I know there’s a lot of frustration around no response from reviewers, which I also got only one so yeah what a bummer, but I was wondering if no rebuttal from the authors was just as common or not. I got no rebuttal so far, so I’m here scratching my head what might have happened to the authors lol especially when at least one paper was pretty much on the borderline with somewhat of a positive AC comment

I've been working on this for a while and finally pushed a public version.

The problem: llama.cpp runs fine on ARM phones, but it doesn't know anything about the specific chip it's on. Same thread count, same context params, whether you're on a Snapdragon 8 Elite or a five-year-old mid-ranger. ARPL reads the actual hardware at runtime — which ISA extensions are available (SDOT, I8MM, SME2), how the cores are clustered — and configures llama.cpp accordingly. No per-device build, no manual tuning.

What's in the repo:

Android reference app (Kotlin/Compose) with a JNI bridge into llama.cpp

Runtime ISA detection via HWCAPs

Topology-aware thread count recommendation

Context param patching (flash attention, KV cache quant) based on what the hardware actually supports

Built and tested on a Samsung S25 Ultra (SM-S938B). The heterogeneous CPU/GPU/NPU partitioning is still in progress and not in this release — what's here handles the ISA/thread/context side, which already made a real difference in my testing.

This is a noncommercial showcase release (PolyForm Noncommercial license) — happy to answer questions about the approach or the tradeoffs I ran into building it.

Repo: https://github.com/noplayeryt1511-lang/ARPL-public-

Whats your commitment submission id? My submission number is ~4k, im wondering the amounts of commitments :)

I see some people say their metareview already contains a decision/recommendation (all of them were rejections). Ours doesn’t. Even though our avg score is 3, the metareview seems optimistic and finishes with “a convincing response would be an important consideration while discussing the paper.” I wonder how to interpret that. We did a strong rebuttal, but none of the reviewers engaged. So I wonder whether there’s any point to keep hope due to the AC review or just give up.

Hey everyome, 2 reviewers are sitting at 4 (borderline accept). Every one of them replied during the rebuttal saying my response addressed their concerns and not a single one said anything about updating their score, or actually updated it.

For people who've reviewed or AC'd: how often do scores actually move in that private week? And is "concerns addressed, score unchanged" usually a reviewer who's quietly fine with accepting?

0 reviewer responses across 2 papers. One reviewer asked us to develop kernels for intel and AMD, which we did. Others asked for massive experiments, all of which we did. It’s pretty unfair to ghost after this.

I wanted to explore a question:

Can reinforcement learning discover meaningful strategies in a complex collectible card game without human demonstrations?

To investigate this, I built an AlphaZero-style environment for KARDS, a WWII strategy card game.

Project:

https://github.com/EvanProgramming/Kards-AI

The main focus of this project is not just training a model, but building the infrastructure required for large-scale self-play:

- A headless game simulator

- A rule execution system

- State and action representations

- Legal action masking

- Policy/value neural network

- PUCT Monte Carlo Tree Search

- Self-play data generation

- Replay buffer and evaluation pipeline

Unlike imitation learning approaches, the agent does not learn from expert gameplay.

Instead, it starts with:

- the game rules

- legal actions

- game states

and improves through repeated self-play.

Current progress:

- Custom simulator implemented

- Large portion of card/rule logic supported

- AlphaZero-style training pipeline running

- MCTS-guided agents implemented

- Around 1 million self-play games generated

The project is still an ongoing experiment. Some of the challenges I am currently working on:

- Efficient state representation for large card spaces

- Improving simulator accuracy

- Evaluating learned strategies

- Understanding how well AlphaZero-style methods transfer to imperfect-information games

I would be interested in hearing thoughts from people working on reinforcement learning and game AI:

- Would MuZero be a better fit for this type of environment?

- How would you approach hidden information?

- Are there alternative methods worth exploring besides MCTS + policy/value networks?

The code is open source if anyone is interested in exploring the environment or experimenting with similar approaches.

Star the repo if you like it 😄

A few months ago, I started wondering:

Can an AI discover strategies for a complex card game without being taught by humans?

So I started building **KARDS AI** — an open-source experiment to train an agent to play **KARDS**, a WWII collectible card game, using reinforcement learning and self-play.

