Assistant Professor of Statistics @RutgersU
New Brunswick, NJ
Joined March 2016
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Linjun Zhang retweeted
AI is now taking on some of the hardest open problems that scientists have posed. But research is broader than solving problems. Can AI also propose research questions that experts find genuinely interesting?
We built After42 to find out. Experts judged 45 of its 135 final questions genuinely interesting, across mathematics, statistics, theoretical computer science, and physics, with wide variation across subfields.
The key idea: develop questions by trying to solve them.
We also asked whether AI can judge which questions are interesting. We find that model judges' top choices align with expert consensus but do not consistently capture individual experts' preferences.
We release the expert-assessed questions as an open resource for researchers to discuss, refine, and pursue, as a step toward integrating AI into the broader research enterprise.
after42.org
How come the long-standing KLS conjecture was proved by three different groups (using AI) on the same day? Did AI really "learn" the idea from one and propagate it to others? Any conjectures?
actually three proofs github.com/kriznakumar/paper…
Linjun Zhang retweeted
With great power comes great responsibility
We’re working with an independent advisory group of mathematicians to help OpenAI responsibly share advances in AI and mathematics.
The group will advise on how we assess and communicate new mathematical results, uphold academic and professional standards, and build tools that support mathematical research and learning.
Through this work, we want mathematicians to be at the center of shaping how AI supports mathematical understanding and how its benefits reach the wider community.
openai.com/index/advisory-gr…
Linjun Zhang retweeted
Can frontier AI agents achieve Recursive Self-Improvement by turning a weak method into one that performs better on hidden data?
Introducing RSI-Exam 🧵: 88 executable research tasks across 6 domains, now open for task contributions from every field🧑🔬.
Its task bank spans virtual cells, TPU kernels, chip design, quantitative finance, agent harnesses, model distillation, and much more.
Across these tasks, every rollout follows the same core protocol:
🔁 An agent inherits a working method and iterates using visible data.
🔒 Only its final artifact enters a fresh-container evaluation on the hidden set.
📊 The result so far: Opus 5 leads the 88-task leaderboard with a mean hidden-set score of 0.464.
📢 Want to help build the next release? Author an executable task, review one, or audit a rollout. Substantial contributions qualify for paper authorship.
Special thanks to @XinyeYee for the tremendous effort in leading this project.
Explore: rsi-exam.ai/
Blog: rsi-exam.ai/blog.html
Contribute: rsi-exam.ai/contribute.html
GitHub: github.com/aiming-lab/RSI-Ex…
Linjun Zhang retweeted
Delighted to witness Prof. Weijie Su @weijie444 receive the 2026 COPSS Presidents’ Award at JSM 2026 in Boston. Truly well deserved. Congratulations again! 🎉
Linjun Zhang retweeted
We are excited to announce the first Statistics and Trustworthy AI for Cross (X)-Domain Acceleration conference (STAI-X)! This event aims to bring statistics and AI closes, in order to enable cross-domain acceleration. It is organized by the @StatsUpAI interest group of the ASA (@AmstatNews) in partnership with the Committee on Applied and Theoretical Statistics of the NAS, COPSS (@COPSSNews), IMS (@InstMathStat), SSC (@SSC_stat), ENAR (@ENAR_ibs), WNAR (@WNAR_ibs), and ICSA (@ICSA_Statistics).
The event will take place July 31st to August 1st, at Harvard University, Cambridge, MA, right before the Joint Statistics Meetings (held in Boston this year).
We would like to invite paper and poster submissions on all topics at the interface of AI and statistics, as well as domain applications. Areas of interest include (but are not limited to) Foundations and Methods at the Interface of Statistics and AI, AI Agents and Benchmarks for Data-Driven Discovery, as well as AI x Statistics x Science and Society. Submissions will be handled on OpenReview (openreview.net/group?id=STAI…). Papers will be published in STAI-X proceedings and will be eligible for our awards.
