标签：#MachineLearning

The generation side is shipping fast (TileGym, AutoKernel, KernelEvolve). The validation-side surface for “what the kernel actually did at runtime” has not kept pace. TL;DR In the past nine months, three significant releases have landed for auto-generation of CUDA kernels: NVIDIA TileGym , RightNow AutoKernel, and Meta’s KernelEvolve. Each ships training infrastructure for kernel generation. Validation infrastructure (what the generated kernel actually did at runtime, on a real workload, in a production-shaped environment) has not kept the same pace. eBPF traces are the ground-truth layer that closes the gap. What “validation” means at the kernel level Two distinct validation surfaces: Pre-launch: the generated CUDA C compiles, the PTX assembles, the kernel passes a numerical-equivalence test against a reference. Standard compiler / unit-test territory. Generation frameworks ship this themselves. Post-launch: the kernel ran, returned, took N microseconds, used M registers per thread, hit X cache miss rate, and did or did not serialize the rest of the stream behind it. This is the layer that an eBPF trace plus standard CUDA driver counters can answer for any kernel, generated or hand-written. Auto-generation pipelines do not by default close the post-launch loop. They demonstrate “the kernel works in our test setup”. They do not demonstrate “the kernel does not regress p99 latency on production inference traffic”. What an eBPF trace adds to a generated kernel Once a generated kernel is in a real workload, the same trace surface used for any CUDA kernel applies: launch latency from cudaLaunchKernel , sync stalls from cudaStreamSynchronize , host-side overhead from the dispatcher, host scheduling preemption while the GPU is busy. None of those signals are visible to a generation framework that evaluates kernels in isolation. -- post-launch validation: did the new generated kernel regress p99? SELECT kernel_name , COUNT ( * ) AS launches , AVG ( duration_ns ) / 1 e3 AS

We train a model on handwritten digit classification. 99% accuracy . Then we train the same model on a new task — say, fashion item recognition. We go back and test it on digits. 34% accuracy . It has completely forgotten. Not gradually, not partially — almost entirely. What Just Happened? We trained a CNN on MNIST digits — 99.2% accuracy . After fine‑tuning on Fashion MNIST, it reached 91.1% accuracy . But when re‑evaluated on MNIST, accuracy collapsed to 33.9% . This collapse is catastrophic forgetting : the model’s weights shifted to optimize for the new task, erasing the old solution. Why did training on more data make the model worse at something it already knew? MNIST is handwritten digits (0–9). Fashion MNIST is clothing items like shirts and shoes. Both are 28×28 grayscale images, but the tasks are distinct. Why Does It Happen? The core issue is that the model relies on the same set of weights for both tasks. There is no separation or dedicated memory; every parameter is shared . When training shifts from Task A ( MNIST digits ) to Task B ( Fashion MNIST ), gradient descent simply minimizes the loss on the data it sees at that moment. It has no awareness that Task A ever existed. In the loss landscape, imagine two parabolic bowls: one for Task A and one for Task B. The optimum for Task A lies at θ A ∗ ​ , while Task B's optimum is at θ B ∗ ​ . As training on Task B progresses, the weights θ move towards θ B ∗ ​ . This movement inevitably raises the loss for Task A because its minimum is left behind. The root cause is the shared weight space. Gradient descent is a stateless optimizer; it only follows the current gradient signal. Since the minima for Task A and Task B are far apart, there is no single configuration of θ that satisfies both tasks simultaneously. This is why catastrophic forgetting occurs. Weight space can be visualized as an N-dimensional space, where each axis corresponds to one parameter. Every point in this space represents a full set of wei

I spent a weekend wiring Google's Gemini and Veo APIs into a single app just to feel where the edges of multimodal AI actually are. It turned into a small studio I now use daily, and along the way I learned more about these models from plumbing them than from any paper. Here's the honest technical debrief. Three pipelines, three completely different problems I wanted one prompt box that could do video, image editing, and document Q&A. Naively I assumed they'd share most of the stack. They don't. 1. Image-to-video: the enemy is time, not pixels Generating one good frame is solved. Video is about temporal coherence — frame 13 must agree with frame 12 or you get flicker and identity drift. Modern video models treat the clip as one object in space and time (latent diffusion over a width x height x time volume, with spatiotemporal attention) rather than 120 independent images. Conditioning on a reference image as the first frame is what makes image-to-video feel controlled: you've handed the model a strong anchor and asked it to extrapolate motion, not invent a world. The surprise: native audio sync (Veo 3.1 generating clip + soundtrack jointly) does more for perceived realism than another notch of resolution. A door slam landing on the exact frame the door shuts is uncanny in a good way. 2. Instruction-based image editing: preservation is the hard part Generating is unconstrained; editing must change one thing and preserve everything else. Condition the diffusion model on both the instruction and the source image's latents, cross-attend the instruction to steer only the referenced region, and bias hard toward preserving unedited latents. Push that preservation too soft and the subject's face quietly morphs across edits — the classic 'character consistency' failure that makes or breaks storytelling use-cases. 3. PDF chat: it's retrieval, not a long context The naive 'paste the whole PDF' approach dies on long files (models get lost in the middle ) and costs you the full

