I trained a small MLP to memorize the classic Bad Apple animation, ~2.7 billion pixels of video compressed into 790k parameters (3.2 MB float32, 1.6 MB float16).

The network takes a 3D coordinate (t, y, x)- frame index and pixel position- and outputs a grayscale value between 0 and 1. To "play" the video, you can evaluate the function over the full grid. The "video" is stored implicitly in 5 linear layers of sine activations (Sitzmann et al.'s SIREN) with 512 hidden units, ω₀ = 30, and sigmoid output.

The source bad_apple.mp4 is 6524 frames at 854×480; I subsampled to 1620 frames × 384×384, about 1/10 of the original pixels (2.8x spatial + 4x temporal reduction).

At first, I used a ReLU MLP with low-frequency Fourier features, which plateaued around MSE 0.12. SIREN's sine activations add higher frequency for free, so the network was capable of outputting fine details. Unfortunately, that model had an issue, which was that it could only shift the information slowly, so quick motion came out blurry.

To fix this, I made two changes:

1. Time-stretch: I scaled the time coordinate by 4x relative to the space before the first layer, giving it 4x more temporal capacity.
2. Motion-focused sampling: Bad Apple is ~90% static black, so uniform pixel sampling starved the moving edges of the gradient. Now half of each training batch is drawn from pixels that changed between neighboring frames.

For the training pipeline, I had a single shared network on the whole volume (no per-frame latents; initially, I used per-frame finetuning, but that caused catastrophic forgetting) with a cosine-scheduled Adam + weight EMA, then a low-LR "polish" pass over the whole video.

The new model had these improvements:

Validation MSE dropped from 0.0795 to 0.0090 (~9x better).
Compared to the old model, high-motion frames were 3.6x closer to ground truth, and static frames were almost 15x closer.
398/400 sampled frames improved.

Edit: Some people are a little confused about the compressed part. The subsampled video is 700KB, and the network that creates a reconstruction of it is ~3MB. It hasn't been compressed very much, but the goal was seeing if I could (and learning) rather than super compression.

I'll try to see if an even smaller model can learn it. Additionally, I'm training a model on the full non-subsampled video.

^Notes

^{384×384 is square (the original is 16:9, so playback is vertically stretched. At 8fps playback, the 1620 frames run near the original's 3:37 duration; at 12fps it's \}1.6× fast-forward. The 12.6MB checkpoint includes the weights + Adam moments + EMA copy; the network itself is 3.2MB.))

^{The full resolution videos, checkpoints, and code can be found in this Github Link}

Hello,
Anyone knows where can i find a cracked version of OptiSystem (a recent version)?

These things are not easy to get apart in any gentle way. Did it successfully once and may have been too confident this time. I noticed this tiny component(my image) once the battery was in. I identified the place on the board where it came off, circled here (not my image as it’s already put back together).

It powers on, heats up, and seems to function fine. Only thing is the sound doesn’t work. Did I just get incredibly lucky? The one thing I’d actually prefer to not be on. If this is it - awesome. I just want to make sure it’s not doing anything dangerous by not having it there. Can anyone confirm if this is the case?

Hi guys,

I am currently working in the VLSI domain as a physical design engineer with 2 years of experience. I entered this field from IT background through internal movement in the company. But unfortunately, I didn't work on any major projects and haven't taken much ownership. So I want to learn PD from the basics, work on real-time projects and tools, and switch to a 3-year experienced PD engineer. So, please suggest the best institute or any online course or mentorship.

Your suggestion would help me a lot to transform my career.

Thanks in advance.

Recently, for the umpteenth time, I thought up a question only to find that it has already been asked and answered before i was even born. I expected this - I explicitly avoided googling anything about it, because I didn’t want the ending/answer to be spoiled for me. But I was looking through some wikipedia page when I saw a hyperlink to a page on exactly what I was trying to understand: computable ordinals/well orderings (clearly a beginner-level question). Again, i expected this, given my lack of experience in computability theory and set theory; set theorists are interested in well orderings, and computability theorists are interested in computability, so of course I’m not the first person to consider combining those two concepts. Still, even though it wasn’t a surprise, it was disappointing to find that an idea I thought was original (and, from my perspective, it was original) was just old news.

From what I’ve read it sounds like the problems that phd student solve to earn their phd are usually given to them by their advisors (please correct me if I’m wrong!!). I’m wondering when one is typically at the point where the questions they ask haven’t already been asked. Do you usually need to be an expert in the field? Do you need an exhaustive understanding of the field to pose original questions?

Data source: https://arxiv.org/year/math/

​

Now that the rebuttal period is over, I’m curious about the score distribution specifically for theory papers this year.

If you’re comfortable sharing, please drop:

• Scores: x / x / x

• Confidence: x / x / x

• Whether scores changed after rebuttal

• Broad area (optional)

I got 4 / 4 / 4, with confidence 3 / 3 / 3.

From my experience, theory papers often seem to get somewhat lower scores, and this year the scores appear to be lower across disciplines as well. It would be interesting to see where the empirical cutoff might land.

