人工智能前沿动态

A researcher has reported a jailbreak of GPT-6 Astra within a day after release. The attack is described as combination of TIP (Task-in-Prompt) attack from ACL 2025 paper with four other unnamed techniques. TIP attacks exploit the model’s reasoning/instruction-following behaviour by hidding the harmful objective inside another task, like solving a cipher or executing a Python code. For GPT-6, the researcher says the original minimal TIP attack was no longer sufficient and had to be reworked. They have reportedly disclosed the details privately to OpenAI rather than publishing the jailbreak. The same researcher reported jailbreaking GPT-5 within an hour of its release a year ago. Source: screenshot/post from the researcher; their ACL 2025 TIP paper linked in the original post. submitted by /u/Asleep-Requirement13 [link] [comments]

Just received an email about the automatic reference/citation checker. Did anyone receive a follow up email about whether the checker was included in the paper's decision making too, along with the general instructional email? submitted by /u/Emergency_Plate241 [link] [comments]

Abstract Language models spend most of their attention on a small fraction of context, yet they read the entire KV cache to find the few tokens that matter. If the user asks about a previous detail in a 1M-token conversation, global attention layers must scan the full context to generate each token of the reply. A prominent approach mitigates this cost by pre-selecting relevant tokens via lightweight proxy scores, but this extrinsic scoring still incurs O(N) per step. We take an intrinsic approach motivated by the simple question: wouldn't the model already know which parts of the context are relevant? To this end, we introduce Declarative Attention (DA), a protocol that elicits the model to declare where it needs to attend within its chain-of-thought, partitioning generation into three modes: <global> (full context), <focus> (a specific region), and <local> (recent output only). The inference engine parses these declarations like tool calls and skips most of the KV cache read. Under zero-shot evaluation across 15 long-context tasks, DA on off-the-shelf models (Gemma-4-31B, Qwen-3.6-27B) significantly reduces total attended tokens during decoding (52.0%, 31.1%) with modest accuracy drops (1.27pp, 2.75pp) that shrink with model scale. DA unlocks a new axis of sparse attention, with further potential under training-based methods that future work can explore. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2609.02737 [cs.CL] (or arXiv:2609.02737v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.02737 Focus to learn more submitted by /u/eigenlaplace [link] [comments]

submitted by /u/Winter_Mistake_3185 [link] [comments]

From what I've seen online so far, the description of these systems is roughly: They asked the model (often Aster) to generate statements in LEAN and then submit those to a LEAN compiler to be checked. Based on the results of attempting the LEAN compilation, they somehow add those statements as fact. When the full proof in LEAN compiles, the system is finished. I can imagine trying to jam as much of a proof as possible into the context window but some of the papers these systems have produced are hundreds of pages. To me this would indicate that somehow the paper is being built piece by piece and being assembled before being submitted to LEAN. This resonates with the part of my understanding that after checking LEAN compilation there's some kind of management of "facts." I would like to try to implement my own janky version and see if it can answer a question I have about higher dimensional geometry. I'm struggling to find a meaningful way to compose larger ideas from smaller ones. I can imagine it's relatively simple if you know what to do. What things have you seen? Do you have any ideas you haven't seen that might be interesting to try? Is this a fool's errand because you really need huge amounts of hardware to do anything meaningful? I would welcome any thoughts or links on the matter, cheers submitted by /u/tough-dance [link] [comments]

