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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:46

Long-Range depth estimation using learning based Hybrid Distortion Model for CCTV cameras

Published:Dec 19, 2025 16:54
1 min read
ArXiv

Analysis

This article describes a research paper on depth estimation for CCTV cameras. The core of the research involves a learning-based hybrid distortion model. The focus is on improving depth estimation accuracy over long distances, which is a common challenge in CCTV applications. The use of a hybrid model suggests an attempt to combine different distortion correction techniques for better performance. The source being ArXiv indicates this is a pre-print or research paper.
Reference

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PermalinkArXiv
Research#Image Generation📝 BlogAnalyzed: Dec 29, 2025 01:43

Just Image Transformer: Flow Matching Model Predicting Real Images in Pixel Space

Published:Dec 14, 2025 07:17
1 min read
Zenn DL

Analysis

The article introduces the Just Image Transformer (JiT), a flow-matching model designed to predict real images directly within the pixel space, bypassing the use of Variational Autoencoders (VAEs). The core innovation lies in predicting the real image (x-pred) instead of the velocity (v), achieving superior performance. The loss function, however, is calculated using the velocity (v-loss) derived from the real image (x) and a noisy image (z). The article highlights the shift from U-Net-based models, prevalent in diffusion-based image generation like Stable Diffusion, and hints at further developments.
Reference

“JiT (Just image Transformer) does not use VAE and performs flow-matching in pixel space. The model performs better by predicting the real image x (x-pred) rather than the velocity v.”

PermalinkZenn DL
Research#GNN🔬 ResearchAnalyzed: Jan 10, 2026 11:58

LGAN: Enhancing Graph Neural Networks with Line Graph Aggregation

Published:Dec 11, 2025 15:23
1 min read
ArXiv

Analysis

This research paper introduces LGAN, a novel approach to improve the efficiency of high-order graph neural networks. The method leverages line graph aggregation, which offers potential advantages in computational complexity and performance compared to existing techniques.
Reference

“LGAN is an efficient high-order graph neural network via the Line Graph Aggregation.”

PermalinkArXiv
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 13:00

Mixed Training Mitigates Catastrophic Forgetting in Mathematical Reasoning Finetuning

Published:Dec 5, 2025 17:18
1 min read
ArXiv

Analysis

The study addresses a critical challenge in AI: preventing large language models from forgetting previously learned information during fine-tuning. The research likely proposes a novel mixed training approach to enhance the performance and stability of models in mathematical reasoning tasks.
Reference

“The article's source is ArXiv, indicating it is a research paper.”

PermalinkArXiv
Research#llm👥 CommunityAnalyzed: Jan 4, 2026 08:53

Smaller, Weaker, yet Better: Training LLM Reasoners via Compute-Optimal Sampling

Published:Sep 3, 2024 05:26
1 min read
Hacker News

Analysis

The article likely discusses a novel approach to training Large Language Models (LLMs) focused on improving reasoning capabilities. The core idea seems to be that training smaller or weaker models, potentially using a more efficient sampling strategy, can lead to better reasoning performance. The phrase "compute-optimal sampling" suggests an emphasis on maximizing performance given computational constraints. The source, Hacker News, indicates a technical audience interested in advancements in AI.
Reference

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PermalinkHacker News