Darshan Deshpande

While I get to learn a lot from Mariya on a daily basis, this is a piece that I resonate with a lot. I have experienced a similar existential reflection on the culture of AI research where people seem to have lost the curiosity that drives novel research. There were times when I pursued the randomest ideas - why would you think about neural nets with complex tensors and their optimizations? who thinks about alternating FFT patterns for steganography? I miss the times when I could pursue topics like these solely from the POV of curiosity. While I say this, I also feel like I am part of the industry and part of the reason why we are in this situation - maybe I as a scientist should step up and build the culture around me. In this attempt, I want to step up for the community and extend an open offer to collaborate if you are working on something that is out of the box and you are really passionate about. I have said this multiple times and I will say this again - I am not scared of my ideas being scooped - I am scared of a world where everyone's ideas are so linear that scooping is a worry for researchers. In this attempt, I will be starting a section on my personal website for curious thinkers where I freely share some of my out of the box ideas for people to use, explore and build on - just to feel the same passion and curiosity within the community again.

Today we're open-sourcing GlobeBench, 1,632 tasks that test whether LLMs can simulate RL environments well enough to train agents on. The top model, GPT-6 Astra, gets 49.6% right. No other model of the 20 we tested reaches 30%. 🧵

Sharing some deeply personal and existential reflections on scientific identity, the changing culture of AI research, the erosion of intellectual autonomy, and the growing disconnect between scientific rigor, professional recognition, and what organizations actually reward. As usual, feedback and debate are welcome and actively encouraged! mariya.fyi/posts/stranger-in…

Attention @arxiv authors: Our Code of Conduct states that by signing your name as an author of a paper, each author takes full responsibility for all its contents, irrespective of how the contents were generated. 1/

Spotlighting our newest benchmark for agentic search: DETOUR When people try to recall something in conversation, they rarely give a perfect query upfront. They say things like “that movie with the scene where…” or “the paper about…” and the assistant has to ask the right follow-up questions to get there. Existing search and agent benchmarks often miss this multi-turn, tip-of-the-tongue behavior. To more realistically evaluate it, we introduce DETOUR: Dual-agent based Evaluation Through Obscure Under-specified Retrieval, an interactive benchmark for dual-agent search and reasoning. DETOUR contains 1,011 prompts across text, image, audio, and video. In the benchmark, a Primary Agent is evaluated on its ability to identify a target entity by querying a consistent Memory Agent, testing whether models can resolve ambiguity through useful follow-up questions. Current state-of-the-art models still struggle: performance reaches only 36% accuracy across all modalities, showing that today’s agents remain weak at clarification-seeking in underspecified, real-world search settings. We hope DETOUR helps push the next generation of search agents toward better reasoning, better questions, and more robust multi-turn retrieval. arXiv Paper: arxiv.org/abs/2602.00352

RL coding agents increasingly game rewards by exploiting their semantic and syntactic weaknesses. Can LLMs detect such behaviors from live training rollouts? We find contrastive cluster analysis is key! 🚀 GPT-5.2 jumps from 45% to 63%. Humans reach 90% Paper + data 🧵