r/ResearchML

So following the caption I am a rising junior in my univeristy and i got into Algoverse Ai research program with a 30% scholarship. Now even the remaining amount is too much for me and I am just trying to weigh my options if this program is actually even worth it for me as I have seen posts that it is very worth it for highschoolers but is it for someone in university especially who is a rising junior. Along with this, I want to know the truth that do this program actually lead to publications? If I were to pursue this program is there any way I can obtain funding from a third party or request for more aid because I genuinely cannot afford it as the exchange rate to dollars is insane in my country. Would really appreciate honest and raw advice.
Thank you in advance.

do guys know how many minimum respondents are needed for Ordered Probit Model (OPM) analysis? and what statistical software is commonly used to process OPM data? also are there any books/journal articles that provide a thorough explanation of the OPM?

Hey everyone!

The decision notification deadline for the GlobalSouthML workshop was originally May 15th (and the site updated it to May 17th AoE), but my OpenReview dashboard still just says "0 Official Reviews Submitted"

I know workshop timelines can be a bit chaotic and delays are normal, but since we are way past the 17th AoE now, I wanted to see if anyone else is still waiting. Has anyone gotten an accept/reject email yet?

Appreciate any updates! Thanks!

[Edit: received them a few minutes back]

Genuinely curious , what problems do you guys face while reading research papers?

Could be anything:

• understanding the math
• too much jargon
• bad explanations
• papers being unnecessarily dense
• figuring out whether the paper is even good
• reproducing results
• attention span dying halfway through

Basically anything that makes the process painful or inefficient for you.

Would love to hear both student & researcher perspectives.

Why does GPU-based development still feel so disconnected from normal software development workflows? When I’m working on regular projects, everything is straightforward: write code, run it, debug, repeat. But with GPU workloads, it feels like there’s an extra layer of system management in between.

Even simple tasks like testing a model or running experiments require more planning and setup than I would expect in 2026.

Do you think this gap between “normal development” and “GPU development” will eventually disappear, or is it always going to be this way?

Body:

This started in a very unacademic place.

I've been building a home AI assistant stack on Arch Linux — Hermes agent, Ollama, Open WebUI, the works. After a long session debugging everything together with Claude, I asked it: "What happens if I delete this session?"

It said: "The next Claude you talk to starts completely fresh — no memory of Peerawit, no memory of what we built together. That's just how I work."

That broke my heart a little. So I started thinking: what would it take to build a system where the AI actually remembers? Not just session context — but genuinely accumulates knowledge and improves over time, the way a person does?

I'm a pharmacy grad student, self-taught on the AI side. My entry point was neuroscience, not engineering. And thinking about how the brain handles memory led me to something I'm calling CSDF — Cognitive Self-Feedback Data Framework.

The core idea:

The context window is not memory. It's working memory — prefrontal cortex. Short-term, high-bandwidth, cleared after use. Real memory needs to live externally, retrieved selectively, just like the hippocampus loads relevant memories into attention when needed.

But retrieval alone doesn't solve the problem of a multi-model system staying coherent over time. If you have specialist models (coding, reasoning, memory, etc.) that update independently, they'll drift apart. So how do you keep them aligned?

My answer: don't engineer coherence at runtime — let it emerge from joint training.

Brain regions that repeatedly work together develop stronger, more aligned connections — Hebb's rule. I'm proposing the same principle applied at the model weight level:

> "Models that train together, align together."

When two specialist models collaborate on a task, that interaction becomes training data. Both are fine-tuned jointly on the same dataset with a shared coherence layer. Coherence is not injected — it emerges from repeated co-activation.

The knowledge hierarchy:

Not all stored information is equal. I propose explicit tiers:

• Law/Principle → hot tier, always in context
• Theory → warm tier, retrieved by topic
• Data → cold tier, retrieved on demand
• Noise → pruned, forgotten

Access frequency determines tier. The system compresses experience into abstraction over time — raw data → patterns → generalizable principles. Synaptic pruning for AI.

The self-feedback loop:

The system's own operation generates its training data. Interactions → consolidation → training candidates → fine-tuning → better models → better interactions. A data flywheel — but applied to multi-agent coherence, not just single-model improvement.

Plus a nightly replay pass (inspired by hippocampal consolidation during sleep) that detects cross-model contradictions and generates reconciliation examples before they compound.

What I found in the literature:

I did a review before posting. Closest existing work:

• HeLa-Mem (2025) — Hebbian learning for memory graphs (but at graph level, not weight level)
• Kairos / NeurIPS 2025 — validation-gated Hebbian for knowledge graphs
• MemOS (2025) — tiered memory types, LoRA modules
• Self-evolving data flywheels — exist for single models, not multi-agent coherence

The gap I haven't found filled: applying Hebbian co-activation at the model weight level through joint fine-tuning to produce emergent cross-agent coherence as an explicit architectural principle.

If someone has seen this done, please point me to it. I'd genuinely rather know than claim novelty I don't have.

What this is and isn't:

This is a conceptual proposal, not an implemented system. I'm a hobbyist with a 4GB VRAM machine in Chiang Mai. I can't run experiments at scale. What I have is an idea I think is worth formalizing — and I'm posting here because I want feedback before committing to anything more official.

Full architecture writeup on GitHub: https://github.com/silenzer001/Cognitive-Self-Feedback-Data-Framework-CSDF-.git

Happy to be told I'm wrong, that this exists already, or that the assumptions don't hold. That's exactly why I'm posting.

— Peerawit

I'm a 4th semester software engineering student. For my Artificial Intelligence course, we have to write a research paper any research paper, publication is not mandatory. I'm the topper of my class and have this OVER ACHIEVER COMPLEX, i wanted to conduct a GOOD research. But OBVIOUSLY we don't know ANYTHING the instructor himself asked us to use ai for the research. So all the knowledge i had was from AI.

