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This is a submission for the DEV x Kaggle Benchmarking Challenge. What I Benchmarked An agent I was running called a ranking tool with limit: 8. The tool had no limit parameter and rejected the call: unknown argument "limit".
Anyone going to WMHS, Physworld AI or Vercodegen? Would love to get to know some people beforehand!
please refresh if stuck, can be slow sometimes so need patience submitted by /u/big_hole_energy [link] [comments]
In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems (DS)” (preprint: https://arxiv.org/abs/2607.14937) we reduce a DS foundation model to the ingredients minimally necessary to faithfully reproduce long-term statistical and geometrical…
i work on reference checking stuff so i was reading through the ICLR 2027 author guidelines and style files this week the sample .bib that ships with the template has the Deep Learning book as "Goodfellow, Bengio, Courville, Bengio" plus a volume 1 that doesn't exist checked their github and it's…
https://preview.redd.it/gkjgii48gfth1.png?width=575&format=png&auto=webp&s=4e1c35bdacf41d18a6409baedcddc9432ec79814 This happened over the past 30 days.
I'm a PhD student in machine learning in the EU and was looking for internships at exciting companies.
Nonobench measures how well LLMs solve nonograms (picross). Each model gets the row and column clues once and returns the full grid. No tools, one attempt per puzzle. Method: - Standard mode: 30 puzzles from 5x5 to 15x15 (from the Nonograms dataset by Moyà-Alcover, CC BY 4.0).
TMLR desk rejected two years of work/efforts. What could be the possible reason? No reason was given in the desk rejection. The work is related to continual motion generation.
In 1978, researchers asked 60 people at Harvard Medical School a single question. A disease affects 1 in 1,000 people. The test for it has a 5% false positive rate. A patient tests positive. What's the chance they actually have it? The most common answer was 95%. The right answer is about 2%.
This is a submission for the Kaggle Benchmarking Challenge What I Benchmarked I built a Kaggle benchmark called Liquidity Event Identification to test whether large language models can reason about liquidity events in controlled price-action scenarios.
Narrow steps that run on every request, on a model stronger than that step needs, are candidates for a cheaper model. Measured spend then shows where the savings lie. Prove the step on the strongest model, move that one job to a smaller or fine-tuned model, and keep the product metric where it was.
Businesses that sell technical products answer the same kind of question every day. "What's the accuracy on this range?" "Can it measure through a coating, and how thick?" "Does it come with a calibration certificate?" The answers sit in product datasheets and manuals.
Originally published at aideazz.xyz — cross-posted here with canonical link. A 429 HTTP status code, "You have reached your ten_secondly_rolling limit," hit my hs-watch-manual-emails process recently.
Almost every modern language model looks impressive on a two-step demo. You ask it to check a database or summarize a document, it calls the right tool, formats the answer, and looks like an autonomous engineer.
"TypeSafe AI sells Jev as a frontier-class reasoner that cannot hallucinate, built by the co-inventor of ChatGPT - fast, and almost free. We ran it live on 16,379 benchmark requests, measured its latency and billing, and probed what it is underneath.
On the NeurIPS 2026 Dates site it says that there is 1 day remaining for the "Accepted papers must be imported" deadline. This is my first research paper ever and I couldn't find anything about how to do this on the internet. Could someone help me out on what to do?
I recently finished The Principles of Diffusion Models, and honestly I think it’s exceptional.
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