c/artificial-intelligence

Hello all! Half of all industrial "chatbots" are just text-to-SQL models in a trenchcoat (and the other half RAG!). I wanted to explore just how small you could make these models if you trained, evaluated, and ran inference in the exact same structured harness, leading to Cleo: a Qwen3.5-2B-Base finetune. Currently, some features of cleo that are only possible/useful in a unified hardel are: Training on the exact same gather, repair, and answer contract it uses at inference time Searching over candidate queries with live execution evidence, not just model likelihood Co-designing the model contract, SQL safety layer, dialect handling, timeouts, and clarification behavior as one system Everything is completely open-source, including the harness, model, and datasets. GitHub: https://github.com/Dreeseaw/cleo Hugging Face model: https://huggingface.co/dreeseaw/cleo PS: If you're also resource-constrained and trying to do RL like me, I would highly recommend experimenting with ECHO: https://arxiv.org/abs/2605.24517 submitted by /u/Dreeseaw [link] [Kommentare]

I'm trying to understand where people doing sensor based ML on microcontrollers (IMU, accelerometer, vibration ,that kind of time-series data) actually lose the most time. When you've built something like this, what was the bottleneck: Getting enough real world data in the first place? Cleaning / labeling / organizing the data you have? Actually building and training the model? Getting it optimized and deployed on the device? I am working on a project that aims to eliminate some of these pains and wanted to get some validation on this topic first before I go and add more features. It is essentially edge impulse, but hardware agnostic, gen ai native, and targeted for time series data. I am still trying to figure out what the best vertical would be as there are many to choose from. submitted by /u/No-Bug-4879 [link] [Kommentare]

Open weights are important and critical, but they are not enough by themselves. If we want open ML and AI research to move forward, we also need open training frameworks: codebases that do more than run jobs. They should make the training process visible, understandable, and modifiable, so researchers/engineers/practitioner can build new algorithms instead of fighting hidden systems. That was the motivation behind FeynRL (pronounced “FineRL”) a framework I built for RL post-training of LLMs, VLMs, and agents. RL is already hard to make work. With LLMs, VLM, and agents, it becomes even messier: rollout engines, reward computation, distributed training, weight syncing, credit assignment problems, long-horizon behavior, and many small implementation details that can quietly break everything. The core idea behind FeynRL is simple: algorithms should stay algorithms, systems should stay systems, and researchers/engineers/practitioner should be able to understand the full training loop end-to-end without spending days or weeks. GitHub: https://github.com/FeynRL-project/FeynRL The framework is designed to keep the framework explicit: from data loading and rollout generation to reward computation, loss construction, optimization, and evaluation. The goal is to make it easier to develop new algorithms, training recipes, reward designs, rollout strategies, and optimization methods without going through a convoluted hidden system. The framework currently includes examples for SFT, DPO, and RL-style post-training for both vllm and llm, with support for single-GPU, multi-GPU, and cluster setups. Would love feedback, issues, suggestions. Also, curious to hear what parts of RL post-training infrastructure people still find too hidden, hard to debug, or hard to modify. submitted by /u/summerday10 [link] [Kommentare]

I am currently a cs graduate student at a top university after completing a comp eng undergrad there My thesis is more to do with embedded system security and maybe applied ml Unfortunately I didn’t get into any ml focused graduate program as they’re extremely competitive even with good grades from a top undergrad (and some projects of course) I want to do a PhD in ml as I’m currently having a tonne of fun taking optimization and ml courses - further I’ve been studying it for years already I think my interests lie mostly in optimization ie shampoo, soap, hessian free/approximations Also inference optimization is pretty cool but I’m more of a math person even though my undergrad was in computer engineering I enjoy learning about things like pca, lda, other stats techniques on my own I’ve had about 7 internships but they were all in software except one where I did a bit of ml near the end ie fitting decision trees to data Currently I couldn’t get a job after 1 year of applying so I’m in a masters program at my school. I have 0 publications and my supervisor puts out maybe 1 paper every 3 years so it’s unlikely I’ll get one from my thesis, if I’m lucky I could do some sort of anomaly detection paper but even that’s unlikely (t-test is pretty much unbeatable) What steps should I take to get into a PhD program after graduating and what classes should I take as my math background feels like it’s lacking when I read something like the shampoo paper submitted by /u/proturtle46 [link] [Kommentare]

