Here's your daily roundup of the most relevant AI and ML news for August 12, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
arXiv:2607.15509v2 Announce Type: replace-cross Abstract: We present a fully automated closed-loop AutoML framework that uses GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture designers for cross-lingual handwritten optical character recognition. Each large language model inde...
Source: arXiv - AI | 10 hours ago
2. NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models
arXiv:2509.03985v2 Announce Type: replace-cross Abstract: In deployment and application, large language models (LLMs) typically undergo safety alignment to prevent illegal and unethical outputs. However, the continuous advancement of jailbreak attack techniques, designed to bypass safety mechani...
Source: arXiv - AI | 10 hours ago
3. Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint
arXiv:2608.09998v1 Announce Type: cross Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks. Despite their benefits, growing attention has been directed toward their environmental implications, primarily du...
Source: arXiv - Machine Learning | 10 hours ago
4. Do LLM Recommenders Know When They're Hallucinating? Auditing Confidence Calibration in Catalog Faithfulness
arXiv:2608.10008v1 Announce Type: cross Abstract: LLM recommenders for top-$K$ item suggestion regularly emit titles outside the target catalog. Prior audits measure this as a binary out-of-domain rate; none ask whether the model knew it was hallucinating. We jointly audit hallucination rate (OO...
Source: arXiv - Machine Learning | 10 hours ago
5. MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Design
arXiv:2608.07544v1 Announce Type: cross Abstract: Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinatorial optimization problems (COPs). Yet existing frameworks optimize for average performance on a small fixed dataset and steer the sear...
Source: arXiv - AI | 10 hours ago
6. Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry
arXiv:2505.22839v2 Announce Type: replace Abstract: Recent studies suggest that diffusion models significantly improve the empirical adversarial robustness of deep neural network models. While intuitive explanations have been proposed, the mechanisms underlying diffusion-based robustness remain ...
Source: arXiv - Machine Learning | 10 hours ago
7. Entropy-based Code Adversarial Translation for Real-world Repository Migration
arXiv:2608.09273v2 Announce Type: new Abstract: LLMs have demonstrated strong capabilities in code generation and automated program repair, but migrating an entire repository rarely produces a runnable application because long-horizon translation challenges LLM-based agents' ability to maintain ...
Source: arXiv - AI | 10 hours ago
8. ACEvo: Adversarial Co-Evolution of Problem Distributions and Solvers for Combinatorial Optimization
arXiv:2506.02594v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to synthesize heuristic programs, yet most existing pipelines optimize solvers against fixed benchmark distributions. This static setup can obscure solver weaknesses and limit understanding of ...
Source: arXiv - AI | 10 hours ago
About This Digest
This digest is automatically curated from leading AI and tech news sources, filtered for relevance to AI security and the ML ecosystem. Stories are scored and ranked based on their relevance to model security, supply chain safety, and the broader AI landscape.
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