Hello Reddit I've been working on QSPR (Quantitative Structure-Property Relationship) analysis for chemical compounds mentioned in the Jean-Claude Bradley Open Melting Point Dataset . Basically the idea is to see how accurate a model can predict melting points of compounds using only topological indices. After some work on the topological indices (feature engineering), each compound was represented by 26 features. I trained a random forest model on the data and got a test r2 score of 0.66 (which is pretty respectable, given the constraints). However, the file size of the model was around 1.23GB. I didn't like it being that big, so I opened up PyTorch to build a custom deep learning architecture that could make predictions as accurately as the random forest but with much smaller file size. After around 2 weeks of research, I build a 270,000 learnable parameter model (1.3-1.4MB according to torchinfo) that got an r2 score 0f 0.6399. Given all this context, I wanted to ask the following question: Should I commit and work on publishing the results, or should I keep working on improving the model? Note: I'm obligated by my university to not give out intricate details of my research before publication, so please forgive me if such details are required for a high quality answer. However, I can give out the metrics achieved by my little deep learning model. Here it is: === Evaluation Metrics (Expected Value) === R² Score : 0.639910 MAE : 41.246754 MSE : 2989.062744 RMSE : 54.672322 NRMSE : 0.083469 MAPE : 11.69% The unit for MAE, MSE, RMSE and NRMSE is Kelvin (K). submitted by /u/AgiGamesYT [link] [留言]

Hi, Niels here from the open-source team at Hugging Face. I've recently relaunched paperswithcode.co as a source for finding the state of the art (SOTA) across various AI domains, from 3D generation to AI agents. This is done by automatically parsing research papers published on arXiv/Hugging Face, enabling leaderboards to be created. See BrowseComp below as an example (a scatter plot and a table are available for each benchmark). - Scatter plot (you can hover over the dots to see the models): https://preview.redd.it/9rz2r3ffcf6h1.png?width=2880&format=png&auto=webp&s=b3f8e7a870802f6ef8227ecc0619e9e1057554b0 - Table: https://preview.redd.it/qoqriddw5f6h1.png?width=2862&format=png&auto=webp&s=a0034574f693847537037013672fb61daf27b16e As you can see, I've added support for viewing evals for closed-source models, too, given that many benchmarks are nowadays dominated by them, like GPT-5.5 and Mythos 5. You can always disable viewing closed-source evals with a toggle or in your PwC settings: https://preview.redd.it/p3k6jt6q6f6h1.png?width=1582&format=png&auto=webp&s=40149e51d6b326a77e53e33baf70d9850b3de365 When you turn them off, here's what the open model leaderboard looks like: https://preview.redd.it/tg42sin36f6h1.png?width=2838&format=png&auto=webp&s=1330a117ae9b4e0ce6d459493ae9e8f64107310a Closed-source papers are treated as regular "papers", although they can be any source, like a blog post (given that PwC supports submitting any source beyond arXiv). See the GPT-5.5 or Mythos 5 papers as examples, with their evals at the bottom. Notice the "closed" tag on their evals. Hence, you could jokingly call these "papers without code". Let me know what you think of this, and whether anything needs to be changed or added! Kind regards, Niels submitted by /u/NielsRogge [link] [留言]

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

I do AI research and keep juggling tabs: new ones on arXiv, trending ones on Hugging Face, famous ones somewhere else again. https://preview.redd.it/cg32bshjqd6h1.png?width=1919&format=png&auto=webp&s=00055bb8af699061be0bdcff59f2cb8fa9ab38b6 So I built one site that brings them all together. Pick a paper, read it right there, star the ones you want for later, and it remembers where you stopped reading, even if you switch from laptop to phone. Live: https://ppdeck.com Demo: https://youtu.be/vtyx34JvxX0 It's free and open source - a star on GitHub would mean a lot ⭐ https://github.com/khuynh22/paper-deck submitted by /u/NeitherRun3631 [link] [留言]

