Perouz Taslakian (@PerouzT) header
Perouz Taslakian (@PerouzT) profile picture

I am an AI Research Scientist at Service Now. I am also an Adjunct Professor at McGill University & an industry member at MILA.

Montreal, QC
Joined September 2009
Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)Photo by Perouz Taslakian (@PerouzT)
๐Ÿš€ Internship Opportunities in AI Agents! At @ServiceNowRSRCH, we have 4 internship positions on AI Agents โ€” exploring robustness, privacy & collaboration. ๐Ÿง  Applicants must be registered students at a Canadian university. ๐Ÿ‘‡ Thread with details & links to apply:
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Perouz Taslakian retweeted
Did you know I gave a 3 hour lecture explaining LLM post-training last summer? Its now on youtube! youtube.com/watch?v=Ha2bAU4Qโ€ฆ Watch it on 1.5x speed and don't forget to like and subscribe. Merci Shat to Armenia LLM Summer school and organizers @PerouzT @h_khachatrian @_kire_kara_
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๐Ÿ”ฌ๐Ÿ‘๏ธ Looking for an AI intern to work on eye disease detection! Build models that combine high-res images with clinical data to automate diagnosis. ๐Ÿ‡จ๐Ÿ‡ฆ Mitacs internship (8 months, full-time, ASAP) (Must be enrolled at a Canadian university) ๐Ÿ“ฉ lnkd.in/enPiEsuJ #McGill
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Perouz Taslakian retweeted
The NeurIPS Datasets & Benchmarks Track is now the Evaluations & Datasets (ED) Track. It now treats evaluation as a scientific object of study in its own right. Datasets/benchmarks still fully in scope. ๐Ÿ‘‰Details: blog.neurips.cc/2026/03/23/iโ€ฆ We look forward to your submissions!
The Datasets & Benchmarks track is now "Evaluation and Datasets", with an expanded scope for NeurIPS 2026! Read the call for papers neurips.cc/Conferences/2026/โ€ฆ, and learn more about the changes in our blog post: blog.neurips.cc/2026/03/23/iโ€ฆ
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Perouz Taslakian retweeted
Thoughtology paper -- the study of reasoning chains of thinking models -- is now published at TMLR. Since we wrote the paper, a lot has changed. Many more models have been released with open-weights. 1. These models are no longer thinking verbosely. GPT-OSS has crisper thoughts than Qwen3/R1. 2. GPT-OSS almost never self-verifies or tries alternate solutions. 3. Qwen3 has a large bloom step (initial solution) than R1. Among commonalities: 4. All of them still have a problem-specific sweet spot (i.e., overthinking doesn't help) 5. Incorrect problems still have a longer chain length. On another note, thanks to @TmlrOrg for allowing us to submit a ridiculously long paper :). 135 pages in total. We thank reviewers and AE for their time. This is the first paper where every member of the group contributed to it! Special thanks to @saraveramarjano and @arkil_patel. We have a documentary around it taken by @CBCNews and @binhanv, hopefully you will get to see it one day. Thanks to @SimonsInstitute for letting us work on this during their LLM2 semester program. @IVADO_Qc for the funding, and @Mila_Quebec members for the feedback. Full paper: openreview.net/forum?id=BZwKโ€ฆ
๐ŸšจThoughtology is now accepted to #TMLR! We've added some new analyses, most notably: ๐ŸŒŸ We quantify rumination; repetitive thoughts are associated with incorrect responses ๐ŸŒŸ We add 2 LRMs: gpt-oss and Qwen3. Both show a reasoning 'sweet spot' See ๐Ÿ“ƒ : openreview.net/pdf?id=BZwKsiโ€ฆ
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Excited to speak at the AAAI-26 Workshop on Agentic AI Benchmarks & Enterprise Tasks (Jan 26, Singapore) ๐Ÿ‡ธ๐Ÿ‡ฌ As agents are rapidly productized, realistic enterprise benchmarks for capabilities and reliability are essential! Submit: openreview.net/group?id=AAAIโ€ฆ ๐Ÿ—“๏ธ Oct 29 cc @gneubig
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3๏ธโƒฃ BlackBox Whisperer Tuning smaller open models to collaborate effectively with large black-box LLMs. Apply๐Ÿ‘‰ tinyurl.com/SNOWBlackBox
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4๏ธโƒฃ Agent Memory Poisoning Attacks Detecting and defending against corrupted memories. Apply ๐Ÿ‘‰ tinyurl.com/SNOWMemPoisoning
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SLAM Labs presents Apriel-1.5-15B-Thinker ๐Ÿš€ An open-weights multimodal reasoning model that hits frontier-level performance with just a fraction of the compute.
