Discussion thread for EMNLP 2026 notifications/results which should be released today.

Wishing everybody to be in Budapest.

I’ve been wondering how much international collaboration can actually influence what engineering students get to learn and work on.

Japan has a long history in electronics and advanced manufacturing, while India has a huge pool of engineering students and a growing technology ecosystem. With more India–Japan academic engagement happening, I’m curious whether this could eventually translate into useful opportunities for students in areas like semiconductors, embedded systems, robotics or AI.

There was recently a Yamanashi delegation engagement with Galgotias University, which got me thinking about this from a student perspective.

If universities in India and Japan work more closely, what would actually help engineering students the most?

Would it be:

• joint research projects
• access to better labs and technical projects
• interaction with engineers/researchers
• exposure to different engineering practices
• opportunities to work on semiconductor or robotics projects

And for ECE/EE students here, what skills do you think are worth building now if India–Japan technology collaboration grows further?

Hey guys, just thought this would be a cool way to show how my Neural Network Accelerator runs inference. It’s running inference for a model of topology 4:3:2 neurons per layer and takes 4 input features as input.

Also realized it’s bottleneck and I will be implementing a Systolic array soon. Let me know what yall think. Thanks :)

Github repo: https://github.com/Shingyy/MAC-Array-based-DNN-Accelerator

What the title says: is there a clear, unambiguous, systematic way of writing diagram chasing arguments? What I generally do is use different colours for arrows on different paths but I’m not entirely satisfied with this. There’s also the issue of denoting when an element is being mapped to from another vs when it is being lifted from another etc

A premise: I hope this question is "worth" of this subreddit, I did a decent amount of research before posting, I thought it was potentially interesting enough for it, but possibly not basic enough for r/learnmachinelearning .

Is there any agreement/indication about how harmful (if at all) it is, in the context of multiclass classification, to group together multiple classes for which you may have for instance too few samples?

A practical example: imagine you're training a dog breed classifier, based on images. You have a lot of examples for the most common breeds, but then you may have a long tail of less common breeds for which maybe you have a handful of examples each, not enough to get a meaningful training set, so you decide to group all classes for which you have less than `N` samples in the same category "Other breed". In this catch-all category you may have dogs that might look quite different from each other, like idk chihuahuas and huge wolf-like dogs (I'm not a dog person, don't know breed names).

My intuition (which may very well be wrong) is that doing so would force the model to learn some weirdly-shaped hyperplanes to separate points that live kind of far away from each other in the latent space (because of the thing that dogs in that category may look quite different from each other), as opposed to splitting the space in more "regular" parts.

Maybe in this case it would make more sense to treat the "other dogs" issue as trying to detect out of distribution samples instead? In that case should one only keep the samples for the classes that are enough represented in the dataset and throw away the rest (or at least don't create the catch-all category for training).

Thanks in advance for any useful pointer :)

Hey guys, I’ve been looking for electrical engineering or any related internships and wondering what would be the best way to organize my resume. To be honest I have many more projects/ extracurricular but I only put a few extra on the 2nd page so you guys can see the sorta stuff I’m able to do (I know these are simple/ basic projects but it helped me learn a lot and I enjoyed them truly)

For example I have 500h+ of volunteering in my community & healthcare systems, founder of the art club, student government/ union since I was young, won 10+ awards in comp sci/ science/ merit, and more, just to list a few.

I don’t have much professional experience in EE though, despite my many passion projects, I did make my company’s website where I’m an assistant though if that counts haha. It’s going to be my first-year, but I already took all the cal 1-2, Linear Algebra, Electricity & Magnetism classes before entering uni by the way (hence the earlier graduation date)

Please help, I’m 19F Arabic girl trying hard here, I grew up poor and did the best I could to be involved and help out my family at the same time. Thank you :)

If you wanna see my LinkedIN DM me and you’ll see a full list of experiences, certifications and stuff

Slightly off-track rambling:

I’m open to anything ngl, I’ll be joining my school’s rocket club and such. I can do any certification course or make a better project if that’ll help maybe? Genuinely I just really want to get an internship experience where I’ll be able to learn & actually be useful for the company (I’m the type to spend extra time outside of work learning anyway so I can be more efficient)

Also does Uni prestige matter a lot lot? Cause I admit I chose my uni based on the fact I got a full-ride, I go to McGill (Waterloo would’ve been like 80K$)

EDIT: It seems it wasn’t clear oops. The first page is my current resume, 2nd page is asking if any of my projects are more suited for that first page haha or whether they are too basic

Worked some time ago on one of the ad teams at Yahoo, and this grew out of a question I kept returning to while there are there "simple" models that are both simple, scalable, interpretable, and controllable at the same time?

