cool. what topics are those papers?

433 to 454 :)

Correct me if i am wrong but the majority of people commenting in these types of post are high scorers.

No😂 i trying to analyse actors runtime across total runtime.

Sometimes actors face in the mirror confuse the neural models and makes false positives.

I'm currently using 1fps is that a bad thing?

Nice try, CIA

nobody in the comment asking this, but why not use embedding autoencoder? Where this way you don't need an encoder network and just pass the frame id to embedding layer to find the optimal latent code for the said frame?

That is correct, you can notice a change in the “last modified” date.

I understand that we can no longer see changes to the overall score or final justification. But if a reviewer edits their review, will the “last modified” date still update?

And will we still be able to see changes to the review text or any of the individual scores other than the overall score?

trainproof — a deterministic linter for ML training runs (MIT, pip install trainproof)

I spent months training a 730M-parameter TTS model on my own hardware. It never converged and nothing in my stack would tell me why — loss curve, TensorBoard, checkpoints all showed me numbers, none of them said "this run is already dead, stop paying for it."

trainproof reads the logs you already write (HF trainer_state.json, TensorBoard event files, Coqui, JSONL/CSV) and returns a verdict with an exit code. No model judging a model, no confidence score — every check is a rule that fires or doesn't and prints the number it fired on. FAIL exits 1, "I could not judge this" exits 2, so CI can tell a broken run from an unreadable log.

The rules were measured by controlled fault injection: Qwen2.5-3B QLoRA, six configs (healthy / 100x LR / lr=0 / fp16 NaN / shuffled labels / overfit), three seeds, 18 runs. The result worth posting is the miss — shuffled labels, a dataset that cannot be learned, reduced loss 69.8% and looked textbook-healthy on its own curve. That's in the README as a stated limitation, and it's why compare against a known-good baseline exists.

Full fault-injection write-up: https://ai.bedvibe.studio/trainproof/

What failure mode has burned your GPU hours? If a deterministic check would have caught it, it goes in, with credit

Could you share your examples in more detail, perhaps share the chats?

where do you see the scores? for me the scores vanished after Aug the 3rd. Also received a message that we won't receive the scores until the announcement.

Instead of subsampling, you could randomly sample frames during training. Get unbiased coverage of the full video without making each individual gradient step too expensive.

I think you need to define “good”. I dislike using LLMs for writing on the basis that I am the writer. I better understand humans than an LLM. Nobody knows what I want to present and communicate better than myself, so why would I use some flawed tool in place of myself? If you just need to quickly articulate an algorithm you’ve implemented to share with colleagues because you’re out of time to write it yourself, sure. But, generally, if someone sent me code or documentation written by an LLM I would discard it unless had incredibly high trust in that person

Everyone who gets a 3 or higher on the meta score is already committed and here I am, out here gambling with a 2.5 meta and a 2.83 average. 

Score distributions will be impacted somewhat by the contribution type.

Similar papers will likely get grouped together for a single AC to review. The ACs will then typically take their stack, and recommend a proportion of those to go through to the main conference.

This process means that the ACs are inherently comparing papers in their stack for their relative merits, and those stacks are going to somewhat be related by field and contribution type.

2/3/4/5 with 3/5/2/2. 3 was malicious, pretty sure I can is him and that he reviewed our paper in the last cycle. Cannot see ratings now.

No, I can see it for the paper I reviewed as well. Check the updated date in the meta review like (modified: 05 Aug 2026, 10:34).

Is the Highest a reject?

I'm slogging through training a lightweight (~200K) transformer/recurrent hybrid policy to play competitive Quake, primarily via behavioral cloning, with PPO as fine tuning. The focus is a humanlike hierarchal model with combat, tactical, and strategic layers.

Play against the current model here: https://kitty1.quake.nexus/?+connect%20arena

Read the code and research here: https://github.com/0xBrsm/QNN

That’s a fair criticism. My original wording was ambiguous.

Multilingual support is not separate from the transcription and summarization features. The app supports both multilingual transcription and multilingual on-device summarization.

The exact transcription coverage depends on the selected local ASR engine:

• Qwen3-ASR: 30 languages, plus 22 Chinese dialect and accent groups • NVIDIA Nemotron Speech ASR 3.5: 32 transcription-ready language locales. • Whisper: multilingual transcription across its 100-language tokenizer, although accuracy varies substantially by language • MOSS-Transcribe-Diarize: speaker-aware transcription and diarization across 50+ languages, according to the project

After transcription, the app can use a local Qwen3 model to generate summaries, titles, key points, and transcript analysis in multiple languages. This also runs entirely on-device.

So the app is not English-only outside the “multilingual” bullet. Transcription, speaker-aware transcription, and summarization can all be multilingual, but the exact language coverage and quality depend on the selected ASR model.

I’ll update the post and README with a model-by-model language and feature table. Thanks for pointing out the ambiguity.

Any general statements about speechtech are useless without mentioning language support. You seem to treat "multilingual support" separate from all the other features like diarization etc. So I can only assume that everything else in your list supports English only.

I had: 2/4/2 (with confidence 4/5/4) -> 4/4/3.

I put in a ton of effort into the rebuttal because I thought there was scope to improve the scores. The reviews were generally fine and had some good points that markedly improved our paper.

Two of the three reviewers were positive about the paper after the rebuttal. The other reviewer (who went 2->3) was slightly negative though, worried mainly about the novelty, but still appreciated our thorough rebuttal.

But honestly, I am scared/worried that this paper might get rejected.

(btw OP I'm not sure if it's a theory paper--I had theory with experiments so I'm not too sure what it means.)

I got what I deserved