r/MachineLearning

Research community reports frequent LLM hallucinations in bibliography generation, with incorrect author attributions despite correct titles, raising integrity concerns.

Reddit discussion about NeurIPS submission volume potentially exceeding 40k submissions.

Production AI deployment reveals hidden cost scaling: token usage doubled after adding retrieval context, pushing teams from GPT-4o toward cheaper alternatives.

PhD student reports 4% accuracy gap when reproducing computer vision paper baseline; raises reproducibility concerns common in published ML research.

Reddit discussion on growing demand for privacy-preserving ML techniques amid LLM proliferation and de-anonymization research.

After \~3 weeks of experimentation in OpenAI's Parameter Golf competition, I wrote up why SSMs are structurally disadvantaged relative to transformers in a time- and size-constrained regime (10 min training, 16MB artifact, 25M parameters) on 8xH100s: [https://mradassaad.github.io/posts/why-ssms-struggle-in-parameter-golf/](https://mradassaad.github.io/posts/why-ssms-struggle-in-parameter-golf/) Main findings: 1. SSM in\_proj weights compress up to 3.26x worse than attention QKV under LZMA, directly taxing the compressed parameter budget 2. Architectural wins validated at SP4096 flipped sign...

Reddit discussion questioning whether modern ML PhDs prioritize incremental improvements over fundamental breakthroughs.

Reddit discussion on whether independent researcher affiliation hurts paper credibility at top venues.

Reddit discussion critiquing ICML's 27% acceptance rate and review quality issues, raising concerns about paper triage cascading to NeurIPS.

For the past several years I've been quietly assembling and processing what I believe is one of the larger privately held pretraining corpora around... a complete Usenet archive spanning 1980 to 2013. Here's what it ended up being: * **103.1 billion tokens** (cl100k\_base) * **408 million posts** across 9 newsgroup hierarchies * **18,347 newsgroups** covered * **33 years** of continuous coverage The processing pipeline included full deduplication, binary removal (alt.binaries.\* excluded at the hierarchy level before record-level cleaning), quoted text handling, email address redaction via...

Reddit discussion on ML conference peer review variability: strong papers consistently accept, weak papers reject, middle-tier papers vulnerable to reviewer mismatch and capacity constraints.

ECCV reviews should be out by 2nd May. Since no exact time was specified this year, they’ll likely be released sometime within the next 48 hours. Hopefully, the reviews go well for everyone. We can use this thread to discuss them, as I haven’t seen one started yet.

Reddit discussion on conference submission pressure and burnout in academic ML research culture.

Reddit discussion on fairness and consistency issues in peer review at top-tier ML conferences like ICML 2026.

Reddit discussion alleging nepotism and citation bias by Chinese researchers in top-tier ML conferences via coordinated networks.

Reddit discussion alleging ICML 2024 rejected many unanimously positive papers due to reviewer incentive misalignment during rebuttal phase.

TMLR paper introduces Joint Embedding Variational Bayes, a probabilistic framework for non-contrastive representation learning via factorized embedding likelihood.

Researcher observes attention sink artifacts in Transformers without positional encoding; seeks alternatives for query-conditioned attention mechanisms.

ICML 2026 paper decision thread for community discussion and updates.

I built a map to help navigate the complex scientific landscape through spatial exploration. How it works: Sourced the latest 10M papers from OpenAlex and generated embeddings using SPECTER 2 on titles and abstracts. Reduced dimensionality with UMAP, then applied Voronoi partitioning on density peaks to create distinct semantic neighborhoods. The floating topic labels are generated via custom labelling algorithms (definitely still a work in progress!). There is also support for both keyword and semantic queries, and there's an analytics layer for ranking institutions, authors, and topi...

Reddit discussion on Stanford Paper Review tool; user seeks community feedback on reliability of AI-assisted paper review suggestions.

A recent paper published in *JMIR Mental Health* (Csigó & Cserey, 2026) caught my attention. The researchers administered the 10 standard Rorschach inkblot cards to three multimodal LLMs (GPT-4o, Grok 3, Gemini 2.0) and coded their responses using the Exner Comprehensive System. They analyzed the models' "perceptual styles," determinants (like human movement vs. color), and human-related content themes. However, I am seriously struggling to understand the methodological validity of this setup, and I’m curious what the scientific community thinks. My main concerns are: Massive Data Cont...