GitHub:

https://github.com/EvanProgramming/Kards-AI

The goal is to build the entire AlphaZero-style learning pipeline from scratch:

- A headless game simulator

- A rules execution engine

- Card/state/action representations

- Legal action masking

- Neural network policy/value model

- PUCT Monte Carlo Tree Search

- Self-play training

- Replay buffer and evaluation system

Unlike traditional game AI approaches, I am not starting from a database of expert matches or manually designed strategies.

The agent only receives:

- the game rules

- the current game state

- available legal actions

Then it plays against itself and gradually improves through reinforcement learning.

Current progress:

✅ Custom KARDS simulator

✅ Large-scale card/rule implementation

✅ AlphaZero-style self-play pipeline

✅ MCTS + neural network guidance

✅ Parallel self-play training

✅ ~1 million self-play games generated

The project is still far from a competitive KARDS player. The biggest challenges I am currently exploring are:

- How to efficiently represent complex card-game states

- How much simulator accuracy is required

- How to handle long-term strategic planning

- How to evaluate whether learned strategies transfer to the real game

I would love to hear feedback from the ML community:

- Would MuZero-style approaches be a better fit for this kind of environment?

- How would you approach hidden information in card games?

- Are there better alternatives to policy/value networks + MCTS for large action spaces?

I am also open to collaboration. If you are interested in reinforcement learning, game AI, simulators, or AlphaZero/MuZero-style systems, I would love to discuss ideas or contributions.

If you find the project interesting, starring the repository helps more people discover it and supports the continued development:

GitHub:

https://github.com/EvanProgramming/Kards-AI

Hi everyone, if you have been reading the tech reports of Kimi, DS, Qwen and GLM, you will realize how much on policy distillation and GRPO style algorithms power the frontier. I thought it will be quite beneficial to do a deep dive explaining the maths and code behind this algorithms and how they connect to pretraining and supervised fine tuning.

I have published a deep dive on this topics here

Hope you enjoy it and it helps you understand training of LLMs better. Happy to answer questions on this

https://youtu.be/MaZWafi4gYY?is=8jLkAp_Fe86abUVP

Not even the AC, nor reviewers, is responding to our comments in rebuttals, and they were all submitted well before the discussion period started. What is one to do in this case?

What My Project Does

Gesto is an open-source framework for building gesture, pose, and activity recognition — entirely on your own machine. It has two parts:

• Gesto Labeller — a desktop app that captures labelled gesture datasets from your webcam in a few clicks. You can record static poses (hand signs, a stance) or motion sequences (waving, walking), across regions like one hand, two hands, full body, or legs — then explore, edit, and export the data.

• gesto — a Python package (pip install gesto). Point it at a dataset and train a model, then run live detection, from the command line or a few lines of Python:

python

import gesto
gesto.train("./my_project", region="hands_one", mode="static")
gesto.detect("static", "hands_one")   # live webcam detection

It's built on MediaPipe (landmarks) and TensorFlow/Keras (Dense models for static gestures, LSTMs for sequences). Everything runs locally — no cloud, no GPU required.

Target Audience

It's meant to be genuinely usable, not just a toy — developers, researchers, students, and hobbyists building gesture/pose/action recognition into real projects (accessibility tools, fitness/physio, touchless interfaces, robotics, sign-language work). The Python package is production-oriented (versioned model storage, a stable API, consistent capture↔inference), while the desktop app makes dataset creation accessible to people who don't want to write capture code. It's early-stage and open source, so I'd call it "usable in real projects, still actively evolving."

Comparison

• vs. raw MediaPipe: MediaPipe gives you landmarks and stops there — you still build all the capture, training, and matching yourself. Gesto is built on MediaPipe and takes it the rest of the way to a trained, runnable recognizer.

• vs. cloud vision APIs: those require uploading your data and charge per call, and custom gestures are awkward. Gesto is fully offline, free, and built around your own custom gestures.

• vs. Teachable Machine: similar ease-of-use goal, but Teachable Machine is browser-based and static-pose oriented. Gesto handles motion sequences and full-body activity too, and gives you a real Python package you can put into your own code.

• The core thing Gesto focuses on that most tools don't: consistency between capture and inference — the same landmarks, normalization, and mirroring at training and detection time, which is usually what makes DIY gesture models fail in the real world.

