Abhinav Java @abhinav_java
navigating the sea of entropy RF @Microsoft Research | Prev @Adobe, @dtu_delhi Looking for PhD opportunities! java-abhinav07.github.io Bangalore, India Joined September 2022-
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Abhinav Java retweeted
[Read] Characterizing Deep Research: A Benchmark and Formal Definition arxiv.org/abs/2508.04183 (1)
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Abhinav Java retweeted
My thoughts on how AI will automate my SWE job in 2026 (I will be plainly honest on this post, even though some people on both sides of this debate will be upset. So, please, respect that these are my predictions. I don't want to start an argument, I just want to share my…
Abhinav Java retweeted
You guys don’t understand. Bad grammar and spelling is becoming high signal. Perfection looks too close to an LLM. Being retarded is the only way to differentiate yourself for a machine
Abhinav Java retweeted
this is peter gregory vs gavin belson irl
Abhinav Java retweeted
Overall, the benchmark is challenging & motivates improvements to the search and reasoning process. huggingface.co/datasets/micro… Joint w/ @naga86 @abhinav_java, @ashmitkx, Sukruta @MSFTResearch Big Q: What would be the key to improving performance further?
Abhinav Java retweeted
Deep research has emerged as a popular task with many recently released models. But beyond lengthy reports, what exactly defines the task? And how to quantify progress? [New Paper!] We provide an objective defn. centered on claim discovery & a 100-problem benchmark spanning…
Abhinav Java retweeted
Good work & impressive gains (though core RL folks critique entropy methods). Idea: - Explore until model's entropy nears natural range. - Adjust adv at token level for shared/unique tokens (which is already in GRPO via token-level importance-sampling) 🔗arxiv.org/abs/2507.19849
Abhinav Java retweeted
A thread 🧵 TL;DR: We’re working on making NumPy’s cross-platform 128-bit float operations go brrr.... 🔥 So why are quad-precision (128-bit) linear algebra ops so slow and how we’re fixing it?
Abhinav Java retweeted
I can't stress enough how useful this trick has been for me in all these years It reduces GPU memory by N equal the number of losses, at literally no cost (same speed, exactly same results down to the last decimal digit) For example ... [1/2]
I can't stress enough how useful this trick has been for me in all these years It reduces GPU memory by N equal the number of losses, at literally no cost (same speed, exactly same results down to the last decimal digit) For example ... [1/2]
Abhinav Java retweeted
Thrilled to share that our paper "ReEdit: Multimodal Exemplar-Based Image Editing with Diffusion Models" got accepted at #WACV2025! - 4x faster than baselines - No fine-tuning required - 1474 exemplar pairs dataset - Code + unified pipeline released! 🧵Thread + GH and Dataset👇
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