Research Scientist, @GoogleDeepMind | Prev: @AIatMeta | CMU @SCSatCMU | @IITKanpur | Audio/Speech, Multimodal AI

Cambridge, MA
Joined June 2016
Anurag Kumar retweeted
Gemini Omni doesn't just build scenes that look real, it reasons about what should happen next. It combines an intuitive understanding of physics with Gemini's knowledge of history, science, and cultural context. Rolling out today starting with video outputs to Google AI Plus, Pro and Ultra subscribers globally through the @Geminiapp + Google Flow, and @YouTube Shorts this week.
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Anurag Kumar retweeted
Today at Google I/O, we introduced Gemini 3.5 Flash! It has become an integral part of our daily research cycle and works with all the tools we have at Google. We used a team of agents in Antigravity 2.0 to recreate the original AlphaZero research paper and build a playable version. They coded the reinforcement learning pipeline in JAX/Flax, trained a ResNet model from scratch via self-play on multi-TPU pods, and shipped a full-stack web app so you can play against it, from just 2 prompts. . Here’s what else makes 3.5 Flash special 🧵
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Anurag Kumar retweeted
1/ Today at #GoogleIO, we’re releasing Gemini 3.5, our latest family of models combining frontier intelligence with action. We’re starting by releasing 3.5 Flash, which is built to help you execute complex, long-horizon agentic workflows. Gemini 3.5 Flash is our strongest model for coding and agent yet.It outscores 3.1 Pro on agentic and coding benchmarks like Terminal-Bench and MCP Atlas, while running 4x faster than other frontier models. Used in Google Antigravity, 3.5 Flash is even further optimized to be up to 12x faster. It’s a powerful engine to deploy sub-agents that collaborate, run high-frequency iterative loops, and solve real-world problems at scale. Some highlights we’re excited about 🔽
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Anurag Kumar retweeted
🚀 Join the ICASSP 2026 URGENT Challenge! Advance Universal, Robust & Generalizable Speech Enhancement. 🗣 Track 1: Universal Speech Enhancement 🎧 Track 2: Speech Quality Assessment 🔗 urgent-challenge.github.io/u… #ICASSP2026 #SpeechEnhancement #AI #AudioProcessing
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An advanced version of Gemini with Deep Think has officially achieved gold medal-level performance at the International Mathematical Olympiad. 🥇 It solved 5️⃣ out of 6️⃣ exceptionally difficult problems, involving algebra, combinatorics, geometry and number theory. Here’s how 🧵
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RL is not all you need, nor attention nor Bayesianism nor free energy minimisation, nor an age of first person experience. Such statements are propaganda. You need thousands of people working hard on data pipelines, scaling infrastructure, HPC, apps with feedback to drive benchmarks and data, tons of research and engineering on generative models, data mixtures, ablations, RL/selftraining, etc etc and we will probably need lots of people working hard to figure out safety, causal world models, awareness, models that create abstractions comparable to infinity and zero and use these to predict the existence of things like black holes and suggest experiments to verify such hypothesis, or come up with novel engineering designs to generate energy more efficiently, robotics, etc etc. It takes thousands of people and many ideas. In the end some simple ideas might become obvious but such obviousness only happens in retrospect. Yes, there is a bitter lesson but if we had followed it, we’d still be doing linear regression with RL. Let’s not oversimplify, but rather honour the research and engineering of thousands of people. Also, people keep rewriting history. When our language understanding start up (darkbluelabs) was acquired by Google about 10 years ago, we joined DeepMind, where the AGI documents were all about concepts, RL, episodic memories and made it clear that there was no room for language. To be honest, back then such a position wasn’t so crazy. Now it seems silly, but only because of the benefit of hindsight. There’s no 1 or 10 heroes in the history of AI. There’s many 1000s of hard working students, profs, engineers, operations and support people, product folks, managers, even hedge funds among others. Let’s honour the whole community and not just ceos or the philosophers of Bayes, RL, deep learning, etc. I look forward to learning from the next generation and seeing what they will achieve. To them: Don’t buy the existing narratives blindly, innovate. Remember that just like mathematics, AI will advance one grave at the time.
