Manuel Cherep

@sanskxr02 Thank you!

@manuelcherep VLMs are criminally underutilised - great work!

Some decisions we make with our eyes 👀, but what about VLMs? Do they have structured, exploitable visual preferences that we can discover systematically before adversarial actors do? In our new paper, we propose a new optimization method for this and show substantial effects on VLMs’ decisions.

Do you see like an agent? Try it yourself: visual-persuasion-website.vercel.app Paper: arxiv.org/abs/2602.15278 Co-Authors: Pranav M R, Pattie Maes (@PattieMaes), Nikhil Singh (@nikhilsinghmus)

In our recent ICLR 2026 paper, we showed how to study other kinds of sensitivities in agent behavior by using counterfactuals with our new framework (ABxLab) x.com/manuelcherep/s…

@_colourmeamused @thegautamkamath Australia is not the global south :) It’s just far from every ML hub worldwide. The closest would be Singapore.

@manuelcherep @thegautamkamath Why do you hate the global south Manuel??

#NeurIPS2026 will be held in Sydney, Australia! #ICML2017 was also in Sydney and was an absolute blast

Everything online exists because someone had a reason to put it there. 🧐 LLMs process internet data, but do they consider why something was said 🤨 in the first place? NO 🛑 - in real online recommendations, LLMs get < .2 correlation with rationally weighing others' intent 🥴

Work w/ Chengtian Ma, Abigail Xu, Maya Shaked, @PattieMaes, and @nikhilsinghmus 🧵9/9

ABxLAB offers: ✅ An open-source man-in-the-middle testbed for real web environments ✅ A scalable consumer choice benchmark for agentic decision-making ✅ A dataset of causal effects of ratings, prices, and nudges across 17 LLMs 📦 Code: github.com/PapayaResearch… 🧵8/9

🚨New Preprint 🚨 Current agent evals mostly measure competence, but miss behavior e.g. are their decisions stable, rational, manipulable, human-like? We introduce ABxLAB, a framework for studying agent behavior. Using it we create an agentic consumer behavior benchmark. 🧵1/9