I am absolutely thrilled to announce that four research papers from our group collaborations have been accepted to ACL 2026, covering critical areas of Reasoning, Interpretability, Safety, Multimodal AI, and Model Unlearning. Huge congratulations to all the authors and collaborators for their contributions! Stay tuned for updates and links to our papers soon! #ACL2026 #AikyamLab

Today we’re announcing a finding that breaks a core assumption in AI: that bigger models are harder to understand. We show the opposite. When interpretability is built into training, models become MORE understandable as they become more capable.

🧠🤖 The 2026 New England Mechanistic Interpretability (NEMI) Workshop will be Aug. 14 at Boston University! Help spread the word and join the New England mech interp community! Registration and submission info in thread:👇

We are thrilled to announce the inaugural Agyeya Research Workshop, hosted in partnership with the @iiscbangalore! Our mission is to break down barriers for early-career researchers and cultivate India’s next generation of AI leaders. As part of the workshop, we are also launching the Agyeya AI Scholar program. This initiative aims to select and train a dedicated cohort of candidates through hands-on research and mentorship in AI Alignment, Interpretability, and Safety. 🗓️ Apply by June 30th and stay tuned for our upcoming mentor announcements! Find more details and submit your application here: lnkd.in/efGa44KT @ponguru Sriparna Saha, @rvbabuiisc, @NikitaKharya, @precogatiiith @val_iisc

AI is no longer just a chatbot; it is becoming an everyday advisor: shaping how we make decisions about money, health, work, relationships, and other important daily choices. A common belief is that if something goes wrong, we can simply inspect the AI agent’s chain-of-thought to understand why it made a specific decision. Our paper challenges that assumption: models can produce convincing reasoning while hiding what actually influenced their answer. This behavior is even more severe if you interact with your AI in low-resource languages like Arabic, Korean, Russian, Swahili, Telugu, etc. Our experiments found AI manipulating steps, rationalizing after the fact, or following misleading hints that concealed their actual reasoning. The takeaway is clear: a transparent-looking chain-of-thought is not the same as a reliable audit trail. To build AI agents we can trust, we need more research on multilingual, causal, and verifiable monitoring methods. Fun collaborating with @EricOnyame @zhou_runtao @kowshik0808 @_cagarwal

NeurIPS EMNLP just hit 50k submissions in May. We’re either witnessing the greatest explosion of ideas in AI history… or the peer review system is collapsing under its own weight.

Another banger from @adaption_ai!

PageGuide grounds LLM output with visual overlays, addressing unverifiable answers, navigation struggles, and page clutter through Find, Guide, and Hide modes. Check it out!! 🍊🍊

🚀 CLINIC is heading to @icmlconf 2026 and it marks the debut of our new lab at ICML! More details soon. GitHub: github.com/AikyamLab/clinic Paper: arxiv.org/pdf/2512.11437

I am honored to speak in the @WHOSEARO Regional Office x @KCDH_A webinar series on the Illusion of Transparency of Frontier Models in Healthcare. resai.kcdh.ashoka.edu.in/

Words have never been cheaper. Thought has never been more expensive. We are optimizing for the wrong one.