Introducing COMPACT: COMPositional Atomic-to-complex Visual Capability Tuning, a data-efficient approach to improve multimodal models on complex visual tasks without scaling data volume. 📦 arxiv.org/abs/2504.21850 1/10

40% with just 1 try per task: SWE-agent-LM-32B is the new #1 open source model on SWE-bench Verified. We built it by synthesizing a ton of agentic training data from 100+ Python repos. Today we’re open-sourcing the toolkit that made it happen: SWE-smith.

🗓️ Mark your calendar: The 1st #ICRA Workshop on Public Trust in Autonomous Systems (PTAS) is just two days away! We'll explore the critical question: How do we build assurances into autonomous technologies from the ground up, shaping public trust before widespread deployment?

🚀 Excited to share the most inspiring work I’ve been part of this year: "Learning to Reason without External Rewards" TL;DR: We show that LLMs can learn complex reasoning without access to ground-truth answers, simply by optimizing their own internal sense of confidence. 1/n

arxiv.org/pdf/2507.07484 machine-bullshit.github.io Princeton University and UC Berkeley published a formalized analysis on the emergent dishonesty that rlhf at scale optimizes for in large language models, They provide a taxonomy and scoring system to allow for direct indexing,

LLMs are picking up weird patterns from humans. Aligning them to be helpful actually teaches them to bullshit? Must be why I like Grok Unhinged the best. :-)

Chain of thought can hurt LLM performance 🤖 Verbal (over)thinking can hurt human performance 😵‍💫 Are when/why they happen similar? Come find out at our poster at West-320 ⏰11am tomorrow! #ICML2025

Awesome, I've been saying this for a while, inspired by Dr John Vervaeke. LLMs are formally bullshitting, yes. medium.com/@balazskegl/on… A couple of threads that may be interesting: x.com/balazskegl/sta… x.com/NandoDF/status… The connection: when we speak, we have an

A short 📹 explainer video on how LLMs can overthink in humanlike ways 😲! had a blast presenting this at #icml2025 🥳