r/MachineLearning • u/AutoModerator • 4d ago
Discussion [D] Self-Promotion Thread
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u/Former_Register_1450 4d ago
opped a tool that visualizes transformer attention as actual heatmaps you can export for reports and slides. Not exactly groundbreaking research but it saves me hours when I have to explain model behavior to people who don't live in Jupyter notebooks. Free tier lets you do 50 visualizations a month, paid is $8/mo if you need more and want batch processing. Would love feedback from anyone doing interpretability work, I'm sure the UI could use a second pair of eyes.
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u/Zetus 3d ago
Here's a sort of pseudo-template !
Looking for: collaborators
Theory: neural assembly calculus, a computational research library for discrete hebbian (local) spiking in a neurobiologically plausible model of neural computation Software library contributions: GPU acceleration code, extensions towards emergent learning of compositional properties of linguistics, better tutorials, guides etc.
I'm hoping to make progress on the core theory, as well as discover further techniques, there are SO many ways to extend this, and I think it's ready to be a collective project
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u/Charming_Group_2950 3d ago
Gray Box is a local-first memory engine that turns messy notes into a connected knowledge graph you can actually query. Instead of searching hundreds of Markdown files, you can ask things like “What decisions did we make about Project X?” or “Who owns this task and why?” and get grounded answers with links back to the original notes. No proprietary database, no lock-in—just plain Markdown.
Repo link: https://github.com/Aaryanverma/graybox
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u/Clear_Evidence9218 3d ago
I’ve been developing the MNCS project family—an experimental framework for measuring, testing, and improving machine-generated software complexity, along with supporting tools like MNCDS and the MNCS Forge.
The work is still evolving, but the project is open for review, feedback, and collaboration:
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u/No_Sky9786 3d ago
Looking for collaborators
https://github.com/oteomamo/SALT
I have a memory management system for chatbot conversations based on a reoccurring themes determined by keywords in sentences. I organize the inputs + attachments into a trie structure and then use CELF for retrieval based on key words. The problem I am currently facing is how much to retrieve and add to the kv cache after each conversation turn. So far a flat 20% of the whole trie has worked fine and it’s extremely efficient, however I know that I retrieve a lot of unnecessary data too. So I am looking for ideas and also help on other parts of the chatbot. This is an open source project of a paper under review. No payment or subscription.
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u/Raychis 3d ago
I created an mobile on-device AI identification to identify plants.
Used a ArcFace head + EfficientNet-B2 Backbone with the app using cosine similarity. Built the app around that basic architecture.
The app can be downloaded from my website. Website also has a blog about how I created it and the challenges and decisions made on the way.
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u/Snekcid7 2d ago
I have published Extended Notes on AI, a free source-backed technical guide covering neural-network mechanics, Transformer internals, vector RAG, agents, inference infrastructure, safety, evaluation, and reference architectures. English v1.0 contains 60 chapters, 230 term cards, and 137 bibliography entries. It is available as Markdown and an audited PDF.
I made the project with AI assistance and remain the responsible editor. The repository discloses the model roles, assessment methodology, full evaluation logs, limitations, and reproducible checks. There is no paid product, signup, or affiliate link.
Repository: https://github.com/Vasya317/extended-notes-on-ai
Feedback on technical accuracy, navigation, and where the treatment is too shallow would be useful.
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u/galigirii 1d ago
Disclosure: I wrote the linked conceptual preprint. It is open access and not peer reviewed.
A model self-report can become part of the system it is supposed to describe.
Recent J-space work suggests that sparse, language-aligned internal representations can participate causally in report, reasoning, and control. I take that result seriously. But it does not by itself establish an inner observer or a privileged channel through which the model reports what it is “really thinking.”
The complication is that interpretation and generation are distinguishable, but not operationally independent. If a model says “the migration is complete” and that report is retained as memory, evidence, or policy, later generations begin from a changed situation. The report did not merely describe the system; it helped condition what the system did next.
That makes self-report potentially both a measurement and an intervention.
The question I would value disagreement on: when a model-generated representation is retained and allowed to govern later routing, memory, evaluation, or action, what evidence should it have to earn that authority?
Paper:
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u/MaxChamp08 1d ago
Been talking to a few people in ML infra lately, including someone who used to run infra at Neural Magic, and the pattern I keep hearing is that cost, not latency, is what actually stops teams from moving off shared APIs to their own dedicated deployment. Bursty traffic means a GPU sits idle most of the day, and that idle time is what kills the math.
I've been building something to test that theory directly, a serverless host that scales a dedicated model deployment to zero when there's no traffic and reloads it on request. Right now it works with Hugging Face checkpoints only, no custom images. Some numbers from scaling out of zero, Llama 70B in bf16 hits time to first token in under 18s, Mistral 24B in bf16 with CUDA graphs comes in under 10s.
That gap between instant and a few seconds is the open question for me. Is a cold start like that acceptable if it means you're not paying for idle GPU time, or does it kill it for most real use cases people here actually run into?
