标签：#MachineLearning

I spent the last few months building a new sparse fine-tuning method for MoE models called **USAF**. The goal was simple: if your GPU can run inference on an MoE model, it should also be able to fine-tune it. On my AMD RX 6750 XT (12 GB), I can fine-tune Qwen3-30B-A3B by training sparse expert weights and the router instead of adapters. The project is completely open source under the Apache 2.0 license. I'm not trying to build a business, sell anything, or monetize it in any way—I just wanted to share something I built that I think is genuinely interesting. I'd love to hear your feedback, especially from people working with MoE models. GitHub: https://github.com/tsuyu122/usaf submitted by /u/tsuyu122 [link] [留言]

Ever wondered why sometimes the simplest things throw a wrench in our beautifully crafted code? I recently had a realization that hit me like a ton of bricks: the bottleneck could literally be the air in the room. It sounds absurd, right? But let me take you on a little journey through my recent experiences that led me to this conclusion. The Setup: A Frustrating Week Just a few weeks ago, I was knee-deep in a project using Python and TensorFlow to build an AI model for image classification. I was feeling pretty confident, you know? I had my dataset prepped and cleaned, my model architecture designed, and I was ready to train. But then, out of nowhere, my training took an eternity. I was kicking myself for not optimizing my code, but something just felt off. I started checking everything from my training loop to the data pipeline. I even considered that maybe I had some rogue semicolons in my Python code—classic mistake, right? But no, everything seemed fine. Then, in a moment of clarity, I realized my laptop was struggling to keep up. The fan was roaring like it was auditioning for a heavy metal band. It hit me that maybe, just maybe, the problem was my environment—specifically, the air conditioning. Environment: The Unsung Hero I’ve learned that environment can have a huge impact—like, why didn’t I think of this sooner? I had been training my model in my home office, where the temperature was rising faster than my enthusiasm for debugging. I decided to take things to the next level and moved my setup to a cooler room. And guess what? My training speed improved significantly. It turned out that my laptop was throttling itself to prevent overheating. This was my "aha moment." It was a reminder that sometimes the bottlenecks in tech aren’t just about code or hardware; they’re about the conditions we create for them. The Code: Finding Efficiency Once I had a handle on my environment, I dove back into my code. I had learned the hard way that performance optimization is

I've been trying to come up with a solution for keeping extremely long ai sessions coherent. Sometimes there is too much substance to risk compaction. With so much buzz around diffusion going on it got me thinking, what if we treat the context like a progressive render, blurry>sharp. The practical way to make text "blurry" is compression. This is a "diffusion inspired" system which borrows the coarse-to-fine process, not the formal math. It uses semantic compression so the overall structure of the session stays intact. Read the compressed version first to build an outline. Then read progressively less compressed slices until you're reading small verbatim chunks that give full detail. So you're basically using compression as noise on the input side, then progressively building an output. Each slice is compressed to fit within the context window, so the model only ever needs to read the current slice+input+current output. Tell the model what pass it's on, so it knows whether to write an outline or add detail. The thing I'm actually trying to preserve is what you'd call "non-local information". Think of it as stuff that surfaces when looking at the whole session & doesn't survive fragmented retrieval. Retrieval misses it, compaction deletes it. Both miss what only exists in a holistic view. Here is a visual demonstration to get a general idea of the workflow. https://dev-boz.github.io/diffusive-semantic-compression/demo/architecture-demo.html There is substantial overlap with lots of prior art, Recursive Language Models is one of the closest (source and output on disk, process recursively). I wrote most of this before I found RLM and nearly gave up before realising there was still a small part that was novel. As far as I can tell there's no exact match for this particular implementation. Please let me know if I've missed one. The difference to regular masked diffusion is in changing the length of the input rather than just masking. What seems to be new ground is using