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Perouz Taslakian retweeted
See you in San Diego ๐Ÿš€ #NeurIPS2025
๐ŸŽ‰ Our paper โ€œ๐ป๐‘œ๐‘ค ๐‘ก๐‘œ ๐‘‡๐‘Ÿ๐‘Ž๐‘–๐‘› ๐‘Œ๐‘œ๐‘ข๐‘Ÿ ๐ฟ๐ฟ๐‘€ ๐‘Š๐‘’๐‘ ๐ด๐‘”๐‘’๐‘›๐‘ก: ๐ด ๐‘†๐‘ก๐‘Ž๐‘ก๐‘–๐‘ ๐‘ก๐‘–๐‘๐‘Ž๐‘™ ๐ท๐‘–๐‘Ž๐‘”๐‘›๐‘œ๐‘ ๐‘–๐‘ โ€ got an ๐จ๐ซ๐š๐ฅ at next weekโ€™s ๐—œ๐—–๐— ๐—Ÿ ๐—ช๐—ผ๐—ฟ๐—ธ๐˜€๐—ต๐—ผ๐—ฝ ๐—ผ๐—ป ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ ๐—จ๐˜€๐—ฒ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€! ๐Ÿ–ฅ๏ธ๐Ÿง  We present the ๐Ÿ๐ข๐ซ๐ฌ๐ญ ๐ฅ๐š๐ซ๐ ๐ž-๐ฌ๐œ๐š๐ฅ๐ž ๐ฌ๐ญ๐ฎ๐๐ฒ ๐จ๐Ÿ ๐œ๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ž ๐ญ๐ซ๐š๐๐ž-๐จ๐Ÿ๐Ÿ๐ฌ between pure SFT, pure RL, and hybrid SFT+RL for multi-step agents. SFT โžก๏ธ RL pushes the Pareto front โ€” and it's the ๐จ๐ง๐ฅ๐ฒ strategy that closes the gap with closed models! ๐Ÿ‘‡๐Ÿงต
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๐ŸŽ‰Congratulations to all the authors for this great work -- specially to @Ahmed_Masry97 for his perseverance through the highs and lows of this project ๐Ÿ˜€ Excited to see AlignVLM accepted to #NeurIPS2025! @ServiceNowRSRCH
Excited to announce that AlignVLM got accepted to NeurIPS! ๐ŸŽ‰๐Ÿฅณ Weโ€™ll be releasing the code and sharing an updated version of the paper with reviewer feedback soon. #NeurIPS2025
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Perouz Taslakian retweeted
๐ŸšจExciting news! Our paper โ€œWebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generationโ€ is accepted for an oral presentation at EMNLP 2025! ๐ŸŽ‰ WebMMU addresses a critical gap in AI evaluation: how well can models understand and build websites? ๐Ÿงต1/n
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Perouz Taslakian retweeted
UI-Vision vs GPT-5: Still holding the crown ๐Ÿ‘‘ and far from saturation. GPT-5 has strengths in coding and reasoning, but when it comes to computer-use tasks, itโ€™s still awkward to rely on it alone. And our team's UI-Vision (ICML 2025) remains a key and still unbeaten multimodal eval framework for screen understanding and grounding. What we continue to see: focused training is essential to beat our evals, and this is exactly where open-source models have been shining. A big thanks to research teams at Microsoft, OpenCUA, and UI-Tars for actively using UI-Vision to push the limits of visual screen understanding. If you are working on VLMs or screen grounding applications for