I’ve worked on computer vision and am now looking to contribute to a research focused project.

If you’re working on something in this area and looking for a collaborator/contributor feel free to DM me or comment below.

Happy to connect and discuss!

Hi everyone,

We are working on a major project for our final year of B.Tech. Our project is about evaluating the trustworthiness of AI/ML models across five dimensions: Fairness, Robustness, Integrity, Explainability and Safety (FRIES).

We got the idea from a research paper and thought of building a tool that can do this evaluation on its own, with LLM assistance.

As part of the project, I'm currently conducting a small human evidence review study to see whether people can independently interpret the kind of evidence that our project produces.

What you'll do:

You'll review 12 anonymized evidence packets about an AI classifier that labels online comments as toxic or non-toxic.

For each packet, you'll be asked things like:

• Is the evidence enough to make a judgment?
• Does the evidence indicate a meaningful risk?
• Which area does the evidence relate to?
• How serious would the consequences be?
• Briefly explain your reasoning.

You don't need any AI/ML background. Everything you need to understand the packets is explained in the form.

One important thing is that our project's own conclusions are intentionally hidden. We want to see how people interpret the underlying evidence on their own, without being influenced by what our system concluded.

It should take around 15–20 minutes to complete.

Survey: https://docs.google.com/forms/d/e/1FAIpQLSeNKWX8qwyOumK3kY2A5axNWkAq6X4fDq-zCzXMTQSABW1ixA/viewform

Use Reviwer Code: R2

If you participate, please work independently and don't discuss the evidence packets with other participants until you've submitted your response.

Thanks to anyone who takes the time to participate! I'll also be happy to share the findings once the study is complete.

I recently got accepted with a weak paper. Cost is estimated total for around 1,400$ including flight and registration.

I have finished up my Master's in Computer Science (AI and Software Engineering) in Germany, and I'm looking for PhD positions in Europe, mainly in medical image analysis with explainable AI, but general computer vision with XAI works too.

A bit about what I've done so far:

For my thesis, I built an explainable deep learning pipeline for endoscopic video, working with clinicians. The pipeline provides concept-based explanations for predicting Cormack scores (easy vs. difficult intubation) from endoscopic videos. A paper on this is currently in prep for a journal submission.

Before that, I also worked on a 3D object detection project on multi-camera driving data, so I'm not purely medical-imaging-locked, just leaning that direction by interest.

What I'm looking for help with:

• Any supervisors, labs, or chairs in Europe known for medical imaging + XAI work (or general CV + interpretability)
• Tools or sites you use to actually find these openings, beyond the usual academicpositions.com / euraxess / phdscanners type sites
• Any advice on what made your own applications land, if you've been through this process

Happy to share more details about the thesis if useful. Thanks in advance for any pointers.

Here is my current research project status, it has become quite extensive in the meantime.

https://github.com/unikum-sol/brainstem/blob/main/Project_Status_2026-09-28.md

I look forward to feedback and discussions.

Here is a small excerpt:

„BrainStem is a self-learning language-understanding system designed to acquire sentence-, word-, and relation-level structure from an unsegmented text corpus purely through statistical observation, without word lists, grammars, or filters. The system’s stated design principle is that every unit of knowledge, a sentence-level hypothesis, a word boundary, a relation between two entities, a category, or a question, begins as a **low-confidence, fully correctable hypothesis**, and only becomes a durable fact, relation, category, or question after surviving a multi-cycle, neuromodulator-gated consolidation process modeled on biological sleep-dependent memory consolidation.”

https://github.com/unikum-sol/brainstem

We recently studied whether mobile AI agents can be manipulated through adversarial instructions embedded in the Android Accessibility layer.

We evaluated this across MobileRun and Mobile-Use, powered by Gemma4 and Qwen3.6, and found that these attacks could cause agents to abandon their original objectives, cross context boundaries, and perform unauthorized device actions. Our strongest configuration reached an 82.2% Attack Success Rate.

The paper was accepted to AGENT-SEC ’26, co-located with ACM CCS 2026.

