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synthesis

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DF3DV-1K: Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis Technology
Artificial Intelligence #dataset#benchmark

DF3DV-1K: Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis

Researchers introduced DF3DV-1K, a large-scale real-world dataset with 1,048 scenes and 89,924 images for distractor-free novel view synthesis. The dataset spans 128 distractor types and 161 scene themes, enabling benchmarking of nine radiance field methods and 3D Gaussian Splatting. Fine-tuning a diffusion-based 2D enhancer on DF3DV-1K achieved average improvements of 0.96 dB PSNR and 0.057 LPIPS.

Jun 21, 2026 1 source
New Tool Synthesizes Adaptive Probabilistic Processors for Combinatorial Optimization Using Ising Model Technology
Hardware #synthesis#adaptive

New Tool Synthesizes Adaptive Probabilistic Processors for Combinatorial Optimization Using Ising Model

Researchers have developed a tool for the synthesis and simulation of probabilistic architectures that map combinatorial optimization problems to the Ising model. The tool automatically constructs the Ising Hamiltonian and selects the best update algorithm among Gibbs Sampling, Simulated Annealing, Simulated Quantum Annealing, and cluster-based methods. Experimental results on benchmark problems demonstrate improved convergence behavior, supporting future hardware implementations based on MTJs and p-bits.

Jun 21, 2026 1 source
DynaDebate: Dynamic Path Generation Breaks Homogeneity in Multi-Agent AI Debates Technology
Artificial Intelligence #multi-agent debate#dynamic path generation

DynaDebate: Dynamic Path Generation Breaks Homogeneity in Multi-Agent AI Debates

A new research paper introduces DynaDebate, a framework that solves the homogeneity problem in multi-agent AI debates by dynamically generating diverse reasoning paths, shifting to step-by-step logic critique, and activating a verification agent to resolve disagreements. Experiments show superior performance across most benchmarks.

Jun 17, 2026 3 sources
New AI Framework Co-Scraper Achieves 94.78% Accuracy for Web Data Extraction with Reusable Scrapers Technology
Software #web scraping#dom pruning

New AI Framework Co-Scraper Achieves 94.78% Accuracy for Web Data Extraction with Reusable Scrapers

Researchers introduced Co-Scraper, a two-stage framework for automated web data extraction that integrates query-aware DOM pruning with a fine-tuned Qwen3-8B model. On the SWDE test set, it achieved an F1 score of 94.78% and a reuse success rate of 90.39%, enabling lightweight, reusable scrapers for heterogeneous web content.

Jun 16, 2026 1 source