Hey guys, I am a researcher and solo founder. I compress models with INT3 at +0.14 nats and built a 2-bit KV cache for long-horizon tasks. I shipped both (INT3 model + INT2 KV) with custom fused Metal kernels for Mac (M-series). Currently Qwen 7B is available in preview. #install brew install reinforceai/spiral/spiral #chat spiral-chat I am optimizing kernels further and working on Triton kernels for GPU support. There is still more room to pack more efficiently, I will share more models soon. I will appreciate any feedback or any model you want me to compress within 100B parameters. github.com/ReinforceAI/spiral submitted by /u/Financial_Buy_2287 [link] [comments]

I am looking at a product that will do runtime quant on DeepSeek V3.2. I want to measure quality loss compared to no quant. What kind of benchmarks can I run? submitted by /u/Chachachaudhary123 [link] [comments]

I recently found a paper accepted to CVPR 2026 reproduced many technical details from my paper submitted to arXiV on June 2025 (5 months before the CVPR 2026 submission deadline). Apart from technical similarities (they rephrased / reframed the term / key ideas), the CVPR paper uses exactly same equation without changes to any notations from our paper without proper citation. Several figures show high similarities in style and pipeline. We tried to contact authors from the CVPR paper, but they framed the technical similarity as "general method" so no need to cite. While they admitted that they refer to our paper for figure design, writing style, and equation, they can only update the arXiv version of their paper (the CVPR camera ready deadline has passed), claiming that they are "inspired" by us. Basically they would not do anything to their proceeding paper. I am wondering how CVPR identify the plagiarism between their accepted papers and arXiv papers? Will it be considered as plagiarism only if they reproduce a published work? Thanks for any advice! Attached part of the reproduction: Our arXiv work applied a multi-turn extension on the basic GRPO algorithm (with notation changes). The CVPR paper directly adopted the exact same equation without citation. Our arXiv paper The CVPR paper submitted by /u/sukays [link] [comments]

I am curious what everyone will be doing. I myself am torn, on the one hand I understand it boosts a paper’s credibility but on the other hand I worry about plagiarism, especially during current times. Thoughts? submitted by /u/undesirable_12 [link] [comments]

Hey everyone, We just open-sourced our reasoning model, Chaperone-Thinking-LQ-1.0, on Hugging Face. It's built on DeepSeek-R1-Distill-Qwen-32B but goes well beyond a simple quantization — here's what we actually did: The pipeline: 4-bit GPTQ quantization — compressed the model from ~60GB down to ~20GB Quantization-aware training (QAT) via GPTQ with calibration to minimize accuracy loss QLoRA fine-tuning on medical and scientific corpora Removed the adaptive identity layer for transparency — the model correctly attributes its architecture to DeepSeek's original work Results: Benchmark Chaperone-Thinking-LQ-1.0 DeepSeek-R1 OpenAI-o1-1217 MATH-500 91.9 97.3 96.4 MMLU 85.9 90.8 91.8 AIME 2024 66.7 79.8 79.2 GPQA Diamond 56.7 71.5 75.7 MedQA 84% — — MedQA is the headline — 84% accuracy, within 4 points of GPT-4o (~88%), in a model that fits on a single L40/L40s GPU. Speed: 36.86 tok/s throughput vs 22.84 tok/s for the base DeepSeek-R1-32B — about 1.6x faster with ~43% lower median latency. Why we did it: We needed a reasoning model that could run on-prem for enterprise healthcare clients with strict data sovereignty requirements. No API calls to OpenAI, no data leaving the building. Turns out, with the right optimization pipeline, you can get pretty close to frontier performance at a fraction of the cost. Download: https://huggingface.co/empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit License is CC-BY-4.0. Happy to answer questions about the pipeline, benchmarks, or deployment. submitted by /u/AltruisticCouple3491 [link] [comments]

Since I felt like I was relying on Claude Code a lot recently, I wanted to see how hard it is to implement a diffusion language model from scratch without the help of AI-Generated code. So I built one while waiting for the training for my master's thesis. This is what I got after a few hours of training on my MacBook Air M2. I trained on the tiny Shakespeare dataset from Karpathy and prompted "to be, " To be, fo hend! First her sense ountier to Jupits, be horse. Words of wisdom! The model has around 7.5M Params and vocabulary size is 66 (65 chars + [MASK]. I definitely did not train long enough, but I ran out of time for this one. Projects like these help me make sense of big scary words like (discrete) diffusion, encoder, decoder, tokenizer. Maybe this encourages someone 🙂 Check out the code here if you're interested: https://github.com/Encrux/simple_dlm Thanks for reading! Be horse. submitted by /u/Encrux615 [link] [comments]

