Abhinav Java @abhinav_java
navigating the sea of entropy | RF @Microsoft Research | Prev @Adobe, @dtu_delhi | Hala Madrid! java-abhinav07.github.io Bangalore, India Joined September 2022-
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Schoolboy football from Khalid Jamil's India. No game awareness, no tactical structure & very little technical quality shown throughout the 90 minutes as India were thrashed by Tajikistan. But don't worry, the preparation for the SAFF "World" Championship is apparently going very well. #IndianFootball
MAI-Thinking-1 is out! Excited to share what we are building and how climbing from scratch (no distillation) actually works: simple recipes, rigorous science, self-distillation, patience, and great infra. Check out our tech report has the full story of our RL climbs. microsoft.ai/wp-content/upl…
Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier. First is our text foundation model, MAI-Thinking-1, exceptionally strong on reasoning and SWE tasks. - It’s a
🚀 How should LLMs sample on hard reasoning problems during post-training and inference where direct rollouts rarely produce a correct answer? Best-of-N (e.g., GRPO) and tree search share two limitations: 🔻 Verification signals are sparse 🔻 Candidates stay within the model's own distribution We introduce BES: Bidirectional Evolutionary Search — a search framework that couples forward candidate evolution with backward goal decomposition. ✅ Works for both post-training and inference.
[1/4] The human eye doesn't process every single pixel of a video continuously—it focuses on what changes. So why are our video AI models wasting compute on redundant frames? Introducing Swift Sampling: a test-time technique inspired by the human visual system. 🧠👇
Unexpected Claude Code hack: instead of letting it write the code, ask it to generate exercises for you. Way more fun. You actually learn the codebase and you stop being a spectator to your own project. #ClaudeCode
Realtime visualizer for @a1zhang’s RLMs is live! Watch recursive decomposition, sub-LLM calls, and REPL state unfold in real time. Built directly on the inference engine with interactive visualizations. Thread with details 👇
🚀 Hiring Alert: Postdoc Positions at Microsoft Research India! I'm especially looking for candidates interested in trustworthy AI reasoning, agentic systems and verification. If you hold a PhD in CS or a related field and want to work on research that truly matters, consider applying. ⏳ Applications are reviewed on a rolling basis through April 15 Happy to answer any questions through replies here.
Legend says Fede Valverde is still running. #REALMADRİD #halamadrid #ucl
Full paper accepted to CVPR!!😀
New paper: Understanding Task Transfer in Vision–Language Models How does finetuning a model on one task affect its performance on other tasks? @karan_uppal3 and @abhinav_java are presenting this work at Unireps, NeurIPS!! 📍 Ballroom 20D ⏰ 3:45 PM – 5:00 PM Come and say Hi!🧵
🚨 Can we truly prove that LLMs won't leak your data or generate unsafe code? Almost all LLM safety evals today only give estimates. Sampling. Benchmarks. Leaderboards. Zero guarantees ❌ We present BEAVER, the first system to provably bound LLM constraint satisfaction. 🧵👇
New paper: Understanding Task Transfer in Vision–Language Models How does finetuning a model on one task affect its performance on other tasks? @karan_uppal3 and @abhinav_java are presenting this work at Unireps, NeurIPS!! 📍 Ballroom 20D ⏰ 3:45 PM – 5:00 PM Come and say Hi!🧵
@SandraWachter5 Same goes for writing, painting, etc. I think humans will continue to do things that bring them joy regardless of the ease of arriving at the final outcome. Or at least I do strongly hope so.
For once I was glad that a reviewer responded within two hours of posting a rebuttal 🥲
After 24 hours of complete silence except for that single social media statement, ICLR has now decided to disregard all author and reviewer discussions during the two week rebuttal period 🤣 Quite a surprising way to wrap up this year’s review process
Excited to share our new work on verifying LLM reasoning! Everyone loves Chain-of-Thought (CoT). LLMs can generate amazing, step-by-step solutions (called CoT). But when they make a mistake, can a human actually find it quickly? The answer is: No, not easily.
@VibeCodeTeddy @ChrisGPotts This directly supports our thesis arxiv.org/abs/2507.07634. We find that prompt optimization can also drastically fuel RL efficiency
Poured a ton into this over the past few months - late nights, endless brainstorming, and lots of debugging marathons alongside @VikParuchuri and @zach_nussbaum. Couldn’t be prouder that it’s finally in the hands of users 🚀
Launch Day 3 of 6: Layout Model Updates 🚀 If your doc parser gets layout wrong, everything downstream breaks. ⚠️ Reading order scrambled → unusable output ⚠️ Blocks missed → footnotes, financial figures, legal text lost We just rolled out major layout upgrades at Datalab to
@ziv_ravid AI reviewers for filtering noise?
Thrilled to share our latest work LiveDRBench at Ploutos! Catch us on Sept 12, 7PM PDT. #Research #Benchmarking #AI #DeepResearch huggingface.co/datasets/micro…
🔍 What is deep research & how can AI master it? Join us for a fireside chat on a new benchmark LiveDRBench w/: 🎙 @abhinav_java of @Microsoft Research India 🎙 @Ceciletamura of @ploutosai world.ploutos.dev/stream/crystal…
@corbtt This is aligned with our findings as well! For small models, prompt optimization + SFT + RL is surprisingly effective. Check out x.com/abhinav_java/s… Also we invite you to try your model on our latest challenging DR benchmark huggingface.co/datasets/micro…
TL;DR 🧠 🔹 Optimized ReAct Prompting + 🔹 SFT to exploit test-time compute + 🔹 RL finetuning to learn when to stop ➡️ Only 1k training examples ⚡ Lower inference latency 📄 arxiv.org/abs/2507.07634…
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Microsoft AI Frontier... @ms_aifrontiers
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Shashwat Gupta @ShashwatGupta23
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Yinghui He @yinghui_he_
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Amey Varhade @ameyvarhade
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Xian Li @xl_nlp
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Olga Loiek @olgaloiek
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