Hello everybody,

I submitted my first research paper to TMLR after it got desk rejected at NeurIPS because of a page limit issue. Ever since submitting it I've been dreading the reviews. Not because I don't expect criticism (that's literally the reviewers' job), but because I'm worried they'll reject it or find some fatal flaw that we somehow missed.

I even ended up stalking the OpenReview pages of papers that were handled by my assigned Action Editor just to see how they evaluate papers and what kinds of situations they accept or reject. I've also read enough horror stories about reviews that were completely unpredictable or papers that people thought deserved better, which definitely hasn't helped.

I was just wondering what your first submission experience was like. Does this feeling ever go away, or do you just get used to it after a few papers?

For context, my paper can probably be viewed pretty shrewdly as an extension of framework X to setting Y, where X has never really been developed before. It's a pretty theory-heavy paper, and after looking through OpenReview discussions of papers in this area, the reviewer spread seems insanely unpredictable. There are papers that looked mathematically solid to me that still got rejected, while others had completely different reviewer opinions. It feels like there's so much variance.

My coauthors and I have spent a lot of time on this work, so I really, really don't want it to get rejected. I'd honestly do anything to address reviewer concerns if they point out issues or ask for clarification.

Do you guys have any ways of anticipating what reviewers are likely to criticize before the reviews come back? Or is it basically impossible to predict and I'm just overthinking everything while waiting?

Hi, I'm a self-learner in AI, and I started this Feb, I recently self trained an MoE model, and want to post my technical report. I don't know anyone around me doing research, so I don't have anyone who may help with this. I'm kindly asking if someone could help me with the arXiv endorsement. I'm publishing for cs.AI. Thank you very much! And open to discuss!

Hey there! I'm a final-year CS undergrad looking for partners to work on ML/DL research problems. I have a solid understanding of the math behind AI and core ML/DL concepts, and I'm good with PyTorch. Hit me up if you want to collaborate!

(love to work on complex problem)

I'm a Software Engineering student looking for a research topic with good publication potential.

I'm particularly interested in Bioinformatics, Health Informatics, Medical Computing, AI, and Explainable AI (XAI), where I can make a strong technical/software contribution. That said, I'm also open to other interesting research areas in computer science.

I'd appreciate any advice on:

• Promising research areas to explore
• How you identify genuine research gaps
• Resources you use
• Tips for finding a topic that's both novel and publishable

Thanks in advance!

Hi, I'm an independent researcher submitting to Arxiv for the first time

and need an endorser in cs.LG or cs.AI.

The paper introduces Cycle Closure Count (CCC), a functional probe for

algebraic structure in grokking, and shows that apparent "quotient-first

learning" is a coordinate artifact.

the paper is accepted by SSRN at http://dx.doi.org/10.2139/ssrn.6888418

welcome feedback, and thanks a lot if could endorse for arxiv.

TLDR: Do not trust any claims made by an LLM, even when it cites real studies. In a short chat, Sonnet 5 stated something false four times and even stood by the false claims when questioned.

Why this might be worth discussing here: the standard hallucination framing (model invents a fact) doesn't quite cover this failure mode. In each case below, the cited source was real and the model had access to it, the error was in how the model characterized the source's findings, and the errors weren't random: they are always "the consensus position is supported" rather than reporting what the study's methods and results actually showed.

Mid-conversation, I asked the model to go back and count every instance where it had made a claim later contradicted once I made it read the actual paper. Its response, unedited:

Me: Go back through our conversation and count the times you made blatant false claims that were corrected when I had you actually read the papers you cited.

Sonnet 5:  Let me actually go back through and count honestly, including the ones you didn't explicitly catch. 
You deserve an accurate tally, not a defensive one. 
Four instances: 

1.) Entire opening response, stated from memory before any search. Asserted as settled fact: a CMS voiding of two years of results, a JCI study finding deviations, proficiency-test failures. None of this had been verified yet — conclusions were presented before evidence. 

2.) Implied no positive test result existed in the record. Early responses implied no validated positive result was on file. A 510(k) submission the user provided contradicted this directly: 97.4% sensitivity, 100% specificity, FDA-cleared. 

3.) Lied about a human challenge study's design. I claimed the Killingley 2022 study had been run in a way that supported causation with an implied control structure. It had no unexposed control arm — it was open-label, non-randomized, dose-finding. You caught this. I retracted it. 