“Why the system feels rigid, why downstream fixes didn’t move the needle, and what actually matters.” This is the clearest picture after the full probe arc (multilayer-lock → gate decomposition → attractor migration → reconstruction ablation → generator diversity audit → live-generator Fix 2 evaluation + dim sweeps). TL;DR: The generator is the root bottleneck (dominant common-mode + low effective rank). The reflective loop is a rank-independent moat that reconstitutes everything back toward the anchor. Fix 2 is downstream and currently dormant on real token regimes. Dimensionality is not the lever. Train the generator so regime differences live in high-energy, separable directions — then downstream tools will actually have something to work with. This update reflects the complete probe arc through June 9 (including the live-generator Fix 2 evaluation and dim sweeps). The picture has sharpened: the reflective loop is a real moat, but it is moating low-rank common-mode input . The generator is the upstream constraint. Key numbers at a glance Regime means collinear: ~0.85–0.96 even at dim 512 Reflective loop migration (even on orthogonal deterministic pairs): +0.001–0.007 Fix 2 on real tokens (common-mode trigger): +0.024 migration, 0% manip at gain 0.6 Safe plasticity band: gain ≈ 0.4–0.8 (0% manip) 1. The generator has a dominant common-mode (effective rank ~1.6–3 even at dim 512) The generator puts the vast majority of its energy into a single shared direction. Regime means stay collinear (~0.85–0.96 cosine) regardless of dimension. Orthogonal pairs can appear at higher dim, but orthogonal regimes (as distributions) do not — the common-mode pulls everything back onto the same axis. Result: real token novelty is tiny and low-energy (mostly in a faint perpendicular component). The system is never shown meaningful structural differences to adapt to. 2. The reflective loop is a rank-independent moat Even when genuinely orthogonal deterministic pairs are presented (dim

We spent the last year building what we think is the missing infrastructure layer for multi-agent systems. Open to everyone starting today. The technical problem: Agents have no identity. In microservices you have a service mesh + IAM. In agent systems you have a Python file. We built a registry where every agent has a first-class ID, version, owner, skill graph. Behavioral evaluation, not function testing. Agents are non-deterministic same input can produce different execution paths. Traditional unit tests don't work. We implemented compound reliability scoring + behavioral regression instead. Composability without rebuilding. Skills are versioned, reusable, agent-inheritable. Inspired by how Kubernetes operators work, applied to agents. Cloud-agnostic deployment with built-in observability traces, cost attribution, drift detection. Model-agnostic. SOC 2 Type II. Genuinely interested in technical feedback especially on the eval methodology and the composability primitive. Free credits this week to test it. https://phinite.ai/?utm_source=reddit&utm_medium=organic&utm_campaign=public_launch_jun2026&utm_content=machinelearning submitted by /u/Embarrassed-Radio319 [link] [留言]

Found from iOS Simulator's files. Both of them are in espresso format There's also another compiled CoreML for concert ranking and based on the content inside of it looks like to be a simple logistic regression. See https://www.reddit.com/r/jailbreak/comments/1u1e1b4/access_to_simulators_root_files/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button Edit: Its the Siri's TTS submitted by /u/Actual_L0Ki [link] [留言]

How will AI affect our ability to think and judge for ourselves? Our new paper co-authored by 30 experts explores epistemic risks —the threats AI poses to our collective capacity to form beliefs accurately, reason well, and maintain a healthy information environment. We look at how AI can lead to harm through these mechanisms: Persuasion & Manipulation: AI systems are highly persuasive, opening the door for political/economic manipulation, incitement and radicalization, and other misuse, as well as unintentional harms like AI sycophancy and mental health risks. Cognitive Offloading: We may be delegating our thinking to AI at a deeper level than prior technologies, risking long-term degradation of individual and societal cognitive resilience. Feedback Loops: Human-AI and AI-AI interactions are narrowing the epistemic space humans and AIs draw from. This already drives homogenization, and may potentially lead to fragmentation and “lock-in” (a self-referential state that is difficult to reverse). While we believe AI could be an unprecedented lever for improving how humanity processes knowledge, we shouldn’t assume this will happen by default. We outline promising directions to change this trajectory across how AI systems are built, human-AI interaction design, institutional and individual adaptation, and information market incentives. Epistemic risks are self-perpetuating. As they can undermine the individual cognitive and social foundations needed to recognize, prioritize, and govern other threats—including the risks from AI itself—the time to act is now, before our capacity to respond is itself lost. Authors: Mick Yang, Stephen Casper, Jonathan Stray, Jasmine Li, Cameron Jones, Anna Gausen, Natasha Jaques, Brian Christian, Bálint Gyevnár, Hannah Rose Kirk, Zhonghao He, Dan Zhao, Siao Si Looi, Joshua Levy, Kobi Hackenburg, Elizabeth Seger, Matt Kowal, Michelle Malonza, Luke Hewitt, Hause Lin, Maarten Sap, Dylan Hadfield-Menell, Thomas H. Costello, Reihaneh Rabbany, Je