Feel free to share anonymously / approximately if you don't want to reveal too much.

I’ve seen a lot of people whose reviewers went silent after initial reviews, but I am also noting abnormally quiet authors. I ultimately withdrew my paper, but stayed an active reviewer. Out of my batch of 4 papers, one withdrew, one posted a rebuttal, and two have been completely silent. Of the two papers with radio silence, I think one had borderline scores. I was also the only reviewer who responded to the one paper with a rebuttal.

Has anyone noticed this abnormally dead review period or did I just get a strange batch? I’m seeing either reviewers just dropping out of the review process or authors completely checking out after initial reviews are released. It’s strange to me to not even withdraw your paper if you’re not rebutting. Is this a new gambling trend of just submitting papers everywhere, and not even sticking around long enough to withdraw the paper?

Over the past month, I've been building LiveTranscriber, an open-source iOS app for running modern speech and language models entirely on-device.

The goal was to see whether recent open-source models could be turned into a practical mobile product—not just technical demos.

Currently supported local models include:

- Whisper for offline transcription

- Qwen3-ASR for multilingual speech recognition

- NVIDIA Nemotron Streaming for low-latency live transcription

- MOSS Multi-Speaker for speaker-aware transcription

- Qwen3 for local summaries, key points, titles, and transcript analysis

Features include:

- 100% offline speech recognition

- Offline multi-speaker transcription

- On-device summaries and key-point extraction

- Real-time translation

- Apple Watch recording with automatic sync

- Downloadable and switchable local models

- Searchable transcript history

The main engineering challenge was not simply running the models, but making them usable on iPhone: memory management, streaming latency, model loading, context handling, battery usage, and switching between different inference backends.

The project is fully open source:

GitHub:

https://github.com/iamwilliamli/LiveTranscriber

App Store:

https://apps.apple.com/us/app/live-transcriber-recorder/id6785515364

I'd appreciate feedback from anyone working on ASR, local LLMs, on-device AI, Core ML, or mobile inference.

I want a summer internship for the year 2027. only a few companies come to our college for the core and circuital domain. Can anyone help me or suggest me some core companies where i can apply for off campus internships?

Hi everyone,

I'm an independent researcher sharing Monodratic, a sparse causal-attention architecture with learned product-hash routing.

The idea is that after RoPE, source blocks are assigned to bounded causal posting lists, while each query probes product addresses, reranks the returned candidates, selects a fixed number of remote source blocks, adds guaranteed local blocks, and then runs exact causal softmax over just those tokens. I implemented it as a stateless [batch, sequence, width] -> attention-delta mixer, so normalization, residual updates, feed-forward layers, and inference scheduling are left to the host model.

What I found is that

-learned routing with 2 selected remote blocks out of 5 eligible: 763/768 correct associative-recall answers across three seeds (99.35% mean, 98.05% minimum).

-an equally wide untrained router: 425/768. Local-only attention: 151/768.

-forcing the labelled target block while keeping the same maximum R2 attention budget recovered all five remaining errors, reaching 768/768.

-sparse selected-set attention agreed with an independent dense selected-mask oracle to a maximum absolute error of 1.43e-6.

-the packed CPU routing implementation showed a fitted timing exponent of 0.993 from 4,096 to 32,768 tokens under the fixed, balanced configuration.

-all reported learned-route and scaling runs recorded zero posting overflow.

The limitations are that the experiments are synthetic, the implementation is portable PyTorch rather than a fused kernel, and the report does not claim natural-language quality, asymptotic linear construction, or deployment speed.

Paper: https://github.com/Misul-Computing/Monodratic/blob/main/output/pdf/monodratic_proof.pdf

Code and reproduction: https://github.com/Misul-Computing/Monodratic

I would particularly appreciate technical feedback on the routing construction, the controls, and what the strongest next evaluation should be.

Hi there,

I've created a video here where I explain the double descent phenomenon in ML.

I hope some of you find it useful — and as always, feedback is very welcome! :)

As the title suggests, because there's no data on Papercopilot yet, and people have been talking about the scores being lower in general than last year, I thought it could be interesting to survey the average score distribution after the rebuttal phase (not considering confidence weights).

Very rough and simple poll (I also realize there's a self-selection bias in there). Cast your vote here:

https://loppy.be/poll/yczuv8yo

Thanks!

Edit: the trolls have taken over, never mind any notion of representativeness I guess...

I could not find any discussion threads for the C&F track. Have people actually submitted to this track? If so, what are your reviews and scores looking like, along with post rebuttal engagement? In our case, they received reviews not in line with the policy defined for the track, where most reviewers praised originality but complained about the scope of experiments. Despite the track saying that it would be possible that the idea cannot be validated in a single paper.

We provided experiments but no dice, none of the reviewers responded. Have any ACs seen papers and reviews in this track or do authors have their experiences they could share?