It turns out LLMs have strong priors over character names that are model-specific and version-specific. If you find Elena Vasquez and Marcus Chen together on a website, there's a good chance Claude generated it. We stumbled on this as a side finding while working on a model diffing method (CDD), and it grew into its own paper. The short version: these names travel as correlated ensembles, appear across dozens of websites as volcano experts, podcast hosts, thriller protagonists, and authors of 1000+ papers published in two months. Then we found a third name in the ensemble. The collage in the comments shows three different websites independently hallucinating the same trio with AI stock photo faces. Preprint: https://arxiv.org/abs/2606.02184 submitted by /u/CebulkaZapiekana [link] [Kommentare]

I’ve been thinking about whether the same basic concept behind Bitcoin could be applied to AI training. In Bitcoin, miners perform proof-of-work and are rewarded for contributing computational resources to secure the network. The actual computation itself isn’t particularly useful outside of the network, but it creates a decentralized system. What if a similar incentive structure could be used for training large language models? Instead of miners solving hash puzzles, participants would contribute GPU resources toward training an open-source AI model. In return, they would receive tokens or rewards based on their contribution. Some questions that immediately come to mind: How could the network verify that a participant actually performed useful training work? How would you prevent people from submitting fake or harmful gradients? Could model improvements be measured objectively enough to determine rewards? Would this be more efficient than training models in centralized data centers? Could a decentralized network eventually compete with large AI companies? I know there are already decentralized AI and compute projects, but I’m specifically interested in whether a true “proof-of-training” mechanism could exist, where rewards are tied directly to improving a model rather than simply renting out compute. Curious to hear thoughts from people who understand distributed systems, machine learning, or crypto economics. Is this fundamentally impossible, or is there a viable architecture that could make it work? submitted by /u/notfinancialadvice0 [link] [Kommentare]

This project distills a model's word embeddings into human-interpretable "concept-vectors", i.e. vectors in which each component tracks concerns like semantics, syntax, and even statistics potentially, while associating each component with a human readable and human definable label. These distilled vector components are then joined with undefined trainable components then passed to a model. Check the readme/repo and supporting docs for details. For transparency, this is a data design project. I have quite a bit of experience with data transformation and manipulation, but limited experience with NNs. I have not tested this on models, and I currently don't have the resources to build a comprehensive database to test it on models. I'm posting primarily for human feedback/criticism, and simply to share the idea since this is as far as I can currently take it. Edit: I forgot to actually add the repo! submitted by /u/true-human-exe [link] [Kommentare]

Hi guys, today is the deadline for acceptance notification from NeurIPS. Has anyone hear back already? Do they send the rejection letter later? submitted by /u/LocksmithAlone242 [link] [Kommentare]

A while ago I realized that intuitively understanding classical ML and calling fit() / predict() wasn't enough to feel confident in my skills or ace interviews. So I did the only thing that actually fixes that: built the algorithms from the ground up in NumPy, then validated them against Scikit-learn and PyTorch. The repo has 10 algorithms as Jupyter notebooks, each implemented as simply and directly as possible: Linear & Logistic Regression Regularization K-Nearest Neighbors Naïve Bayes Decision Tree, Random Forest Gradient Boosting, XGBoost Neural Network Three things I noticed while building these: Structure matters more than you think. A neural network becomes much clearer when you model it as a collection of blocks (linear layers and activations), each capable of a forward and backward pass. The breakdown into small pieces makes backprop feel obvious instead of complex. The same ideas keep showing up. Gradient descent isn't just one algorithm – it's the backbone of most of what's in this repo. Once you implement it by hand the first time, everything after gets easier. When something goes wrong, fixing it is the most rewarding part. You can't blame the library – you have to understand exactly what broke and why, which forces a real depth of understanding. Everything is free: https://github.com/ml-from-scratch-book/code Each notebook runs locally or opens directly in Google Colab. If you're studying for ML interviews or just want the fundamentals to feel solid, this might be useful. submitted by /u/OleksandrAkm [link] [Kommentare]

