
PhD student @SCSatCMU @mldcmu Prev - @MSFTResearch @Adobe @dtu_delhi Hala Madrid!
Pittsburgh
Joined September 2022
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Abhinav Java retweeted
‼️The Bitter Lesson for context management: Giving LMs unrestricted control over their context beats human-designed SOTA!
Introducing 🩵Context Language Models (CLMs)🩵
- Natively manage their own context
- Treat context as a file
- Learn policies in CLM weights, no harness
Inspired by recent thoughts from senior researchers whom I deeply respect (e.g. @JitendraMalikCV @Michael_J_Black @Ken_Goldberg @phillip_isola), I wrote down some thoughts as a junior researcher too.
The recent Astra demos in dexterous manipulation are truly impressive. But they also raise two questions that I don’t think academia has good answers to yet:
- How should credit be assigned when an agent synthesizes many prior works into a new research result?
- How can academia attract and retain talents when so much of its incentive structure relies on credit?
I reflect on these questions in more detail in a blog post. Curious to hear what others think, and debates are very welcome!
toruowo.github.io/blog/posts…
Abhinav Java retweeted
Encouraging frontier labs to train on my research notes, just to set them back a few years. Doing what I can for AI safety
Abhinav Java retweeted
On a more personal note:
I am wrapping up my internship and looking for full-time roles in post-training and adjacent areas. At MSR I worked on agentic RL for general tool-calling on benchmarks like BFCL, Taubench, and MCPBench and on self-distillation and synthetic-environment data curation and training.
More of my work can be found on my github and website, linking below.
Please RT/QT for helping w the algo:)
Abhinav Java retweeted
Replying to @RDrfx_RMCF
Matty F is good defensively whilst providing passing range, Martinez is a muppet
Abhinav Java retweeted
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
Abhinav Java retweeted
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/uplo…
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 35B active parameter MoE with a 256K context window. Independent human raters on Surge prefer it for overall quality in blind side-by-sides versus Sonnet 4.6, and it’s achieved 97% on AIME 2025, the key measure of its general-purpose reasoning abilities.
- It's at 53% on SWE Bench Pro, placing it right alongside Opus 4.6 on one of the toughest coding benchmarks.
- And since we co-designed our models with our own silicon, MAI-Thinking-1 is optimized on our MAIA 200 chip. Benchmarking head-to-head against the GB200, we see 30% better performance per dollar as well as a 1.4x performance-per-watt gain when running our MAI models on the MAIA 200 end-to-end.
Next is MAI-Image-2.5 and its Flash variant. Two super strong models now at #2 on the leaderboards, surpassing the score of Nano Banana 2 on image editing.
Last for now is MAI-Code-1-Flash, our new inference efficient coding model, especially tuned for VS Code and GitHub Copilot CLI.
- Code-1-Flash achieves 51% on SWE Bench Pro, despite having just 5B parameters, putting it closer to Haiku in size but cheaper in cost.
All of this is the foundation for Microsoft Frontier Tuning. It lets you customize our models to create custom, company-specific agents that only you control. You can make our model, your model. Your data. Your agents. Your moat.
Early adopters are already seeing a difference. When we tuned our models for McKinsey’s tasks, MAI delivered the highest win rate, outperforming GPT-5.5 on quality, while being 10x lower on cost.
Also really excited to be collaborating with the amazing team at Mayo Clinic to jointly train a new frontier AI model for healthcare.
Our announcements today mark another milestone on the road to humanist superintelligence. You can learn more and about our other new models in our latest blog: microsoft.ai/news/building-a…
Abhinav Java retweeted
🚀 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.
Abhinav Java retweeted
[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. 🧠👇
Abhinav Java retweeted
🚀 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.
Abhinav Java retweeted
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!🧵
Abhinav Java retweeted
🚨 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.
🧵👇
Abhinav Java retweeted
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!🧵
Abhinav Java retweeted
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.
Abhinav Java retweeted
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 🚀
Abhinav Java retweeted
[Read] Characterizing Deep Research: A Benchmark and Formal Definition arxiv.org/abs/2508.04183 (1)
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 thoughts with people who might be interested.)
So, through the last few days, I've been running a crazy experiment: rebuild HVM from scratch, with AI only. Turns out there is now a model (you know which - no more ads 😑) that is already capable of writing most of my code. I give it instructions, go do something else, and come back to a (possibly) working implementation. And that's really incredible.
What I wonder is: what is still consuming my time?
I'm an experienced developer. Given enough time™, I could accomplish a lot. In a year or two, I could write a browser, an operating system, a game engine, an MMORPG. So, if AIs are truly automating my job, how come I didn't ship all these things yet? What would objectively prevent me from becoming a single-man massive SWE company, if I wanted to?
Well, other than the obvious bureaucratic matters, even in the pure coding sense, that is obviously not possible, for one reason:
The AI can only work for so long before it needs me.
Here's how this experiment went:
1. I wrote a full spec of the "next-gen HVM"
2. I asked the AI to make the first part (parser)
3. It wrote 80% correct code, but got key points wrong
4. I corrected these points (expert intelligence injection)
5. It wrote 100% correct code
6. I asked the AI to make the second part
7. ... repeat 2-5 over and over ...
About 3 days later, I have a working prototype.
I didn't write more than 1% of that code.
I spent 95% of that time playing games.
From a point of view, the AI automated 95% of my job, if we measure by time alone. Yet, from another point of view, it automated 0% of my job. After all, without the expert (me) stepping in every 30 minutes, the AI wouldn't be able to move past the very first module. That is, if I just said "implement the spec" and left it working alone, it would not complete the job. Not in 3 days, not in 3 years. I'd just come back to a bug-ridden, useless codebase, and a traumatized codebot.
Is a way, this is kinda the best case scenario for the desperate about job loss: today AI can automate most of your job, as long as it is you doing it! How convenient, isn't it? Of course, we can't count on that being true forever. If the "autonomous work time span" keeps increasing - as many expect - then, as soon as it hits the 3 day mark (48x times from where we are now), it would have been able to complete the job without any intervention. At least in theory.
Now, when (or whether) that happens, it depends on the big AI labs, and I really have no control over it. Yet, what I can do is ask:
Given whatever is the SOTA model's autonomous work time window, can I implement a new language and tooling so good that it extend that time window by a constant, but substantial, factor? Can I make the Bend2→NeoGen→AI loop so good that this same model would be able to complete this very task with way less interventions - or, even better, fully autonomously?
I'm really excited because I think the answer is "yes", and it seems like we're really close to that point here. If that really works, that would be a huge, because we'd then be able to automate the development of massive scale software, a few years before models are able to do so on their own. (I wonder if HOC should just become a SWE company at that point?) Plus it would be a massive bump on my quality of life, as I'd finally be able to stop playing League of Legends, and go play Ragnarok Online instead, like the old times 🥳
Anyway that's what's currently on my mind
Thanks for reading and have a great day
(Today I'll let the AI keep polishing ""HVM4"" as I do other things😏 on the background. I really want to see how far this goes.)


