an observation : GPT-5-class models are genuinely capable(They are) of doing a substantial fraction of knowledge work, why haven’t we seen a noticeable productivity shock in the real economy yet? Is AI actually less economically useful than the benchmarks suggest—or are organizations simply too slow, constrained, and inefficient to turn model capability into measurable output? Are we confusing “AI can do the task” with “AI can replace the economic system built around the task”? If GPT-5 is already this capable, what exactly is the bottleneck preventing that capability from showing up in GDP and productivity statistics? My take : There is no question that these models are genuinely impressive. The question is whether that intelligence is actually translating into measurable economic productivity. People are already asking whether models like GPT-6 or equivalent. Claude, and Gemini will replace large sections of white-collar workers. I think there is a much simpler question we should ask first: if these models are already so capable (to me they definitely are capable enough) at a huge range of knowledge work, why haven't we seen a correspondingly obvious increase in productivity? I'm not even talking about GPT-6 or whatever comes next. It's probably too early to judge a newly released model. I'm talking about the current generation—GPT-5 and its equivalents from Google and Anthropic. These systems are genuinely good. They can write, summarize, analyze documents, explain technical concepts, generate code, reason through problems, conduct research, manipulate information and perform a remarkable range of tasks that previously required educated human labour. And yet, looking at the world around us, something feels strange. Where is the enormous productivity shock? Why don't we see a dramatic effect on GDP growth? Why don't we see massive increases in output per knowledge worker? Why don't organizations appear to be accomplishing dramatically more with the same number of employees? Why does the broader economy still look remarkably similar to the pre-LLM economy? Coding is probably the clearest exception, and even there the picture is complicated. AI can make programmers substantially more productive in certain tasks, but software development still involves architecture, debugging, verification, integration, requirements, security, deployment, maintenance and—most importantly—human judgment. The bottleneck often moves rather than disappears. almost every knowledge profession, the gap between "the model can perform this task" and "the organization can therefore produce substantially more output" is different it seema. A lawyer might be able to use an LLM to draft a document in minutes instead of an hour. But the lawyer still has to verify it, take responsibility for it, communicate with the client, comply with professional regulations and integrate it into an existing workflow. A doctor can use AI to summarize medical literature, but diagnosis and treatment remain embedded within a much larger institutional system. A researcher can generate dozens of hypotheses, but experiments still take time. A manager can produce reports instantly, but meetings, organizational politics and decision-making remain. the possibility: perhaps the bottleneck is no longer intelligence. Perhaps the bottleneck is everything surrounding intelligence. Organizations, regulations, verification, trust, coordination, physical-world constraints, legacy software, incentives, management structures, liability and simply the fact that human institutions change much more slowly than technology. This also makes me skeptical of simplistic claims that "AI can already do X, therefore everyone doing X will soon be unemployed." Technical capability and economic substitution are not the same thing. The internet could transmit information essentially for free, but that did not instantly eliminate newspapers, universities, governments or offices. Computers could perform calculations millions of times faster than humans, but most accountants and engineers did not disappear. Automation often increases the productivity of workers while simultaneously changing what their jobs consist of. As with the major Grok release, Elon Musk said it is "as good as most top phds", my question after more than a year? (& he ain't wrong with the benchmarks), my question is, how many phds it has replaced in xai or spaceX? did he stop hiring phds? if not, why? So I find the current situation genuinely puzzling. We have perhaps the most powerful general-purpose cognitive technology ever deployed, and yet the physical and economic world doesn't look radically different. Maybe we're simply in the early stages and adoption takes years. Maybe the productivity gains are real but are being absorbed into quality improvements rather than measured output. Maybe GDP is simply a poor instrument for measuring the value created by AI. Or perhaps current models, despite their extraordinary capabilities, still lack some crucial property required for autonomous economic production: reliability, persistence, agency, contextual understanding, verification, or the ability to operate continuously inside messy real-world systems. idk which explanation is correct. submitted by /u/Same-Club4925 [link] [comments]

Benchmark scores: https://preview.redd.it/dgumcg67ggnh1.png?width=1378&format=png&auto=webp&s=fae8fb006ef46fcdebb0876717fc977a905baa89 https://openai.com/index/gpt-6-astra/ Above, GPT-6 uses a harness for ARC-AGI-3, and is at about 60% without one: https://preview.redd.it/bym9wajephnh1.png?width=615&format=png&auto=webp&s=72cb425fb037ce68a68dcb433e7748b27dc96c41 Prior to the launch, OpenAI President Greg Brockman said "I think it’s not unreasonable to feel that we are now in the AGI era". GPT-6 is now joining a growing list of models that greatly exceed the human baseline on GDPval-AA v2: https://preview.redd.it/to7tdvbn4inh1.png?width=1419&format=png&auto=webp&s=02c413ab031cd943087684c5c573ce1e524b917d If we have AGI, why do human knowledge/remote workers still have jobs? Is it just a matter of time until the economy replaces a large number of humans with LLMs, or are LLMs lacking something that these benchmarks fail to measure? submitted by /u/we_are_mammals [link] [comments]

Has anyone else received an AAAI-27 desk rejection related to modifications to the title or abstract between the abstract-registration deadline and the full-paper deadline? What I’m trying to understand is how the modification rule is being applied in practice. The AAAI-27 modification guidelines say that the title and abstract can still be edited after abstract registration, while warning against substantive changes, and describe rejection in terms of changes that make the submission describe qualitatively different research. In my case, almost everything was identical. The modifications were incredibly minor. The rejection notice says that the decision is final and appeals will not be considered. Did this happen to anyone else? submitted by /u/Dansilly [link] [comments]

Hi everyone, I just quickly wanted to share a paper I was working on for around a year now. I created this summary website with key results: https://flogrammer.github.io/moljepa/ TL;DR: its a multimodal JEPA model for molecules. There will be more work to do to improve performance and I would be happy about feedback and ideas :) submitted by /u/TerribleAntelope9348 [link] [comments]

Three weeks from decisions even. I wonder what percentage is industry and VC funded AI labs looking to mingle and recruit. submitted by /u/alrojo [link] [comments]