Hi everyone, I shared PrintGuard here about a year ago as a few-shot FDM failure detector built on a ShuffleNetV2 backbone classified by a prototypical network — the model from my dissertation, packaged with a hub and a web UI. v2.0 ships today and is a complete rewrite of everything around the model, so I wanted to walk you through what's changed and what hasn't. What hasn't changed is the model. It's still a ShuffleNetV2 encoder classified by nearest prototype, trained for few-shot FDM fault detection in Edge-FDM-Fault-Detection (with a technical write-up in the repo). What has changed is the runtime: the model is now a ≈5 MB TFLite export via LiteRT, classified by nearest prototype, with per-printer sensitivity and threshold sliders that map directly onto the prototype distances — so you can tune for camera and lighting without retraining. The interesting bit for this sub is the architecture around the model. v2.0 is a single Python engine that runs unmodified on CPython (hub mode) and on Pyodide in the browser (local mode). Everything mode-specific is confined to one Platform implementation per runtime — the two modes cannot drift apart because they execute the same files. The methods on the Platform contract are exactly the ones that aren't portable: infer(rgb), discover_cameras(), open_camera(id, source), http(...), encode_jpeg(rgb), load_state / save_state. On the CPython side, infer is ai-edge-litert on CPU threads, discover_cameras walks the MediaMTX path list, and open_camera is a PyAV reader thread per RTSP stream. On the browser side, infer is LiteRT.js in WASM via a JS bridge, discover_cameras is enumerateDevices(), and open_camera is getUserMedia + canvas grabs. The UI is presentation-only and speaks one JSON command/event protocol — over a WebSocket in hub mode, over an in-page Pyodide bridge in local mode. The engine cannot tell which transport it is on. No mode-specific logic lives anywhere else; if a feature needs a runtime service, it extends the Platform contract on both sides. Inference scheduling is fully dynamic and fairness-aware: A smoothed estimate of observed inference latency continuously yields the sustainable total rate (workers / latency). That capacity is water-filled across in-use cameras (max-min fairness): no camera is allocated beyond its native fps, and surplus flows to cameras that can use it. A free worker takes the most overdue camera and grabs its freshest frame at dispatch time. Frames carry a sequence identity, so the same frame is never inferred twice, and results always describe the present, not a backlog. On RTSP, MediaMTX bursts the buffered GOP on connect, so stream fps is trusted from the SDP average_rate where available, and measured only after a warm-up otherwise. The defect pipeline is a monitor on top of a per-printer score stream. score ≥ threshold for N consecutive frames triggers the configured action (alert only, pause, or cancel) on the linked OctoPrint or Moonraker service, with retries on failure; the alert event carries the action and its outcome, the UI error feed gets a copy, and the snapshot goes out to every enabled notification channel (ntfy, Telegram, Discord). The fail-safe behaviour is the part I most want feedback on, because I have strong opinions about it. A printer's watching state gates inference: Linked service reports Watched? Why no service linked yes nothing to gate on printing yes the job needs eyes no state yet / unknown yes can't tell → watch offline (unreachable) yes losing the signal must not stop monitoring idle / paused / error no (standby) positively not printing Only a positive "not printing" stands inference down. The watchdog then warns on the dashboard and through notification channels when a camera drops, a feed freezes or a printer service stops answering, and a failed pause is announced, never swallowed. I'd be very interested to hear how this stance interacts with people who run multiple printers with mixed reliability on their printer services. There's a live browser demo (the whole engine in Pyodide + LiteRT.js WASM), the Docker image is multi-arch, and the architecture doc goes into all of the above in more detail with diagrams of the engine layout and the defect pipeline. This is a major version — nothing from 1.x migrates, and a 2.0 hub starts from a fresh configuration. Issues, especially around the fairness scheduler, the CORS / mixed-content / host.docker.internal edge cases, and the LiteRT ↔ Pyodide bridge, are very welcome. Let's keep failure detection open-source, local and accessible for all. submitted by /u/oliverbravery [link] [Kommentare]

Hi everyone, I’m a recent CS graduate working mainly on NLP/LLMs and VLMs failures. I’m currently in a phase where I can dedicate a lot of focused time to research, but the main bottleneck holding me back is compute. I know “asking for GPUs” can sound vague or unserious, so I want to be transparent. I’m not looking for free compute to casually experiment or waste cycles. I have already been actively publishing and submitting research, including papers at EACL 2026, IJCNLP-AACL 2025, MICCAI 2026, an EMNLP 2025 workshop paper, and a recent ARR submission. I’m happy to share my Google Scholar/CV/papers privately with anyone interested. The ideas I’m currently working on are GPU-intensive, mostly around LLMs, NLP, and VLMs. I’ve discussed some of them with PhD friends/peers, and the feedback has been encouraging. The goal is to develop these ideas into strong, publishable work, ideally targeting top conferences such as *CL venues, CVPR, ICLR, and related ML/AI conferences. To run the experiments properly, I likely need more than a single consumer GPU. Ideally, I’m looking for access to something like a 4x or 8x GPU setup, L40S, A100, H100, H200, or similar. I understand that asking for H100/H200-class compute is a big ask, so I’m also open to scheduled access, partial access, university/lab cluster time, unused credits, or any practical arrangement. What I can offer: Serious research effort and consistent execution Weekly progress updates, logs, and experiment summaries Clear compute usage reports so the resources are not wasted Reproducible code, experiment tracking, and documentation Open discussion of ideas before running expensive experiments Proper acknowledgment of compute support Co-authorship To be very clear: this is purely for research work, no mining, no commercial misuse, no unrelated jobs. I’m comfortable discussing the project scope, risks, expected compute needs, and authorship/acknowledgment expectations before using anything. I know this is a long shot. Maybe nothing comes out of it. But I also know many early-career researchers face this same wall: you may have the time, motivation, and ideas, but not the infrastructure to test them properly. So I’m putting this out here in case someone has unused compute, lab access, cloud credits, or is interested in collaborating on publishable research. If this sounds relevant, please DM me or comment, and I’ll be happy to share more details about my background and the research directions. Thanks for reading. submitted by /u/Academic-Success9525 [link] [Kommentare]

