I am currently doing research on world models, specially in tje field of robot learning, and, as probably most of you alredy know, JEPA-like models are mentioned over and over. I read the main recent papers from lecun as well as other research groups, and I personally think the whole approach is very promising and can really go somewhere. But after listening a bunch of the recent Y Lecun conferences his ideas looks even too cool compared to "literally everything else" (as he's dissing LLM, RL, etc and pitching his ideas are the "only next big things"...). So I am asking myself if there are red flags about his approaches that I do not see yet and maybe I need somebody being the "devil advocate" with whom breaking down ideas. Where do you think are the biggest downside of this models, compared to other world models approaches? submitted by /u/Amazing-Coat5160 [link] [comments]

So, from what I've seen across twitter(x) and reddit, I've inferred that we'll be seeing NeurIPS reviews drop on July 22nd 5:30 pm AoE(Anywhere on earth), what's your thoughts to those who've submitted to NeurIPS 2026 ? Would love to hear your opinion by the reviewers, the people who've submitted to the workshops (who should've already gotten their decisions too by now I think) and to the main tracks and other available tracks. submitted by /u/Practical-Buddy6323 [link] [comments]

I have a technical interview for a machine learning position coming up, and I'm really nervous. Can you tell me about your experiences with this process? It's my first technical interview, and I don't know what to expect submitted by /u/Abatistta [link] [comments]

Hey everyone, Seeing a ~170× slowdown running a point-tracking model on an NVIDIA T4 compared to an A100. On A100 the tracker takes ~0.5 seconds per half-video. On T4 the same call takes ~85 seconds. Video is 47 frames at 256×256, batch 1. I expect a meaningful gap between these cards, but 170× feels too large to explain by generational hardware differences alone. Setup: Precision: pure FP32 Architecture: builds local 4D correlation volumes (dense matching between frames) followed by transformer layers for temporal context Already ruled out: GPU is at 99% utilization during the call (via nvidia-smi) Model is actually on GPU (torch.cuda.is_available() = True, device prints "cuda") Enabling torch.backends.cudnn.benchmark = True had no effect Same slowdown on two independent T4 machines, so it's not a driver/setup issue Given the architecture (4D correlations + transformers) and pure FP32 execution, what would cause a T4 to be this much slower than A100? What should I look for or profile first? submitted by /u/Future-Structure-296 [link] [comments]

I’ve been deeply interested in AI and machine learning since around 2019, back when GPT-2 was still one of the major talking points. Since then, I’ve been amazed by how quickly the field has evolved. It genuinely feels like one of the most exciting times to be involved in technology, research, and innovation. My background is in audio. I’ve spent most of my life working as an audio engineer, and I’ve always loved learning about sound, digital signal processing, and the technology behind audio systems. Since 2022, I’ve been working toward a long-term goal of becoming an AI researcher, specifically in the audio and music technology space. To move toward that goal, I went back to school, completed coding bootcamps, studied the mathematics behind machine learning, and I’m currently working on a master’s degree in artificial intelligence and machine learning. I’m also planning to pursue a PhD after graduation. Many of my classmates and colleagues are interested in business applications of AI, but I’m still completely committed to audio. I currently work as an AV systems designer and consultant, and while I’m grateful to have a career, I often feel disconnected from the work. Most days, I would much rather be studying AI, audio, machine learning, DSP, and research. I’ve started applying for roles, but I’ve faced several rejections. I also recently wrote and submitted a research paper to ISMIR. Unfortunately, it was rejected, but the process was still incredibly valuable, and I received feedback that will help me improve. I think what I’m ultimately trying to say is that this is not a career path I’m pursuing because AI is popular or because I expect to make a huge amount of money. I genuinely love audio and AI, and I want to spend my life working on problems that combine the two. I want to wake up each day and feel like the work I’m doing matters to me. For anyone currently working as an AI or machine learning researcher, especially within audio, music, speech, or signal processing, I would really appreciate your perspective: What did it actually take for you to get your first research role? What qualifications, education, projects, publications, or previous experience helped you stand out? What are the best and worst parts of being a researcher? What do you wish you had known before entering the field? And if someone came to you today and said they wanted to become an industry researcher, what advice would you give them? Thank you in advance to anyone willing to share their experiences. Even honest or difficult feedback would be genuinely appreciated. submitted by /u/Consistent_Sundae540 [link] [comments]