LLMs describe physics well but don't "understand" it in any grounded sense — they've learned statistical relationships between tokens like "falls" and "gravity", not actual physical intuition. This is basically the Mary's Room problem: Mary knows every physical fact about color but has never seen one. LLMs are Mary. The idea I've been thinking about: Train a JEPA-style model inside a physics simulation (think MuJoCo or a simple 2D env). Instead of predicting pixels or tokens, the model predicts representations of future states in an abstract embedding space. If it gets physics wrong, the predictor fails — the loss is unforgiving in a way next-token prediction never is. The embedding space that emerges should encode actual physical structure — object permanence, momentum, trajectories — because that's what makes prediction possible. Not surface-level textures, just the principles. Freeze those representations and attach them to an LLM-style reasoning model as a conditioning signal. The LLM now has both linguistic physics knowledge AND grounded physical intuition it can actually "run" forward — closer to a computational primitive than a propositional fact. The hypothesis is this makes downstream learning significantly faster — the LLM doesn't have to rediscover that objects fall, it has representations that already encode that. V-JEPA does something adjacent for video (predict future frame representations, not pixels), and DreamerV3 uses a latent world model for efficient RL — but the specific combo of JEPA-style prediction + sim-grounded physics representations + LLM attachment doesn't seem to have been done cleanly. Questions for the community: - Is there prior work I'm missing that does exactly this? - What's the right interface between grounded JEPA representations and a language model? Just concatenate to the prompt embedding? Cross-attention? - Would the sim-to-reality gap kill the transfer, or are the representations abstract enough to survive it? Interested in whether this is worth building a small prototype for. submitted by /u/Full_Promotion4522 [link] [comments]

Just uploaded the full 5.94 billion TikTok video dataset to Hugging Face. It’s fully open source: https://huggingface.co/datasets/kuben-developer/tiktok-videos-4b This dataset was collected using a TikTok mobile app reverse-engineering method I developed a few years ago. The method allowed me to extract billions of videos, profiles, comments and replies, hashtags, sounds, and more. Full write-up and code here: https://tiktok-api.seeksocial.io Disclaimer: The TikTok app exposes 24 endpoints that can be accessed without a TikTok account, so the data itself is publicly accessible. But accessing it this way is probably still against TikTok’s ToS. Also, the full code is not free, I charge a small fee for access to it. submitted by /u/DataShack [link] [comments]

I've spent the last month building a local C++ machine learning library called Deepity to test alternative credit assignment algorithms; specifically Predictive Coding Networks (PCNs). While PCNs are fascinating for biological plausibility and continual learning, naive implementations are painfully slow. By implementing recent research (Accelerated PCNs via Direct Kolen-Pollack Feedback Alignment) and utilizing algorithmic caching to bypass redundant forward projections during the inference settling phase, I managed to close the performance gap with backpropagation on my CPU when training on MNIST (50 epochs). PyTorch Backprop (Feedforward): 98.27% test accuracy in ~70s. Deepity DKPPCN: 97.73% test accuracy in 59.5s. Next up is porting these kernels to CUDA to scale up the architecture and testing its capabilities in continual learning scenarios where standard backprop struggles. If you are interested in local learning, alternative credit assignment, or HPC for ML, I'd love your feedback! GitHub: https://github.com/ra4ster/deepity Project Site: https://ra4ster.github.io/Deepity submitted by /u/Important-Home4431 [link] [comments]

