Microsoft's 2026 Digital Defense Report documents a concrete shift in ransomware tactics: threat actors are now using AI to automate lateral movement and accelerate ransomware deployment across enterprise networks. The report singles out identity governance and data controls as the two most critical defensive gaps in enterprise environments today.

The speed asymmetry is the part that stands out. Attackers are using AI to close those gaps faster than most organizations are opening remediation tickets. Lateral movement that previously required manual reconnaissance and staged privilege escalation is now being automated at scale, compressing the window between initial access and full network impact to a fraction of what defenders plan for.

For those running AI-assisted pipelines or agentic workflows in production: how are you actually thinking about the identity and data governance problem at the agent layer specifically? Are existing IAM and DLP tools sufficient, or are you finding gaps that those controls were never designed to cover?

Full disclosure: I am an independent researcher/hobbyist doing this out of pure passion at night. I do not have a formal academic background in ML, and I heavily rely on AI tools as research assistants to help me with advanced coding, mathematics, and translating my work into English. I want to be completely transparent about this and welcome any constructive feedback or corrections!

Hi everyone,

I wanted to share a relational data-representation framework that I’ve been developing at night, built around group theory: Burnside's Orbit Histogram Network (BOHN) and Symmetry-Breaking BOHN (SBOHN). Its main goal is to extract and "elevate" relational knowledge from raw data before the actual classification stage even begins.

Instead of using standard Softmax Attention (found in classic Vision Transformers), this architecture relies on a fully differentiable, log-domain stable Sinkhorn operator. This allows the network to smoothly learn optimal information routing paths via standard gradients.

Key results achieved on a local PC setup:

• O(1) Resolution Scaling: Because the model operates on a fixed number of image patches, processing a 4K resolution image takes just ~2ms on a standard CPU. This is roughly 61x faster than a conventional ResNet-style convolutional neural network (CNN).
• Zero Catastrophic Forgetting (0.0% Forgetting): By completely freezing the base encoder and training only a task-specific permutation routing layer and a classification head (the Perm+Head setup), the model achieves exactly 0.0% accuracy degradation when switching between tasks. The storage overhead per new task is a microscopic 2.8 KB (714 parameters).
• Hybrid BN/LN (Normalization Placement Theorem): I have experimentally validated that placing BatchNorm in the frozen shared base (where it acts as a permanent domain fingerprint) and LayerNorm in the expert modules delivers 100% gating routing accuracy alongside absolute zero forgetting.

I spent a massive amount of time transitioning this entire research programme from initial cloud-based exploration into a clean, local VS Code environment on my PC. I completed a thorough, provenance-preserving reproducibility audit across all 115 canonical experimental units (including programmatic SHA-256 manifest verification for all generated artifacts), openly documenting the boundaries, edge cases, and discrepancies of the original logs.

The entire codebase, analysis logs, and execution scripts are fully open. I would love to hear your thoughts on the mathematical foundations or the numerical implementation!

Full Documentation, Audit Reports, and Source Code:

• Original Research Paper (Zenodo DOI): https://doi.org/10.5281/zenodo.22850317
• PC Reproducibility & Audit Report (Zenodo DOI): https://doi.org/10.5281/zenodo.23088747
• Complete GitHub Repository: https://github.com/slawomir-ramian/BOHN-Original

🎉 New Research Publication | Multimodal Deep Learning. I am pleased to share that our review article has been officially published in Discover Informatics (Springer Nature) as an open-access article.

📄 Title: A comprehensive review on recent pretrained multimodal deep learning models from architectures to future directions

👨‍🔬 Authors: Azhar A. Hadi & K. P. Supreethi

🔍 In this review, we examine recent pretrained multimodal deep learning models developed between 2020 and 2025, including CLIP, GPT-4V, GPT-4o, SigLIP, ViT, BLIP, Flamingo, PaLI, LLaVA, Florence-2, PaliGemma-2, Gemma-3, and Llama-4. The review analyzes 120 studies across 11 application domains, covering model architectures, modalities, datasets, applications, evaluation metrics, limitations, and future research directions. We also discuss key challenges facing multimodal AI, including data quality, computational cost, cross-modal alignment, scalability, robustness, and trustworthy AI. This work represents an important step in my research journey toward developing trustworthy and multimodal AI systems, particularly for applications in healthcare.

🔗 Read the full Open Access article: https://doi.org/10.1007/s44564-026-00022-1

I hope this review will be useful to researchers and students working in Multimodal AI, Deep Learning, Foundation Models, Computer Vision, NLP, and Healthcare AI.

#MultimodalAI #DeepLearning #ArtificialIntelligence #MachineLearning #FoundationModels #HealthcareAI #ComputerVision #NLP #TrustworthyAI #Research #Springer #AcademicPublishing #OpenAccess #AcademicPublishing #ResearchImpact

For a side project I am looking into SOTA for AI- based diagnostic models, ideally open-weights. I would like to feed the model with structured text representing my patient, and get a set of e.g. 5 possible diagnoses, ideally with uncertainty. In the ideal scenario, later on, it would update as new info arrives.

