Big congrats 🎉🎊🍾 to UCLA PLUS lab @VioletNPeng and collaborators won 3! !outstanding paper awards at #EMNLP2024 👏👏👏

Another great paper from Google. Shows general LLMs can solve formal math by planning proofs and checking each step. Raised general LLM performance from under 10% to 70%. A general LLM failed badly when asked to write full formal proofs in 1 try, but became much stronger when it planned, split the work into smaller claims, reused past claims, and learned from Lean’s feedback. The paper shows the weakness was not just the model’s math ability, but the way it was being used - the absence of structured interaction with a verifier. The key idea is that the model does not try to write one giant perfect proof at once, because that usually fails on long and tricky problems. Instead, LEAP stores the proof as a graph of goals and subgoals, so useful lemmas can be reused instead of rediscovered every time. The authors tested LEAP on Putnam 2025 and a new Lean benchmark built from 60 IMO-style problems, where ordinary one-shot proof writing did very poorly. LEAP solved all 12 Putnam 2025 problems and raised general LLM performance on the Lean IMO benchmark from under 10% to 70%. ---- Link – arxiv. org/abs/2606.03303 Title: "LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks"

Another great paper from Google. Shows general LLMs can solve formal math by planning proofs and checking each step. Raised general LLM performance from under 10% to 70%. A general LLM failed badly when asked to write full formal proofs in 1 try, but became much stronger when it planned, split the work into smaller claims, reused past claims, and learned from Lean’s feedback. The paper shows the weakness was not just the model’s math ability, but the way it was being used - the absence of structured interaction with a verifier. The key idea is that the model does not try to write one giant perfect proof at once, because that usually fails on long and tricky problems. Instead, LEAP stores the proof as a graph of goals and subgoals, so useful lemmas can be reused instead of rediscovered every time. The authors tested LEAP on Putnam 2025 and a new Lean benchmark built from 60 IMO-style problems, where ordinary one-shot proof writing did very poorly. LEAP solved all 12 Putnam 2025 problems and raised general LLM performance on the Lean IMO benchmark from under 10% to 70%. ---- Link – arxiv. org/abs/2606.03303 Title: "LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks"

Another great paper from Google. Shows general LLMs can solve formal math by planning proofs and checking each step. Raised general LLM performance from under 10% to 70%. A general LLM failed badly when asked to write full formal proofs in 1 try, but became much stronger when it planned, split the work into smaller claims, reused past claims, and learned from Lean’s feedback. The paper shows the weakness was not just the model’s math ability, but the way it was being used - the absence of structured interaction with a verifier. The key idea is that the model does not try to write one giant perfect proof at once, because that usually fails on long and tricky problems. Instead, LEAP stores the proof as a graph of goals and subgoals, so useful lemmas can be reused instead of rediscovered every time. The authors tested LEAP on Putnam 2025 and a new Lean benchmark built from 60 IMO-style problems, where ordinary one-shot proof writing did very poorly. LEAP solved all 12 Putnam 2025 problems and raised general LLM performance on the Lean IMO benchmark from under 10% to 70%. ---- Link – arxiv. org/abs/2606.03303 Title: "LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks"

🚨Model editing in practice often collapses with catastrophic forgetting! Meet SPHERE🌐: an energy-regularized method that keeps weights uniformly distributed on hyperspheres, making sequential editing stable. Paper: arxiv.org/abs/2510.01172 Code: github.com/PlusLabNLP/SPHERE

🚨 You can bypass ALL safety guardrails of GPT-OSS-120B 🚨❗🤯 How? By detecting behavior-associated experts and switching them on/off. 📄 Steering MoE LLMs via Expert (De)Activation 🔗 arxiv.org/abs/2509.09660 🧵👇

Introducing @NeoCognition, the agent lab for specialized intelligence. Everyone needs experts, but human expertise does not scale. Backed by $40M seed funding, we build self-learning agents that specialize across domains to make expertise abundant.

Accelerating Science track: A century of scientific progress in one decade - that's the target. 👑Accelerating the Queen of Sciences, @UCLA Teaching AI to wonder, conjecture, and discover the way a mathematician does. Every hard science benefits if this works. @sahaiamit @kaiwei_chang Raghu Meka @VioletNPeng @terrence_tao @WeiWang1973 🌦️Actionable AI Weather Forecasts for Developing Economies, @UChicago Open-source AI weather forecasting infrastructure for developing economies, with millions of farmers already reached with better monsoon forecasts. @WillettBecca @ianfoster @turbulentjet Michael Kremer

Multilingual Routing in Mixture-of-Experts LLMs We present (1) an in-depth analysis of how MoE LLMs route multilingual texts, with very clear patterns (2) a router intervention (steering) method that leads to consistent multilingual improvements! 🧵1/4 arxiv.org/pdf/2510.04694

(2/4) Nanyun (Violet) Peng @VioletNPeng @UCLA is developing MAP-SE, a Multi-Agent Persuasion Simulation Engine that studies how influence emerges across adaptive agents with long-term memory—moving beyond simple one-on-one interactions. #AIResearch #AgenticAI

✨ Very overdue update: I'll be starting as an Assistant Professor in CS at University of Minnesota, Twin Cities, Fall 2026. I will be recruiting PhD students!! Please help me spread the word! [Thread] 1/n

🌏How culturally safe are large vision-language models? 👉LVLMs often miss the mark. We introduce CROSS, a benchmark of 1,284 image-query pairs across 16 countries & 14 languages, revealing how LVLMs violate cultural norms in context. ⚖️ Evaluation via CROSS-EVAL 🧨 Safety fine-tuning & DPO for alignment 📄 Paper: arxiv.org/pdf/2505.14972 🔗 Data & Code: github.com/haoyiq114/CROSS

@VioletNPeng served as the Program Co-Chair for #EMNLP25, one of the largest NLP conferences, which received over 8,000 submissions and drew 6,000 participants.