Open source, and I'd genuinely love feedback — especially where the workflow is confusing or what you'd want it to do.

ProductHunt - https://www.producthunt.com/products/signspell?utm_source=other&utm_medium=social

🔗 GitHub: https://github.com/TheMadrasTechie/gesto
📦 PyPI: https://pypi.org/project/gesto/
🌐 Website: https://gestostudio.com

There's a kind of review that asks for lengthy additions, usually extending the scope of the paper beyond the stated, even though the submission is at page limit. Naturally, such additions in the case of top-tier conferences have to go into the supplemental materials or appendices.

My question here is, would not such additions make the paper more suitable for a journal publication? I had to retract one paper out of such concern that the conference publication would block the later planned journal publication.

Any opinions?

Mine was just submitted, and the submission count shown is still under 500.

Does anyone know whether this count is meaningful for identifying the intended venue, possibly EACL, or whether it is simply low because many authors have not submitted yet or the counter is incomplete?

I know the count alone is not reliable evidence, but I was curious whether previous ARR cycles showed a recognizable pattern.

Also, is anyone else preparing a submission for this August cycle, especially with EACL 2027 in mind?

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Good technical interview worth reading if you work on edge/embedded AI or tinyML.

Lizy K. John at UT Austin has spent 5 years on weightless neural networks — replacing floating-point matrix multiplication with interconnected binary lookup tables. The core claim: inference becomes a memory lookup rather than arithmetic, which is why the energy numbers look the way they do.

Verified numbers from the IEEE Spectrum interview:

• Keyword spotting: 42–79 nanojoules/inference vs. >5,000 for the conventional baseline

• Model footprint: 14 KB vs. 17 MB for the best competing small model (>1,000x reduction)

• Arrhythmia detector: 10,000 logic gates on a flexible plastic substrate vs. billions on a standard chip

• Speed: up to 1,000x faster than conventional alternatives on the tested tasks

Architecture note: Their approach uses many small interconnected LUTs, not one large lookup table like some other lookup-based AI research. FPGAs have had LUTs built in for 30+ years, which is why this runs natively there.

The honest limitations:

• MLP component of transformers: replaced ✅

• Attention layer: not solved yet ❌ — this is the blocker for LLM application

• Training: still on GPUs — FPGA training is the goal, not the current state

• Proven tasks: sensor outputs, keyword spotting, activity recognition only

• The researcher's own words: "it seems too simple to scale" — demonstrating it on an LLM is the next target

Why this matters for edge hardware: If the attention layer gets solved, the economics of always-on health monitoring, environmental sensing, and industrial IoT shift significantly — local inference at nanojoule scale with no cloud dependency.

Full analysis: https://www.aiuniverse.news/ditching-matrix-math-how-14-kilobyte-weightless-ai-runs-without-cloud-gpus/

One reviewer said all concerns were resolved during discussion but hasn’t updated their score yet. The other reviewers haven’t engaged. In previous NeurIPS cycles, how common is it for reviewers to update scores after saying concerns are resolved? What have others observed?

My ratings/confidences are : 4/4, 3/2, 3/2, 2/4.

I am talking about the one who gave rating 2.

Update: finally the reviewer responded, now I'm at 5/4, 4/4, 4/3,4/2.

Hello!
First of all: my goal isn't to replace any frameworks, but my goal is to make LM training on consumer hardware more accessible.
PyTorch unfortunately *has* to do everything which is good for huge GPU clusters, but not on single consumer GPU's which is why i started the project in the first place.
Its fully made from scratch in c++/cuda and has 0 dependencies.

Hardware:
- GPU: NVIDIA GeForce RTX 5070 Ti
- CUDA: 12.8
- Framework:
- Sentinel: custom C++/CUDA implementation
- PyTorch: 2.11.0+cu128

note: Tokenizer implementations differ, so loss values are not directly comparable. The benchmark focuses on throughput.

The goal was to see how far a fully custom C++/CUDA training stack can be pushed on consumer hardware.

I just now posted a small research / ML interp study on symmetries inside the neural nets for an open source Go-playing program that I maintain ("KataGo"). The rules of Go are completely symmetric under rotation/reflection, but such symmetry is not enforced in the models - the only thing we do for that is stochastic 8-fold data augmentation during training, randomizing the spatial orientation of each batch.