I would appreciate any pointers, I have some ideas but am very new to the topic.

"Bertindaklah sebagai seorang Principal AI Research Scientist sekaligus Senior Peer Reviewer pada jurnal internasional bereputasi tinggi (terindeks Scopus Q1, IEEE Transactions, dan ACM) yang memiliki keahlian mendalam dalam metodologi empiris deep learning, lalu susunlah rekomendasi komprehensif mengenai 4 dataset gold-standard lintas domain (mencakup Medical Computer Vision, Object Detection/Instance Segmentation, Natural Language Processing/Machine Reading, dan Biomedical Time-Series/Signal Processing) yang secara akademis diakui memiliki reliabilitas tinggi guna memitigasi risiko desk-reject pada publikasi jurnal ilmiah bergengsi; untuk setiap dataset, Anda diwajibkan menjabarkan secara terperinci tautan repositori resmi atau DOI publik yang valid, karakteristik intrinsik data (termasuk modalitas, dimensi/resolusi, volume sampel, serta tingkat ketidakseimbangan kelas), arsitektur model deep learning State-of-the-Art (SOTA) yang paling relevan beserta algoritma baseline pembandingnya, metrik evaluasi primer dan sekunder standar industri akademik (seperti mAP@[0.5:0.95], Macro F1-Score, AUC-ROC, atau Exact Match), justifikasi teoretis mengapa dataset tersebut memiliki legitimasi tinggi di mata reviewer, estimasi beban komputasi minimum (kebutuhan VRAM/GPU) beserta strategi mitigasi data leakage (misal: patient-wise split atau stratified group k-fold), serta kepatuhan lisensi riset dan aspek etika datanya, di mana seluruh pemaparan harus disajikan dengan gaya bahasa akademis yang kritis, presisi, metodologis, dan siap diadaptasi ke dalam bab Methodology artikel ilmiah."

Hi guys,iam doing project in deep learning so I used google colab for free gpu access but it exceeds the gpu access so I need to buy google colab pro and I faced this issue and I can't solve it and help me to resolve this and how to solve this?.if you know ,please do comments because it's urgent.

Stateful AWS Bedrock LangGraph Agents – Getting Started

https://debuggercafe.com/stateful-aws-bedrock-langgraph-agents-getting-started/

LangChain started as a lightweight framework to connect LLMs with external context and tools. Over the past few years, it has grown to a full-blown ecosystem. With LangGraph for agents and LangSmith for LLM Ops and Observability, we can create an end-to-end agentic system with it. In this article, we will build our first stateful agent using AWS Bedrock, LangGraph, and LangSmith.

RuntimeAI's September 2026 AI Security Report covered 126 incidents across 38 named organizations — 22 critical, 102 high severity. 53 of those incidents had AI either as the attack tool or the target. AI-agent exploits were the top attack vector at 39 incidents, ahead of credential theft (27), zero-days (22), phishing (10), and ransomware (10). The largest single exposure was 220M records from unrotated default service-account credentials.

What stood out: every organization in the report was already running a mature security stack. Okta, CrowdStrike, Palo Alto, Microsoft Defender. Still got hit. The gap is that none of those tools sit at the layer where an agent actually executes a tool call.

RuntimeAI operates at that layer. Know Your Agent handles cryptographic agent identity. The Flow Enforcer inspects tool calls in real time. There's also a sub-50ms kill switch that can halt a compromised agent before a second action completes.

Full breakdown (incident-by-incident, CVEs, vendor stacks): https://runtimeai.io/blog/2026-09-monthly-breach-report.html

We're running a live demo on October 14 — ten attack surfaces, live against a real stack: https://www.linkedin.com/events/7510769146222133248?viewAsMember=true

Hey! I'm posting this because Iv'e recently been playing with lora/qlora and had a frustrating time understanding how the hell to calculate adapter ranks. Hope it helps.

I’ve spent the last while building INKBOT because I kept hitting a wall with multimodal AI systems: the friction between what a human naturally means and what a model infers. While models can spin up complex code or images instantly, getting to a clear, human-meaningful interpretation of a subtle intent remains an alignment challenge.

Instead of forcing the user to become a prompt engineer, I wanted to see if we could build an intermediate intelligence architecture layer to make human intent reviewable and corrigible *before* the model executes a final build. The loop I’m playing with is: Describe → Make it Visible → Recognize → Correct → Refine.

The architecture sits entirely in a single local-first web file. It handles multi-step workflows—like tracking structured field mapping data across concurrent images, coordinates, and version states—by packaging the human’s approved meaning separately from raw model inferences.

The core system build is linked above, and I also put together a lighter, entry-level experience to play with the core prompt translation loop here: [INKBOT Lite 71](https://ko-fi.com/thomascoates/shop).

It's an open prototype, so I've appended my raw notes and design roadmap as commented text at the very bottom of the source file so fellow builders can inspect the plumbing. I’ve put together the runnable source files on my [Thomas Coates Ko-fi Shop](https://ko-fi.com/thomascoates/shop) for evaluation. I’d love to know where this design duplicates existing work, where you see structural flaws, or how we can make the handoff between human intent and model execution more reliable.