I'll be at #ICML this week presenting three projects generally structured around self-evolving agents. Go to atharvas.net/icml to mark these on your calendar! Always happy to chat! Feel free to email me ([email protected]) or book a time through: calendar.app.google/Ws8qzdpLRRh...

Excited to share FormulaCode, a continually updating benchmark for evaluating the holistic ability of LLM agents to optimize codebases. Our current dataset consists of 957 tasks curated from 245477 pull requests in 70+ repositories (and growing!). 🌐 formulacode.org 🧵👇

You’ve generated 10k concepts with your favorite XAI method -- now what? Many concepts you’ve found are fairly obvious and uninteresting. What if you could 𝑠𝑢𝑏𝑡𝑟𝑎𝑐𝑡 obvious concepts away and focus on the more complex ones? We tackle this in our latest preprint!

Deadline Extended! Submit to the LM4Sci Workshop @ COLM 2025 in Montreal 🇨🇦 🧠 Large Language Modeling for Scientific Discovery (LM4Sci) 📅 New Deadline: June 30 📢 Notification: July 24 📍 Workshop: Oct 10, 2025 📝 Non-archival short (2–4p) & full (up to 8p) papers welcome!

🚨 Call for Papers: LM4Sci @COLM_conf 2025 🚨 Excited to announce the Large Language Modeling for Scientific Discovery (LM4Sci) workshop at COLM 2025 in Montreal, Canada! Submission Deadline: June 23 Notification: July 24 Workshop: October 10, 2025

I’m presenting Escher (trishullab.github.io/escher-web) at #cvpr2025 Saturday morning (Poster Session #3). Escher builds a visual concept library with a vision‑language critic (no human labels needed). Swing by if you’d like to chat about program synthesis & multimodal reasoning!

Just julia things.

Happy to announce the PySR v1.0 release! github.com/MilesCranmer... PySR lets you do high-performance symbolic regression from Python. Now, you can learn multiple symbolic expressions simultaneously! Also: + Parametric expressions + TensorBoard support + Improved search + Julia-based inference

Missing NeurIPS this year but wanted to highlight our new paper on LLM-guided genetic programming: trishullab.github.io/lasr-web/ Our method, LaSR, conditions mutation/crossover operators on (1) an LLM's general domain knowledge, and (2) LLM-generated abstractions of high-performing programs. (1/2)

Arya and I'll be at #NeurIPS presenting LaSR (trishullab.github.io/lasr-web/) on Wednesday morning 11AM PST to 2PM PST (East Exhibit Hall A-C #4003). Drop by and say Hi!