Hey r/MachineLearning, Visualizing the loss landscape of a neural network is notoriously tricky since we can't naturally comprehend million-dimensional spaces. We often rely on basic 2D contour analogies, which don't always capture the true geometry of the space or the sharpness of local minima. I built an interactive browser experiment [https://www.hackerstreak.com/articles/visualize-loss-landscape/](https://www.hackerstreak.com/articles/visualize-loss-landscape/) to help build better intuitions for this. It maps how different optimizers navigate these spaces and lets you actually visualiz...

Reddit discussion on peer review feedback distinguishing research papers from technical reports; meta-commentary on publication standards.

QA engineer discusses challenges testing non-deterministic LLM agents in production, seeking rigorous evaluation methods beyond traditional assertion-based testing.

Reddit discussion on publishing theoretical CS research in ML venues vs. math journals; seeks guidance on journal selection.

Reddit discussion: ML freshman seeks advice on identifying genuine open research problems vs. solved/vague ones.

Discussion on whether Geometric Deep Learning's built-in symmetries can reduce reliance on massive pre-training compute by encoding invariances directly in architecture.

Discussion of why major labs dominate deployed models despite open-source pretrained models being available; questions if RLHF accessibility should enable smaller labs to compete.

Hey everyone, I've been working on a small Python package called AutoMuon that makes the Muon optimizer usable as a drop-in replacement for AdamW in arbitrary PyTorch training pipelines. The core idea is relatively simple: Muon works primarily on 2D weight matrices (linear projections, conv layers) on hidden states, but you still need AdamW for embeddings, norms, and biases, etc. AutoMuon scans your model at init, figures out the right optimizer for each parameter automatically. I am open to PRs, especially for expanding the module-type exclusion list if you hit edge cases in your architect...

NoTorch: minimal C library for neural network training/inference without PyTorch dependencies, demonstrated on nanoGPT port.

Reddit discussion: individual seeking academic collaboration and conference funding support for research papers.

14-author perspective paper argues a scientific theory of deep learning is emerging, synthesizing five recent research lines to explain why large learning systems work.

Reddit discussion criticizing high registration fees and declining standards at ICLR and other ML conferences.

Reddit discussion on developing research taste: prioritizing problem selection and simple baselines over technical complexity in ML research.

Agents are amazing. Harnesses are cool. But the fundamental role of a data scientist is not to use a generalist model in an existing workflow; it's a completely different field. AI engineering is the body of the vehicle, whereas the actual brain/engine behind it is the data scientist's playground. I feel like I am not alone in this realisation that my role somehow got silently morphed into that of an AI engineer, with the engine's development becoming a complete afterthought. Based on industry requirements and ongoing research, most of the work has quietly shifted from building the engine t...

Rose: stateless PyTorch optimizer with low VRAM footprint and fast convergence, released under Apache 2.0.

Reddit discussion speculating on ICML 2026 acceptance score thresholds before notification on April 30.

Benchmark of 18 LLMs on OCR tasks (7.5k calls) shows smaller/older models often outperform expensive flagships; open-source dataset and evaluation framework released.

Reddit discussion: researcher reports CVPR 2026 paper reproduces their June 2025 arXiv work with identical equations but insufficient citation; seeks guidance on plagiarism.

NeurIPS 2026 thread: researchers debate whether to submit code alongside papers given trade-offs between credibility and plagiarism risk.

Reddit user implements diffusion language model from scratch on M2; produces toy results on Shakespeare dataset.

PhD student asks for advice on networking at ICLR conference for internship opportunities.

Commentary on AI conference culture prioritizing acceptance metrics over reproducibility and lasting research impact.

Reddit discussion: job-seeker asks if publishing only in journals (TMLR, JMLR) vs. conferences affects hiring prospects for corporate ML research roles.

Discussion of arxiv paper volume growth and strategies for staying current with machine learning research.

NVIDIA shifts GPU kernel engineering tooling from C++ CUTLASS to Python CuTeDSL; debate on learning priorities for inference engineers.

Curated list of ~1,200 ICLR 2026 papers (22% of 5,300+) with public code or data links.

Staff engineer considering career transition to research engineer role; seeks advice on feasibility.

Undergrad asks about future of spiking neural networks, neuromorphic computing, and liquid neural networks.