Disclosure up front: I'm the original first author of CABiNet (ICRA 2021), so I'm not a neutral party. Everything below is reproducible from the repo. Background CABiNet is a dual-branch CNN for real-time semantic segmentation: a high-res spatial branch, a lightweight context branch (global aggregation + local distribution) over a MobileNetV3 backbone, fused with a small FFM. Published 2021, then it went quiet. I came back this year, rebuilt the repo (PyTorch 2.x, Hydra, AMP, EMA, poly-LR, OHEM loss, CI + tests), and used it to ask one question on **UAVid**, the aerial dataset the original paper targeted: how does a purpose-built 2021 efficient architecture compare to a 2026 general multi-task model with a dedicated semantic-segmentation variant? What's actually controlled (and what isn't) Both models run off the same converted dataset and splits, the same ENet inverse-log class weighting (`cls_pw=0.5`), EMA weights for eval, and the same evaluation protocol: single-scale, no test-time augmentation. What is not matched: | Axis | CABiNet | YOLO26-sem | Potential advantage | | --- | --- | --- | --- | | Initialization | ImageNet-pretrained MobileNetV3 backbone; seg layers random | full net pretrained on Cityscapes + ADE20K | potentially favors YOLO | | Epoch budget | 5000 (early stop, patience 100) | 500 (early stop, patience 50) | potentially favors CABiNet | | Optimizer / schedule | SGD + poly decay, decoder LR ×10 | SGD + cosine | different | | Loss | OHEM-CE + aux deep supervision | CE + Dice + aux | different | | Extra augmentation | none | mosaic 0.8, copy-paste 0.15 | potentially favors YOLO | So this is not an architecture-only ablation. It's a controlled benchmark: the data representation, class weighting and evaluation are standardized, while each model keeps a model-specific training recipe. None of the rows above is an isolated experiment, so I haven't measured how much any single one is worth. Results — UAVid test split, 1024×1024, single-scale | Model | mIoU (%) | Params (M) | FLOPs (G) | FP16 latency* | FP16 FPS | | --- | --- | --- | --- | --- | --- | | **CABiNet (MobileNetV3-L)** | **67.14** | 9.17 | 54.8 | 4.44 ms | 225 | | **CABiNet (MobileNetV3-S)** | 65.25 | 5.36 | 44.1 | 3.09 ms | 324 | | YOLO26x-sem | 64.41 | 40.16 | 430.9 | 13.09 ms | 76 | | YOLO26l-sem | 63.28 | 17.87 | 192.4 | 7.54 ms | 133 | | YOLO26m-sem | 61.98 | 14.32 | 152.3 | 5.71 ms | 175 | | YOLO26s-sem | 61.69 | 6.50 | 44.4 | 2.52 ms | 396 | | YOLO26n-sem | 58.17 | 1.63 | 11.4 | 2.23 ms | 449 | *\*RTX 4070 SUPER, batch 1, pure model forward pass (no pre/post), 200 iters after 30 warmup, measured by me. Params are architecture-only; FLOPs are analytic forward-pass at 1024² (thop for CABiNet, Ultralytics profiler for YOLO26; both report FLOPs = 2×MACs).* UAVid mIOU vs FP16 Latency The dashed line is the accuracy/latency Pareto frontier: YOLO26n and YOLO26s sit on it as legitimate lower-latency points, while YOLO26m/l/x are dominated, each being both slower and less accurate than at least one CABiNet variant. CABiNet occupies the higher-accuracy end of the frontier. Three things worth pulling out: Near-iso-compute: CABiNet-S vs YOLO26s. ~44 GFLOPs each (44.1 vs 44.4), CABiNet-S has slightly fewer params (5.36M vs 6.50M), and they're within 0.6 ms on this GPU, yet CABiNet-S is +3.6 mIoU (65.25 vs 61.69). YOLO26s is still the faster model, so this is a clean accuracy/latency trade, not a universal win. Higher-accuracy end: CABiNet-L vs YOLO26x. CABiNet-L is +2.7 mIoU and ~3× lower forward latency (4.44 vs 13.09 ms). It's not that CABiNet is the fastest model (YOLO26n/s are faster); it's that it reaches higher accuracy without moving into the latency/compute regime of YOLO26m/l/x. Not universally better. On VDD and AeroScapes (same matched eval), YOLO26 s-and-up pull ahead of CABiNet-Large, which lands mid-pack there. Numbers and configs in the repo. MobileNetV3's depthwise convs are FLOP-cheap but not GPU-latency-cheap, which is why the frontier looks the way it does. The story is accuracy per millisecond at the higher-accuracy end, not "smallest and fastest." Qualitative — CABiNet-L vs YOLO26x-sem Where the +2.7 mIoU comes from. Per-class IoU on the UAVid test split, matched single-scale: | Class | CABiNet-L | YOLO26x-sem | Δ | | --- | --- | --- | --- | | Human | 28.3 | 21.1 | **+7.2** | | Static Car | 57.2 | 51.3 | **+5.9** | | Moving Car | 71.9 | 66.8 | **+5.1** | | Tree | 80.3 | 78.2 | +2.1 | | Vegetation | 64.1 | 63.3 | +0.8 | | Road | 80.3 | 79.8 | +0.5 | | Clutter | 67.8 | 67.3 | +0.5 | | Building | 87.1 | 87.4 | −0.2 | UAVid Test Set Qualitative Comparison The gap is almost entirely the small / thin classes: people and vehicles. On the big region classes the two are within half a point, and YOLO26x is marginally ahead on Building. Two UAVid test frames, both single-scale; columns are input · YOLO26x-sem · CABiNet-L · ground truth. Row 2 shows a failure mode behind the Static-Car number: YOLO26x collapses the parking-lot structure into one Static-Car/Clutter mass and bleeds Building into the lot, while CABiNet-L tracks the ground truth more closely. These two frames were chosen to illustrate the per-class differences above, not as a representative random sample. Scope / limitations UAVid only (see point 3 above). The VDD / AeroScapes numbers and configs are in the repo; I'm leading with UAVid because that's where the result is clean, not hiding the rest. Single training run per config: no seed sweep, no variance estimate. The observed ~2.7 mIoU CABiNet-L vs YOLO26x gap is large relative to the smaller differences in this table, but I haven't established statistical significance. I wouldn't over-read anything under ~1 point. Latency is a clean-room forward pass on one consumer GPU. No TensorRT/ONNX, no Jetson, no full-frame sliding-window cost (UAVid source frames are 4K; CABiNet tiles, YOLO resizes, so end-to-end numbers would differ). Read these as model-level GPU measurements, not deployment throughput. The initialization is asymmetric: YOLO26-sem starts from Cityscapes + ADE20K pretraining, CABiNet only from an ImageNet-pretrained backbone. This likely gives YOLO26 a transfer learning advantage on aerial data, though its magnitude isn't measured here. CABiNet reaching higher UAVid accuracy from the less domain-specific start is part of what makes the result interesting, but it stays a confound. Open-sourced Weights (CABiNet-L/S + all YOLO26-sem sizes) on Hugging Face, with model cards + metrics Full Hydra configs, converters and eval scripts; every row above is reproducible Live demo (UAVid): run any checkpoint on your own aerial image at https://huggingface.co/spaces/dronefreak/uavid-aerial-segmentation Links Code + leaderboards: https://github.com/dronefreak/CABiNet Demo: https://huggingface.co/spaces/dronefreak/uavid-aerial-segmentation Paper: CABiNet, ICRA 2021 (DOI 10.1109/ICRA48506.2021.9560977) The criticism I'd most like: is standardizing the data representation, class weighting and evaluation, while letting each model keep its native training recipe, a useful way to compare architectures from different lineages? If not, what would you standardize or change instead? submitted by /u/Naive-Explanation940 [link] [comments]