So ICLR is coming and apparently the biggest value one can get from these conferences is to network. Let's take my example: I'm a PhD student looking for industry internships. Say I have located about 15-20 posters regarding topics adjacent or directly related to my area of research, some of which are by authors from industry labs. I go to the poster, ask the authors about their paper, discuss a bit, perhaps ask some insightful questions and mention that I work in similar things, and then after the conference I email them asking if they have internships? Is this how I should be extracting the networking value of it? Also, how overwhelmed are authors with these kind of requests? Seems like cold emailing vs this doesn't make that much of a difference, besides the fact that they might remember me from the conversation we had during 15 minutes during their poster session. submitted by /u/howtorewriteaname [link] [comments]

I implemented two recent ideas for long-context inference / KV-cache compaction and open-sourced both reproductions: Cartridges: https://github.com/shreyansh26/cartridges STILL: https://github.com/shreyansh26/STILL-Towards-Infinite-Context-Windows The goal was to make the ideas easy to inspect and run, with benchmark code and readable implementations instead of just paper/blog summaries. Broadly: cartridges reproduces corpus-specific compressed KV caches STILL reproduces reusable neural KV-cache compaction the STILL repo also compares against full-context inference, truncation, and cartridges Here are the original papers / blogs - cartridges - https://arxiv.org/abs/2506.06266 STILL - https://www.baseten.co/research/towards-infinite-context-windows-neural-kv-cache-compaction/ Would be useful if you’re interested in long-context inference, memory compression, or practical systems tradeoffs around KV-cache reuse. submitted by /u/shreyansh26 [link] [comments]

Did anyone get an email? I emailed the chairs. They say every participant got an email titled: "CVPR26 BP Scholarship Decision Has Been Released", and participants got a separate email with the award and details. But I got no such email, yet. submitted by /u/Erika_bomber [link] [comments]

The current AI conference acceptance culture feels like it leaves little room for the kind of spark we once cherished in research (at least in my own experience). It seems to run on tons of evaluations to let reviewers believe solid, often far beyond the level of interest that can be realistically sustained for any single project, and almost nobody will verify them again. submitted by /u/NuoJohnChen [link] [comments]

I saw many discussions about TMLR and other journals lately and how their review processes are considered fairer and less random. My question is, how much does it hurt one's chance much of getting interviewed/hired as a ML research scientist if they choose to publish at only journals like TMLR, JMLR, or Neurocomputing, instead of conferences? Edit: just to clarify, I mean corporate research scientist positions instead of academic positions. submitted by /u/dontknowwhattoplay [link] [comments]

I’m tired of training a lot of models and trying different datasets but still my model is trash and can’t detect clearly it sometimes has mAP50 pf 80% but it is only in numbers not practical, what can i do to have a good model that can be used? I trained using YOLO11n to use it in RPI5 16GB RAM no AI hat, but still can’t get the results i want, i tried searching and learning what could go wrong but I can’t seem to find the right solution+ i’m not that big of an AI expert so. submitted by /u/vDHMii [link] [comments]

Only counting those categorized as cs.LG. I'm sure there are multiple other subcategories with even more ML papers uploaded such as cs.AI, and math.OC How are you keeping up with the research in this field? submitted by /u/NeighborhoodFatCat [link] [comments]

For people just starting out in GPU kernel engineering or LLM inference (FlashAttention / FlashInfer / SGLang / vLLM style work), most job postings still list “C++17, CuTe, CUTLASS” as hard requirements. At the same time NVIDIA has been pushing CuTeDSL (the Python DSL in CUTLASS 4.x) hard since late 2025 as the new recommended path for new kernels — same performance, no template metaprogramming, JIT, much faster iteration, and direct TorchInductor integration. The shift feels real in FlashAttention-4, FlashInfer, and SGLang’s NVIDIA collab roadmap. Question for those already working in this space: For someone starting fresh in 2026, is it still worth going deep on legacy C++ CuTe/CUTLASS templates, or should they prioritize CuTeDSL → Triton → Mojo (and keep only light C++ for reading old code)? Is the “new stack” (CuTeDSL + Triton + Rust/Mojo for serving) actually production-viable right now, or are the job postings correct that you still need strong C++ CUTLASS skills to get hired and ship real kernels? Any war stories or advice on the right learning order for new kernel engineers who want to contribute to FlashInfer / SGLang / FlashAttention? Looking for honest takes — thanks! submitted by /u/Daemontatox [link] [comments]