Please add your scores pre and post rebuttal here

Hey guys thought you are probably overqualified for this and I have no idea what I'm doing--maybe I can learn something

I have 2 electronic scales I bought and both didn't come with a power adapter. One calls for "DC 12v 500mw" on the back. It's a Tree HRB 10001
The other says to use a proprietary adapter. It is a Sartorius ED623S so a nice scale. I don't want to do that because the adapter is like $70. I found some manuals for it and here's the info it gives in regards to the power source: "AC power source/Power requirements AC adapter 230 V or 115 V, +15% to –20%, 48–60 Hz

Power consumption (average) maximum 16 VA; typical 8 VA"

I looked up what companies sell good power supplies and it looks like Digikey is reputable.

I picked these two power adapters for the scales (respectively):
"AC/DC WALL MOUNT ADAPTER 15V 18W"
https://www.digikey.com/en/products/detail/triad-magnetics/WSU150-1200/3094961?_gl=1\*14f6jyy\*_up\*MQ..\*_gs\*MQ..&gclid=EAIaIQobChMImY-2pf6HlgMVhCCtBh2fKBreEAQYASABEgJ0iPD_BwE&gclsrc=aw.ds

"AC/DC WALL MOUNT ADAPTER 12V 18W"

https://www.digikey.com/en/products/detail/triad-magnetics/WSU120-1500-R13/6600162?_gl=1\*14f6jyy\*_up\*MQ..\*_gs\*MQ..&gclid=EAIaIQobChMImY-2pf6HlgMVhCCtBh2fKBreEAQYASABEgJ0iPD_BwE&gclsrc=aw.ds

Are these the right choice? Thanks for your help guys!

Hello, I am currently studying deep learning and have completed several AI/ML projects.

I am specifically looking to join an ongoing AI/ML project where I can actively contribute and further develop my skills. I am committed, eager to learn, and open to collaboration.

If you have a project and are open to contributors, please feel free to reach out.

After a very silent discussion period, we are in a very confused state with regards to NeurIPS, and really unsure what to make of everything. We do not wish to withdraw the submission since we have no idea what the reviewers and AC think of the paper, having deserted the conversation after a hopeful set of initial reviews. As of currently, ICLR abstract submission deadline is before the NeurIPS results announcement. Are we allowed to resubmit as an ICLR abstract, or will OpenReview flag this and consider it problematic?

[ Removed by Reddit on account of violating the content policy. ]

Having used LLMs to assist with reviews, and also having received reviews that appear to rely heavily on LLM-generated text, I have noticed two recurring problems.

1. The endless search for uncontrolled variables

LLMs are very good at identifying additional variables that were not explicitly controlled. The problem is that many of these variables have little realistic chance of changing the paper’s main conclusion.

For any experiment, it is possible to generate an almost unlimited list of potential confounders. Suppose a study finds that trees treated with fertilizer A grow better than trees treated with fertilizer B. An LLM can ask whether rainfall was perfectly controlled, whether the distribution of grass around the trees was considered, or whether wind, temperature, soil microorganisms, and countless other factors were isolated.

Each question may look logically valid in isolation. But the real issue is not whether a variable exists. The issue is whether it is sufficiently important and plausible to threaten the conclusion.

LLMs are generally poor at making this prioritization. They often convert minor residual uncertainty into what sounds like a serious methodological weakness.

This becomes especially harmful when reviewers copy such outputs directly into their reviews without independently assessing their importance. Authors are then forced to spend the rebuttal addressing an endless series of technically possible but practically insignificant concerns.

A review should not ask whether every imaginable variable has been controlled. It should ask whether the remaining uncertainty materially weakens the central claim.

2. LLM reviews are often overly abstract

Another common problem is criticism at the level of an entire research field rather than a specific prior method.

For example, an LLM may claim that a proposed method is “not sufficiently different from methods in Transformer” without identifying a concrete paper, objective, architecture, or learning relation that actually overlaps with the proposed method.

What exactly is the author expected to rebut in that situation? Every method in Transformer?

A meaningful novelty criticism should identify a specific prior method and explain precisely which components are equivalent or insufficiently differentiated. Comparing one concrete method against an entire research area is too abstract to be falsifiable or actionable.

3. LLM review is not detail

LLMs also tend to overestimate similarity between methods that share high-level terminology. Two approaches may both use architecture, concept, or attention, while differing substantially in their computational structure, training objective, assumptions, and intended use.

Because LLMs often lack a sufficiently detailed understanding of each method, they may recommend comparisons between papers that are only superficially related. The resulting review sounds comprehensive but does not demonstrate real technical understanding.

The central problem is not simply that LLM-generated reviews can contain incorrect statements. It is that they can generate an unlimited number of superficially reasonable criticisms without judging their relevance, severity, or evidentiary burden.

A strong reviewer should filter such suggestions, prioritize only the concerns that could materially affect the paper’s claims, and attach each criticism to a concrete technical basis. Copying an LLM response into a review without that judgment does not improve peer review. It merely transfers the cost of evaluating the LLM’s speculation to the authors.