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So happy to share that our work has been accepted to @SIGIRConf. Thank you to my amazing collaborators! @NegarEmpr, Andrea Tupini, Yuxuan Sun, @Tviskaron, @artemZholus, @Cote_Marc and @julia_kiseleva Pre-print: arxiv.org/pdf/2407.08898
What a way to wrap up @IgluContest! Our paper “IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents” accepted to @SIGIRConf including: 1) rich multi-modal dataset 2) A data collection tool 3) An online eval framework #SIGIR2025
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Anurag Kumar retweeted
``Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment,'' Joanna Hong, Sanjeel Parekh, Honglie Chen, Jacob Donley, Ke Tan, Buye Xu, Anurag Kumar, ift.tt/5JkZ0Gp
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Excited to share our work at @coling2025! While I couldn’t attend in person, @jad_kabbara will be presenting today at the 1:30 PM poster session. Come by to learn how we’re using LLMs to improve understanding in social conversations! #COLING2025 #NLProc
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Anurag Kumar retweeted
``SyncFlow: Toward Temporally Aligned Joint Audio-Video Generation from Text,'' Haohe Liu, Gael Le Lan, Xinhao Mei, Zhaoheng Ni, Anurag Kumar, Varun Nagaraja, Wenwu Wang, Mark D. Plumbley, Yangyang Shi, Vikas Chandra, ift.tt/sxluwgt
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If you are still around in Vancouver for Neurips, tomorrow we will have the URGENT challenge workshop from 1.30 pm. Come by if you are interested in generalizable speech enhancement (also wind will be up to 70km/h tomorrow and is cozy inside 😉) Lineup: neurips.cc/virtual/2024/comp…
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You don't (and shoudn't) need to make reviewer's identities known to authors to improve reviewing quality! There is a simple solution that WAS USED for a long time--get rid of inter-reviewer anonymity! #ICLR2025 Reposting xitter.cf/rao2z/status/177127225… <Unpopular opinion> Inter-reviewer anonymity is a big cause for low review quality at mega #AI conferences (e.g. #AAAI #NeurIPS #IJCAI #ICLR #ICML..). When reviewers don't have to worry about the censure of the other peer reviewers looking at the same paper, there is little downside to bad reviewing. [Note that we are of course not talking about reviewer names being visible to authors; just among the reviewers of the same paper] When I started reviewing for AI conferences, the reviewers looking at the same paper will all know each other's names. When you have to stand behind your opinions in front of your peers, there is a natural moderating force at work. Over time, however, there was a movement to anonymize the reviewers among themselves (as well as, in some conferences, at the senior PC member level). The justification given for this (at least in the AAAI/IJCAI family of conferences) was that junior reviewers will be intimidated into writing , and discussing what they really think about the paper . This justification may have had some support at one time, but I contend that it barely makes sense in the current AI Zeitgeist. To begin with, everyone knows that very few senior folks actually review for conferences anymore. Much of the reviewing is really done by junior researchers--often graduate students who themselves haven't had much experience in publishing (because, face it, we have exponentially growing number of papers). Secondly, in most conferences, inter-reviewer discussion has really become negligible (see xitter.cf/rao2z/status/170508056…). So why do we still have inter-reviewer anonymity? I contend that the knowledge that your reviews will be seen along with your name by other 3-4 reviewers of that paper may well help reign in the short/contentless/spiteful "why didn't cite my paper?"/chatGPT generated reviews. Currently, the downside for putting up such reviews is quite low--as unless the review is truly and horrendously egregious, it is not likely to get flagged to a level where it will be associated with the reviewer name. Even then, there is little practical/legal censure options available to conference organizers. (I heard countless discussions about keeping track of longitudinal reviewer behavior--but face it, AI conferences are not reddit with easy to accumulate/track karma!) We all complain eternally about reviewing quality. But we can't change the number of papers being submitted, or the fact that we can't do reviewing with just people who have a significant reputational loss to fear (there just aren't enough of them). This inter-reviewer anonymity is something we can actually change.. If you agree, consider retweeting.
