Nanyun (Violet) Peng

This account is verified. Learn more

@VioletNPeng

Associated Professor@UCLA (leave), Scientist and manager@Google. AI and Infra, AI for science, AI creativity, event. (she/her/hers). Opinions are my own.

Los Angeles, CA violetpeng.github.io Joined August 2012

569 Following 8K Followers 848 Posts

Latest posts 5

Pinned post

Nanyun (Violet) Peng @VioletNPeng Nov 15, 2024

Thrilled, grateful, and humbled to have won 3 outstanding paper awards at #EMNLP2024!!! Not even in my wildest dreams. Immense thanks to my amazing students and collaborators!
All three works are on evaluating LLM’ abilities in creative narrative generation. 🧵👇

uclanlp @uclanlp Nov 15, 2024

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

Nanyun (Violet) Peng @VioletNPeng Oct 20

Thanks AK @_akhaliq for featuring our work!
Excited to share OpenVLHarness, a multimodal agent that inspect, compute, and reason over evidence.

The technical core:
• A capability layer gives consistent interfaces for various tools such as perception and search, and renders their outputs into observations the model can inspect.
• Persistent multimodal memory retains images, masks, numerical arrays, and web references under reusable handles.
• A coding agent computes over those artifacts to combine evidence across tools.

Across 22 benchmarks and 8 backbones, we see average gains of 9.7–12.6 points over matched no-tool baselines. Evidence acquisition and additional reasoning are complementary!

A joy collaborating with this UCLA × Apple team. Huge shoutout to @yu_bryan_zhou, @cfyang58, @RuiSun94013021, @DiWu0162 , Sibo Peng, Mingyang Zhang, Zhen Yang, Ruiyi Zhang, @zhegan4 and @kaiwei_chang! 🎉

AK Verified account @_akhaliq Oct 20

OpenVLHarness

Real World Visual Agentic Tool-Use with Multimodal Memory

paper: huggingface.co/papers/2610.26…

View quoted post on X

Nanyun (Violet) Peng @VioletNPeng Oct 14

Super excited to share this work from our amazing interns and team at @Google, co-led with awesome @chunliang_tw!

Meet DuoDiscover: a full-stack code optimization agent. Its secret sauce is co-discovering the optimization target AND a cheap proxy evaluator (a "yardstick") at the same time.

A quick spoiler of the results:
🏆 #1 on NVIDIA's SOL-ExecBench
🐍 2× the human experts on SWE-fficiency
🚀 1.92× on vLLM; 1.44× on FreeToken
🔥 2.68× over production TPU kernels
🖥️ Beats the best human and AI-designed CPU prefetchers

Optimizing AI infra. Accelerating science. Dive into more details below 👇

Dilara Soylu Verified account @dilarafsoylu Oct 14

AI is cracking math problems. What's holding it back in engineering?

The cost of evaluation. Agents can generate ideas in seconds, but testing them can take hours or days.

💡 Meet DuoDiscover: where evaluation is part of what agents discover 👇🧵

Some highlights:
🏆 #1 on NVIDIA's SOL-ExecBench
🐍 2× the experts on SWE-fficiency
🚀 1.92× on vLLM; 1.44× on FreeToken
🔥 2.68× over production TPU kernels
🖥️ Beats the best human and AI-designed CPU prefetchers

View quoted post on X

Nanyun (Violet) Peng @VioletNPeng Jun 5

My first paper at Google is out! Thank you @rohanpaul_ai for highlighting LEAP.

To share more thoughts on this direction: I strongly believe that as models generate longer and more complex proofs, automatic formal verification will be the key to the future of AI for math, and I'm bullish on using general LLMs + agentic framework for this task.

As we started with competition math in LEAP for rigorous benchmarking purposes, we've already started to venture into research math.
- Solved Erdős problem 527 (zero web search).
- Partially formalized Knuth's cycle problem even case which resulted in ~4000 lines of Lean code.

Please check out all of our solutions here: github.com/google-deepmin…

I'm incredibly proud of this work, and we are just getting started. More to come!

Rohan Paul Verified account @rohanpaul_ai Jun 4

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"

View quoted post on X

Nanyun (Violet) Peng @VioletNPeng Apr 21

Congrats @ysu_nlp for this exciting launch! Cannot wait to see what will come out from @NeoCognition!

Yu Su Verified account @ysu_nlp Apr 21

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.

View quoted post on X