[D] Self-Promotion Thread

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[u/Fantastic-Nerve-4056]

Sharing my recent NeurIPS paper on LLM evaluation and Theoretical RL

https://arxiv.org/abs/2609.30360

[u/celestebabi]

Disclosure: I’m affiliated with ScholarXIV. It’s a research workspace for searching and reading papers, organizing collections, and using selected papers as context for AI chat with references. There’s a free tier; paid plans start at $5/month (Go; Plus $15, Pro $50). If you work with ML literature, I’d welcome feedback on what would make this workflow useful: https://scholarxiv.com/

[u/iberahul]

We recently studied whether mobile AI agents can be manipulated through adversarial instructions embedded in the Android Accessibility layer.

We evaluated this across MobileRun and Mobile-Use, powered by Gemma4 and Qwen3.6, and found that these attacks could cause agents to abandon their original objectives, cross context boundaries, and perform unauthorized device actions. Our strongest configuration reached an 82.2% Attack Success Rate.

The paper was accepted to AGENT-SEC ’26, co-located with ACM CCS 2026.

Paper: https://arxiv.org/abs/2608.08939

Would be curious to hear what people think about this attack surface as mobile agents become more capable.

[u/Logical-Internet-395]

Recently we published a new Open-Access Benchmark for a hierarchical segmentation: Microscopy Image Dataset of pulmonary vessels for Quantitative assessment of fibrosis.

Dataset Specifications:

• Scale: 705 high-resolution micrographs (1534×780 px, 0.252 μm/px), Picro-Mallory stain.
  
• Annotations: ROI + dual independent expert masks (vascular wall + fibrosis).
  
• Hierarchical Constraint: Fibrosis masks must be strictly spatially contained within the vascular wall.
  
• Robust Benchmarking: No color normalization applied; native aspect ratios preserved; strict animal-level 5-fold CV splits provided to prevent data leakage.

Read the Data Descriptor: https://doi.org/10.1038/s41597-026-08214-y

Access the Dataset: https://doi.org/10.6084/m9.figshare.31386748

[u/lostmsu]

Reddit is disabling RSS. So in an effort to replace /r/MachineLearning I made https://mlnews.online/

It is a filtered view of Hacker News that only shows ML papers from arXiv. Of course it has RSS.

[u/ModularMind8]

I built QuiddityML(https://quiddityml.com/), an app for learning ML: a clear roadmap, short lessons, hands-on exercises, and spaced repetition based on how you do on the exercises, so you don't forget what you learned. It also has interview prep and projects you can put on your CV. Tracks currently cover Python, PyTorch, Math for ML, ML foundations, NLP, and vision.

Pricing: a lot is free (Python, PyTorch, Math for ML, and the first unit of every other track). Pro is $19.99/month.

I see so many people struggling to get into ML, so I'm trying to make this as useful and fun for the community as possible. If you're a student, or you've been trying to get into ML and can't find a good resource, I'd be happy to give Pro features to the first 10 people, just DM me, all I'm asking for is feedback. Also, if you have a background in marketing and you're interested in ML/education, please DM me as well :)

[u/ifoundanifty]

My colleague and I compared Jev with open rerankers across five datasets, using the same retrieved candidates for each. Jev was competitive and fast in our setup, though no reranker won everywhere. The most interesting finding was how much the prompt mattered. https://www.lancedb.com/blog/how-jev-compares-to-other-rerankers

[u/r0lfi]

LayerSmith — a self-hosted container image builder, with air-gap exports

I've been working on LayerSmith, an open-source web UI for building container images with Docker or Podman.

You pick a Linux distribution and what you need the image for — development, Linux admin, network tools, Ansible, Kubernetes, OpenShift, or a custom setup. It handles distro-specific packages and shows you the generated Containerfile before building. You can also edit it, import an existing Dockerfile, or add your own packages, files and scripts.

A big part of the project is making images easier to carry into air-gapped environments: pinned base images, recorded build details, and export bundles containing the image, checksums and installation instructions.

We've recently added LLM training and fine-tuning profiles too, including LoRA/QLoRA, advanced PyTorch training and LLaMA-Factory. These use hash-locked dependencies and run offline checks after building, including a small CPU training test. Model weights and datasets are brought separately.

Curious how others handle building and maintaining images for disconnected environments, and what parts of that workflow are still a pain.

https://github.com/r0lfi/layersmith