ICLR submissions, UI-Vision is the place to measure and improve your systems. And we are only getting started: our next, UI-Vision-Grounding, is on the way๐Ÿš€. It brings a larger dataset that the community can make use of, harder grounding tasks, and new training recipes to help models level up in grounding abilities. ๐Ÿ”—uivision.github.io/ ๐Ÿ“œarxiv.org/abs/2503.15661 Big kudos to all our partners and collaborators who made this possible! @ServiceNowRSRCH, @turingcom, @Mila_Quebec, @PShravannayak, @EdwardJian2, @aarashfeizi, @gspandana, @PerouzT, Qinghong Lin, @chrisjpal, @_rabiulawal, @dvazquezcv, @joanrod_ai, @RajeswarSai
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๐Ÿš€ We just released the final test split of #RepLiQA โ€”our dataset for evaluating QA on truly unseen content! ๐Ÿ“š Dataset: huggingface.co/datasets/Servโ€ฆ ๐Ÿ“ NeurIPS โ€™24: neurips.cc/virtual/2024/postโ€ฆ Big thanks to my amazing co-authors @ @ServiceNowRSRCH ! ๐Ÿ™Œ #RAG #LLMs #NLP #QA
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Perouz Taslakian retweeted
New to ML research? Never published at ICML? Don't miss this! Check out the New in ML workshop at ICML 2025 โ€” no rejections, detailed feedback, awards, and ICML tickets for selected authors. Deadline: June 10 (AoE) Submit: openreview.net/group?id=ICMLโ€ฆ Info: newinml.github.io/
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Perouz Taslakian retweeted
Thanks @_akhaliq for sharing our work! Excited to present our next generation of SVG models, now using Reinforcement Learning from Rendering Feedback (RLRF). ๐Ÿง  We think we cracked SVG generalization with this one. Go read the paper! arxiv.org/abs/2505.20793 More details on the demo, code, and models coming soon! Stay tuned ๐Ÿ’ซ
Rendering-Aware Reinforcement Learning for Vector Graphics Generation RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization
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๐Ÿš€ New paper from our team at @ServiceNowRSRCH!โฃ โฃ ๐Ÿ’ซ๐’๐ญ๐š๐ซ๐…๐ฅ๐จ๐ฐ: ๐†๐ž๐ง๐ž๐ซ๐š๐ญ๐ข๐ง๐  ๐’๐ญ๐ซ๐ฎ๐œ๐ญ๐ฎ๐ซ๐ž๐ ๐–๐จ๐ซ๐ค๐Ÿ๐ฅ๐จ๐ฐ ๐Ž๐ฎ๐ญ๐ฉ๐ฎ๐ญ๐ฌ ๐…๐ซ๐จ๐ฆ ๐’๐ค๐ž๐ญ๐œ๐ก ๐ˆ๐ฆ๐š๐ ๐ž๐ฌโฃ We use VLMs to turn ๐˜ฉ๐˜ข๐˜ฏ๐˜ฅ-๐˜ฅ๐˜ณ๐˜ข๐˜ธ๐˜ฏ ๐˜ด๐˜ฌ๐˜ฆ๐˜ต๐˜ค๐˜ฉ๐˜ฆ๐˜ด and diagrams into executable workflows. ๐Ÿ–๏ธโ†’โš™๏ธโฃ โฃ ๐Ÿ”—arxiv.org/abs/2503.21889โฃ ๐Ÿ“tinyurl.com/3utdbn97โฃ #Sketch2Flow #AI #VLM
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