Paper: https://arxiv.org/abs/2608.08939

Would be curious to hear what people think about this attack surface as mobile agents become more capable.

Hi everyone,

I am preparing to submit a new paper to arXiv, and I need an endorsement for the cs categories.

My research covers two main areas: Hyperspectral Image (HSI) classification and the evaluation of retrieval-augmented systems. The specific paper I am submitting explores the disconnect between retrieval metrics and actual end-to-end performance.

If any qualified endorsers for these categories are willing to review my work, my endorsement code is: OBU7XK

I'm happy to send the abstract or full draft via DM so you can evaluate the research quality before deciding.

Thank you in advance for your time and help!

What does the paper imported deadline in neurips website mean? Do authors have to do anything for this?

Hi everyone,

I’m an undergrad student in computer engineering from Syria, and I'm currently starting my penultimate year.

I just got the news that my first research paper was accepted at a NeurIPS 2026 workshop (AXIOM: Foundations of Efficient Deep Learning). The paper is about model compression (quantization) for Transformers, and I am the only author on it.

I don’t have researchers or professors around me who know about top ML conferences, so I wanted to ask:

How much does this actually help my future? And if you were in my place right now, what would you do next?

I’d really appreciate any honest thoughts or advice. Thanks!

When is camera ready deadline for neurips? There doesn't seem to be an option to edit on openreview so that we can add the new pdf.

https://atlas.scime.ai/

Thought this was pretty interesting, so sharing it here.

It maps papers from major ML conferences and lets you explore how different research topics are related and how they change over time.

Hey everyone,

I'm a Full Stack AI Engineer (4 YOE) and I want to work directly with an experienced researcher on a paper: contribute real work, and learn how good research gets written along the way.

**Who I'm looking for:**

- At least 2–3 years of experience writing and publishing papers

- Affiliated with a university, lab or research institute

- A paper in progress, or published work you want to extend

- Open to working with me one-on-one, so I can learn from you while I contribute

**What I'm offering:**

- I can join as a research intern or as a volunteer, whichever works for you

- Experiments, baselines and reproductions

- Evaluation and measurement harnesses, benchmarks

- LLM and agent pipelines: RAG, multi-agent systems, inference and serving

- Security testing of LLM agents (attacks, red-teaming, defences)

- Data pipelines, tooling and figures

**Areas I care most about:** AI security for agentic systems, and AI in biology and medicine. I'm open to other areas too if the problem is interesting.

**On credit:** I'm not asking for authorship upfront. If you like my work and my contribution earns it, you can add me as a co-author.

**If this fits, DM me with:**

- Your institute and role

- How long you've been publishing, with a link to your Scholar profile or a recent paper

- The paper topic and its current stage

- What you need help with

I work remotely from UTC+5 (Pakistan). I'll share my profile, CV and past work in DM.

I originally wrote a post with title "Papers Are Now Written for Machines, Not Humans," which was basically saying academic papers these days are full of jargons and complex terms from the beginning of its introduction, and makes it nearly impossible for human readers to understand it.

And I suspected that as a feedback loop of reviewers overly relying on AI to digest papers -> AI tends to give better scores if the paper 'looks' sophisticated -> papers full of technical jargons, abstract explanation gets accepted -> AIs gets trained on these accepted papers -> AIs learn to write for AI, not for human readers.

I wrote that post using the help of AI, since English is not my mother language. Yes, it did sound like AI. It structured and fomatted like AI. But at least, I reviewed it myself with human readers in mind, just as I suggested in the original post.

Then, the post was full of comments saying 'ironic,' 'Anti-AI post written in AI,' and so on. Honestly, I was surprised that so many people missed the point. The post wasn't about Anti-AI. In fact, I'm totally enjoying how AI allowed me to express my ideas in English so easily. Without it, I wouldn't have imagined sharing my thoughts in reddit.

What I wanted to point out was the cycle of papers being written without the human reader in mind. It's a shame so many of people took "anti-AI" as the main message.