Building a sports prediction model ,I found consistent edge when backtesting against closing lines. At inference time tho, I predict 12-24 hours before the event where closing lines don't exist yet. I use the current line instead. My strongest feature is line movement (opening to closing implied probability). At prediction time this feature is incomplete as the market hasn't fully moved yet. This creates a paradox: Closing lines are considered nearly impossible to beat because they contain all available information : sharp money, injury news, everything. Yet the backtest shows consistent edge against them. If closing lines are truly efficient, beating them implies genuine model signal. But at inference time we're betting against earlier, less efficient lines with an incomplete version of our strongest feature. The question: does edge against closing lines transfer to earlier bets where lines are less efficient ? Or does the incomplete line movement signal hurt prediction enough that the edge disappears before close? My intuition is the edge is smaller earlier because the market is less efficient but the model signal is also weaker. These two effects might cancel out or one might dominate. Curious if anyone has studied this tradeoff in sports or financial prediction. submitted by /u/MrProbability101 [link] [comments]

I have recently started working in mechanistic interpretability independently, starting with distill circuits thread My work is on disentangling and closely studying a single neuron, a 1x1 convolution in inceptionv1 model (and applying the method to other neurons in the same layer). The key insight was that the hadamard product of the receptive field and the weight of a neuron is what the neuron is 'seeing' or detecting. We can cluster the hadamard product to get all the patterns a neuron detects. It gave clean monosemantic clusters (cars, cats, dogs which it was known to activate on). We also get more clusters however, letters, human faces, and many more low valued activations. This gave me a new technique to analyse the neuron very closely. On close analysis the most peculiar thing I found was that the low valued clusters (like letters) had all its dependent neurons also firing on the same concept (letter), and the positive and negative weights were evenly distributed between them to bring down the sum. An evidence of gradient descent working deliberately to put patterns and concepts in a noisy range. I've tried to keep it very distill like with good visualisations. I hope you give it a read. https://pages.narang99.in/posts/2026-07-12-disentangling-mixed4e-55/ I made a mistake honestly by starting with convolutions, nobody seems to care about it. I'll start working on language soon, but it would be good if anyone can read this, it would be good to have some feedback on whether I've actually found anything useful. Thank you 🙂 submitted by /u/narang_27 [link] [comments]

Does anyone else miss when conferences like BMVC, ACCV, FG, ICIP, and ICASSP had much bigger communities? FG was the place for face analysis, ICASSP for signal processing, and BMVC/ACCV regularly featured strong papers. Now it feels like everything is concentrated into a handful of flagship conferences. With exploding submission numbers, limited capacity, and inconsistent reviews, I wonder how many good papers end up as non-archival submissions, arXiv-only, or never get shared at all. I also miss the focused communities. Is it just nostalgia, or has the research ecosystem become too concentrated? submitted by /u/Sep29493919 [link] [comments]

So I was belatedly reading Matthew Colbrook’s paper on unstable neural networks https://www.pnas.org/doi/10.1073/pnas.2107151119, and the paradox therein sent me back to my university days and reminded me of Kurt Godel. It kind of amazes me that so few people have heard of him given Einstein clearly thought he was more than an equal. Anyway, with the default assumption currently being that any problem will yield to more data and compute, it was timely to revisit this. It’s a long read and I’m not sure it’s fully coherent but if you like logic then you might enjoy this and I‘d welcome your thoughts and feedback. https://iain.so/infinities-impossibilities-and-the-man-in-the-white-linen-suit submitted by /u/iainrfharper [link] [comments]

I've been working on incremental indexing pipelines lately, basically keeping a vector store in sync as the source data changes, and I keep finding the same bugs never show up until it's been running a while. Biggest one for me is deletes. I tested the "new doc comes in, gets embedded" path a hundred times and it was fine. Never really tested what happens when a doc gets deleted upstream. Turns out if you don't handle that, your index just keeps growing with stuff that shouldn't be there anymore, and you don't notice until search starts returning weird results. Partial updates got me too. I didn't want to re-embed a whole doc every time something small changed so I did partial updates instead. Cheaper, but I ended up with drift between what's in the index and what's actually true in the source, especially once chunk boundaries moved around. Didn't notice until a query happened to hit the stale part. Also learned the hard way that idempotency isn't optional. My pipeline gets retried and backfilled all the time, and if reprocessing the same input twice doesn't give the same result, I get duplicate docs every time something routine reruns. None of this feels like new information, it's just normal distributed systems stuff, but I feel like it gets way less discussion than embedding models or chunking strategies. Anyone else dealt with this or have a setup that's actually held up long term? submitted by /u/Whole-Assignment6240 [link] [comments]