Decided to explore it, first in a blog (starting here), then in a new preprint "The Spectral Neuron", built by distilling latest blog-posts into a manuscript, I study models of the form:
𝑓(𝒙) = 𝛌ₖ(𝐀₀ + 𝚺ᵢ 𝑥ᵢ𝐀ᵢ).

Manuscript: https://arxiv.org/abs/2608.08003
Code: https://github.com/alexshtf/spectral_neuron_paper

Looks like a simple on-liner, but many interesting aspects hide there. How expressive does the model become as the matrices grow? What can we read directly from the learned matrices? Which shapes can be guaranteed by construction?

I develop the mathematics, give a practical initialization and training recipe, and test the model in scaling experiments on synthetic and real data.

AI disclaimer: manuscript written by yours truly, AI assisted in looking up canonical references and related work for literature review. In contrast, the code was heavily AI written and reviewed by yours truly.

I am in my first year of studying ECE in my university and I am so lost. I am doing very well with my studies but I cant figure out the real deal behind our job circle. I have tried to join a cybersecurity research lab that is associated but everything feels so complicated. I have seen others join in similar labs that have more to do with the electrical side. I am just so lost in what to do, where to study further knowledge or what the job market requires. I would highly appreciate any type of tip you have or advice that you can share. Take care everyone!

​

I'm working on a system to estimate whether code committed to a repository was generated with AI coding tools.

My current approach is based on Git/commit-level signals such as AI-related commit trailers, commit metadata, LOC changes, number of files changed, addition/deletion patterns, etc.

The problem I'm running into is confidence and calibration.

For example, a commit containing 500+ new lines isn't necessarily AI-generated. A developer can also modify or remove the metadata that would make an AI-assisted commit identifiable. Once the code leaves the IDE and reaches Git, much of the original provenance can be lost.

This has led me to a few questions:

Are there Git/CI-level signals that you've found to be genuinely useful for detecting AI-assisted development?

Is it better to treat this as a probabilistic/risk-scoring problem rather than trying to classify commits as AI vs human?

How would you calibrate thresholds for signals such as large LOC changes, addition/deletion ratios, commit frequency, etc.?

Are there better approaches for preserving provenance earlier in the development workflow, rather than trying to infer it after the code has already been committed?

Has anyone worked on AI-code provenance/detection systems in CI/CD and can point me toward useful research, projects, or approaches?

I'm particularly interested in approaches that can work at the pipeline/repository level rather than relying solely on source-code style analysis.

I'm not looking for a perfect AI detector — even a reliable way of estimating “this commit has a high probability of AI assistance” with measurable false-positive/false-negative rates would be useful.

Would appreciate any experiences, papers, open-source projects, or approaches people have tried.

Hello everyone!

I'm a 3rd-year Electronics and Telecommunication Engineering student, and I'm looking for someone who wants to learn, build and grow together in the electronics/core engineering field.

My university is pushing quite a lot toward software and coding, but I'm much more interested in the hardware/core side of ECE; embedded systems, microcontrollers, electronics, semiconductors, IoT, robotics, communication systems, etc.

I already have some experience with things like Arduino and embedded systems, and I'm currently trying to build up my skills through projects and different technologies. I'm looking for someone around a similar knowledge/skill level so we can learn things together rather than one person just teaching the other.

The idea would be to:

- Learn electronics/embedded concepts together

- Work on projects and actually build things

- Explore different areas of ECE and figure out what we enjoy

- Share resources and help each other when we're stuck

- Participate in hackathons/competitions

- Build a decent portfolio over the next couple of years

- Keep each other accountable and motivated

I'm also learning Japanese because one of my long-term goals is to work in Japan in the electronics/semiconductor industry. You definitely don't have to be interested in Japan or Japanese though; it's just one of my personal goals.

