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Technology#AI Tools📝 BlogAnalyzed: Jan 3, 2026 06:12

Tuning Slides Created with NotebookLM Using Nano Banana Pro

Published:Dec 29, 2025 22:59
1 min read
Zenn Gemini

Analysis

This article describes how to refine slides created with NotebookLM using Nano Banana Pro. It addresses practical issues like design mismatches and background transparency, providing prompts for solutions. The article is a follow-up to a previous one on quickly building slide structures and designs using NotebookLM and YAML files.
Reference

“The article focuses on how to solve problems encountered in practice, such as "I like the slide composition and layout, but the design doesn't fit" and "I want to make the background transparent so it's easy to use as a material."”

PermalinkZenn Gemini
Research Paper#Security, Compiler, CFI🔬 ResearchAnalyzed: Jan 3, 2026 19:43

Automated CFI for Legacy C/C++ Systems

Published:Dec 27, 2025 20:38
1 min read
ArXiv

Analysis

This paper presents CFIghter, an automated system to enable Control-Flow Integrity (CFI) in large C/C++ projects. CFI is important for security, and the automation aspect addresses the significant challenges of deploying CFI in legacy codebases. The paper's focus on practical deployment and evaluation on real-world projects makes it significant.
Reference

“CFIghter automatically repairs 95.8% of unintended CFI violations in the util-linux codebase while retaining strict enforcement at over 89% of indirect control-flow sites.”

PermalinkArXiv
Research Paper#Software Engineering, AI, Graph Neural Networks, Causal Reasoning🔬 ResearchAnalyzed: Jan 3, 2026 20:01

GraphLocator: Causal Reasoning for Issue Localization in Software

Published:Dec 27, 2025 05:02
1 min read
ArXiv

Analysis

This paper introduces GraphLocator, a novel approach to issue localization in software engineering. It addresses the challenges of symptom-to-cause and one-to-many mismatches by leveraging causal reasoning and graph structures. The use of a Causal Issue Graph (CIG) is a key innovation, allowing for dynamic issue disentangling and improved localization accuracy. The experimental results demonstrate significant improvements over existing baselines, highlighting the effectiveness of the proposed method in both recall and precision, especially in scenarios with symptom-to-cause and one-to-many mismatches. The paper's contribution lies in its graph-guided causal reasoning framework, which provides a more nuanced and accurate approach to issue localization.
Reference

“GraphLocator achieves more accurate localization with average improvements of +19.49% in function-level recall and +11.89% in precision.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 28, 2025 21:57

Introducing AutoJudge: Streamlined Inference Acceleration via Automated Dataset Curation

Published:Dec 3, 2025 00:00
1 min read
Together AI

Analysis

The article introduces AutoJudge, a method for accelerating Large Language Model (LLM) inference. It focuses on identifying critical token mismatches to improve speed. AutoJudge employs self-supervised learning to train a lightweight classifier, processing up to 40 draft tokens per cycle. The key benefit is a 1.5-2x speedup compared to standard speculative decoding, while maintaining minimal accuracy loss. This approach highlights a practical solution for optimizing LLM performance, addressing the computational demands of these models.
Reference

“AutoJudge accelerates LLM inference by identifying which token mismatches actually matter.”

PermalinkTogether AI
Research#5G and AI📝 BlogAnalyzed: Dec 29, 2025 07:47

Deep Learning is Eating 5G. Here’s How, w/ Joseph Soriaga - #525

Published:Oct 7, 2021 16:21
1 min read
Practical AI

Analysis

This article from Practical AI discusses how deep learning is being used to enhance 5G technology. It highlights two research papers by Joseph Soriaga and his team at Qualcomm. The first paper focuses on using deep learning to improve channel tracking in 5G, making models more efficient and interpretable. The second paper explores using RF signals and deep learning for indoor positioning. The conversation also touches on how machine learning and AI are enabling 5G and improving the delivery of connected services, hinting at future possibilities.
Reference

“The first, Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking, details the use of deep learning to augment an algorithm to address mismatches in models, allowing for more efficient training and making models more interpretable and predictable.”

PermalinkPractical AI