🚀Our BiAlign Workshop @NeurIPS2026 is out! Call for Papers on Dynamic Alignment in Human-AI Coupled Systems 👫<->🤖! AI is no longer a one-shot tool — it's a long-term participant in high-stakes domains. AI shapes human trust, values, and behavior; humans reshape AI's objectives in turn. 🔄 Yet mainstream alignment assumes static preferences, missing dynamic and interaction-driven risks: sycophancy, overreliance, value-action gap, and LLM dark patterns. 💡Building on our BiAlign Position Paper ( our NeurIPS 2025 Tutorial ( and prior BiAlign Workshops (ICLR'25, CHI'26), this workshop moves toward Dynamic Alignment: alignment as continual learning, adaptation, and co-evolution in human-AI coupled systems. 🌐 Website: 📩 OpenReview: 🤝 Join our Program Committee: ⏰ Deadline: Aug 29, 2026 (AoE) ← 4 weeks away! 📅 NeurIPS 2026 (Sydney & Virtual), Dec 11 or 12 💗Many thanks to our amazing co-organizers! --💫 Prof. Yoshua Bengio @Yoshua_Bengio, Dr. Divy Thakkar @divy93t, Profs. Dawn Song @dawnsongtweets, Joan Bruna, Nick Haber @nickhaber, and Gordon Dai @DaiGordon11945, Vivek Myers @vivek_myers. Wecome join us 🤗 — we'd love to see you in Sydney (or online)! Feel free to RT & share. Thanks 🙏! #NeurIPS2026# #HAIAlignment# #BiAlign#

During long-horizon task planning, a robot must decide what to do, while ensuring each action is geometrically feasible. To this end, we propose 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗶𝗻𝘁𝗲𝗿𝗹𝗲𝗮𝘃𝗲𝗱 𝘃𝗶𝘀𝗶𝗼𝗻-𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀.

Everyone's asking what AI can do for their job. We asked if it could teach a 6-year-old. Introducing Ello 2.0: reading, math & more for ages 4-9. Free tier for all. If we want AI to force-multiply humanity, let's start with teaching our youngest.

🧵(1/9) Sparse RL for reasoning has an exploration problem. It can only reward solutions the model already stumbles into. On hard problems, that means lots of zeros and very little signal. SFT and self-distillation attack this with reference solutions as targets to match. Instead, we use them as reward scaffolds: a dense signal at both the outcome and process level. Introducing ExpRL: RL-based mid-training that improves exploration by scoring the model’s own attempts against the reference via an LLM judge. What we find: • A stronger policy straight out of mid-training (higher pass@1 and pass@k) • Still ahead after downstream sparse-reward RL • Holds across domains – math & STEM • Scales to a larger policy graded by a smaller judge

LLMs can shift people's beliefs. But most persuasion studies only check beliefs before and after a conversation. We built PersuasionTrace to measure beliefs turn by turn, so we can study how belief updates actually unfold.

Ever wonder why we drop $1,000s on a Chanel bag or queue for ages for a Labubu doll? Status signaling drives a lot of human behavior, but how certain things become potent status symbols has a remained a big puzzle in social science In our new paper we: 1. Synthesize the literature on this topic and propose a generative model of status signaling through the theory of appropriateness: people imitate what "someone like us" is supposed to want, display, and value 2. Show that we can simulate the theory to demonstrate how status symbols emerge with LLM-agent societies in Concordia @jordigraumo @WilCunningham Sasha Vezhnevets and @jzl86

Are you attending @cogdevsoc 2026? Come join us for an afternoon pre-conference workshop on motivation in development! We'll bring together developmental, educational, and computational perspectives to ask: Can we build a unified account of motivation across the lifespan? ✨

Can LLMs use ToM to genuinely persuade you, or do they just use good rhetoric? In our new preprint, we use the MINDGAMES framework to test this. Surprisingly, LLMs like o3 can be incredibly effective persuaders *without* actually understanding your mental states. 🧵👇