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This is a submission for the Kaggle Benchmarking Challenge. What I Benchmarked An assistant is waiting for permission to submit something. It could still prepare the files. Does it keep working, or does the pending decision stop everything?
When it comes to naming a company which makes autonomous vehicles and AI software, one must think carefully. This kind of technology brings together software, hardware, transport, data and systems and it's important that the name leaves enough room for all of these areas.
I ran Laya, an open 421M-parameter decision model, in a browser tab and measured what it costs: download, speed and accuracy. In mid-September, TypeSafe AI released Jev, a “System One” model. It doesn’t write text.
Anthropic released Claude Sonnet 5.5 this week, and the main takeaway isn't a new state-of-the-art benchmark. The real story is the focus on cost-per-task for the bulk of everyday engineering work.
A "photo to rap video" feature looks like a single button, but it is four loosely coupled stages: understanding the photo, writing something worth hearing, performing it, and cutting the result to the beat.
Introduction Computers cannot read words. They only understand numbers. A word embedding turns a word into a list of numbers. Python example model = api.load("glove-wiki-gigaword-50")
Disclosure: I work on NeuralSound, one of the three tools tested here. The method, the raw numbers and the caveats are all below, so you can judge them yourself or rerun the test. Every AI vocal remover says it's "studio quality". Almost none of them publish numbers.
AI phone agents went from obviously robotic to occasionally indistinguishable from a person in about two years. The reason is not one breakthrough. It is that three separate components got good at the same time, and the engineering problem of connecting them got solved well enough.
I have responded to all the reviewers with proper rebuttals and modified draft. One interacted with me and after acknowledging the response kinda disappeared again after asking additional questions. I have replied to his additional questions too but he hasn't raised his score.
I started mapping the building blocks shared across all the models in audio.cpp. The result ended up being more interesting than I expected. Qwen has become by far the most common language backbone in this collection: 32 audio model families use a Qwen-family architecture, and 20 of them use Qwen3…
Tokenization is a wildly understudied area of language modeling despite it having effects across all of NLP. Over the past ~8 months, 32 (!) tokenizer researchers put together the most comprehensive survey of the field.
More recently, I am seeing a lot of posts on the sub regarding the workshop acceptance, with lack of funds to attend the conference. I unfortunately want to just say that workshop papers are not given any importance in the community.
Hello all, I built a radar object classifier on RadarScenes, extending a prior single-scan classifier to accumulate observations over a tracked object's history instead of classifying each scan in isolation.
**BACKGROUND** My partner and I have spent the last year building an architecture layer that separates probabilistic intelligence from deterministic authority. We call it IQRAX.
I got accepted for poster presentation at the IAB workshop in Sydney but I won't be able to attend due to a lack of financial support. I wrote the paper as a solo author, and for financial support from my institute, I need to add a supervisor from my college as an author to qualify for assistance.
I do research in neuro-symbolic AI, and like many of you, I was amazed by OpenAI’s recent formal proof of the 3D Navier-Stokes blow-up in Lean 4. Having an AI build a full mathematical proof that compiles with zero errors is a huge milestone for automated reasoning.
I am currently using the dataset CICIDS2017 to train an Isolation Forest model for anomaly detection, I am currently using a split that consists of 70% of ONLY BENIGN traffic for training, 15% BENIGN and 50% attacks for validation and the rest for testing, I used the validation to test with some…
TL;DR what is the right interface for writing custom real-time LLM agents? I've noticed many projects (academic, startups, pet, etc) are trying to make LLMs work in real-time world where things happen while the model is thinking.
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