Can LLM agents coordinate in long-horizon, open-ended worlds? We evaluate 13 modern LLMs in a new benchmark where agents must work together to explore, communicate, trade resources, craft tools, build structures, and fight mobs. TL;DR: Most agents struggle, averaging only ~6% normalised return. Yet on the hardest setting, zero-shot Gemini 3.1 Pro performs comparably to the best MARL agent trained for 1 billion environment steps. More broadly, we find coordination is a distinct bottleneck beyond long-horizon task competence, with communication having the largest effect in our harness ablations. Paper: https://arxiv.org/abs/2606.08340 Project page and leaderboard: https://alem-world.github.io Code: https://github.com/alem-world/alem-env Interactive traces: https://alem-world.github.io/traces.html Feel free to ask any questions! submitted by /u/ktessera [link] [comments]

Hello guys. I want to introduce my recent research presented at ICML workshop. github link : genji970/SRM-LoRA: official implementation of "SRM-LoRA: Sub-Riemannian-Metric Updates for Mitigating LLM Hallucination in Low-Rank Adaptation" ICML2026 Workshop FoGen Shot summarization of Paper. """ SRM-LoRA is a sub-Riemannian-inspired LoRA method designed to reduce LLM hallucination. It builds a sensitivity-based Riemannian metric that reshapes backward gradients in the LoRA parameter space. This metric suppresses high-cost update directions while leaving the forward computation and inference cost unchanged. Trained only on HaluEval-QA, SRM-LoRA improves factual reliability on both related and out-of-distribution benchmarks. """ Experiment """ https://preview.redd.it/3uyvbtoa76dh1.png?width=740&format=png&auto=webp&s=4431233186dffae885db8e989f009bf37710fe22 """ In my view, the reason mathematics is not effectively used in the context of improving the performance of the latest AI systems, such as LLMs, is that progress in discussions about what should serve as the elements of mathematical theories has been slow. For example, suppose that we use a Riemannian metric. In the parameter space of an LLM, the update vector produced by backpropagation arises from a loss objective that contains the training data. However, if we introduce learnable parameters in order to construct the Riemannian metric and train those learnable parameters by passing through them the same signal as the main training signal, then this may simply amount to a more complicated form of training and may only increase the possibility of overfitting to the training data. Then, how can we obtain the benefits of mathematical theory while moving in a direction that can generalize? In this paper, the Riemannian metric is constructed based on the rate of change of the LLM model parameters with respect to the loss signal. The reason for defining it in this way is as follows. No matter how good the data or the distribution that can be learned may be, in practice there is still a high possibility of overfitting that results in hallucinations. Therefore, the cost used to construct the Riemannian metric, where a higher cost indicates a worse path, is defined using this sensitivity, which can be understood simply as gradient(loss)/gradient(parameter). In other words, rather than merely introducing a more complicated metric, the Riemannian metric acts as a brake on the updates generated from the training data, which is the main signal. I believe that mathematics can be incorporated more deeply into AI if, when using theory A and theory B, the elements of theory A and the elements of theory B are each designed appropriately for the specific situation. submitted by /u/Round_Apple2573 [link] [comments]

Does anyone know of any evidence/forum/paper analyzing benchmark result differences between cloud inference platforms (togetherai) and running models locally with vLLM under greedy decoding? submitted by /u/No_Cardiologist7609 [link] [comments]

Hello community, just a short reminder that Raffi Krikorian (CTO @ Mozilla) is live today for an AMA to discuss Mozilla's inaugural State of Open Source AI report. Topics include enterprise adoption, the real cost of "free"models, developer trust, Chinese open models and their impact, agentic AI infrastructure, and the future of open source with respect to Machine Learning & Artificial Intelligence. Drop your questions in the thread here: https://reddit.com/r/MachineLearning/comments/1upxdvc/raffi_krikorian_cto_mozilla_ama_on_the_state_of/ The AMA starts at 1pm ET/10am PT/6PM BST His team reached out to us and he provided proof via Linkedin here: https://www.linkedin.com/feed/update/urn:li:activity:7481380478365880321/ Thank you! submitted by /u/Benlus [link] [comments]

