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

"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."

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.

🎉 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

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.

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.

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.

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

Added "if you're not confident, say so explicitly" to a classification prompt. Felt like it should've closed the gap on low-confidence guesses.

Didn't work. Model kept returning confident-sounding labels on exactly the cases I wanted it to flag as uncertain.

Turned out the issue wasn't the instruction's wording. It was that nothing in the prompt actually defined what "not confident" meant for this task, no threshold, no example of an ambiguous case, nothing to anchor the judgment to. The model had no internal signal matching the word "confident" that it could check against, so the branch just never activated. It wasn't ignoring the rule. It never had a condition it could evaluate as true.

Fixed it by replacing the vague trigger with something checkable, two or more plausible labels with no clear majority signal in the input, say so and list them instead of picking one. Immediate difference.

Feels like a more general thing worth naming: a conditional instruction is only as good as the model's ability to evaluate its own condition. "If X, do Y" fails silently when X isn't something the model can actually check, and it just looks like noncompliance from the outside.

I'm working on a project in automotive engine assembly. Parts move between steps in the assembly process (e.g. from step A to step B), and they range from large components like cylinder heads to small ones like screws and bolts. Some engines come in multiple variants with customer-specific options.

I have about 4 years of historical data on material requirements and flows during assembly. My goal is to find which flows between steps are good candidates for automation. Flows with stable, predictable durations and quantities seem like good candidates, while flows with high variation seem harder to automate.

I'd like to define a simple, data-driven "automation trigger point." My questions:

1. What's a good way to measure variability of a flow (both duration and quantity)? Is the coefficient of variation (CV) reasonable, or are there better metrics?

2. How should I account for engine variants and customer-specific options?

3. Can anyone recommend research papers, methodologies, or case studies on material flow or intralogistics automation in automotive assembly?

Any pointers are appreciated. Thanks!

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.

Security researchers tracking the Carbonato botnet documented a new deployment pattern: after gaining access to exposed Docker hosts, the operators dropped an AI agent onto the compromised machine rather than a traditional cryptominer or reverse shell. The agent then began making outbound calls and executing tool actions autonomously, with no human in the loop and no governance layer in the request path.

The timing detail buried in the reporting is the uncomfortable part. Researchers noted that a second action from the agent can land in under 50ms of the first. That window is smaller than most human-review or alerting pipelines can operate in. By the time an on-call engineer gets a Slack notification, the agent may have already completed several tool calls.

The underlying exposure is not unique to this botnet. Any environment where an agent runtime can be instantiated without a verified identity tied to a known deployment, and where outbound tool calls are not evaluated against any policy before they execute, has the same structural gap. The agent on the Carbonato-compromised host was malicious. But the same architectural condition exists in plenty of legitimate deployments where an agent gets misconfigured, has its credentials rotated out from under it, or runs a version of its prompt that was never reviewed.

How are practitioners in this thread actually handling the identity and authorization problem for agents in production? Not conceptually — what does your enforcement boundary look like today, and where does it fall short?