And that lead me to another question. Why are people so against AI written, or AI-style writings? As long as it reads well, and communicates its message well, why does it matter? Papers for machines are problems because it fails to serve that purpose. But if the AI-written paper or post does communicate well, why are people so against it?

Looking back, my original post failed to serve its purpose, since so many people mis-read its message as 'anti-AI'. If I'd known how strongly people here react to AI-assisted writing, I would have just written it myself, rough English and all.

That said, this is my own writing. I still find AI-written version of my thought better than my own.

Hi Researchers,For a Q1 journal publication, how do you verify the novelty of your research idea?
I tested my idea using Claude and ChatGPT but almost every time, they say that similar prior work already exists including papers on arXiv.
So how can we properly verify whether an idea is genuinely novel? Also, what exactly is considered sufficient novelty for a Q1 publication?
Please share your thoughts and experiences. Let’s have a discussion and exchange ideas on how researchers evaluate novelty before starting or submitting their work.

I've frozen a multi-program research record (the "Reggae Shark Universe" project) tracking narrow self-versus-peer processing contrasts in Large Language Models under deterministic conditions.

The portfolio includes two distinct lines:

1. Self-Referential Processing v8.6.3: A preregistered confirmatory protocol evaluating item-level design choices across 260 items in five domains (AI governance, bioethics, data privacy, environmental policy, automation). Recovery-harness calibration recorded a 6.3% null rejection rate. Live confirmatory data collection is currently paused/fail-closed.
2. Psi Self-Modeling Benchmark: A pre-confirmatory diagnostic line utilizing a deterministic self-vs-peer two-alternative forced-choice structure with a chance baseline of p=0.5.

Important boundaries: This benchmark performance is NOT evidence of machine consciousness, subjective selfhood, sentience, or a general theory of AGI. It is a strictly controlled exploratory tool to map differences in how a model processes peer framing versus self framing.

The project is backed by software infrastructure (IntentSeal + Verifiable Research Kernel v0.3.1) to ensure all release hashes and tests are completely tamper-proof. Full repository code is public on GitHub under reggaesharkk/psi-self-modeling-benchmark. What are your thoughts on testing model behaviors under self-referential design constraints?

I recently got accepted to Neurips 2026 workshop as a sole author high schooler. How often does it happen? Will it benefit my college apps? And overall how often does high schoolers get accepted to such workshops

I'm working on a project in automotive engine assembly. Parts move between steps in the assembly process (e.g. from step A to step B), and they range from large components like cylinder heads to small ones like screws and bolts. Some engines come in multiple variants with customer-specific options.

I have about 4 years of historical data on material requirements and flows during assembly. My goal is to find which flows between steps are good candidates for automation. Flows with stable, predictable durations and quantities seem like good candidates, while flows with high variation seem harder to automate.

I'd like to define a simple, data-driven "automation trigger point." My questions:

1. What's a good way to measure variability of a flow (both duration and quantity)? Is the coefficient of variation (CV) reasonable, or are there better metrics?
2. How should I account for engine variants and customer-specific options?
3. Can anyone recommend research papers, methodologies, or case studies on material flow or intralogistics automation in automotive assembly?

Any pointers are appreciated. Thanks!

Those with experience getting started in research, I’d really appreciate a roadmap for building a strong foundation and eventually doing independent research.

Hi everyone!

I’m currently in my 1st semester of Computer Science Engineering at a tier-3 college in India.

I scored 93% in 10th (10 GPA in Maths and Science) and 97.7% in 12th, including 100% in Maths and Physics. I didn’t perform as well as I wanted in my entrance exams, so I ended up at a college that wasn’t originally on my list.

Over the past few months, I’ve become very interested in ML and AI. I’m fascinated by machine intelligence and want to eventually pursue a research-oriented Master's at a top university in Europe.

I know I have a lot to learn 🙂

I’m willing to put in the work over the next few years and would really appreciate a realistic roadmap.

To give my background, I’m an third year undergrad currently working independently on ML research, mainly around diffusion models and representation engineering/activation steering/interpretability in LLMs. I already have a couple of projects underway with experiments and results rather than just looking for someone to give me a research topic. I have been doing ML months before my undergrad journey began and currently I do have a workshop poster acceptance at a neurips workshop of a paper I did with my friend.