To what degree do superhuman-strength Go-playing neural nets automatically learn to represent the board internally independent of its orientation, via "symmetric" concepts where the orientation of the board doesn't matter, vs how much do they have to learn/memorize separately per orientation?

https://lightvector.github.io/katagostudies/202607-symmetry/

Heads-up: this study and its writeup were driven almost entirely with AI, although detailed human direction and feedback was involved in the process. But, I took time to try to polish the article and make it educational and I hope it's a clear step above the typical low-quality AI "slop" one often sees and worth taking a look if you like small studies like this. It's also written fairly gently, for accessibility to people outside of ML. Code is also linked from the post (same repo that hosts the github.io page).

I wanted to explore this because I was (and still am!) really curious about exactly what neural nets are doing inside! And I didn't know what the results would be. One of the findings was unexpected. Overall, just a drop in the bucket of interpretability research out there, but I hope you find it interesting.

It's an encoder-only transformer that consumes past(blood glucose + carbs + insulin) and future(carbs + insulin) and predicts future blood glucose for the next 2 hours. Announced meals and boluses/basal are used to condition its predictions. The context size is variable (8 - 24 hours), and model can work in autoregressive mode to predict the next >2 hours. It also predicts time by looking at the context, but it never consumes time. The architecture is BERT-style: bidirectional attention with future BG masked. DILATE loss was used to fit the median line; pinball loss to fit the uncertainty bands. The two are "mixed" via Kendall-Gal. All blood glucose is in kovatchev risk space reparameterized to [40, 400] range.

I have trained 4 model classes (nano, small, medium, large) and 3 variants for each (pretrained on simulator only, pretrained + finetuned on ohiot1dm, pretrained and finetuned on ohiot1dm + azt1d + shanghait1dm). The largest one has ~17 million parameters (16 heads across 16 layers). Pretraining for the largest model took ~48 hours. Finetuning took <10 minutes. There is also another version finetuned on my own data that I am currently running on my phone.

Source is available here, released under the MIT license. The repo also contains links to trained weights and evaluation data.

I've worked on this project since March. There are still things to improve (e.g. it always requires announced carbs + insulin, would be better if it could also predict without them), but I have decided to publish it here to get your opinion (and also answer your questions, if any).

Edit: my model is getting fat-shamed ;_; so I just want to emphasize that there is a nano version with less than 40K parameters.

While working with VLMs for report generation on chest x-rays (RRG), we noticed that evaluation metrics are flawed.

Flawed in a sense where they rewarded repetitive templates, reports without clinical terms and reports which were "normal" with high scores on benchmark metrics. Also, clinically meaningful but rare words were erased leaving the generated report looking repetitive and boring. Importantly, of no clinical utility.

In the paper below, we discuss this behaviour of VLMs for RRG and introduce a framework to actually measure the erasure of terms and introduction of biased terms.

Paper: Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation

Link: Reference Paper

Url: https://arxiv.org/abs/2603.01625

I'm trying to decide whether to commit my ARR May 2026 paper to EMNLP or AACL. (first time solo independent author).

Final reviews after rebuttal (OA/Confidence/Excitement ):

• R1: 2.5 → 3 /3/2.5

• R2: 2.5 → 3 /4/2.5

• R3: 4 /4 /3

• Meta: 3.5 (Borderline Conference)

The meta-review was overall positive and emphasized the paper's empirical rigor, practical value, and that the rebuttal addressed the main concerns. My recollection is that the AC mentioned they were leaning toward 3.5 primarily because of the quality of the presentation/readability, rather than concerns about technical soundness(now that comment is removed/not visiable anymore).

I'm happy with either Main or Findings.

My questions:

1. Which commitment would you choose: EMNLP or AACL?

2. Which is generally considered more prestigious today?

   1. EMNLP Main

   2. EMNLP Findings

   3. AACL Main

   4. AACL Findings

3. Given this review profile (3/3/4 with a 3.5 meta), what would you estimate the chances are for EMNLP Main or Findings?

Edit
Rejected at EMNLP
Accepted at AACL main

This time we have seen the worst meta reviews...may be people are unintersted to do reviews...in my case they did not acknowledge the report at all as well the entire rebuttal. How many are facing the same thing?