels.

AI Tools & Products · 5 min ·
De-aged casts, ChatGPT-generated programs: How AI is changing Korean TV
Llms

De-aged casts, ChatGPT-generated programs: How AI is changing Korean TV

Artificial intelligence is transforming every corner of industry, and television is no exception. Major networks in Korea have recently a...

AI Tools & Products · 4 min ·
AI in Online Advertising: 5 Key Trends from March 2026

AI in Online Advertising: 5 Key Trends from March 2026

March was a busy month for AI in advertising. AI shopping agents moved from conference demos into real consumer behavior, Google shipped ...

AI Tools & Products · 10 min ·
[2603.23899] SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries
Machine Learning

[2603.23899] SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries

Abstract page for arXiv paper 2603.23899: SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries

arXiv - AI · 4 min ·
[2603.16629] MLLM-based Textual Explanations for Face Comparison
Llms

[2603.16629] MLLM-based Textual Explanations for Face Comparison

Abstract page for arXiv paper 2603.16629: MLLM-based Textual Explanations for Face Comparison

arXiv - AI · 4 min ·
[2603.15159] To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation
Llms

[2603.15159] To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

Abstract page for arXiv paper 2603.15159: To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

arXiv - AI · 4 min ·
[2602.09678] Administrative Law's Fourth Settlement: AI and the Capability-Accountability Trap
Computer Vision

[2602.09678] Administrative Law's Fourth Settlement: AI and the Capability-Accountability Trap

Abstract page for arXiv paper 2602.09678: Administrative Law's Fourth Settlement: AI and the Capability-Accountability Trap

arXiv - AI · 4 min ·
[2603.14375] The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
Machine Learning

[2603.14375] The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics

Abstract page for arXiv paper 2603.14375: The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics

arXiv - AI · 4 min ·
[2602.08316] SWE Context Bench: A Benchmark for Context Learning in Coding
Llms

[2602.08316] SWE Context Bench: A Benchmark for Context Learning in Coding

Abstract page for arXiv paper 2602.08316: SWE Context Bench: A Benchmark for Context Learning in Coding

arXiv - AI · 4 min ·
[2603.14267] DiFlowDubber: Discrete Flow Matching for Automated Video Dubbing via Cross-Modal Alignment and Synchronization
Machine Learning

[2603.14267] DiFlowDubber: Discrete Flow Matching for Automated Video Dubbing via Cross-Modal Alignment and Synchronization

Abstract page for arXiv paper 2603.14267: DiFlowDubber: Discrete Flow Matching for Automated Video Dubbing via Cross-Modal Alignment and ...

arXiv - AI · 4 min ·
[2602.08277] PISCO: Precise Video Instance Insertion with Sparse Control
Generative Ai

[2602.08277] PISCO: Precise Video Instance Insertion with Sparse Control

Abstract page for arXiv paper 2602.08277: PISCO: Precise Video Instance Insertion with Sparse Control

arXiv - AI · 4 min ·
[2601.13227] Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?
Llms

[2601.13227] Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?

Abstract page for arXiv paper 2601.13227: Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?

arXiv - AI · 3 min ·
[2602.07374] TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Layer-wise Scaling
Llms

[2602.07374] TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Layer-wise Scaling

Abstract page for arXiv paper 2602.07374: TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Lay...

arXiv - AI · 4 min ·
[2602.00095] EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions
Llms

[2602.00095] EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions

Abstract page for arXiv paper 2602.00095: EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM...

arXiv - AI · 4 min ·
[2601.22440] AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations
Llms

[2601.22440] AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations

Abstract page for arXiv paper 2601.22440: AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Value...

arXiv - AI · 4 min ·
[2601.13622] CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language Models
Llms

[2601.13622] CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language Models

Abstract page for arXiv paper 2601.13622: CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language...

arXiv - AI · 3 min ·
[2601.13222] Incorporating Q&A Nuggets into Retrieval-Augmented Generation
Nlp

[2601.13222] Incorporating Q&A Nuggets into Retrieval-Augmented Generation

Abstract page for arXiv paper 2601.13222: Incorporating Q&A Nuggets into Retrieval-Augmented Generation

arXiv - AI · 3 min ·
[2601.07855] RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution
Machine Learning

[2601.07855] RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution

Abstract page for arXiv paper 2601.07855: RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution

arXiv - AI · 3 min ·
[2512.11798] Particulate: Feed-Forward 3D Object Articulation
Machine Learning

[2512.11798] Particulate: Feed-Forward 3D Object Articulation

Abstract page for arXiv paper 2512.11798: Particulate: Feed-Forward 3D Object Articulation

arXiv - AI · 3 min ·
[2512.16378] Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs
Llms

[2512.16378] Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs

Abstract page for arXiv paper 2512.16378: Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs

arXiv - AI · 4 min ·