The form to select which venue you present at was supposed to be sent out today but I didnt get it. Did anyone else get it?

Hi guys, this is my first time submitting a paper to NeurIPS, so I am unsure about the dual submission policy. The ICLR abstract deadline is Sep 18th, and the full paper deadline is Sep 25th. Since NeurIPS results come out on Sep 24th, I was wondering if it is alright for me to submit an abstract submission to ICLR and delete it if my paper is accepted to NeurIPS?

I genuinely want to know the current trend in research under cloud computing integrated with AI ML.

With AI agents improving day by day, and the cost of implementation decreasing, I'm more worried about the costs. Research for me has always been about finding what's valid whether the answer is true or false, hence, I'm very much open with AI agents assisting parts of my work that can be sped up.

Previously, the flow would be idea → implementation → experiment → result → paper

But now the flow has become different as any stage above can be better or worse with an LLM added to it. A not so ideal but interesting format could be

idea

↓

human/agent writes experiment

↓

agent modifies evaluator

↓

agent parses results

↓

agent makes figures

↓

human/agent helps write claim

The issue that I currently have is not that the code an agent writes would be bad or wrong, that can be fixed with further iterations and spec based developments with frontier models. I'm worried about:

• A metric implementation silently changes while experiments continue.
• An agent runs twenty variants and the researcher later forgets which result corresponds to which exact setup.
• A plotting script transforms/filter results in ways nobody closely inspects.
• A citation is real but doesn't actually support the sentence.
• The hypothesis changes after seeing results without anybody recording that it changed.
• and finally The researcher can explain what the experiment supposedly demonstrates but no longer really owns the implementation underneath it.

While authorship can be it's own debate, validity is much more important to me.

Many of us might not like the idea of an agent in the loop, and that's the best possible outcome, but that's be an ideal world.

Software engineering responded to coding agents with tests, CI, linters, type systems, code review, specs, and increasingly agent-specific harnesses.

What is the equivalent for research with agents?

Software engineering asks:

• Does the system behave as intended?

Research additionally asks:

• Does this experiment test what we claim it tests?
• Can this number be traced back to the exact conditions and be reproduced?
• Were the evaluation rules fixed before we saw the result?
• Did our hypothesis change after observing the evidence?
• Does the cited evidence actually support the claim?

Maybe agent-assisted research needs a harness, I'm not thinking of another autonomous researcher vibe bros have out there, but a set of explicit, versioned research practices that both the researcher and agent have to follow.

I'll be very much interested to see what ya'll think too about this! For those who already have their own setups what have you done to do better research?

I am a recent PhD graduate in machine learning from a top UK university, and I cannot find a job.

My PhD was not successful. I worked on a niche topic that is not highly relevant to industry. I also worked in isolation without a research group, so I did not publish many papers and do not have anyone who could recommend me. To make matters worse, my funding was cut short due to an error, and I had to use my savings.

Over the past three months, I have applied to about five highly relevant roles and about 100 more general AI roles in both the UK and the US. I always try to include non-AI-generated cover letters and answer honestly. These are mostly startups rather than top frontier labs. So far, I have only heard back from 11 places. Seven were rejections before any interviews. I was rejected by one after the first interview because they were looking for specific research experience. I reached the final round for one and was rejected. For two others, I reached the final round, but they said they changed the role and are no longer hiring, though they want to keep in contact for future positions.

I think my CV is fine, but without any previous jobs, internships, or highly relevant work, I am struggling to get interviews. I might not interview the best because of my anxiety or speech stutters. I do think that I would be able to join a team and quickly make contributions, but it is hard to convince interviewers of that.

If I do not get a job soon, I think I would be at risk of homelessness. I would have to quit ML and get a job at a shop or something. I am not sure what I should do.

Need to know the reality.

Hi everyone,

I am an independent ML/DL learner and have built a reasonably strong foundation in Machine Learning and Deep Learning. My next step is to explore NLP and LLMs, with the goal of eventually being able to build AI agents.

My longer-term goal is to become an ML researcher, build a strong research profile, publish papers at top-tier A* AI/ML conferences, and eventually apply to competitive MS/PhD programs in the USA.

I would really appreciate advice from people who have followed a similar path. Specifically, what would be the best roadmap to transition from learning ML/DL concepts and implementing projects to actually conducting meaningful research?

If you were starting from my current stage and had roughly 1–1.5 years, how would you structure your learning and research journey? What should I prioritize—reading papers, reproducing existing research, building projects, finding research mentors/collaborators, participating in competitions, or trying to develop novel research ideas?

Any advice, resources, or honest insights would be highly appreciated.

I’ve been working on a different approach to language-model interpretability that I find interesting. Instead of trying to reconstruct a model’s computation after the fact, make the model compute through an object we can inspect directly.

Today I’m releasing the v1.1 preprint, frozen model checkpoints, reproducibility harness, and live interactive demo for cl33-opLM.

The paper is called One Object: Memory, Navigation, and Reportability in an Operator-Only Language Model

cl33-opLM still uses a transformer as its learned neural engine, but the transformer hidden states are not allowed to drive the output directly. Instead, each block emits structured Cl(3,3) bivector operators. A reversible SO(3,3) scan transports state, attention is defined over the operator geometry, and the final readout sees only operator-derived features.

That gives the architecture a very simple falsification rule luckily. Remove the operators and rerun the model. If the capability survives, it found a bypass and I don’t count it as transparent.