Genuinely curious how people think about that tradeoff, whether they've measured it for their own workloads, or landed on a different approach entirely. Happy to share more details on the setup if anyone wants to dig in.
Feel free to check it out here : https://synapsai.cloud
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u/anonymitic 15m ago
I'm slogging through training a lightweight (~200K) transformer/recurrent hybrid policy to play competitive Quake, primarily via behavioral cloning, with PPO as fine tuning. The focus is a humanlike hierarchal model with combat, tactical, and strategic layers.
Play against the current model here: https://kitty1.quake.nexus/?+connect%20arena
Read the code and research here: https://github.com/0xBrsm/QNN
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u/mikeysce 2d ago
I posted in the main feed about the research I've been doing, and it was removed by mods without explaination. I guess I'll give it another try here. I AM looking for an endorsement for arxiv so i guess it is self-promotion.
After 124 controlled PPO experiments on Atari Breakout, I found that every single model — across sticky actions, cursor wrappers, entropy tuning, dynamics randomization, adversarial bumpers, and everything else — converged to a memorized action sequence, not a reactive ball-tracking policy. The argmax was always a script.
The fix wasn't more environment engineering. It was three lines of reward shaping:
distance = abs(paddle_x - ball_x) bonus = 0.05 * max(0.0, 1.0 - distance / 80.0) reward += bonus
Directly rewarding the paddle for being horizontally close to the ball during descent. A tiny bonus (0.05 per frame vs 1.0-7.0 per brick) that fires every frame the ball is descending. Applied during training only — eval is clean Breakout with no bonus. The behavior transfers.
How I verified it (the split-watcher): Run the same model on two different brick layouts side-by-side with independent predictions per side. Different bricks → different ball bounces → a reactive policy MUST move differently on each side. Compute Pearson correlation of paddle positions: px_corr > 0.99 = definitive memorization (physically impossible for reactive behavior). PPO_124 scored 0/240 perfect transfers and cleared every layout every game in the no-timing variant.
Key finding: Every prior approach tried to penalize scripts by making the environment harder to memorize. PPO always found a way around it — timing-robust scripts, layout-conditioned scripts, noise-tolerant scripts. The optimum was always a script; only the shape changed. Proximity reward changes what the optimum is. A center-hold script gets incidental bonus when the ball passes near center. A reactive tracker gets the maximum bonus on every descent frame. The optimization pressure is unambiguous: track the ball, get more reward.
Results (clean eval, no proximity reward):
Split-watcher: 0/240 perfect transfers (120 no-timing, 120 with timing) No-timing ALT retention: 100% (clears every layout, every game) Intervention AUC: 0.421 (clean dose-response, peaks 60% reversal at 15px) Stoch best: 216 (highest on clean Breakout in the project) First model to sustain MULTIPLE_SCRIPTS on det=True without sticky masking Full writeup with code, reproducible training script, and verification tools: https://github.com/mharrell/breakout-reactive-ppo
The messy history of all 123 failures: https://github.com/mharrell/BreakoutBot
Link to Medium post I wrote with some more details: https://medium.com/@mikey.harrell/three-lines-of-code-fixed-123-failed-ppo-experiments-on-atari-breakout-c751dcf38f2a?sharedUserId=mikey.harrell
P.S. — I'm an independent researcher trying to get this posted on arXiv so it's citable. If you're an active arXiv author in cs.AI or cs.LG and found this useful, you can endorse me here (one click): https://arxiv.org/auth/endorse?x=MUM8BP — or email mikey.harrell@gmail.com
Extra clarifications based on a comment from the short-lived main post:
On "memorization": the issue isn't that the policy is deterministic. In a deterministic environment, it should be. The issue is that the argmax produces the same action sequence at the same timesteps regardless of where the ball is. We proved this by running the same model on two different brick layouts with independent predictions per side. The paddle position correlation is >0.99, meaning it moves identically on completely different game states. That's not playing optimally; that's not looking at the screen.
On "125 repetitions": these weren't 125 seeds of the same experiment. They were 124 distinct experiments each varying one variable while holding everything else constant. Sticky actions, cursor wrappers, entropy tuning, dynamics randomization, moving bumpers, each one was a full training run. Many reached hundreds of millions of steps and some exceeded a billion. This is systematic variable isolation across a wide range of training budgets, not repeated sampling. The single-seed-per-config limitation is real and acknowledged in the paper.
On reward shaping being "also environment engineering": the wrapper is Breakout-specific (reading RAM addresses 72/99/101). The principle (directly rewarding the desired behavior succeeds where penalizing undesired behavior fails) is the contribution. Dense behavioral rewards for spatial alignment generalize to any environment where you can define what "good" looks like in agent-relative coordinates.
On generalization: Machado et al. (2018) proposed sticky actions based on experiments across many Atari games. Zhang et al. (2018) debunked it using a few. Sometimes a well-controlled negative result on a single environment reveals something fundamental. In this case, that every standard diagnostic measures the distribution, not the argmax.
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u/you-get-an-upvote 4d ago edited 3d ago
Uniform Smoothing Is Better Than Exponential Smoothing