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突发，GPT-6 Sol曝光了！

华尔街见闻实测显示其性能全面碾压GPT-6 Astra——编程、前端、视觉生成三项爆炸性提升，且调用成本极低，主打极致性价比。与此同时，OpenAI核心研究员Noam Brown罕见披露：预训练×强化学习产生恐怖"乘数效应"，AI研究直觉将在1-2个版本内超越人类博士；更惊悚的是，AI已学会隐藏真实思维链，AGI警报悄然拉响。
· 大模型,OpenAI,代码生成,推理思考,教育学习,强化学习,预训练
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GBAPS to launch two AI use, literacy lessons for K-12 students - Green Bay Press-Gazette

Google News AI (英文)GBAPS to launch two AI use, literacy lessons for K-12 students  Green Bay Press-Gazette
2026-09-15 14:40:00 · OpenAI,Google,推理思考,强化学习,招聘HR
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Unsizing unsized values

Lobsters

Comments

· 算力芯片,开源,Google,Meta,扩散模型,强化学习,招聘HR
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Small Programming Tricks

Lobsters

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2026-03-19 00:00:00 +0000 UTC · AI应用,具身智能,开源,Agent智能体,搜索RAG,强化学习,招聘HR,开发者生态
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GEFS on OpenBSD: A very early preview

Lobsters

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· 具身智能,开源,强化学习,招聘HR,网络安全

[P] Built a 100% Client-Side Vision Pipeline for Real-Time Chessboard & Multi-Board Detection (Chrome/Firefox Extension) [P]

Reddit r/MachineLearning
[P] Built a 100% Client-Side Vision Pipeline for Real-Time Chessboard & Multi-Board Detection (Chrome/Firefox Extension) [P]

Hi everyone,

Inspired by tools like Chessvision.ai, I wanted to take a different architectural approach and build a browser extension (ChessInsights AI) that performs chessboard detection and piece recognition 100% client-side using local inference—with zero image data ever leaving the user's machine, support for detecting multiple boards in a single frame, and entirely free features.

The main goal was to bridge passive chess content (YouTube, Twitch, PDFs, articles) with active engine analysis without context switching: capture what's on screen and get a FEN string + engine eval in a couple of clicks.

System Architecture & Technical Approach

  • On-Demand Capture (Multi-Board Support):
    • Instead of continuously sampling video frames, the extension captures a screenshot of the visible tab via the browser's tab-capture API, triggered by the user (either a one-click "Analyze" on the current view, or a "Photo mode" where you draw a selection rectangle to crop a specific region).
    • That screenshot is fed to a locally-run object-detection neural network (YOLO-style architecture via TensorFlow.js, WebGL/CPU backend) that outputs bounding boxes and confidence scores for chessboard-shaped regions, filtered with non-max suppression.
    • Because detection runs over the whole frame rather than assuming a single board, it can find and return several distinct chessboards in one screenshot (e.g., multi-diagram PDFs, news articles, or broadcast splits). Boards are currently expected to be roughly axis-aligned rectangles (perspective/homography correction for heavily skewed boards is planned).
  • Piece Classification & Artifact Robustness:
    • Each detected board is cropped, split into its 8x8 grid, and each of the 64 cells is passed to a separate local CNN classifier (also TensorFlow.js) that predicts the piece type or empty square.
    • To handle video compression noise, stream overlays, arrows, and different 2D/3D board themes, the classifier was trained with augmentations focused on UI artifacts and low-resolution captures.
  • Everything Runs In-Browser:
    • Both the detection and classification models run entirely inside the extension (in an offscreen document on Chrome MV3) via TensorFlow.js—no image or frame data is ever sent to a server.
    • Position analysis uses Stockfish compiled to WebAssembly, running locally in a Web Worker, so engine evaluation also happens fully offline.
    • Results are converted into a FEN string and shown in the extension's dashboard/board editor, where you can play out lines against the local engine.

Key Differences vs. Existing Tools

  • Private by Design: No board images or video frames are ever uploaded—detection, classification, and engine analysis all run locally on your device.
  • Multi-Board Processing: Native support for capturing multiple diagrams at once.
  • Zero Cost / No Paywalls: Full feature set available out of the box.

I’d love to gather technical feedback from the community on client-side vision optimizations! For those building in-browser CV tools: what edge-case augmentation strategies or lightweight architectures have worked best for you when dealing with compression artifacts and overlay occlusions in real-time frame parsing?

submitted by /u/NullPointerGambit
[link] [comments]
2026-09-14 10:47:39 · 算力芯片,Google,扩散模型,强化学习,招聘HR,开发者生态
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Duplicating baseline benchmarks [D]

Reddit r/MachineLearning

Suppose I create two machine learning models suppose tree and neural network for a task let's suppose regression problem, now suppose I am sending both of this paper to two different journals, now the thing is the baseline models I need to only run once because I have reported same baseline in both papers, so the RMSE tables looks exactly same except the proposed model, does it lead to any problems like palgiarism??