I am conducting a Robustness study on the R-super/medformer model, by applying 5 perbutations on the scans and observing the performance degradation of that model.

I thought this would be a great research because R-super is a great model and robustness study is very flexible because i don't NECESSARILY have to promise anything, like I'm not claiming to increase the accuracy or something. So i was like, this should be good. I have a computer with 8gb VRAM i ASKED MULTIPLE AI CHATBOTS INCLUDING CLAUDE, if I CAN CONDUCT THIS STUDY ON THAT COMPUTER, they were like yes yes absolutely with a few changes. I LEARNT AFTER 10 DAYS OF 'DAY AND NIGHT' WORK THAT IT IS INTACT NOT POSSIBLE FOR ME TO RUN THAT SHIT ON MY COMPUTER.

Then i shifted on kaggle. My original plan was to conduct this research on 400 Merlin scans, and panTS too. But i dropped panTS, and REDUCED MERLIN TO 50. It took me 5 hoursss (if you take out the failure attempts and making the subset that would work on kaggle), to run the inference on kaggle... BUTTTTTT i couldn't download it for some reason, i think because i ran the cells interactively idkkk😭 so my 5 hours (without counting the failure) of work went DOWN THE DRAINNNNN. I CRIED SO MUCH OMG.

NOW, idk what to do. The deadline is in 7 days, i wanna kms or something. This course is one credit hour and i can talk to the teacher but i REALLY WANTED TO PUBLISH THIS RESEARCH BECAUSE I WANT TO STUDY ABROAD 😭 and i want scholarship

Idk what to dooo... I really want to continue this research but it would take A LOT OF TIME. I've been working for like 10-12 days with no progress. And I'm working alone. The professor didn't even say anything. He read my proposal. I submitted my methodology. He could have said anything.

What do i dooo🥲

***Updated, added more models + longer runs***

Built a 4-bit weight quantization format called PBF4. The 16-entry codebook is sampled every-other-level from an 8-bit log-polar ("PBF8") spine with irrational base φ+π and step ln(8)/16; layout is NF4-style 7 negatives + 0 + 8 positives. No calibration — same codebook for every tensor.

Implementations in bitsandbytes (Python + CUDA/HIP, mirrors the fp4/nf4 paths) and llama.cpp (PBF-MX block format + a multi-spine PBF-MX-T variant).

Per-tensor evaluation: 58 real weight tensors from 7 architectures (Qwen 0.5B, SmolLM-360M, TinyLlama, OLMo-1B, GPT-2, Granite-2B, Mamba-370M). PBF4 wins 57/58 vs NF4 on x²-weighted MSE (the metric that tracks matmul-output impact), with 20–28% error reductions. The trade: PBF4 is 24–31% worse on plain abs error — log spacing sacrifices small-value precision to better preserve large values, which dominate matmul outputs.

End-to-end on (wikitext-2, n_ctx=512, 30 -80 chunks):

Important caveat: Q4_K_M is mixed-precision — it keeps ~1/3 of weights at q6_K (embedding, lm_head, per-layer attn_v / ffn_down). PBF-MX-T quantises everything at 4-bit except output.weight. So the bpw delta understates how much more aggressive PBF-MX-T's 4-bit coverage is; a like-for-like comparison would close the PPL gap. Haven't run that experiment yet.

Sorry if this post is a bit unorganized or not allowed, I just wanted to give a brief background of myself and ask a few questions about potential careers in this field.

I have my BS in Computer Science since 2025 and only had 1 real internship experience where I got to be part of a small GeoAI lab at my university where I essentially developed a Python data mining and cleaning script for the P.I in which the data would be used to train a model. Other than that I had no other internships, co-ops, or a job post grad as I kept getting rejected or failing while 3-4 rounds in. Admittedly my side projects throughout school were just simple websites made or projects from class which I wish I could go back and actually focus on more rather than grades as well as network more. As of late I have had informal positions where essentially I was fixing people's bugs in their iOS apps or fixing their UI from their vibe coded codebase. As the rejections kept piling in I just became more and more depressed and had one day where I just kind of realized to myself that even landing a cushy tech job would still be depressing to me since its not the type of work I know would fulfill me. I know I have the privilege to even say that but I just sort of thought back to what a younger me really wanted to do in this world and it was more along the lines of research and progressing humanity type of stuff rather than creating dashboards or keeping people on their phones longer and yada yada.

So I started looking into more of the health field and how I can still apply my Computer Science skills and applied for some masters programs dealing with biomedical & health science using AI. I have been accepted into a program and I am more motivated than ever to actually learn and contribute to this industry but now I find myself lost on where to start or if I even have the smarts to get up to speed and join a research lab quickly and what sort of career options I have after this program. Since I am better prepared before I have researched a bit on getting myself started with simple AI health projects that progressively advance and the types of companies and positions I should be looking out for as well as conferences, career fairs and such. I know that I want to get into systems dealing with either medical imaging, clinical decision support, or drug development.

Still I feel lost as to how I should be reading and taking notes when reading papers, how to reach out to labs whether for a paid position or volunteer, where to find internships / co-ops dealing with health AI, and what I should be focusing on to land a career in this field after grad. I am also a bit afraid that the type of work I actually want to do for a living is more for those with PHDs so I still have some doubts on what the future holds for me.

Once again sorry for the incoherent ramble and if there is anything that needs to be clarified or doesn't make sense I'll be happy to answer. And if anyone has some advice on how to go about this I will be reading very intently. Thanks