Research Scientist @MSFTResearch. NLP/HCI Research.
New York City
Joined April 2022
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Incredible work from Hita on a timeless challenging NLP+HCI problem: how do we help readers effectively navigate a complex answer with citation to effectively retrieve evidence. Check it out!
Commencing the annual defrost of my academic social media to share delightful projects done with delightful collaborators that I’m excited to present at #UIST2026, the first of which is: Attribution Gradients, which also got a 🏆 Best Paper Honorable Mention!
Philippe Laban retweeted
Very excited to be featured in @Nature for our work showing how GenAI is creeping into bestselling novels.
Philippe Laban retweeted
Must-read paper from Microsoft Research and Georgia Tech.
This one has lots of great insights.
They used 163,487 Reddit posts about AI agents and surveyed 73 experienced agent users to map how delegated agent work goes wrong.
The result is MANDATE, a taxonomy of 23 failure categories in seven groups. Each describes an agent that finishes the job in a way the user didn't expect.
Users rated the most severe failures as the hardest to notice.
People who had already run into a failure found it easier to spot than people who only thought it was possible.
The most common failures are often the least studied.
Agents that keep running and spend time and compute without making progress are among the most commonly reported failures, and they have the thinnest research base.
arxiv.org/abs/2610.34748
Chat with Paper: academy.dair.ai/papers/babys…
Philippe Laban retweeted
We're hiring PhD research interns to join the MSR AI Interaction and Learning team for Summer 2027. We advance the science of large-scale AI systems through research on behavior, collaboration, evaluation, learning, and adaptation.
Learn more and apply: apply.careers.microsoft.com/…
Philippe Laban retweeted
Heading to #EMNLP2026! Excited to share two papers on LLM user simulation:
🧠 KnowSim: Simulating how users learn through AI interactions, validated against real human–AI interaction data across different knowledge levels.
🎭 User Role Vector: Investigating assistant bias in LLM user simulators through internal representations and activation steering.
I’m also on the job market! I’d love to connect with folks interested in user simulation, interactive AI evaluation, and oversight for agentic systems. Feel free to DM me!
AI assistants can provide correct, comprehensive information and still be poorly calibrated to the user: too difficult for novices, redundant for experts.
KnowSim is a user simulator that tracks what a user knows and updates that knowledge as the conversation unfolds, so we can evaluate information calibration over multi-turn interactions.
Across 9 LLMs, model rankings changed with the user’s knowledge level.
📄 arxiv.org/abs/2608.17150
Philippe Laban retweeted
Time flies and I am looking for full time research positions! I am finishing my PhD at Cambridge early 2027. My research focuses on simulating human behavior with LLMs, LLM personalization, evaluating and shaping model behavior, and understanding human–AI interaction. Please get in touch if you are hiring in relavant areas!
Philippe Laban retweeted
User simulators are only as faithful as the user representations they are conditioned on.
Missing details force simulators to fill gaps with assumptions, leading to generic behavior⚠️
Introducing UserRepGen-4B, which generates representations optimized for simulation fidelity.
Philippe Laban retweeted
User simulators are trained to talk like users. But users don’t just talk, they judge.
We introduce simulators that model both: what users say and what they think of the assistant.
Philippe Laban retweeted
Please come to our poster today afternoon (session #6) at #COLM2026 on *Weightless Fine-tuning for Personalizing LLMs without actual Fine-tuning*
Supervised fine-tuning (SFT) is one of the strongest ways to personalize a model to an individual user. (In one of our recent papers with @TuhinChakr and Jane Ginsburg, for example, we used per-author SFT to emulate the writing styles of award-winning authors.)
But what if we want to personalize a model to thousands or millions of different authors/users?
SFT gets expensive very quickly:
- run a separate optimization for each user;
- store separate model/LoRA weights for each user;
- retrain as new user data arrive.
Philippe Laban retweeted
WildChat, LMSYS, ShareChat… each human-LLM chat dataset aims to capture "real-world" AI use. But do they paint the same picture?
Not quite. From user messages alone, a classifier can name a chat's source dataset, and user models trained on them inherit these signatures 🧵👇
I'm at COLM this week, presenting:
- LLMs Corrupt Your Documents When You Delegate. At today (Wednesday's) afternoon poster 4.30-6.30. Come check out our very (!) colorful poster.
- Giving a fun talk on mostly ongoing work at the Workshop on Lifelong Agents early Friday morning.
Old and new friends, let's chat!
Philippe Laban retweeted
Why can some people manage multiple coding agents while others can only manage one at a time? Led by @taolongg we worked on understanding this and piloting solutions to bridge the gap arxiv.org/abs/2609.33113
Philippe Laban retweeted
Accepted #NeurIPS2026
The Narrative Gap: Can LLMs help us Navigate Diverse Narratives Across Languages?
What information capabilities should #AI systems support to help us navigate diverse multicultural narratives in current and future information ecosystems?
Preprint out soon
Philippe Laban retweeted
Excited to share that our paper was accepted to #NeurIPS2026 E&D🎉
and stay tuned for our follow-up work that will be released soon! We present an approach for producing less homogeneous simulations that better preserve the population-level diversity of real users.
What happens when you compare the distributions of real and simulated user behaviors?
🔍 The gap is large.
We introduce a method to measure this gap and evaluate 24 LLM-based user simulators across coding and writing tasks.
@convai_uiuc @MSFTResearch @berkeley_ai
🧵 1/N
Philippe Laban retweeted
We can't build cumulative knowledge about #AI's impact on humans if we can't reproduce studies and evals
‼️ 97% of recent conference papers had replication issues👇
gricea.com is built to solve this problem
📄 arxiv.org/pdf/2609.22039
#HCI #NLProc #OpenScience
Philippe Laban retweeted
We put a 35B ternary LLM on an iPhone—in ~4 GB RAM.
Millie beats Bonsai 27B 1-bit across every benchmark we tested, with ~60% faster generation and lower memory use.
Works offline. Open weights + runtime
Try it on the App Store: apps.apple.com/us/app/millie…
Philippe Laban retweeted
there is no single "correct" behavior for user simulators. the goal is to simulate diverse behaviors, and we want to be as close to the distribution of real users as we can
our earlier work shows that there is a large distributional gap, and scaling up assistants do not lead to better user simulators --> 8b trained simulators are very competitive with GPT-5.4 and Gemini 3.1 pro.
really cool to see a large-scale effort for user simulation and excited to try out the model!
Philippe Laban retweeted
Excited to share that our paper 𝐃𝐢𝐠𝐢𝐭𝐚𝐥𝐂𝐨𝐚𝐜𝐡 was accepted to the #EMNLP2026 𝐌𝐚𝐢𝐧! 🎉
Agents are getting better at automating software tasks. But can they teach us how to use software ourselves?
We studied real expert–novice coaching interactions: 🧵