I’m training a single-class segmentation model for large rectangular artwork placed on the floor and photographed from above. We have around 3,000 accurately masked original images taken by six different photographers. They are not the same height and do not hold the camera in exactly the same way, so the photos naturally vary in: roll pitch yaw camera distance object coverage in the frame centering and X/Y shift orientation perspective lighting The photos taken with flagship iPhone. I want to use on-the-fly augmentation to simulate realistic human-hand variation and save our designer from adjusting each time to make it flat. is 100 augmentation combinations per original be useful, or excessive? Should the policy be: mostly isolated transforms, mostly crossover combinations such as orientation + roll + pitch + yaw + coverage + shift, or a controlled hybrid of both? The goal is maximum segmentation accuracy, especially around the object boundary, not speed. I plan to train for around 300 epochs and keep validation and test images unaugmented. submitted by /u/Loganbirdy [link] [comments]

NB: Reposting due to a typo in the title Putting this here instead of the other subs since I figured a question on deep learning theory is out of place there -- I "recently" found (actually, a few months ago but only just got to reading) a monograph claiming to provide a unified theory of deep learning (and possibly SSL) through the lens of information theory, with one of it's headline claims is that you can design a "white-box" (I disagree with that, more on that later) transformer through the principle of coding rate reduction. I looked through the works the book claimed to be synthesizing and got a decidedly mixed picture: a JMLR and a NeurIPS on the one hand, but another frankly terrible paper concerning mechanistic interpretability (with which I am more familiar) published in a venue I've never heard of. And if it means anything, the book itself was endorsed by Kevin Murphy As I've alluded to, I'm more familiar with the interpretability side of ML as opposed to SSL/theory (where this book seems more relevant) so I'm unsure what to make of this. In particular, the apparent result that their bespoke transformer learns image segmentation on non self-supervised tasks seems interesting, I'm not sure how this relates to how machines learn more broadly. Also, their "white-box" transformer consists of a bespoke MLP suspiciously similar to a regular one with a sparsity penalty, and an attention mechanism strictly less expressive than those currently used (obtained by setting Q=K=V=OT.) I know it seems like I've done my research on this topic, but really I've just skimmed a few of these papers (which all seem to be originating from one lab) with no context to situate them in, so some help would be appreciated. Thanks in advance! submitted by /u/Carbon1674 [link] [comments]

Disclosure: I built it, it is open source, Apache-2.0 licensed, and currently alpha. Repository: https://github.com/mireklzicar/gpuhedge I started working on it after benchmarking a 17 GB AI model across several serverless GPU providers. On the primary provider, requests usually either completed in roughly 6–8 seconds or took around 90–122 seconds after a fresh GPU cold start. Simply switching to another provider did not remove the problem because every provider had its own tail. GPUHedge treats this as a speculative-execution problem. It starts a request on a primary provider, watches the job’s lifecycle state, and conditionally launches or switches to a backup. The first result that passes a validator wins, and the losing job is cancelled through the provider’s native API. You can try the policy engines without creating provider accounts or spending money: pip install gpuhedge In the initial benchmark, a fixed RunPod → Cerebrium hedge launched after 10 seconds. On the 36-request evaluation portion, it changed: observed p95 latency from 116.6 s to 29.4 s; requests over 60 seconds from 11/36 to 0/36; modeled active-compute cost from $0.0114 to $0.0083 per request. What is your experience with cold start latency? Which provider to add next? Can something like this help what you are building? UPDATE: Commenter's already noted that with the cost-saving it is more complicated (because of idle time, cancellation costs and actual invoice spent differences). I would say this tool is not primarily for saving money, it is rather for getting better latency and reliability without significantly higher costs. An actual "invoice spent" benchmark is needed to quantify this. submitted by /u/Putrid_Construction3 [link] [comments]