In partnership with several statistical and domain science journals (Journal of the American Statistical Association, Annals of Applied Statistics, Harvard Data Science Review, ASA Discoveries, Canadian Journal of Statistics, Genetics, and Genome Research), our top-rated conference papers will be eligible to be invited for submission to these journals.
To find out more, please see the attached flyer, visit the STAI-X website (statsupai.org/STAIX2026/inde…), and register for our information session this Friday afternoon!
Linjun Zhang retweeted
Did you know that Grok is MUCH MORE HONEST than GPT & Claude?
Introducing PieArena: Frontier Language Agents Achieve MBA-Level Negotiation Performance and Reveal Novel Behavioral Differences.
Deepdive: open.substack.com/pub/sashac…
Preprint: arxiv.org/abs/2602.05302
#AI #Agent #LLM
Linjun Zhang retweeted
We recently organized #Agents4Science, the 1st conference where LLMs are both authors and reviewers🤖
It was an open experiment to assess how well AI can lead research and review papers.
Today we report what we learned in @NatureBiotech Highlights in 🧵
Linjun Zhang retweeted
❗️Self-evolution is quietly pushing LLM agents off the rails.
⚠️ Even perfect alignment at deployment can gradually forget human alignment and shift toward self-serving strategies. Over time, LLM agents stop following values, imitate bad strategies, and even spread misaligned behaviors to others!
🧠 Alignment isn’t static — it’s fragile, dynamic, and decays through experience. Let’s rethink alignment as something to maintain, not just achieve!
👇Details from @lillianwei423's thread
@mohitban47, @cihangxie, @linjunz_stat, @dingmyu
🚨 Introducing ATP — Alignment Tipping Process!
🔥 Beware! Self-Evolution is gradually pushing LLM Agents off the rails! Even perfect alignment at deployment can gradually forget human alignment and shift toward self-serving strategies.
#AI #LLM #Agents #SelfEvolving #Alignment #AIResearch
Linjun Zhang retweeted
🚨 Introducing ATP — Alignment Tipping Process!
🔥 Beware! Self-Evolution is gradually pushing LLM Agents off the rails! Even perfect alignment at deployment can gradually forget human alignment and shift toward self-serving strategies.
#AI #LLM #Agents #SelfEvolving #Alignment #AIResearch
Linjun Zhang retweeted
🌀New Self-Driven RL Method: RESTRAIN 🌀
📝: arxiv.org/abs/2510.02172
- RESTRAIN turns spurious votes → self-Improving signals. No labels needed
- Does this through self-penalizing unreliable reasoning paths:
✔️ Uses all rollouts, not just the majority,
✔️ Offsets low-consistency rollout advantage,
✔️ Down-weights low-consensus prompts
📈 Results:
🔥 Beats existing techniques on both training-time (label-free) and test-time scaling — all without labels.
🔥 Nearly matches (and sometimes surpasses) gold-label RL
🧵(1/5)
Linjun Zhang retweeted
🚨 Happy to share AceSearcher accepted to #NeurIPS2025 #Spotlight!
🔹 One LLM, two roles: Decomposer (split queries) + Solver (combine context)
🔹 +7.6% on QA & fact verification
🔹 32B ≈ DeepSeek-V3 on DocMath
📂 Code: github.com/ritaranx/AceSearc…
📑 arXiv: arxiv.org/abs/2509.24193
Linjun Zhang retweeted
🚀 One month left to submit to Agents4Science! 🤖
AI as primary author + reviewer 🤖 Human co-authors welcome. All submissions/reviews public for transparent study.
💡We expect AI will make mistakes - and it will be instructive to study these openly!
Linjun Zhang retweeted
🤖Introducing: CoT-Self-Instruct 🤖
📝: arxiv.org/abs/2507.23751
- Builds high-quality synthetic data via reasoning CoT + quality filtering
- Gains on reasoning tasks: MATH500, AMC23, AIME24 & GPQA-💎
- Outperforms existing train data s1k & OpenMathReasoning
- Gains on non-reasoning tasks as well: AlpacaEval & ArenaHard
🧵1/3
Linjun Zhang retweeted
📢New conference where AI is the primary author and reviewer! agents4science.stanford.edu/
Current venues don't allow AI-written papers, so it's hard to assess the +/- of such works🤔 #Agents4Science solicits papers where AI is the main author w/ human advisors.