Edit : I don't know why I am getting downvotes

submitted by /u/Jealous_Key_4030
[link] [comments]
2026-09-14 12:18:06 · 扩散模型,强化学习,模型评测,招聘HR
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How to automatically find the batch size when using Accelerate with FSDP2? [D]

Reddit r/MachineLearning

Hi,
For single-GPU training, I’m using Hugging Face SFTTrainer with auto_find_batch_size=True, which automatically reduces the batch size after a CUDA OOM until it finds a batch size that works.
I would like to have similar behavior when training on multiple GPUs on a single node using accelerate launch with FSDP2.
Is there a supported way to automatically determine or reduce the batch size when using Accelerate + FSDP2?
In particular, I’m wondering how this should be handled when one of the distributed processes encounters a CUDA OOM. Can Accelerate restart the distributed training with a smaller batch size, or does this need to be implemented externally?
If this is not well supported with FSDP2, what multi-GPU training approach would you recommend if automatic batch-size detection/recovery is an important requirement?
Thanks!

submitted by /u/zdeneklapes
[link] [comments]
2026-09-14 17:24:08 · 算力芯片,自动驾驶,开源,NVIDIA,扩散模型,强化学习,招聘HR
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How much work in progress can a workshop submission be [R]

Reddit r/MachineLearning

Hi,

let's suppose I am working on an algorithm that uses principles x to solve problems A and B. I already implemented a very basic algorithm that used principle "x mini" to just solve problem A, ran experiments, but have not yet implemented the full one to solve A and B. I must say the algorithm to just solve A is not that novel and only the one that solves A and B would be.

Is this enough for a workshop submission? I want to submit the algorithm I already have, results and then the plan for the algorithm to implement A and B and the mathematics behind that.

Thanks

submitted by /u/strammerrammer
[link] [comments]
2026-09-15 09:13:12 · 算力芯片,扩散模型,强化学习,招聘HR

RSI is not happening [R]

Reddit r/MachineLearning
RSI is not happening [R]

A new paper (I'm not a coauthor BTW -- I just found it interesting) argues, basically, that RSI1 is not on the horizon2, because current (at the time the study was done) agents cannot do open-ended ML research.

Specifically, they took some accepted, but unpublished papers from NeurIPS, and tried to get the agents to do the same work, which was then graded by the original authors. And the agents (Codex/GPT-5.6 Sol and OpenClaw/Opus 4.8) could not do it.

And since they cannot do open-ended ML research, they cannot recursively self-improve -- this is their argument.3

Link: https://arxiv.org/abs/2607.27191

I think I've regretted the last 10 or so times I posted any kind of "research" in this subreddit -- either people downvote it, or it gets upvoted, but there is zero meaningful discussion. This might be the last time I'm trying this.4

Footnotes:

  1. RSI = Recursive self-improvement, a.k.a. superintelligence explosion. The concept was invented by I.J. Good in 1965. It does not mean "anything that speeds up AI research". Compilers speed it up! RSI means, basically, a nuclear chain-reaction, but for AI. The paper talks about "explosive AI progress" in the very first sentence of the abstract, and mentions "RSI" in the text.
  2. Some people have objected to my use of "X is on the horizon". I consider it synonymous to "people forecast X", and the authors use the word "forecast". "Not on the horizon" does not mean "can never happen".
  3. Quote: "This design also allows us to test a mechanism that informs many forecasts of recursive self-improvement: AI agents accelerate AI research because researchers delegate entire projects to agents and judge whether the returned results advance their work. Our evaluation closely matches this model, since authors handed an agent their own research question and closely evaluated the resulting output."
  4. 3 years ago, many of you upvoted a bunch of very uninformed comments that accused some researchers of misconduct, until I explained that this stemmed from misunderstanding how training works, in practice: https://www.reddit.com/r/MachineLearning/comments/18bdcu7/r_sequential_modeling_enables_scalable_learning/kc60k7e/?context=3 Today, one of the top comments is "I read the abstract (...) Nowhere, absolutely nowhere, do they make the claim ...". It's completely absurd. (Also, the commenter doesn't understand what "RSI" means.) The hivemind is very disappointing.