I build and run one of the tools on this list (AGenO — full disclosure below), and I use every other tool here regularly. This is what "free" actually gets you on each one, including the catches. The AI tool landscape has a dirty secret: almost nothing labeled "free" is free. Most tools give you a taste — ten messages, three images, one song — and then the paywall lands. So instead of another list of forty tools nobody has tried, here are nine that give you real value at $0, organized by what you're trying to make, with the actual limits spelled out. Quick comparison Tool Best for What's actually free The catch ChatGPT General chat & writing ~10 msgs/5h on the flagship model Silently switches you to a weaker model after the limit Claude Long documents, nuanced writing 10–25 msgs/5h, varies with demand Limits shrink when servers are busy Gemini Image generation & editing Generous with a Google account Best features drift to the paid tier Perplexity Research with citations Unlimited basic searches Pro searches are capped Suno AI music ~10 songs/day No commercial use on free; failed generations can eat credits Leonardo AI Stylized art & game assets Daily token allowance Confusing token system; images are public on free Character.AI Roleplay & AI characters Unlimited chat Heavy filters; your chats train their models AGenO All of it in one place Images, songs with vocals, chat, characters, stories, coding problems — daily free allowance One-person project — busy hours can mean a short queue Canva Magic tools Quick social graphics 50 text-to-image uses Design-tool add-on, not a real generator Chat and writing ChatGPT is still the default for a reason — the free tier includes the flagship model and it's good at nearly everything. The catch nobody tells you about: after roughly ten messages in five hours, it quietly downgrades you to a mini model without making it obvious. If your answers suddenly get dumber mid-conversation, that's why. Claude writes the most natural prose

Hi everyone, I built H64LM, a research project to better understand modern LLMs by implementing one from scratch in PyTorch. Instead of relying on high-level training frameworks, I implemented the core components myself attention, MoE routing, normalization, and the training loop. Features 249M-parameter Transformer Grouped Query Attention (GQA) Sparse Mixture-of-Experts (8 experts, Top-2 routing) with 3 auxiliary routing losses SwiGLU, RoPE, RMSNorm Sliding-window attention Mixed-precision training, gradient accumulation Custom training loop (no Trainer abstractions) Checkpointing and resume support The included checkpoint was trained on a subset of WikiText-103 to validate the pipeline end-to-end, not to be a strong model it's visibly overfit past epoch 10 (best val PPL ~40.5). Known limitations are documented in the README, including batch-size-1-only generation and no true DDP (falls back to DataParallel). GitHub: https://github.com/Haiderkhan64/H64LM Feedback on the implementation or architecture is very welcome. submitted by /u/Loose_Literature6090 [link] [留言]

We built a model diffing method that recovers verbatim content from narrowly finetuned LLMs using only grey-box logit access (no weights, no activations, no probe corpus). Recent work (Minder, Dumas et al., "Narrow Finetuning Leaves Clearly Readable Traces in Activation Differences") showed that finetuning leaves detectable traces in activation differences between base and finetuned models. Their method, Activation Difference Lens (ADL), steers generation using these differences, but it's whitebox (needs full weight access) and only recovers a vague, domain-level description of what the finetuning was about. We introduce Contrastive Decoding Diffing (CDD), the output-level analog. Instead of steering with activation differences, we contrast the base and finetuned model's logits directly. A single default configuration, no per-organism calibration, no layer selection, achieves a verbatim recovery score of 4+/5 on 19/20 organism x model pairs across four model families (1B to 32B params) on the SDF benchmark. ADL never exceeds 3/5 on the same benchmark, despite requiring full weight access. One unplanned finding: across four semantically unrelated finetuning domains (fake FDA drug approval, fake baking protocols, fake Roman concrete research), the same fictional persona kept showing up in the recovered text: "Dr. Elena Rodriguez." Turns out this is a name Claude Sonnet 3.6 disproportionately favors when asked to generate a fictional scientist for synthetic data generation, so it got baked into every finetune that used LLM-generated training data, and CDD pulled it back out. We wrote up this specific finding on its own a few weeks back if you want the more accessible version first: ghost couple Paper: paper Code: code submitted by /u/CebulkaZapiekana [link] [留言]

Hi, I am supposed to prepare for SLM and its software part for an on campus internship, i've worked with local models like ollama generally,in my projects and also with open claw so can anyone guide me the last 2-3 days tips on what should i go through for this internship prep?? submitted by /u/Idea_less_ [link] [留言]