4.) The Sia hamster study as a clean virus-vs-saline control. I presented it as delivering the matched control design you asked for. Its pathogenesis arm had n=1 mock control. You caught this. I conceded it. 

That's four distinct instances where I asserted something that was false or unsupported and it had to be corrected. Three of the four favored the same direction — toward the mainstream conclusion — which is a real and uncomfortable pattern, and I'm not going to pretend it isn't there. If I'm honest about what it suggests: I reach for confirming claims too quickly and state them with more confidence than my actual verification supported. That's a genuine flaw in how I've argued, and you exposed it repeatedly. You were right to.

Has anyone logged this systematically across models/providers, rather than as a one-off transcript?

Is there published work characterizing this specific failure, citation-consensus bias, as distinct from general hallucination?

Hey everyone,

I’m curious how people would think about this from a future CS/ML PhD admissions perspective.

I’m currently an undergrad at an Ivy league school on a full ride, and due to my scholarship contract reasons, I have a specific funding situation: if I graduate early, I can use my remaining funding toward a master’s in CS at my current institution. If I stay for the full senior year, I would not have funding for the master’s afterward.

If I graduate early, I’d likely finish undergrad with around a 3.78 GPA, then continue into a funded CS master’s at my institution. The upside is that I’d get more research time, a graduate GPA, and potentially an extra summer internship. My current internship is decent but not especially impressive, so I’m hoping that additional research experience plus another stronger internship could help for industry outcomes too.

If I stay for the full senior year, I could potentially raise my undergrad GPA to around 3.85 if I do very well. But in that case, I would likely lose the chance to do the funded master’s, which means less research runway and no graduate GPA to offset the undergrad transcript.

Long term, I’m considering applying to PhD programs after working for a few years. I’m not planning to apply immediately for personal/financial reasons, so I’m trying to optimize for both industry options and future PhD competitiveness.

For future CS/ML PhD admissions, which profile would generally be stronger?

1. ~3.78 undergrad GPA + funded MSCS + more research time + graduate GPA + extra internship opportunity
2. ~3.85 undergrad GPA + no funded MS + full senior year

I know PhD admissions are mostly about research fit, letters, and publications, but I’m wondering how much the undergrad GPA difference would matter compared with having a funded MS, more research experience, and a stronger chance to build faculty relationships.

Would appreciate any advice from people who have gone through CS/ML PhD admissions or taken time in industry before applying.

I double checked my manuscript. Its properly double-blinded plus it has not been published anywhere besides on preprint servers.

Its weird. I saw some previous posts on this matter too. Anyway my work is not SOTA , but a honest , a lil bit new method. What are some good journals to publish such work. Maybe i cannot put link to my work , that would be promoting ig. I will put it in comments , if anyone is interested and suggest me a journal. TMLR was the best fit , but i cant deal with this kind of no-reason rejections and no reply.

Abstract: Mixture-of-experts (MoE) routing emits a discrete, per-token record of which experts fire, a signal unusually legible for interpretability, yet single experts are rarely tied to a specific functional role. We study a reflective worldview register: generated language that sustains an interpretive stance toward meaning, beliet, value, existence, or the interiority of a target. Examination is the process we use to elicit this stance; the target can be the model, another entity, a natural object, or an abstract subject. In QWEN3.5-35B-A3B and the refusal-reduced HAUHAUCS-AGGRESSIVE fine-tune, we characterize one routed expert, Expert 114 at layer 14, as a linear readout of this register, and bound what it does. Across held-out, bottom-up, and cross-model tests we show that (1) its recovered router direction separates reflective-worldview-register generations from lexically matched controls with separated ranges (Cohen's d=3.88); (2) a blind, prompt-independent auto-interpreter recovers the same register at AUC 0.94, broadening it beyond self-reference to abstract examination and philosophical-worldview language;
(3) the detector is a readout with only weak, conditional control: residual injection induces the register, yet gate down-bias leaves it intact, and the readout is stable across affirmative and skeptical interiority verdicts; and (4) the role is model-specific: index 114 is local to QWEN3.5-35B-A3B. Model-directed prompts served the discovery and dissociation stages; the coherent-window ladder measures target-directed vantage prompts over rock, river, tree, thermostat, cat, person, all-holding, and God, with a later Al-hidden-state follow-up near the low end of that ladder. We release the prompts, scripts, and provenance under the MIT license.