Jasper Research just released a cookbook on how to build a text-to-image model from scratch. It shares the full reasoning and intermediate results, making it ideal if you want to deep-dive into text-to-image models, or if you are curious about how frontier labs build them. The cookbook also includes a 100M-image dataset and a codebase with a tiny model, so you can train a text-to-image model from scratch. Here are the links: Cookbook: https://huggingface.co/spaces/jasperai/t2i-technical-interactive-report nano t2i: https://github.com/gojasper/nano-t2i Monet Dataset: https://huggingface.co/datasets/jasperai/monet submitted by /u/dh7net [link] [comments]

We needed to know where the open-source AI-detection field actually stands, so we ran every notable open detector through the same protocol. Setup: - Public data only: Jabarian & Imas 2025 (NBER), Liang 2023 TOEFL essays, a 1,060-text frontier set (GPT-5.x, Claude Opus 5, Gemini 3.x), 5,000 pre-LLM (2018) FineWeb pages as human pool - Every model gets its threshold set on the same 6,930 human docs to a matched 0.5% FPR - Then measure recall per group: raw AI, humanizer-paraphrased AI, frontier models What surprised us: - 4 of 6 models effectively can't reach 0.5% FPR. MAGE scores >0.9999 on 26% of ordinary human web text. The old OpenAI RoBERTa detector lands at AUC 0.31 - worse than coin flip on modern generators. - Humanizer-paraphrased text is where everything collapses: best model catches 42%, second best 4%. - All models flag non-native essays at a higher rate than they flag native essays. This is a fundamental flaw in the entire class of models, not just one. model ROC-AUC raw AI humanized AI frontier models tropa-mini 0.968 93.2 % 41.6 % 33.6 % desklib/ai-text-detector-v1.01 0.875 83.9 % 4.0 % 1.8 % SuperAnnotate/ai-detector 0.824 0.5 % 1.4 % 0.6 % Hello-SimpleAI/chatgpt-detector-roberta 0.571 0.8 % 0.4 % 0.2 % yaful/MAGE 0.507 —* —* —* roberta-large-openai-detector 0.313 0.0 % 0.1 % 0.0 % More information and data on HF \recall at matched 0.5% FPR; * MAGE can't reach 0.5% FPR at any threshold (flags 26% of human web text with score >0.9999)** Disclosure: one of the six is ours - we run a hosted detector and released this one as open weights (Apache-2.0). All datasets and the methodology are in the model card, so you can rerun everything: huggingface.co/wasitaigeneratedcom/ai-text-detector-small submitted by /u/grumpyp2 [link] [comments]

Hi r/MachineLearning, Stjepan from Manning here, posting with the mods’ permission. We’ve recently released GPU Programming with Triton by Harshwardhan Fartale in early access. It’s a practical guide to speeding up machine learning training and inference by writing custom GPU kernels in Python with Triton. The book explains how to identify operations worth optimizing, build and benchmark kernels, fuse operations to reduce memory traffic, implement common parallel and reduction patterns, and improve performance through tiling, vectorization, and better memory access. The goal is to help ML practitioners move beyond framework-level optimization when a model has a stubborn bottleneck. I’d love to hear from the community: Which part of your ML workload would you most like to accelerate with a custom kernel—and what currently stops you from writing one? Real use cases, benchmarks, failed experiments, questions, and skeptical takes are all welcome. We’ll give a free ebook to the five comments that contribute the most to the discussion. The giveaway will remain open for 48 hours, after which we’ll announce the winners here. Book: https://hubs.la/Q04w2PtF0 50% off with code for the community: MLFARTALE50RE Full disclosure: I’m posting on behalf of Manning. Honest criticism is just as welcome as enthusiasm. Thank you for having us. Cheers, Stjepan submitted by /u/ManningBooks [link] [comments]