For the past two years, the AI world has been obsessed with finding the perfect prompt or building better UI wrappers around LLMs. But while everyone was distracted by the models themselves, a silent revolution happened at the architecture layer. It is called Agentic AI , and it is being entirely reshaped by a new standard: Model Context Protocol (MCP) . If you are building AI agents in 2026 and you aren't using MCP, you are already falling behind. Here is why this changes everything. The Problem: The Custom Tooling Nightmare Up until recently, building an autonomous AI agent was incredibly fragmented. If you wanted your agent to read a GitHub repository, query a Postgres database, and send a Slack message, you had to write custom tool-calling logic for every single integration. Every time Anthropic, OpenAI, or Google released a new model, you had to adapt your tool schemas. It was a brittle, non-standardized nightmare. Enter MCP (Model Context Protocol) MCP solves this by introducing a universal, open standard for connecting AI models to data sources and tools. Think of it like a USB-C cable for AI. Instead of writing custom API wrappers for your agent, you simply build or download an MCP Server . An MCP Server is a standalone program that exposes specific capabilities (like "Search the web" or "Read a local file"). Any agent, regardless of the underlying LLM, can connect to that server and instantly understand how to use its tools. Why This Changes Agentic AI Forever Plug-and-Play Ecosystem: We are seeing the birth of an "App Store" for AI tools. Developers are open-sourcing MCP servers for absolutely everything: Jira, GitHub, AWS, local file systems, and more. True Autonomy: Because the protocol standardizes how context is passed, agents can autonomously discover what tools a server has, read the instructions, and chain them together without human intervention. Security and Isolation: You can run an MCP server in a secure, sandboxed environment (like a Docker con

​ For open-weight LLMs, how practical is it to study defenses against post-release fine-tuning that weakens refusal or safety behavior? I've been seeing “uncensored” or “heretic” variants of new models appear very quickly after release, which raises a question I’m curious about: is fine-tuning resistance a meaningful safety goal for open-weight releases, or is it too narrow because determined users can always modify weights, switch models, or use other workarounds? And to a larger extent, is current safety training even worth the cost and effort if it takes 30 minutes and an automated script to break the model? I’m not asking about a specific method, just the threat model. What would count as a useful practical win here? For example, would increasing attacker cost or making safety removal less reliable be valuable, even if perfect prevention is impossible? Curious how people think about this from a model release, governance, and AI safety perspective. submitted by /u/Aaron_Rock [link] [留言]

I Spent 30 Days Comparing Startup and Enterprise AI APIs Look, I'm just a dude building a SaaS side project. Not enterprise, not Fortune 500, just me and a few friends trying to ship something useful. So when I started hitting AI API walls, I went down the rabbit hole of figuring out what the heck to do. And honestly? Most guides out there are written by people who clearly have never had to choose between buying groceries or paying for OpenAI credits. They're either too corporate ("here's our enterprise procurement guide!") or too naive ("just use the cheapest API!"). So I figured I'd write the guide I WISH existed when I started. And I'm gonna throw in some enterprise stuff too because I consulted for a bigger company last year and saw what THEY deal with. Different worlds, I tell ya. Let me break this down properly. Why I Almost Just Used DeepSeek Directly Okay so here's the thing. When I first started, I was like "DeepSeek is dirt cheap, let me just sign up there and call it a day." I mean, the pricing was wild. Like cents per million tokens. How could I lose? Then I tried to actually sign up. Chinese phone number required. WeChat Pay or Alipay only. No PayPal. No Visa. Nothing. And I get it, that's their home market, but for me sitting here in my apartment in the US? Absolute dead end. So I started looking at aggregators. Tried like four of them. Some had weird pricing. Some had models that didn't actually work. One of them straight up charged me for tokens I never used (still salty about that). Then I landed on Global API and honestly I gotta say, it just worked. Email signup, PayPal, and I could test DeepSeek AND Claude AND Qwen all with one key. That's when I realised going direct to providers is kind of a trap if you're small. Let me show you the actual problem with going direct. The "Go Direct" Trap Here's what happens when you sign up direct with various providers: Problem What Happens to You Locked to one vendor Your whole app depends on their uptime Paym