It is always frustrating as an author to receive a bad quality review, and worse still it could have been wrong or comes with a inherent rejection bias. Still, making reviewers non-anonymous at any point is a slippery slope. Regardless of the current status quo, we need to preserve the ability to have critical discourse without attracting the ire of the authors. Perhaps a solution is multi-party rating system All participants of the process: Authors, Reviewers, and ACs should rate each other anonymously (double blind) with both scores and possibly comments (although trickier) based solely on the interaction during the review cycle. These ratings when aggregated across papers, across multiple review cycles in different venues would paint a better picture of the individual as a participant in this process. And this ratings page should then be made public like google scholar or a github badge. These community service ratings become a currency of goodwill, and hopefully harder to game as an individual. This could enable simple mechanism design for fair play. - don't accept a service assignment if you dont have time/expertise or expect to do a bad job. - Providing voluntary service results in goodwill currency. - If you submit papers, then you must accept service assignment. perhaps inspired by the driver/rider ratings in ride-sharing apps or host/guest ratings in Airbnb. Wonder what the community thinks.
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New tutorial! I spent 3 weeks realizing flow-matching/rectified flows can be viewed in a simple way that end-runs the usual pages of math: "Basic physics provides a 'straight, fast' way to get up to speed with flow-based generative models" Colab included! drscotthawley.github.io/blog…
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Super excited to share that our paper, "On the Relationship between Truth and Political Bias in Language Models", has been accepted to EMNLP 2024 Main! 🎉 We explore how models trained on truthful data might exhibit a political bias. #EMNLP2024
📣📣Do language models only trained to predict truth from falsehood still display a political bias? 🤔Surprisingly, it seems so!  Our paper "On the Relationship between Truth and Political Bias in Language Models”, has been accepted to EMNLP 2024
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Better late than never: announcing SANE 2024! Thursday 10/17 in Kendall Square, Cambridge, MA. Confirmed speakers: Mark Hamilton (MIT) @mhamilton723, Paola Garcia (JHU) @leibnyPaola, Zhiyao Duan (U. Rochester), Chris Donahue (CMU) @chrisdonahuey Info/RSVP: saneworkshop.org/
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Anurag Kumar retweeted
We are thrilled to announce the URGENT 2024 Challenge - a new speech enhancement (SE) competition at NeurIPS 2024: urgent-challenge.github.io/u… This challenge aims to unify diverse distortions and sampling frequencies using a single universal SE model. #URGENT2024 (1/4)
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Anurag Kumar retweeted
YODAS from @WavLab is finally here! 370k hours of weakly labeled speech data across 140 languages! The largest of any publicly available ASR dataset, now available on @huggingface datasets under a Creative Common license. huggingface.co/datasets/espn… Paper: ieeexplore.ieee.org/abstract…
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Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.
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Can machines recording an audio-visual scene produce realistic, matching audio-visual experiences at novel positions and novel view directions? We present AV-NeRF to synthesize new videos with spatial audios along arbitrary novel camera trajectories in that scene. #NeurIPS2023
At #Neurips2023 next week to talk about our paper/AV-NeRF and all things AI, Audio/Speech & Multimodal😀 AV-NeRF:Learning Neural Fields for Real-World Audio-Visual Scene Synthesis Demo: t.ly/UuXkG Paper t.ly/0oIhV (w. Susan,Chao,@YapengTian,@ChenliangXu)
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Anurag Kumar retweeted
Amazing night at @ISMIRConf - thanks to Uri, Romain, Genís, and André for playing with me. Also, "Fear of MATLAB" was the best thing I heard in years! 😭😭 – at Milan, Lombardy
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