Read a long piece on the future of LLM reasoning that makes a provocative claim: Chain of Thought is a useful hack but we've started to confuse a readable trace with the actual computation. All in all, "generating text is not the same as thinking." There are two practical problems here: Faithfulness: CoT style traces can decouple from what the model actually "did." u can get plausible steps with a wrong answer, or messy steps with a right answer (so the trace isnt a reliable audit trail) Systems cost: Autoregressive reasoning serializes intermediate work into tokens. Longer traces inflate latency, cost and context usage The latent turn (stop making models "think in public"): A lot of recent work is shifting the inner loop into latent space and decoding language only at the end: Coconut (continuous / latent "thought" steps) HRM / HRM Text (separating slower planning from faster recursive execution) RecursiveMAS (agents passing latent embeddings instead of long text messages) A framing i propose: language as interface vs language as the compute substrate. I agree that language is essential for communication and abstraction but forcing search / constraint solving to be serialized into text is awkward and expensive. The black box wall If the "thinking" happens in dozens of latent loops, u lose the already imperfect window u had with CoT. In production, especially in high stakes domains, "no visibility" is a real blocker. One proposed solution is an outer loop governance layer (e.g., a symbolic / planning manager that builds an auditable DAG of subgoals + deterministic verification at each node: unit tests, constraints, rules, etc.). Auditability shifts from "read the model's inner monologue" to "audit the plan + checks + verified outputs." Where BDH fits BDH (Dragon Hatchling) is interesting in this landscape because it aims to keep language modeling capability while adding recurrent or stateful latent computation, rather than being "just" a supervised puzzle solver. Pathway reports 97.4% top 1 accuracy for a BDH based system on ~250k Sudoku Extreme puzzles, without CoT or solution backtracking. Sudoku is a useful diagnostic for constraint solving but not a complete measure of general reasoning. One point i found clarifying: many recursive latent reasoners excel at depth recurrence (iterating on a fixed snapshot of the problem) but real agentic / language settings are a stream: new tokens arrive continuously. That introduces time recurrence questions: when do u advance time, and what state / memory do u carry forward? BDH's stated research direction is basically trying to bring these together i.e. high bandwidth latent iteration and a principled state / memory story over time. It also provides a recoverable graph view and sparse, localized state, offering some native interpretability hooks but that is complementary to, not a replacement for, system level verification. I want views on: Is CoT increasingly a costly interface artifact rather than a scalable reasoning path? For high stakes use, do we inevitably need a DAG / verification outer loop, or can native model analysis hooks meaningfully reduce the governance burden (even if they cant replace it)? If latent recursion is the inner loop, what should the outer loop be in practice, DAGs, unit tests, formal specs, proof assistants, something else? submitted by /u/meowsterpieces [link] [comments]

Left: Telegram digest (optional); Right: detailed digest on HTML Like probably everyone here, my to-read list only grows. Skimming arXiv listings or my feeds takes 30-60 minutes a day, 95% of it is irrelevant to what I actually work on, and newsletters don't really help: they surface what's popular, not what's relevant to my research. So I built Research Radar, a daily cron job that: Fetches every new paper in your arXiv categories (RSS + API, deduped) Scores every abstract 1-10 against a markdown file describing your research interests (cheap model, batched) Deep-reads the top scorers: downloads the PDF, extracts full text, and a strong model writes a summary, key insights, limitations, and how it relates to your own work Delivers a morning HTML digest + optional Telegram ping with the must-reads Design decisions: Nothing domain-specific in the code. Your interests live in one markdown file. Edit it and the same pipeline works for ML, physics, bio, econ, whatever Only the two scoring passes touch a model. Fetching, dedup, PDF extraction, and rendering are deterministic Python Model-agnostic backend layer: Claude Code / Codex CLIs run it on the subscription you already pay for (no API key), or any OpenAI-compatible endpoint, including fully local via Ollama / vLLM. Backends mix per pass: cheap model for skimming, strong one for the 5-10 deep reads Approximate costs, benchmarked in the repo (tokens, cost, latency, quality grades per model). Rough sizing: a 10-abstract scoring batch is ~18k input tokens with a small JSON out; a deep read sends the whole paper, 40-70k input tokens. I've been using it daily for about a month and it's been genuinely useful in my field. The repo is a generalization of my personal setup to any domain, and I haven't deeply tested fields other than mine, so feedback and GitHub issues are very welcome. The model has to say "not relevant, 3/10" a lot without drifting toward score inflation, and the whole scoring is just prompt + markdown context. So I'm curious how others would approach calibrating an LLM judge like this GitHub: https://github.com/ramazan793/research-radar submitted by /u/usedtobreath [link] [comments]

My TMLR paper was assigned to reviewers on April 23, and as of July 13 I've received 2 reviews, but the third is still pending. The discussion phase hasn't opened yet, so I can't respond to the existing reviews. Is this normal for TMLR, or is it reasonable to send the Action Editor a polite status email? asking for the 3rd review, apprecite the suggestions submitted by /u/Ok_Ant_4311 [link] [comments]