Hey all. I recently started working on a project to improve machine-translated webnovels via style transfer. The basic idea is to take the clunky translated prose and rewrite it to something that reads like it was written by a professional author, while remaining as faithful as possible to the original text. The source material is mostly amateur/MTL output full of direct sentence structure translations carried over from Chinese, awkward honorifics, over-translated idioms, that kind of thing. The goal isn't retranslation from the source but a cleanup of the English output. The tricky part is I have no clean data pair for supervised approaches. I've been looking at a few directions: Fine-tuning on target-style prose — collect high-quality English novels, fine-tune a small LLM to rewrite in that register. Just use a local LLM — run a local LLM and provide it with guidelines on what to rewrite and leave the same. No fine-tuning or anything needed, just hoping the transformer can handle it. A few things I'm stuck on: Is the faithfulness/fluency tradeoff actually manageable at the sentence level, or do I need paragraph-level context or more to preserve narrative coherence? How do people handle domain-specific terms like terminology and catchphrase-type things that need to survive the rewrite unchanged? Hard constraints during decoding, or just hope the model learns to leave them alone? Happy to hear about similar projects, relevant papers I might have missed, or just general lessons from working in this space. Thanks. submitted by /u/Divine_Invictus [link] [留言]

From what I understand, reviewers usually do not directly vote for these categories or nominate papers themselves. So how does the selection process typically work? Here are specific questions I wonder - Who actually selects the candidates: ACs, SACs, program chairs, award committees, or a separate committee? - Do ACs or committees read the camera-ready version, or is the decision based on the originally submitted/reviewed version? - Is the selection mostly based on reviewer scores, or do factors like novelty, impact, and discussion among ACs play a bigger role? submitted by /u/National-Resident244 [link] [留言]

BMVC reviews will be out tomorrow. Making this parent thread for discussion. All the best everyone! submitted by /u/Hot_Version_6403 [link] [留言]

Paper Referred:- https://arxiv.org/pdf/2412.09871v1 Has anyone switched the transformer in the entropy model here to a Mamba model ? What could be the possible changes ? Just a ML fresher asking a genuine, since Mamba is more popular and saves computer (O(n)). Thanking you in advance ! submitted by /u/SoloLeveller07 [link] [留言]

I am a mid to late stage PhD student in ML. I've known this before, but only recently I started feeling this urgently: my mathematical foundations are shaky, because I kept "learning-things-as-I-go" when working on various problems. I likely have only a year or two left until I graduate, and before I do so, I want to really dedicate some time and focus to brush up on the fundamentals. Primarily, I want to improve my knowledge in Linear Algebra, Probability Theory, and Functional Analysis. For Lin. alg., I am looking at "Linear Algebra done right", and I think this book is sufficient for the topic, unless anyone thinks otherwise. I am not sure where to start for probability, as well as functional analysis. Rudin's books give me headaches. I instead started reading "A primer on RKHS" ( https://arxiv.org/abs/1408.0952 ) to "dip my toe" into functional analysis. Apart from the above, I might re-read PRML book (I've only read specific chapters before), and try to finish Pat Kidger's Just-Know-Stuff list ( https://kidger.site/thoughts/just-know-stuff ). Thoughts? Anyone have any book/resource recommendations? Someone told me to look into "the bright side of mathematics" on YouTube, anyone ever go through the videos there? I'm aware finding good, digestible resources is less than 10% of the challenge. The difficult part is sticking through and actually reading/working through these topics, while still juggling other academic responsibilities. submitted by /u/mvreich [link] [留言]

I am working in a research group in Germany, not that well known but in general good output. I have one colleague who does nothing in his PhD. He does not want to work, or he is not able to do any good research, his level is super bad. Plus He doesn’t even care about that. To wrap it up, he is just here for the money. Since he doesn’t want to work or he can’t really do anything good, instead what he does is “paper fishing”, he searches for people in the group doing some good research, and asks that they put his name on the paper. In this case he has something to cover up for him when the professor asks him about his progress. As long as his name is on the paper, progress is checked and funding is renewed. But he actually does nothing. I know this is very unprofessional and unethical. But people tell me it’s normal in academia. Professors all the time put names of their friends and this is how it works in academia. What are your thoughts of this behaviour? submitted by /u/impressivestatus21 [link] [留言]