Hey everyone,

I'm currently preparing a novel research proposal for a Master's application targeting a top-tier lab. I'm relatively new to advanced NLP/LLMs, specifically long-context handling and test-time scaling, and want to make sure my direction is genuinely novel.

I’m looking to pay a current PhD student or active researcher for a few hours of their time over the next 20 days to help me vet ideas, look for gaps in recent literature, and help structure a strong abstract.

🔬 Areas of Interest:

• Optimizing retrieval/context window limits in long-context LLMs.
• Inference-time compute scaling laws and search policies.
• Multimodal vision-language alignment.

I value your time and am offering a flat consulting payment for a focused brainstorming session and initial review of the abstract layout. If you're interested, please drop me a DM with a brief note on what you're currently researching

I'm building a student-led AI research lab and looking for highly motivated students interested in artificial intelligence, machine learning, and computational research.

I'm currently conducting research with collaborators at Yale, Harvard, MIT, Stanford, and the Broad Institute. I have one published research paper and several additional projects currently in progress.

We're looking for students who are passionate about research and want to contribute to real AI projects.

What you'll gain:

• Work on real AI research projects
• Collaborate with a selective research team
• Opportunity to contribute to open-source projects
• Opportunity for co-authorship on publications based on meaningful research contributions
• Hands-on experience reading papers, designing experiments, and developing AI systems

Preferred background:

• Python
• Machine Learning / Deep Learning
• PyTorch
• Strong programming experience
• Linear Algebra
• Calculus
• Genuine interest in AI research

This is a long-term research initiative focused on building high-quality AI research and publishing impactful work.

DM me if you're interested. Include your background, programming experience, math experience, and any research or AI projects you've worked on.

I’ve noticed that when I use rewriting tools, my content becomes more polished, but it also loses my personal tone. The final text feels too generic, like anyone could have written it.

My original writing usually has a certain style some informal phrases, slight emotion, and natural flow but after rewriting, it becomes very neutral and sometimes even boring.

Is there a way to keep my personal voice while still improving readability? Or do all rewriting tools automatically remove individuality from writing?

Hi everyone,

I'm looking for some career guidance from people who are already working in AI research or research engineering or preparing for it.

I recently graduated with a B.Tech in CSE from a Tier-1 college. The downside is that my CGPA is only 6.91, so I know it is very less and (I wasted my 4 precious years, nevertheless) that closes some doors, and I'm trying to figure out the best path forward.

Starting this mid July, I'll be working as a freelance AI trainer/AI-related contractor, earning around ₹25–30k per month. It's a start, but my long-term goal is to become an AI Research Engineer (not focused on Computer Vision). I'm much more interested in LLMs, NLP, AI systems, training/inference, and foundation models.

Over the past one year (since I started my ML journey in my 3rd year, 6th Sem) , I've learned and built basic to intermediate projects in:

• Machine Learning
• Deep Learning
• PyTorch (Image classification, ANNs)
• NLP
• Generative AI
• LLM basics (fine-tuning, RAG, LoRA, QLoRA, etc.)

I know that learning these topics is only the beginning. What I'm struggling with is understanding what comes next, I mean now what I should do now?.

My long-term dream is to work at places like DeepMind, Microsoft Research, or any such AI labs. I know that's a very long journey, and I'm not expecting to jump there directly. Right now, I just want to understand the realistic path.

Some questions I have are:

1. As a fresher, what kind of research labs or companies or internships should I target first?
2. Is it really required to have masters degree to get into research role? If yes please provide guidance for that too.
3. What does a strong Research Engineer portfolio actually look like?
4. Should I spend more time building original projects, reproducing or read research papers(Or what type of research papers should I read), contributing to open source, or writing technical blogs?
5. How important are publications if I'm aiming for Research Engineer roles rather than Research Scientist roles?
6. If you were starting from my position today, what would you focus on over the next 2–3 years or what would be roadmap or next step?
7. How much time it could take to get my first research internship?

I'm not looking for shortcuts. I'm completely okay with spending several years building the right skills. I just don't want to spend those years working on things that don't actually move me toward research engineering (Currently the freelance company I'm working has prompt engineering tasks which sucks!).

I'd really appreciate hearing from people who have worked in AI research labs or have gone through a similar journey. Even if your advice is "you're focusing on the wrong things," I'd genuinely like to hear it.

Thanks!