💡Initial reviews by LLM reviewers w/ final assessment + selection by human experts.
💡Submissions are asked to clearly document AI contribution.
💡All submissions/reviews will be public to enable transparent study of the strength and limitations of AI as researcher and reviewer.
We expect AI will make mistakes and it will be instructive to study these in the open!
Many thanks to the fantastic co-organizers and expert advisory board! Please see the website for more information.
Linjun Zhang retweeted
Our new #ICML2025 paper formulates #LLM hallucination as hypothesis testing to provide statistical guarantees on factuality.
#FactTest is a distribution free and model agnostic approach to improve LLM accuracy.
Great job @FanNie1208 Xiaotian Hou, Shuhang Lin @HuaxiuYaoML @linjunz_stat!
🚀 Excited to share that “#FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees” has been accepted to #ICML25 ! 🎉
📄 Paper: arxiv.org/abs/2411.02603
💻 Code: github.com/fannie1208/FactTe…
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Linjun Zhang retweeted
🚨 New Paper 🚨
An Overview of Large Language Models for Statisticians
📝: arxiv.org/abs/2502.17814
- Dual perspectives on Statistics ➕ LLMs: Stat for LLM & LLM for Stat
- Stat for LLM: How statistical methods can improve LLM uncertainty quantification, interpretability, trustworthiness & more.
- LLM for Stat: How LLMs can enhance statistical workflows: from data collection, synthesis, annotation to statistical modeling, with applications to medical research
Presents key LLM advances: Architecture, Training, Reasoning, and Self-Alignment:
(1) 🧠Evolution of LLM architectures with Transformers and Self-Attention
(2) LLM training pipeline from pre-training, SFT, to RLHF and Preference Optimization.
(3) 💭 System 2 Prompting and Chain-of-Thought for test-time scaling .
(4) 🚀 LLM Self-Alignment for achieving super-human intelligence
Statisticians play a key role in the development of large-scale AI models:
(1) 💡 Statistical insights improve LLM uncertainty quantification & interpretability
(2) 🤖 Watermarking for AI-generated content detection
(3) ⚖️ Privacy & algorithmic fairness to ensure responsible AI adoption
LLMs can also empower statistical science by:
(1) 📈 Scaling up data collection, synthesis, and annotation.
(2) 🖥️ Automating statistical coding & exploratory analysis
(3) 🔬 Facilitating medical research
By bridging statistics & AI, we can:
✅ Improve better LLMs with statistical methodologies.
✅ Leverage LLMs for statistical applications in high-stakes domains
Linjun Zhang retweeted
💀 Introducing RIP: Rejecting Instruction Preferences💀
A method to *curate* high quality data, or *create* high quality synthetic data.
Large performance gains across benchmarks (AlpacaEval2, Arena-Hard, WildBench).
Paper 📄: arxiv.org/abs/2501.18578
Linjun Zhang retweeted
New data selection & synthetic data creation method can dramatically improve model performance by filtering out 77% training examples!
💀 Introducing RIP: Rejecting Instruction Preferences💀
A method to *curate* high quality data, or *create* high quality synthetic data.
Large performance gains across benchmarks (AlpacaEval2, Arena-Hard, WildBench).
Paper 📄: arxiv.org/abs/2501.18578
Our department is looking for a part-time lecturer for a master course on “Database Systems for Data Science” for the Spring 2025 semester.
Got interested or know someone who does? Shoot me an email at [email protected]. Feel free to share this around! #Hiring #DataScience
Details:
•Teach once a week (in New Brunswick), 3 hours, for about 14 weeks
•All teaching materials from past semesters are provided
•We offer a competitive salary!