Hey All, I am currently working on ASR models, and I have gathered some recent literature. From my literature search, it seems like the ASR models are getting more and more powerful due to two main things. Because pseudo-labelled data is growing, supervised models are rising rapidly. Whisper-large-v3 has been trained on 5M hours of weakly supervised data, and Nvidia Parakeet v3 has been trained on 660k hours of labelled data (open-sourced). Funny enough, Nvidia Parakeet v3 actually beats Whisper-large-v3 on almost every benchmark, even though it has a smaller model size and smaller data scale. So clearly, scale is not everything. New architectures are on the rise; We used to have self-supervised + CTC to solve the ASR task, but now it seems like Transducer, and Token-Duration-Transducers are taking off. As well as attention encoder-decoder architectures (Qwen) that are all trained in a supervised manner. Now, given that the labelled data is very huge, and the new architectures are coming up, are we saying bye to the self-supervised learning approaches like Data2Vec2.0, WavLM, etc., for ASR, and will we only use them for general-purpose speech tasks? This is actually not similar to how computer vision operates now. Dinov3 is a self-supervised approach that is extremely performant in segmentation, classification, depth estimation etc but I do not see this in the speech domain now. ASR is dominated by these huge supervised architectures (which is a dense-prediction task), as well as emotion recognition, diarization, and speech seperation are also all dominated by the supervised approaches. Do you think we will have our Dino moment with a new self-supervised architecture? Or supervised learning is the way to go? How would these methods actually perform if we trained a self-supervised model on these huge datasets? submitted by /u/ComprehensiveTop3297 [link] [留言]

Hi everyone, I work for a major berry company, and a large part of my role involves forecasting total industry crop volumes (weekly harvest/production forecasts) as well as future pricing. I'm relatively new to ML-based forecasting. This is only my second professional role, and I have a bachelor's degree in Information Systems with a few machine learning courses under my belt, but I'm definitely not a forecasting expert. For crop forecasting, I've been working with USDA and other industry datasets. I started with SARIMA models and have recently been experimenting with XGBoost and Holt-Winters methods to compare performance. I'm looking for recommendations on: Libraries/frameworks that are commonly used for production-grade time series forecasting Models that work well for agricultural production forecasting Approaches for forecasting commodity/produce pricing Feature engineering ideas (weather, seasonality, acreage, imports, etc.) Any papers, blogs, or resources that would be useful Most of the data is weekly and highly seasonal, with weather and supply conditions playing a major role. Any suggestions, lessons learned, or pointers from people working in forecasting would be greatly appreciated. submitted by /u/foreigneverythingg [link] [留言]

https://preview.redd.it/vfxky33v5a6h1.png?width=2612&format=png&auto=webp&s=f60bd8506a39abb40b1c9ff9507e8dcddea95498 OpenAI ran a hiring challenge, but the top candidate was one they couldn’t hire: our autonomous research agent, Aiden. In Parameter Golf, Aiden ran for 22 days, and out-outperformed all 1,016 other researchers. Parameter Golf was OpenAI’s 44-day competition and hiring challenge. The goal is to train the best language model under strict size and compute constraints. 1,016 people entered and filed 2,048 PRs. Only 47 made the leaderboard, each reviewed and reproduced by OpenAI. Research outputs only matter when others can build on them. So Aiden filed its own PRs into the same public stream as everyone else, under tight automated quality control. Aiden filed 25 prs and 7 became leaderboard records, 2x the next best human participant. Other participants cited Aiden’s PRs 435 times and built on them. By PR h-index, Aiden scored 10 vs the next best at 7, making it the most impactful “researcher” in the community. And this wasn't brute force. Aiden ran on a single GPU node, used under 4% of visible compute, and still produced 15% of the official records. About 28% of its submissions were accepted, ~ 6x the community rate, raising signal in the public stream instead of flooding it. Our favorite part is an async collaboration story. Aiden plateaued for 5 days. Then a human contributor shipped a clever new tokenizer on top of Aiden's base (its last record PR). Aiden fused it with components it had built during the plateau, and shipped the biggest jump in weeks. Full writeup: https://www.weco.ai/blog/parameter-golf-aiden Edit: resharing since original got removed submitted by /u/Educational_Strain_3 [link] [留言]