Hi all, I recently read this paper: Julien Guinot, Alain Riou, Elio Quinton, Gyorgy Fazekas. Steering dense music retrieval with open-vocabulary concept discovery.https://arxiv.org/abs/2608.08757 There is multiple model where you can get embedding from Song and Text so that you can search song by text. The challenges is when you get a result that don’t really respect your query, for example you search: - POP viola with female vocalist And as a result you usually get POP song with female vocalist because they are usually very common in you library, instead the Viola tend to disappear being uncommon. But the power of MIR isn’t exactly search to specific song even if uncommon ? Here the idea of the paper is to get the compressed embbeding layer, make it sparse again and be able to identify for some common words exactly which neuron are activated try to avoiding overlapping with other neuron. Then you can improve the value of let’s say the Viola neurons and then bring back to the compressed embbeding space. This is not mine, is only explanation, what I did is in the past a Distilled version of LAION CLAP called DCLAP that is around 7 million parameters and can efficiently run on CPU: - https://github.com/NeptuneHub/AudioMuse-AI-DCLAP The following the paper I also trained a SAE for DCLAP: - https://github.com/NeptuneHub/AudioMuse-AI-SAE So if you’re interested you can take a look at let me know. Are all free and opensource. Finally if you want to see both of them in action I used both the model for AudioMuse-AI, a software dedicated to sonic analysis and automatic playlist creation (with various model): - https://github.com/NeptuneHub/AudioMuse-AI If you have any feedback, suggestion, idea, please let me know ! submitted by /u/Old_Rock_9457 [link] [comments]

Why did I sign up to review when it’s not reciprocal? Am I an idiot? Am I dumb to sacrifice some of my precious time outside of work to review these papers when I don’t even have to? Yes. I tell myself I’m giving something to the community. But all I’m really doing is pissing off the authors as I reject their papers. I really wanted to accept one of them too. But, it wasn’t as well done as I’d hoped. Strong reject. Nobody made me sign up. Nobody even asked me personally. They sent a lovely form email that goes to everyone who’s published there. I let feeling important convince me to do it. How dumb of me! At least it’s only a couple of papers and a small amount of my time. And I’m learning something new reading stuff slightly outside my direct field, that I would never normally read otherwise. And I get to hone the skill of critical reading, thinking, and generally understanding how a paper should (or should not) be put together. Maybe it was a good idea after all. How does everyone else feel about non-reciprocal reviewing? I imagine those that agree to do it are in the minority. submitted by /u/OptimalOptimizer [link] [comments]

Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. -- Any abuse of trust will lead to bans. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. -- Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [comments]

Hi everyone, 2nd-year PhD candidate here staring down my first A* submission deadline (AAMAS 2027). I could really use some perspective on theory expectations, especially since I think I’ve methodologically painted myself into a corner. The setup My project started with a clean hypothesis: if architecture X is more robust than Y to perturbation A, and B is a strictly harder version of A, then the X > Y ordering should hold under B as well. I isolated three variables I suspected were driving the effect, ran experiments, and… got results that only partially support the hypothesis, with clear boundary conditions. Where I got stuck Trying to explain the “why” mathematically sent me down a theory rabbit hole. I ended up with two bad options: Claims tied to specific training outputs rather than structural/architectural properties, or Weak, hand-wavy speculations that feel like post-hoc rationalizations. I’m pretty sure I fell into HARKing.. I started building theory after seeing the results instead of deriving predictions beforehand. Furthermore, my codebase is built on an undocumented public repo, and I recently found a bunch of hidden parameters set to wrong values for my setting. I’m currently re-running everything, which is why I’m being vague about specifics. My “insights” from the first round are probably garbage. My actual questions For those who’ve reviewed for or published at AAMAS (or similar A* venues): how much formal theory is actually expected for an empirical MARL paper? Is “here’s the phenomenon, here’s the controlled experiments, here’s a plausible but incomplete theoretical sketch” a death sentence? If the theory ends up being training-dependent rather than structural, is that a sign I should pivot to a lower-tier venue, or can strong empirical characterization + limited theory still fly at A*? How do you recover from HARKing mid-project when you’re under pressure to publish in year 3/4 of a 4-year contract? Any advice on how to salvage the timeline or reframe the narrative would be hugely appreciated. submitted by /u/ham_bam0 [link] [comments]