Hello everyone! I've been independently researching & developing small-but-powerful vision-language models (VLMs) and noticed a gap in visual datasets - none were teaching my model to simply ground text in imagery, but trying to get it to reason about the text or about the scene itself. This lead me down a 2 week side-side-project to create SGOCR, an open source dataset pipeline for generating spatially-grounded, OCR-focused VQA tuples with tons of rich metadata to support diverse VLM training strategies. Code v1 dataset My development began with simply prompting Qwen2.5-VL locally and grew into a multi-stage beast. At one point, my OCR-stage looked for concensus between 3 text recognition models (Parseq), my anchor stage did the same between GroundingDino, Florence 2, and SAM 3.1, and verification required passes from both Gemini 3.1 Pro & ChatGPT 5.3 Codex to pass. I discovered that less is more in this case, and landed on using Nvidia's nemotron-ocr-v2 for text extraction, a combination of Gemma4 with a Qwen3-VL fallback for anchor discovery & labeling, and then gemini-2.5-flash as a teacher model with simple grounding checks for verification. I got away with using the smaller 2.5 Flash teacher model due to the highly grounded annotations provided in context allowing flash to focus on semantics. I utilized an agentic loop for development after first creating a dataset review frontend that would store my personal accept/reject/maybe marks to be referenced as human-grounded context later. I bootstrapped this process into a quality score that reflected the aspects of questions I accepted, and from there the rest was much easier to automate. I run a custom optimization loop agent, based on Karpathy's autoresearch (which I found a bit too hyperparameter-searchy), that uses a sweep-based process that allows better holisitc observation, an oppurtunity to make code changes, and less risks of good ideas dying earlier due to their evals being slightly less than another variant's. I'm looking for general feedback and interested if other people were looking for something like this, or building similar VLMs. Thanks for reading! submitted by /u/Dreeseaw [link] [comments]

I just noticed that the reviews and discussion for our submitted paper have vanished, but I can see the discussions for other papers in my reviewer view. Do others notice the same? submitted by /u/Massive-Bobcat-5363 [link] [comments]

Here is a list of ~1,200 ICLR 2026 accepted papers that have associated public code, data, or a demo link available. The links are directly extracted from their paper submissions. This is approximately 22% of the 5,300+ accepted papers. The List: https://www.paperdigest.org/2026/04/iclr-2026-papers-with-code-data/ The 'code' link in the last column takes you directly to the code base (GitHub, official site, etc.). Some code repositories may not be made fully public until the conference officially begins. ICLR 2026 will be in Rio de Janeiro, Brazil, starting April 22nd 2026. submitted by /u/Lonely-Dragonfly-413 [link] [comments]

I am thinking about becoming a research engineer, and want to ask your advice on how realistic it is, and which strategies make sense in my situation. About myself: I am in the US, have extensive experience as a Software Engineer (including Staff+ position at one of the top companies), have a math heavy CS degree, and have taken additional ML courses from one of schools offering them to outsiders. I also had applied ML work some time ago, but I didn't like it (that's why I am considering research engineer position, and not a fine tuner or a prompt engineer). I am also a bit over 40, which I feel might be a problem for some companies/positions. What organization hiring for these positions are looking for? What kind of experience is required? Which strategies could I use. P.S. It's realistic for me to invest into unpaid/lower paid positions at least part time, where I could get the required experience. UPD1: I thought about getting a master degree, but I don't see what it will get me except connections/publications (I have a good base in classical numerical stuff, and covered almost all relatively modern areas of ML with additional courses). Getting PhD doesn't look like a good idea to me, but I might give it a thought. submitted by /u/ArtisticHamster [link] [comments]

​ I am trying to convert XQuery statements into SQL queries within an enterprise context, with the constraint that the solution must rely on locally run LLMs. A key challenge is the limited availability of training data (pairs of XQueries and their corresponding SQL queries), especially with enough diversity to cover different patterns. I initially experimented with a parsing-based approach. The idea was to extract elements such as table names, columns, and conditions from the XQuery (using a Python script), map them to SQL components, and pass this structured representation to an LLM. However, this approach depended heavily on regex-based parsing and broke down when the input queries varied in structure. I then tried a prompt-engineering approach, defining strict rules and templates for how SQL queries should be generated. While this worked to some extent for simpler inputs, the outputs became inconsistent and often incorrect for more complex or longer XQueries. At the moment, I am considering fine-tuning a local LLM using PEFT (QLoRA) with a Qwen2.5-Coder 7B model. However, the dataset available is quite small (\~110–120 samples) and not very diverse. The main issues observed so far: Sensitivity to variations in how XQueries are written. Missing conditions or columns in generated SQL for longer inputs. Given these constraints, I am trying to understand the most effective direction to take. Would fine-tuning with such limited data be sufficient, or are there better approaches for handling this kind of structured query translation problem? Happy to provide more details if needed. submitted by /u/genius03noob [link] [comments]

Question to discuss. I'm an undergrad and stumbled across these new forms of neural networks but I haven't seen mainstream adoption of these and was wondering are these something to look forward to learn about (maybe make a project or 2)? submitted by /u/GodRishUniverse [link] [comments]