Hi everyone, I'm a PhD researcher at Mainz University of Applied Sciences, Germany. My dissertation looks at how interface and UX design shape user trust in AI/LLM-based chatbots, specifically how to support calibrated trust, where users neither over-rely on a system nor dismiss a capable one. As part of this, I've developed a structured method that helps designers or developers decide which trust-related interface elements to use in a chatbot, and how strongly to apply them, depending on the use context. I'm looking for practitioners to apply the method to a worked example and tell me whether it's understandable, useful, and applicable in practice. Critical feedback is exactly what I'm after; there are no right or wrong answers. Who I'm looking for: People who design, build, or research AI/LLM-based products, e.g.: UX, product, or interaction designers AI/ML engineers, data scientists, or applied-AI / conversational-AI practitioners Advanced students or researchers in these areas You should be comfortable reading and responding in English. What's involved (~20-30 min, at your own pace): Read a short description of the method and a sample chatbot case Apply the method step by step to that case, noting your reasoning as you go Rate it on three dimensions (clarity, usefulness, applicability) and leave open feedback Details: Fully anonymous online survey. Voluntary, no compensation. No personal data is required beyond a few optional questions about your professional background. Responses are used only for my dissertation, and you can stop any time before submitting. Consent details are on the first page. Survey link: https://ww3.unipark.de/uc/ux4ai/ Happy to answer questions in the comments or by DM. Thanks for considering it! submitted by /u/pparker20 [link] [Kommentare]

Recent years I see plenty of folks from OpenAI and Anthropic attending conferences like ICML/Neurips, yet obviously few are presenting. Are they mainly recruiting? Following emerging research? Curious if anyone with firsthand experience can shed some light on how attendance is justified internally and what the main objectives usually are. submitted by /u/snekslayer [link] [Kommentare]

How does the ML research community feel about evolutionary algorithms? Should I do a PhD in this area? Quick remark: I know some people in the ML community dunk on evolutionary algorithms because there’s often a better optimizer, but they do have their place, which is what researchers in my community aim to quantify. Background: I just finished my first year as a mathematics master’s student working on the theory of evolutionary algorithms (EAs)/randomized search heuristics. I’m fortunate to be on a research assistantship and have already coauthored several papers in strong conferences in our area. I’ve always been more interested in classical ML/deep learning theory but haven’t had anyone to work with. Researchers in my field, including my advisor, occasionally publish in mainstream ML venues such as AAAI and NeurIPS, but it’s primarily the EA venues. For a while now, I’ve been independently studying deep learning and statistical learning theory, and I have found intersections with my current research that I plan to pursue for my thesis. With my current CV, it’s looking like I could get into some of the best PhD programs in my area, but I’m wondering if I should try to go to a more ML-centric PhD, even if it means going to a less prestigious institution/group for the sake of my career. I’m not sure yet what I want to do after my PhD and a possible postdoc, but I want to keep myself competitive for top-tier opportunities. What implications might doing an EA PhD have for my career? With strong EA publications, could I get into a good ML PhD program if I pitch myself appropriately? Could staying somewhat outside mainstream ML actually be a good career move, given how competitive and crowded ML has become? submitted by /u/NullRecurrentDad [link] [Kommentare]

I’d like to apply for a Google PhD internship, but I currently have only one publication and three ongoing projects that will hopefully be published someday. Do I still have a realistic shot? submitted by /u/Sea_Muscle_4281 [link] [Kommentare]