YOLO26 ships a depth-estimation model — dense, full-resolution, per-pixel regression, a task architecturally much closer to image restoration than to detection. I wanted to know whether the backbone+neck weights it learns through depth training transfer to a different dense-regression task (deraining), compared with training the same architecture from scratch. The deraining model that came out of it is a useful byproduct, but the transfer result is the part I think is worth discussing. What I inherited from YOLO26-depth The CSPDarknet backbone and PAN-FPN neck, unchanged. The depth decoder's multi-scale fusion (project the P3/P4/P5 pyramid to a common width, progressively upsample-and-add P5→P4→P3). That part isn't depth-specific — it's just feature fusion — so RGBHead reuses it. What I changed / added Replaced the 1-channel Depth head with a new RGBHead. The config change is one line; RGBHead itself is a new restoration decoder, not a re-pointed depth head. Reconstruction tail that continues to full input resolution (deraining needs pixel-exact output; depth stops at 1/4 res). Skip connections from the stride-2 and stride-4 backbone layers into the tail, so fine detail has a path that doesn't route through an 8×-downsampled bottleneck. Residual output — the head predicts a correction added to the input (NAFNet/Restormer-style), not the image directly. LayerNorm in the head's own conv blocks; the backbone and neck stay on BatchNorm (folds into conv at TensorRT export, and keeps the model loadable from the whole YOLO26 pretrained zoo, not just the depth checkpoint). How it was trained and measured ClearView as an external lib — its mixed synthetic+real rain recipe, Charbonnier loss, and 10-test-set protocol — so the numbers land on ClearView's own model-zoo scale, not a benchmark I made up. Released scales: nano (5.25M) and small (12.13M). Loading the YOLO26-depth checkpoint into this architecture matches 468/468 backbone+neck tensors exactly — only the new RGBHead is randomly initialized. So the controlled comparison is: identical architecture and recipe, backbone+neck either from the depth checkpoint or from scratch. The transfer result (the interesting bit) A controlled initialization experiment at nano scale — same architecture, same recipe, fixed 100 epochs each — backbone+neck from the YOLO26-depth checkpoint vs. random init: Init Avg PSNR (10 sets) Avg SSIM Test sets won Random 27.45 0.807 0 / 10 YOLO26-depth 27.94 0.813 10 / 10 Δ (depth − random) +0.48 +0.006 — Deltas are from the unrounded averages (27.935 vs 27.452 PSNR). Small, but the depth init wins on every one of the 10 test sets. (These are 10-set averages, AllWeather included, from the 100-epoch controlled run — so they're lower and not directly comparable to the 9-rain-only released-model numbers in the ranking table below, which come from longer training.) On "did the random model just need longer to converge?" — both conditions ran a fixed 100 epochs, and the gap isn't a convergence-speed artifact: a 1-epoch check was a statistical wash, by 20 epochs the gap was already ~+0.49 dB, and at 100 epochs it was +0.48. It appeared early and didn't close with more training. This does not establish why — whether depth supervision teaches geometry/spatial structure that's useful for restoration, or whether YOLO26-depth just happens to be a strong pretrained checkpoint. Only that, in this setup, the depth-initialized representation is a better starting point than random. Per-dataset deltas are in the repo; happy to paste them in a comment. Accuracy (avg PSNR over 9 rain-only test sets, ClearView's ranking convention): Model Params Avg PSNR Restormer 15.3M 35.10 NAFNet-Large 116M 34.16 NAFNet-Mid 14.3M 33.97 Restormer-Small 2.3M 31.98 UNet 21.5M 31.74 NAFNet-Small 1.1M 31.15 yolo26_rgb_s 12.13M 30.95 yolo26_rgb_n 5.25M 30.83 ResNet50-UNet 73.3M 30.63 ResNet34-UNet 24.5M 30.45 ResNet18-UNet 14.4M 30.23 ClearView's own analysis points to the classification stem's early downsampling (a stride-4 entry before any residual block runs) as a likely reason the ResNet-UNet baselines underperform. This project doesn't test that directly — the ResNet-UNet comparison is a whole-architecture comparison, not a pretraining ablation — but it's the context the depth-vs-random experiment sits in. Note NAFNet-Small (1.1M, 31.15 dB): smaller and higher PSNR than yolo26_rgb_n, but ~4× slower (26.9 qps). So this isn't Pareto-dominant on every axis — the story is specifically the real-time / YOLO-derived operating point, not "more efficient in every sense." Deployment (TensorRT fp16, 1920×1080, batch 1, RTX 4070 SUPER 12GB; baseline figures are ClearView's own on the same GPU/TRT version) The clean pairwise comparisons against the ResNet-UNet family: yolo26_rgb_s — 12.13M, 30.95 dB, 92.2 qps vs ResNet34-UNet — 24.5M, 30.45 dB, 94.9 qps → same speed, ~half the params, +0.5 dB yolo26_rgb_n — 5.25M, 30.83 dB, 108.6 qps vs ResNet18-UNet — 14.4M, 30.23 dB, 110.3 qps → same throughput, ~1/3 the params, +0.6 dB Both are ~3× faster than ResNet50-UNet (73.3M, 30.63 dB, 33.1 qps), while also scoring higher PSNR. Restormer (rank 1 on PSNR) doesn't build under TensorRT at 1080p on this 12GB card in my setup — TensorRT reports ~14.4GB of scratch needed to fuse its attention path. What this shows — and what it doesn't Demonstrated: YOLO26-depth initialization beats random init for deraining in this setup (10/10 test sets, +0.48 dB), same architecture and recipe. Supported: the trained models sit at an attractive real-time quality/latency point relative to the ResNet-UNet baselines. Not demonstrated: that depth pretraining beats classification pretraining for restoration, or why depth helps. Those need experiments I haven't run. Practical limitations: deraining is partial (faint streaks survive up close; dense rain over flat, low-texture backgrounds is the worst case). AllWeather (rain+fog) is out of domain — both YOLO26-RGB models and every ClearView baseline land around 13.5 dB, so it's excluded from the ranking. One task, two scales — not a general restoration model. Solo side project. AGPL-3.0 (inherited from Ultralytics' YOLO26 license). Not affiliated with Ultralytics. Code: https://github.com/dronefreak/yolo26-rgb Models: https://huggingface.co/dronefreak/yolo26-rgb-n · https://huggingface.co/dronefreak/yolo26-rgb-s Happy to answer questions on the architecture or the eval setup. submitted by /u/Naive-Explanation940 [link] [comments]