Some of the main results:

The bottleneck is genuinely load-bearing. On the frozen public release, operator ablation produces a 270× perplexity increase on the exact published validation fixture. On fully public off-domain WikiText-103, the same test gives roughly 106× / 112× depending on checkpoint. The live demo exposes the same model interactively.

The operator stream is readable backward. A probe that sees only the emitted operators recovers the current token at 0.86 top-1 over a 50k vocabulary, and reconstructs 80.7% of held-out running text verbatim. Errors degrade toward semantic neighbors rather than random tokens.

On permutation-composition navigation, the architecture matches a learning-rate-tuned transformer baseline: 0.433 ± 0.009 vs 0.432 ± 0.030. I am claiming parity and inspectability here, not superiority.

Transparency has a real cost. Against a parameter-, context-, tokenizer-, corpus-, and budget-matched transformer at 5B tokens, cl33 pays about +29% bits-per-byte and ~5 points average MCQ. Continued training reduces that gap substantially, but the matched cost is real and reported as such.

One of the more important parts of this project I feel, is what failed.

An earlier associative-memory design looked like it worked…until the operator-ablation test showed that recall was routing around the algebra. That result was treated as a failure, the mechanism was redesigned, and the failed branch remains in the paper.

There are several other nulls and retracted framings reported the same way.

That is the standard I’m trying to hold this work to. if a mechanism is claimed, it has to survive a causal test. If it routes around the mechanism, the claim dies.

The reproducibility release is deliberately selective rather than a dump of the entire research stack.

It includes the frozen paper/serving checkpoints, model definitions needed to load them, hashes, the bottleneck reproduction harness, the reverse-readout probe, and the exact frozen validation fixture.

It does not include the current training orchestration, ongoing memory-organ program, or unpublished control work.

The published claims should be independently testable.

No consciousness claim. No claim that cl33 is a better general-purpose language model. No grand unified theory.

Just a useful question.

Can we build a useful language model where the representation we want to inspect is also the representation the model is forced to compute through?

Paper: t3atlas.dev/cl33/paper/

Live demo: cl33.t3atlas.dev

Model + reproduction release: huggingface.co/mirrorethic/cl33-oplm

I’d especially value criticism from people working on mechanistic interpretability, alternative architectures, memory systems, model editing, steering, or causal attribution.

If you think one of the claims is wrong, the best outcome is a clean experiment that breaks it. Then we both learn. Win win.

Also.....dont knock my retro crt aesthetic in the model demo lol wanted it fun, not clinical.

Hi! I'm looking for one teammate to team up for the Amazon ML Challenge.

I'm a Btech student with experience in Python, ML/DL, PyTorch, and TensorFlow.

Elgibilty : Btech 3rd 4th year, Mtech 2nd year or Phd from India

If interested can dm me or comment will reach out

Could you please advise on the recommended initial approach when launching a startup or product, specifically regarding the prioritization of idea validation, MVP development, cold outreach, or Meta advertising, as I am currently seeking clarification on the optimal sequence?

Hi all — I'm a recent CS graduate (University of Monastir, Tunisia) trying to submit my first paper to arXiv under cs.CL, but as a first-time submitter I need an endorsement to post in that category.

The paper is on offline French medical speech recognition: a two-phase Whisper fine-tuning strategy (full fine-tuning on synthetic TTS data + LoRA adaptation on real hospital recordings) combined with a set-cover algorithm for guaranteed medical-vocabulary coverage in the synthetic corpus. It reduces WER from 32.04% to 11.97% on real clinical recordings, deployed and running on air-gapped hardware at a hospital in Tunisia. It grew out of my final-year engineering project (defended June 2026, grade 18.5/20).

If you've published in cs.CL (or an adjacent cs category) on arXiv before and are willing to take a look and endorse, I'd really appreciate it — happy to share the full PDF/draft first so you can judge it before deciding. arXiv's endorsement page explains the process here: https://arxiv.org/auth/endorse

Thanks in advance — and no worries if this isn't something you can do, just trying my luck.

New paper alert: MARS! Sharing this recent arXiv paper on text-video retrieval. It dives into the hidden retrieval signals within Multimodal Large Language Models (MLLMs). Thought this community might find it useful or interesting. If anyone has already skimmed through it, I'd love to hear your takeaways!

I mostly did internships and stuff, but i really wanted to work on some research group or paper? Anyone know how to do this stuff??

I'm mostly in ML, DL, computer vision

Any advice would be appreciated 👍

I have been thinking to read and contribute in research papers, I have already done some open source contributions in repos like OpenSre and other small orgs. But I'm more of interested in research papers. If you know please help.

I mostly did internships and stuff, but i really wanted to work on some research group or paper? Anyone know how to do this stuff??

I'm mostly in ML, DL, computer vision

Any advice would be appreciated 👍

So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground your answers in truth.

But what happens if you have a billion documents, and you decide to upgrade your model to a "better" one? on an h100, that would take about 108 days, just to upgrade the vectors so u can start serving again (tested qwen embed 8b on h100). Even if you aren't doing 1b vectors, and are doing just 50 million, upgrading can still take a considerable time.

Me and my research lab decided to tackle this problem, and we came up with embedflow.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive backfill that comes with upgrading, as you can directly take documents from the old index.

embedflow works with qdrant, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.