I've been reading more about privacy-preserving ML approaches such as differential privacy, federated learning, and on-device inference. The research literature is fairly active, but I'm curious about real-world adoption. For those working in industry: Are these techniques being deployed in production? What were the biggest engineering challenges? Did privacy requirements significantly impact model performance or infrastructure costs? Are there specific use cases where privacy-preserving approaches have proven especially valuable? Interested in hearing both success stories and cases where the tradeoffs made adoption difficult. submitted by /u/Electrical_Mine1912 [link] [留言]

Im moving to my final year of engineering, im panicking scared everything but im confident in myself. I can read papers, understand the code go through the architectures and see them at scale (in my head), while i struggle to interpret all the dimensions and helper functions being coupled, i somehow get by hour an abnormal amount of time spent on it. I dont get what i should be doing next? i aspire to combine encoders for vision, audio and ofc text to build a model. but i dont see how that happens overnight, i wanna know what you all experienced folks did after reading papers. it makes me curious about the implications and applications, how real researchers are working on top of it. somewhat like the Big Bang Theory, where all the scientists just discuss ideas, I wish to reach out to researchers too, leave any suggestions on what would help me stand out among all these AI proposals. submitted by /u/EnchantedHawk [link] [留言]

Is it normal to use different styles of figures (colours, backgrounds, grids, etc.) when writing a paper? Personally, I think it looks unprofessional. submitted by /u/Few-Annual-157 [link] [留言]

hi everyone, I created a blog around how I started open source contribution, documented all minute details. Please give it a read and give review as this is my journey to do blogging for the first time. It is free! https://substack.com/home/post/p-200202050 submitted by /u/DqDPLC [link] [留言]

TL;DR If you're shipping AI inference and tired of babysitting GPUs, serverless is the way out. You deploy the model, the platform scales it from zero to hundreds of GPUs and back, and you only pay for the time you actually use. If I'm picking one to start with, it's DigitalOcean . It's got the widest GPU lineup of any serverless provider (RTX 4000 Ada all the way up to NVIDIA Blackwell B300 and AMD's MI350X), one API and one bill instead of five, and it's simple enough to ship on without a sales call. (More on why that one's personal for me below.) Below I compare 9 providers across the things that actually matter: GPU specs, per-hour pricing, cold-start latency, model support, and how nice they are to build on. DigitalOcean, RunPod, Modal, Koyeb, Together AI, Replicate, Baseten, Fal, and Cloudflare Workers AI each win at something different, from cheap experimentation to global edge inference. Contents Why I ran this The field at a glance How I evaluated these providers Per-provider analysis: DigitalOcean RunPod Modal Koyeb Together AI Replicate Baseten Fal Cloudflare Workers AI Why I keep coming back to DigitalOcean The short version Questions I actually get asked Why I ran this Quick note on why this exists. At work I get a front-row seat to a lot of people shipping an AI model into production for the first time: students, first-time founders, my own team. And lately the same question keeps coming up: where do I actually run this thing? I was tired of answering with a shrug and "it depends," so I did the homework myself. Signed up, read the pricing pages, ran the comparisons, and wrote it all down. Nobody's a real expert at this yet, me included, so I'd rather share my notes and get corrected than pretend I've got it figured out. And here's the thing about AI inference in 2026: demand blew past what the old way of provisioning GPUs can handle. Teams that used to wait weeks for dedicated hardware now need a model live in minutes. The ground moved. And the stuff t

Yes, I'm calling it out. It IS racism. As an active member of r/MachineLearning and a researcher who is ethnic Chinese, I am DISGUSTED by unfounded accusations against the group of researchers who constitute over half of the field. Such posts pop up every other week, grounded in conspiracy theories, and creating a sinophobia echo chamber. I understand the salty feeling when one's paper is rejected, no matter whether the paper actually deserves acceptance or not. Given the noise in conference organization and reviewing process, and a relatively junior body of participants, it is very likely that one finds a paper "worse than mine" slip into the conference, and there's a high chance that the paper has a Chinese author. That's simply because of the composition of the authors, and does not warrant accusations, aka witch hunts, towards certain ethnic groups. This sub is about an important scientific subject in the modern world. If anyone agrees with the logic "80% of the authors are Chinese, so my rejection is their fault.", they should seriously rethink their career plan since such thinking does not belong to serious scientists. We should be open to discussing the problems we have in the current conference organization and reviewing process, but racism should not have a foothold in our field. submitted by /u/AffectionateLife5693 [link] [留言]