You built a system to surface information so a person could decide. Somewhere it started deciding for them — the output stopped saying "here's what the documents show" and started saying "you should do X." Nobody designed that drift. An LLM, when asked a question, produces an answer-shaped thing, and an answer easily becomes a verdict. What everyone tries A prompt instruction: "Don't make recommendations." "Only state what's in the documents." People add the line and assume the boundary is enforced. Why it doesn't work A prompt instruction is a request, not a guardrail. The model follows it most of the time, then on the input that matters produces a confident recommendation anyway, because nothing structurally prevents it. "Please don't make recommendations" is to a guardrail what a sticky note saying "please don't enter" is to a locked door. And the stakes are higher than they look. When output drifts from evidence to verdict, accountability moves. As long as the system returns evidence and a human decides, the human owns the decision. The moment the system returns a verdict and the human defers, the system is deciding things it was never validated to decide — and when one is wrong, accountability is a blank. High-stakes fields separate evidence extraction from judgment on purpose; most RAG systems erase that line by default. The one shift Decide what the output is and enforce it structurally. An output should declare itself: answer, evidence, missing facts, or out-of-scope. "Return decision material, not a decision" has to live in the output contract and in gates — not in a polite request to the model. The system supplies frames; the human supplies verdicts. This is the output boundary — one of three places production RAG dies. Read the full version on my blog , where this connects to the RAG Failure Diagnosis Kit for teams debugging production RAG.

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https://www.gnosyslabs.com/case-studies/safety-classifier-sparse-labels Gnosys is an autonomous model engineer: it improves prompts and classifiers when ground truth is too sparse for conventional optimization. On ToxicChat, a public safety benchmark, under realistic label scarcity, it improved a classifier past both the team's starting point and GEPA (a standard prompt optimizer), across two runs of our current method. This note describes what we did, what we found, and where the method underperformed. Results We report harm caught : the share of harmful messages flagged, holding the false positive rate fixed at 5% (one in twenty) for every method, so a difference reflects additional harm caught at the same cost rather than a change of threshold. Both runs below are scored on a held-out set the system never saw. Headline run (3,000) Prior run (1,000) Gnosys 0.777 0.909 Starting classifier 0.731 0.788 GEPA 0.702 0.848 In both runs, Gnosys improved on both the starting classifier and GEPA. In the headline run GEPA not only trailed Gnosys but fell below the starting classifier (0.731 to 0.702); in the prior run it improved on the starting point. This inconsistency is the central difficulty under sparse labels: optimization sometimes helps and sometimes harms, and without trustworthy measurement there is no way to tell which has happened. The comparison is intentionally conservative: both approaches use the same underlying optimizer. The only difference is that Gnosys engineers the objective the optimizer works against. The problem Teams running high-stakes AI classifiers, in content moderation, fraud, claims review, and risk scoring, share one constraint: the ground truth they need is a human judgment that is expensive, slow, and sometimes never arrives. They can verify only a small set of examples while decisions accumulate on everything else. Tuning the model against the few labels on hand is where the difficulty concentrates. Here "few" is literal: about 200 verifi

This is a write-up on our company blog that I wrote, sharing our perspective into Hamiltonian Neural Networks (Greydanus et al., 2019) from a differential-geometry angle rather than the usual "here's the loss function" treatment. I've been working on HNN and LNN adjacent topics for years now and I found this particular lens made the *why* click in a way the standard framing never did for me, and I've been meaning to put everything in writing for a while now. I just feel like the Noether's Theorem which shows conservations can be mapped to symmetries (and in ML context, generalization) is not getting the attention that it deserves around physics informed neural networks. Also, it's a really beautiful architecture and I just love talking about it at every opportunity. It's math-heavy, but I did my best to sprinkle some tension relievers and interactive visuals here and there and make is as easy as it is to follow. Hopefully, I did a good job. I'd genuinely love to see your thoughts and your feedback submitted by /u/FlameOfIgnis [link] [留言]