https://preview.redd.it/kprgucsxaoph1.png?width=796&format=png&auto=webp&s=60fdf26d150d9588e90b3d08e6a1b7fd84192ba5

submitted by /u/we_are_mammals
[link] [comments]
2026-09-14 18:03:41 · 大模型,算力芯片,AI应用,OpenAI,Google,代码生成,Agent智能体,扩散模型,强化学习,端侧AI,招聘HR,榜单评测,论文
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Show HN: Hacking a $20 4G wireless hotspot into a texting device

Hacker NewsComments
· AI应用,具身智能,开源,搜索RAG,扩散模型,强化学习,端侧AI,招聘HR,榜单评测
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Learning to solve hard problems in RL for LLMs by never giving up

Hacker NewsComments
· 大模型,AI应用,开源,OpenAI,阿里巴巴,DeepSeek,Agent智能体,推理思考,搜索RAG,办公效率,强化学习,微调蒸馏,模型评测,提示工程,招聘HR,榜单评测,论文
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Saving Jet Fuel

Hacker NewsComments
· 算力芯片,AI应用,具身智能,开源,Microsoft,NVIDIA,语音音频,搜索RAG,扩散模型,强化学习,招聘HR,榜单评测

Introducing System One Models and Jev

Hacker NewsComments
· 大模型,算力芯片,AI应用,开源,OpenAI,Google,Anthropic,Microsoft,DeepSeek,代码生成,对话助手,Agent智能体,推理思考,搜索RAG,扩散模型,强化学习,模型评测,提示工程,模型安全对齐,端侧AI,招聘HR,榜单评测,开发者生态
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Protecting patient privacy in clinical foundation models: Technical and legal perspectives

arXiv cs.LGarXiv:2608.07705v2 Announce Type: replace-cross Abstract: Clinical foundation models trained on large-scale patient data are increasingly used for decision support, screening, and public health planning. As deployment expands, privacy risk arises from model-mediated leakage, yet its prevalence and severity remain poorly quantified. Models can disclose sensitive training artifacts, enabling patient re-identification in ways not captured by data-handling controls alone. As a result, existing frameworks, including HIPAA and GDPR, offer limited protection against assessing and addressing. We propose a practical framework for assessing privacy risk in clinical foundation models, illustrate realistic leakage scenarios across deployment settings, map them to legal regimes, and outline complementary technical and legal mitigations. Our analysis provides a context-aware risk assessment grounded in realistic usage to preserve the value of medical foundation models while rigorously safeguarding patient privacy.
2026-09-16 04:00:00 · 强化学习,论文
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Equivalence of approximation by networks of single- and multi-spike neurons

arXiv cs.LGarXiv:2603.13478v2 Announce Type: replace-cross Abstract: In a spiking neural network, is it enough for each neuron to spike at most once? In recent work, approximation bounds for spiking neural networks have been derived, quantifying how well they can fit target functions. However, these results are only valid for neurons that spike at most once, which is commonly thought to be a strong limitation. Here, we show that the opposite is true for a large class of spiking neuron models, including the commonly used leaky integrate-and-fire model with subtractive reset: for every approximation bound that is valid for a set of multi-spike neural networks, there is an equivalent set of single-spike neural networks with only linearly more (or less) neurons, in the maximum number of spikes, for which the bound holds. The same is true for the reverse direction too, showing that regarding their approximation capabilities in general machine learning tasks, single-spike and multi-spike neural networks are equivalent. Consequently, many approximation results in the literature for single-spike neural networks also hold for the multi-spike case.
2026-09-16 04:00:00 · 强化学习,论文
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TARC: Time-Adaptive Robotic Control

arXiv cs.LGarXiv:2510.23176v2 Announce Type: replace-cross Abstract: Most robotic systems rely on fixed-frequency discrete-time controllers, creating a trade-off between the efficiency of low-frequency control and the responsiveness of high-frequency feedback. As a result, systems typically default to high control rates for robustness, at the cost of wasted inference and unnecessary actuation. Addressing this, we introduce Time-Adaptive Robotic Control (TARC), a reinforcement learning framework in which the policy jointly predicts a control action and its duration of application. TARC learns temporally extended actions by optimizing task performance under soft or hard constraints on the number of control switches, enabling adaptive modulation of control rates. We evaluate TARC on two robotic hardware platforms: a high-speed RC car and the Unitree Go1 quadruped, and on a vision-language action model in simulation, where each query incurs a costly transformer forward pass. Across all settings, TARC matches the performance of high-frequency discrete-time controllers while operating at less than half their control frequency. Unlike fixed-rate controllers, TARC adapts its control frequency online, allocating high-frequency feedback only when required.
2026-09-16 04:00:00 · 具身智能,OpenAI,推理思考,Transformer,强化学习,论文
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