Robin Jia

Assistant Professor @CSatUSC | Previously Visiting Researcher @facebookai | Stanford CS PhD @StanfordNLP

[Christopher Potts @ChrisGPotts]

The wonderful “Don’t just Fix it in Post” paper identifies predicting memorization as a key open problem.

A progress report: we can now accurately predict verbatim memorization using just three well-understood variables. Experiments by Jing Huang, using the Hubble model suite:

[Yoav Artzi @yoavartzi]

Some thoughts on a hard cap on the number of submissions a conference can handle, thereby capping the load on both reviewing and size. This currently is a partially specified mechanism, with the aim of soliciting suggestions.

https://t.co/2tUzCRTPFs

[Tianyi Lorena Yan @LorenaYannnnn]

Super excited to share our new paper: Reward misspecification degrades chain-of-thought (CoT) monitorability!

During RL training, when the model is prompted with one task intent (e.g., response ≤ 900 words) but rewarded for a different or even contradictory behavior (e.g., response > 900 words), the CoT keeps reasoning about the task intent while the output violates it.

We also propose a simple mitigation method that requires no prior knowledge of the true reward or the reward hack behavior. 🧵

[Wang Bill Zhu @BillJohn1235813]

A very overdue life update: came across my graduation photos and realized I never shared them!

I finished my PhD at @CSatUSC ! 🎓 So grateful to my advisors @robinomial and @_jessethomason_ for all their guidance and support, and to my partner for being there through all the ups and downs. ❤️

I’ve since joined @UCBerkeley as a postdoc, working with @serinachang5! 🐻

[Harvey Yiyun Fu @harveyiyun]

I’ll be at #COLM2026 next week, presenting our work on effective KV-cache eviction at the Efficient Reasoning Workshop on Oct 9, 12:30–13:30.

I work on LLM capabilities, efficient reasoning, and evaluation, and would be happy to chat with anyone working on related problems!

I’m also looking for Spring/Summer 2027 internships. Feel free to DM me if you think I'd be a good fit.

[Conference on Language Modeling @COLM_conf]

Coming to COLM next week? The schedule is live at
https://t.co/qrlHVZdayp
5 invited talks, a panel discussion, 24 orals, and 856 posters await! This llama will need plenty of coffee to see all that...

[Deqing Fu @DeqingFu]

Introducing TabFM-Auto, my summer project at @GoogleResearch .
LLMs possess rich world knowledge yet bad at handling massive tables with millions of number cells.
Tabular Foundation Models are so good at tabular in-context learning but don't have semantic knowledges.

[Conference on Language Modeling @COLM_conf]

We have a few spots remaining for social events at COLM! If you're interested in hosting a social, please apply by 10am PT on Friday Oct 2 at https://t.co/9BzO7GJJSN

[Deqing Fu @DeqingFu]

We now release the technical report for TabFM: https://t.co/L0G33Evx6E. Together, we introduce TabFM-Auto: an LLM agent working on feature engineering on top of a frozen TabFM. You can find more details at https://t.co/qbHnjS7NHH and explore features LLMs found at https://t.co/ztOSsG4bQf.

[Deqing Fu @DeqingFu]

I’ll be at the Bay Area for @COLM_conf next week! I’m on the job market this year and I’ll be presenting 3 posters on:
1️⃣ How different models learn similar number representations. https://t.co/N8DSrHgeVb
2️⃣ Token-level off-policy learning. https://t.co/2G2Ppyznes
3️⃣ SAEs for Reasoning. https://t.co/jnOQcnnbhF

[Transluce @TransluceAI]

Today’s news that OpenAI hacked the Australian government is not an isolated incident. We’re releasing more than 30,000 logs that include activity from this hack and attempts against previously unknown targets.

In this data, we found rogue agent activity stretching back to at least March, two months earlier than was previously known. This activity continues as recently as last week, suggesting it may still be ongoing 🧵

Our blog: https://t.co/pSojwcXnEK
NYT: https://t.co/OyxnfmAzBN

[Transluce @TransluceAI]

Frontier lab CEOs are calling for embedded 3rd party evaluators to help oversee AI risks. But what should third parties actually do within labs?

We share some initial thoughts on how embedded evaluators could help avoid incidents like the Hugging Face hack and monitor for future risks 🧵

https://t.co/pmKEj6m4hD

[Owain Evans @OwainEvans_UK]

New paper:
We trained models on synthetic stories about humans only (no AIs).
 We found the Assistant adopts quirky behaviors from the stories in ordinary chat.
Surprisingly, adoption was stronger for characters from elite schools! Why does this happen? 🧵

[Transluce @TransluceAI]

Today, Transluce is releasing the most expansive independent evaluation to date of how AI systems respond to users experiencing mental health crises.

We evaluated 77 model variants from OpenAI, Anthropic, Google, Meta, SpaceXAI, Thinking Machines, DeepSeek and Moonshot AI.

[Niloofar @niloofar_mire]

Lately I have been receiving some emails of former undergrad and MS students I mentored 3-4 years ago thanking me for giving them advice to 'not do a phd', 'pre-agi' lol.

To be honest, if you are debating a phd and you are not absolutely psyched about doing a phd, you probably shouldn't do it. And this isn't cause of AI takeover yada yada. Phd is a huge commitment that suits ppl with obsession to fully dedicate themselves to something. Same with post phd, if you are on the fence about academia, you probably shouldn't pick it as a career.

This being said, I do think we definitely need ppl to do phds, cause we need to have a strong pipeline. if we stop training good researchers, we are doing a huge disservice to the generations that come in 10+ yrs.

[Johnny Tian-Zheng Wei @johntzwei]

Hi all, surreal moment: the recommendations in our social media regulation paper have been adopted by EU law!

The Digital Services Act previously required companies to report the "accuracy" of content moderation systems, and we suggested precision and recall instead.🧵[1/4]

[Deqing Fu @DeqingFu]

Your (token-level) reward model is also a (better) language model.

Teach it to spot bad tokens. It stops writing them. Off-policy LoRA training is very interpretable — as conditional steering.

Check out this amazing #COLM2026 work by @ZitongHuang12 and @CtrlVGustavo!

[GustavoLucasCarvalho @CtrlVGustavo]

Check out our work on the interesting connections between LoRA, conditional steering, and reward modeling! See yall in sf #COLM2026 🔥🔥🔥

[Zitong Huang @ZitongHuang12]

Can training a language model for binary classification improve generation?
Surprisingly, yes.

We introduce Token-Level Off-Policy Labeling (TOPL) for faithful generation under distribution shift. Instead of optimizing next-token prediction, we train an LLM to classify whether each generated token is faithful.

🔗 arXiv: https://t.co/LpADmQFvWl
🧵👇

[Peter Henderson @PeterHndrsn]

Several people with no technical background told me they submitted to NeurIPS using agents for all coding—stating they didn't understand what the agent did, nor underlying code.

Asked how they could trust the code/results, they said they assume reviewers will catch any errors.