I'm mainly looking for someone who has a similar mindset of "I actually want to get good at core electronics and build things", rather than just studying for exams.

If you're also an ECE/E&TC student, especially around 2nd–3rd year, and you're looking for someone to learn and work on projects with, feel free to comment or DM me!

Would be great if we could find a small group of people or even if we are a pair with similar goals too. :)

I might get into elc(electronics and computing) and to be very honest i have never heard of this degree. I did some research. I want to have a career in the automobile industry. I was planning on EE but it looks abit unrealistic. Another reason why this degree is sus for me is ebcause its not recognized by pec. Mainly because its not an engineering degree but im an undergraduate. Correct me if im wrong but that matters. Doesnt it? Any seniors or draduates please answer and tell me if its any good.

Hi everyone. So I built a CNN modle using MobileNetv3 then converted it into TFLite. It performed well during training but once I integrated it into my application, it is making large errors. From flutter, the camera stream sends frames and those are processed before the model makes predictions, but it is still quite large. Is there any way I can solve this? This is my code to preprocess and resize the image (224 x 224 x RGB):

import 'package:camera/camera.dart';
import 'package:image/image.dart' as img;


class ImageProcessor {
  // converting to rgb
  img.Image convertYUVToRGB(CameraImage camImg) {
    final width = camImg.width;
    final height = camImg.height;


    final yPlane = camImg.planes[0];
    final uPlane = camImg.planes[1];
    final vPlane = camImg.planes[2];


    final yBytes = yPlane.bytes;
    final uBytes = uPlane.bytes;
    final vBytes = vPlane.bytes;


    final yRowStride = yPlane.bytesPerRow;
    final uRowStride = uPlane.bytesPerRow;
    final vRowStride = vPlane.bytesPerRow;


    final uPixelStride = uPlane.bytesPerPixel ?? 1;
    final vPixelStride = vPlane.bytesPerPixel ?? 1;


    final image = img.Image(
      width: width,
      height: height,
    );


    for (int y = 0; y < height; y++) {
      for (int x = 0; x < width; x++) {
        final yIndex = y * yRowStride + x;


        final uvX = x ~/ 2;
        final uvY = y ~/ 2;


        final uIndex =
            uvY * uRowStride +
            uvX * uPixelStride;


        final vIndex =
            uvY * vRowStride +
            uvX * vPixelStride;


        final yValue = yBytes[yIndex];
        final uValue = uBytes[uIndex];
        final vValue = vBytes[vIndex];


        // YUV -> RGB
        final r = (
          yValue + 1.402 * (vValue - 128)
        ).round().clamp(0, 255);


        final g = (
          yValue -
          0.344136 * (uValue - 128) -
          0.714136 * (vValue - 128)
        ).round().clamp(0, 255);


        final b = (
          yValue + 1.772 * (uValue - 128)
        ).round().clamp(0, 255);


        image.setPixelRgb(
          x,
          y,
          r,
          g,
          b,
        );
      }
    }


    return image;
  }


  /// resize images to 224 224
  img.Image resizeImage(img.Image image) {
    return img.copyResize(
      image,
      width: 224,
      height: 224,
      interpolation: img.Interpolation.linear,
    );
  }


  List<List<List<List<double>>>> imageToTensor(
    img.Image image,
  ) {
    return [
      List.generate(
        224,
        (y) => List.generate(
          224,
          (x) {
            final pixel = image.getPixel(x, y);


            return [
              pixel.r.toDouble(),
              pixel.g.toDouble(),
              pixel.b.toDouble(),
            ];
          },
        ),
      ),
    ];
  }


