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Research Paper#Formal Verification, LLMs, Software Engineering🔬 ResearchAnalyzed: Jan 3, 2026 08:53

Automated Verification with LLMs for Large Programs

Published:Dec 31, 2025 03:31
1 min read
ArXiv

Analysis

This paper addresses the challenge of verifying large-scale software by combining static analysis, deductive verification, and LLMs. It introduces Preguss, a framework that uses LLMs to generate and refine formal specifications, guided by potential runtime errors. The key contribution is the modular, fine-grained approach that allows for verification of programs with over a thousand lines of code, significantly reducing human effort compared to existing LLM-based methods.
Reference

“Preguss enables highly automated RTE-freeness verification for real-world programs with over a thousand LoC, with a reduction of 80.6%~88.9% human verification effort.”

PermalinkArXiv
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 07:43

Deductive Coding Deficiencies in LLMs: Evaluation and Human-AI Collaboration

Published:Dec 24, 2025 08:10
1 min read
ArXiv

Analysis

This research from ArXiv examines the limitations of Large Language Models (LLMs) in deductive coding tasks, a critical area for reliable AI applications. The focus on human-AI collaboration workflow design suggests a practical approach to mitigating these LLM shortcomings.
Reference

“The study compares LLMs and proposes a human-AI collaboration workflow.”

PermalinkArXiv
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 09:56

Enhancing LLMs: Integrating Deductive Reasoning for Improved Information Processing

Published:Dec 18, 2025 17:27
1 min read
ArXiv

Analysis

The article likely explores novel methods for improving Large Language Models by incorporating deductive reasoning capabilities. This is a crucial area of research, potentially leading to more accurate and reliable LLM outputs.
Reference

“Integrating Deductive Reasoning in Retrieval-Augmented LLMs”

PermalinkArXiv
Research#Transformer Quantization📝 BlogAnalyzed: Dec 29, 2025 07:28

Quantizing Transformers by Helping Attention Heads Do Nothing with Markus Nagel - #663

Published:Dec 26, 2023 20:07
1 min read
Practical AI

Analysis

This article summarizes a podcast episode from Practical AI featuring Markus Nagel, a research scientist at Qualcomm AI Research. The primary focus is on Nagel's research presented at NeurIPS 2023, specifically his paper on quantizing Transformers. The core problem addressed is activation quantization issues within the attention mechanism. The discussion also touches upon a comparison between pruning and quantization for model weight compression. Furthermore, the episode covers other research areas from Qualcomm AI Research, including multitask learning, diffusion models, geometric algebra in transformers, and deductive verification of LLM reasoning. The episode provides a broad overview of cutting-edge AI research.
Reference

“Markus’ first paper, Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing, focuses on tackling activation quantization issues introduced by the attention mechanism and how to solve them.”

PermalinkPractical AI