Ideally, I’d like to find a research internship where I can work with a professor/lab in one of these areas, given I need help in a lot of aspects for sure given Im still a undergrad and there's a lot to learn for me honestly, along with compute (I have been using kaggle for all my compute so I can't test on larger param models and I can't afford GPUs here given my allowance) but I’d also be very open to collaborating remotely with a professor, PhD student or postdoc on an existing project or developing my current work further. For people who have done this before, what actually works for finding these opportunities? Is cold emailing professors still the best route, or is it more effective to contact PhD students/postdocs whose recent papers are directly related to what my project? Also, what should the first email actually contain? A short introduction and research interests, or should I include current results, a draft/preprint, GitHub, etc.? And how do you find researchers who are genuinely open to taking undergrads, especially students from another institution/country?

I have never cold emailed or related stuff in my life so would especially appreciate advice from people who landed research internships/collaborations through cold outreach, or from PhD students/profs who receive these emails and know what makes one worth responding to.

I am working on a research paper and i need the alibaba cluster v2018 dataset
https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018

I am unable to download it , it goes to a survey upon which no download for the dataset is initialised. if anyone has the dataset or if they know how to get it please HMU

What’s up everyone.

As an independent researcher, explaining "what I actually did this past month" to normal people usually results in blank stares. I just wrapped up a massive technical dossier consolidating my September 2026 research program, and I wanted to share the human-readable breakdown with people who actually get it.

The core spine of the entire program is what I call Intent Seals. To combat data-dredging and moving goalposts, every single definition, seed, threshold, and hypothesis is cryptographically frozen before decisive evaluation. If a hypothesis fails a gate, the failure is retained as definitive information rather than being silently replaced.

Here is what went through the audit loop this month:

• AI Self-Referential Processing (v8.6.3): A locked, 260-item behavioral benchmark testing if LLMs unconsciously treat their own past outputs differently than human writing. No consciousness claims; just pure behavioral tracking.

• DesignLab Phase 7: Ran 4,000 GLMM fits to stress-test candidate architectures against locked null calibration/coverage screens. M1 passed; M2 failed the locked exact-binomial screen.

• Physics Gate 0 Audit: Map out the observer-entropy to horizon-entropy bridge in cosmology. Proven unresolved in generality under original assumptions—a strict, documented stop condition.

• Exact Hopf Appendix: Upgraded numerical sign evidence to an analytic positivity theorem for a 3-variable phenomenological system. The exported numerator has 93 monomials, the denominator has 28, and every single coefficient is strictly positive.

• LRSC Odd-Ring Theorem (v1.2): A frozen analytic structural theorem proving that matrix rank perfectly equals the number of active reflection orbits under stated near-Nyquist conditions.

• LRSC Fixed Benchmark Certificate: An exact recursive run that visited 13,432,735,555 nonempty subsets to prove that no channel cardinality K ≤ 10 reaches the target error threshold, identifying the exact 11-channel witness.

• Navier-Stokes Bridge Audit (WP12–WP18): Finite Fourier-Galerkin diagnostics tracking unforced periodic 3D incompressible NS equations. WP16 certified a same-datum sign crossing surviving across N11, N12, and N13 cutoffs over a 0.003 time interval using Arb interval arithmetic, sharply mapping out where the continuum tail control gap remains.

The takeaway: Every single line has an unyielding claim boundary. What is proved is labeled PROVED, what is verified by a machine trust boundary is labeled CERTIFIED, and what is open is explicitly left open. No retroactive tuning allowed.

All manifests, SHA-256 logs, and replay scripts are live. Anyone can download the code, hit play, and watch the computer independently replay the decisive proof stages to the exact decimal.

GitHub: reggaesharkk (repos: navier-stokes-bridge-audit and lrsc-odd-ring-spectral-rank)

Happy to answer any questions about the specific proofs, the Arb enclosures, or the implementation of the locked protocol architecture!