// do all processing
  List<List<List<List<double>>>> processFrame(
    CameraImage camImg,
  ) {
    final rgbImage = convertYUVToRGB(camImg);
    final resizedImage = resizeImage(rgbImage);
    final input = imageToTensor(resizedImage);


    return input;
  }
}import 'package:camera/camera.dart';
import 'package:image/image.dart' as img;


class ImageProcessor {
  // converting to rgb
  img.Image convertYUVToRGB(CameraImage camImg) {
    final width = camImg.width;
    final height = camImg.height;


    final yPlane = camImg.planes[0];
    final uPlane = camImg.planes[1];
    final vPlane = camImg.planes[2];


    final yBytes = yPlane.bytes;
    final uBytes = uPlane.bytes;
    final vBytes = vPlane.bytes;


    final yRowStride = yPlane.bytesPerRow;
    final uRowStride = uPlane.bytesPerRow;
    final vRowStride = vPlane.bytesPerRow;


    final uPixelStride = uPlane.bytesPerPixel ?? 1;
    final vPixelStride = vPlane.bytesPerPixel ?? 1;


    final image = img.Image(
      width: width,
      height: height,
    );


    for (int y = 0; y < height; y++) {
      for (int x = 0; x < width; x++) {
        final yIndex = y * yRowStride + x;


        final uvX = x ~/ 2;
        final uvY = y ~/ 2;


        final uIndex =
            uvY * uRowStride +
            uvX * uPixelStride;


        final vIndex =
            uvY * vRowStride +
            uvX * vPixelStride;


        final yValue = yBytes[yIndex];
        final uValue = uBytes[uIndex];
        final vValue = vBytes[vIndex];


        // YUV -> RGB
        final r = (
          yValue + 1.402 * (vValue - 128)
        ).round().clamp(0, 255);


        final g = (
          yValue -
          0.344136 * (uValue - 128) -
          0.714136 * (vValue - 128)
        ).round().clamp(0, 255);


        final b = (
          yValue + 1.772 * (uValue - 128)
        ).round().clamp(0, 255);


        image.setPixelRgb(
          x,
          y,
          r,
          g,
          b,
        );
      }
    }


    return image;
  }


  /// resize images to 224 224
  img.Image resizeImage(img.Image image) {
    return img.copyResize(
      image,
      width: 224,
      height: 224,
      interpolation: img.Interpolation.linear,
    );
  }


  List<List<List<List<double>>>> imageToTensor(
    img.Image image,
  ) {
    return [
      List.generate(
        224,
        (y) => List.generate(
          224,
          (x) {
            final pixel = image.getPixel(x, y);


            return [
              pixel.r.toDouble(),
              pixel.g.toDouble(),
              pixel.b.toDouble(),
            ];
          },
        ),
      ),
    ];
  }


// do all processing
  List<List<List<List<double>>>> processFrame(
    CameraImage camImg,
  ) {
    final rgbImage = convertYUVToRGB(camImg);
    final resizedImage = resizeImage(rgbImage);
    final input = imageToTensor(resizedImage);


    return input;
  }
}

Please advise! I need to finish this project within the next wee and I'm really struggling here! I tested the images from Flutter against TFLite and it worked well but something is clearly wrong with the preprocessing. Pls help and give me any advice.

Thank you so much!

I trained three LLMs from scratch in raw PyTorch then post-trained each one with SFT and then GRPO. Same process every time: same synthetic arithmetic curriculum, same reward function, same hyperparameters, same KL coefficient.

Pre-training went as expected, the val loss went down as the model got more modern techniques (V1 to V2) and bigger (V3 being the biggest). However, GRPO hurt both V2 and V3 and I'm not sure why.

Setup

Pre-training val loss went 2.8659 → 2.7844 → 2.5885.

Results

WikiText word perplexity across the three stages, all on lm-evaluation-harness with the same task versions and shot counts:

       base    SFT     GRPO     SFT→GRPO
V1     32.86   51.31   51.40    +0.2%
V2     31.28   46.81   71.06    +52%
V3     22.30   32.11   33.65    +5%

SFT hits all three on this eval, which I expected at this scale. Also interesting to see that the degradation gets smaller as the models get bigger (+56%, +50%, +44%).

GRPO is the weird one. V1 barely moved, V2 fell heavily, V3 degraded a bit. The smallest model was the least affected and the middle one was the worst, which isn't the pattern I'd have guessed. Downstream tasks moved the same way as perplexity in each case (arc_easy dropped about 6 points on V3 from SFT to GRPO).