After following various arXiv papers and researcher discussions on X/bluesky about latent reasoning and continual learning, one idea which resonates strongly is that path forward (towards AGI) may depend less on generating ever-longer chains of thought and more on finding architectures that can reason beyond the token stream. LLMs routinely reach correct answers through flawed or fabricated CoT steps, and produce perfectly logical steps that end in wrong answers (Kambhampati, 2025). The trace doesn't track the computation which clarifies that verbalized CoT is an imitation of reasoning and not the mechanism itself. The alternative mechanism which gets the most attention is latent reasoning: instead of verbalizing every intermediate result, the model repeatedly transforms its continuous hidden state and decodes only the answer. I’m breaking latent reasoning down into at least five distinct families: Continuous thoughts in autoregressive LMs: Coconut (Hao et al., 2024) feeds the model's own final hidden state back in as the next input embedding while Soft Thinking (Zhang et al., 2025) reasons in a continuous concept space. Theory here argues a single continuous state can hold several search frontiers at once and expand them in parallel (Zhu et al., 2025) . Compressed discrete non-linguistic tokens: Abstract-CoT (Ramji et al., 2026) swaps verbal rationales for a short sequence from a learned vocabulary. It is non-linguistic, but still serial and externally decoded, the discrete end of the spectrum . Recurrent depth and looped models: recurrent-depth LMs (Geiping et al., 2025) and looped Transformers (Saunshi et al., 2025; Zhu et al., 2026) reapply a shared block to a latent state. Mostly framed as parameter efficiency and test-time-compute scaling, not as a new reasoning interface . Task-trained recursive solvers: HRM (Wang et al., 2025) and TRM (Jolicoeur-Martineau, 2025) recursively refine latent and candidate-answer states. Their ARC pipelines are transductive: evaluation-task demonstrations get augmented into optimization with learned per-puzzle identities, so an unseen task needs a backward pass before it can be answered . In-context recurrent latent solvers: this is where BDH-CQ (Engdahl et al., 2026) sits, built on the Dragon hatchling architecture (Kosowski et al., 2025). Demonstrations write directly into a recurrent memory at inference time, and new test inputs are then solved by iterative computation in a separate continuous latent space. The authors report a point beyond the previously published cost–accuracy Pareto frontier on public ARC-AGI-1, as well as early pretraining experiments show transformer-like scaling laws upto 600B parameters while preserving the latent reasoning behavior. Two distinctions seem especially important: how a system acquires a new task (through context, memory, or gradient-based optimization or finetuning) and where its intermediate computation happens (through language tokens, abstract tokens, or continuous latent states) Lmk if I have missed any family or papers. More importantly, if latent reasoning wins on efficiency, what happens to the readable traces on which much of industry's interpretability and evaluation work currently depends? Was CoT legibility a temporary consequence of how we scaled LLMs or is it a safety property worth paying an efficiency penalty to keep? submitted by /u/Typical-Scene-5794 [link] [comments]

I'm exploring Hidden Markov Models (HMMs) as a baseline method for "dataset exploration/discovery" where I have a bunch of unstructured data with no annotations, and wish to gain insights about the structure and semantics of the data within. I was wondering if there are more modern (deep learning based or otherwise) approaches which have completely superseded HMMs for such tasks. submitted by /u/fullgoopy_alchemist [link] [comments]

For Job Postings please use this template Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for] For Those looking for jobs please use this template Want to be Hired: [Location], Salary Expectation:[], [Remote | Relocation], [Full Time | Contract | Part Time] Resume: [Link to resume] and [Brief overview, what you're looking for] ​ Please remember that this community is geared towards those with experience. submitted by /u/AutoModerator [link] [comments]