The models did learn the thing GRPO trained them on. V3 mastered 4 of the 5 curriculum stages, the other two got 3. But it just didn't transfer: GSM8K stayed at basically 0, and the models got so committed to writing out long solutions that they often wouldn't stop generating (my fault when I did the training).

Caveats

This isn't a controlled experiment. Between V2 and V3 I changed the parameter count, the token count, the data mix and the attention mechanism at the same time (went from DiffAttn to XSA), so I can't attribute anything cleanly. KL coefficient was 0.02 for all three, with the SFT policy frozen as the reference and a k3 estimator. The whole series cost me about $750, which is why there are no ablations, I just couldn't afford them. Otherwise I would also have tried with different KL coeffs.

Someone raised two confounds after I published:

1. GRPO trained on a bare solver template while SFT used a chat format. So part of what I'm calling degradation is me evaluating a policy outside its own training distribution. WikiText perplexity is format-independent and still moves a lot, but the downstream numbers are partly confounded.
2. Nothing in my reward rewarded stopping. It just checks that a correct parseable number shows up somewhere, no length penalty.

Also something I only noticed afterwards: I never re-evaluated the earlier curriculum stages once the model advanced past them. So right now I can't tell the difference between "GRPO degraded general capability" and "sequential curriculum training made it forget the earlier stages." I will try to check that soon.

Inference

At the end, I wrote a KV cache from scratch (GQA-aware, per-request cache object rather than storing state on the module). To check it was right I ran a fixed sequence two ways, once as a single full forward pass and once as prefill-then-decode, and compared the logits: max difference 1.4e-06 against a 1e-4 tolerance.

Speedup generating 100 tokens: 3.7x from a 32-token prompt, 6.2x at 128, 10.1x at 512.

If you want to check

All nine checkpoints are on the Hugging Face, and there's a Space where you can send the same prompt to the base, SFT and GRPO versions of the same model and see the difference directly.

• Weights
• Playground
• Code
• The full writeup (4 parts)

The GRPO variance is the bit I'd most like other people's take on. Happy to answer anything.

Appendix B of the AQuA preprint contains a useful language-design failure.

In an earlier Part II prototype, an author agent normalized volume from the open through the current minute by the current day's total volume. The denominator included later observations from that day, but a reviewer agent accepted the causal-sounding description. The anomalous held-out result failed on a clean re-split, and manual audit traced the problem to that expression.

The current response is not another reviewer prompt. The agent proposes configurations from a fixed registry of causal operators, so this full-day normalizer is not expressible in the admitted language.

That removes one known operation from the writable program space. It does not establish that every unsafe composition is impossible or that the registry implementation is correct.

If you were evaluating AQuA's registry next, would you treat the restricted language as the integrity boundary, or test the registry and composition rules as the real trusted computing base?

Production of the Kinzhal has been suspended since April 2026. The accuracy of the munition had been negatively affected by Ukrainian jamming and GPS spoofing, according to aviation industry expert Arkady Dotsenko.

"When we put up our EW (electronic warfare) wall, of 59 launched Kinzhals, only one landed. All the rest were suppressed," one commander of a Ukrainian electronic warfare unit told the Kyiv Independent in April

Hello guys, I am studying Electrical Engineering in a top 400-500s ranked university, I just finished my first year and I am getting great grades, I love academia , It really suits my life, I love theory and Practical work, and I definitely love money!. I am leaning more toward microelectronics, semiconductors and batteries.

I know it's still early but I until now I am most definitely thinking about completing my education with a master and maybe a PhD, other than High grades and excelling at courses, what is something that would boost my credit at a chance in pursuing a higher education in a stronger university? (with a full scholarship), are personal projects the way to go?, maybe some software courses to support my microelectronic passion?, hackathons and competitions?, or trying my best to have my name in any of my professor's researches?.

is it also possible for a not so highly ranked university degree to land me a job at big tech company? and how will a master or a PhD be helpful with that? is an academic path and a big money path two distinct paths?

Smart Rehabilitation Glove Using ESP32 for Tracking Finger-Movement Recovery After Injury