Deqing Fu @ COLM @DeqingFu
phd-ing @csatusc on theoretical foundations of LLMs | alum @uchicago, B.S. '20 | student researcher @googleresearch | prev research intern @metaai deqingfu.github.io Los Angeles Joined November 2020-
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@zahid_pichen @GoogleResearch The TabFM model is open weight on huggingface: huggingface.co/google/tabfm-1…
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.
What's the role of small-scale research in the era of massive models? Tell us where you stand and help decide what the @MOSS_workshop panel debates at #COLM2026 by voting on the poll below! 🌱
Help us shape the panel at MOSS @COLM_conf ! We've put together a quick poll. Questions with the most split opinions will become panel discussion topics. Vote: form.typeform.com/to/UOyzADcF See more info on our website: sites.google.com/view/moss-colm…
Come and talk with Harvey at COLM! If you know any spring/summer opportunities, please DM him as well!!!
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
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.
Introducing VaSE: Value-Aware Stochastic KV Cache Eviction. Reasoning models think in CoT, bloating the KV cache. Eviction caps memory but suffers capability drop. VaSE is a training-free recipe that cuts that cost: keep large-magnitude value states, evict stochastically.
I’ll be at #COLM2026!! I’m excited to talk about (long-horizon) human–AI interaction, personalization, and multi-turn policies (and sf bakeries 😭) Alsooo, I’m on the job market and looking for postdoc/research roles in related areas! I’d also love to chat if you’re hiring :)
I'll be at COLM next week! I'll present my work on the fragility of self-improving agents at the Lifelong Agents workshop (Friday 10-11am). If you work on self-improving agents, especially on how to evaluate them, oversee them, and make them safe, I'd love to chat! ☕
@sunjiao123sun_ Thank you for bringing back Gemini 🎉
🔬 What do auto-research agents actually do differently from top human researchers? Despite all the excitement around auto-research agents / RSI, we still lack lack a clear picture of how they approach research tasks and how their strategies differ from those of expert humans. We introduce TraceML, a tool for trajectory-level analysis of agents and systematic agent-human comparisons. We compare Codex agents with Kaggle Grandmasters and uncover clear differences in their research strategies. For example, humans draw on a broader set of research skills, while agents tend to concentrate on a narrower subset. Even more interestingly, we distill the human research skills uncovered by TraceML back into Codex agents and find that they can substantially improve final outcomes! 🚀 TraceML will be presented at NeurIPS 2026 E&D! More results in the thread ↓ jerryyan123.github.io/TraceML/
Auto-research agents run ML experiments all night, but the leaderboard only shows where they end up. TraceML shows how they got there: 4,465 human and 207 agent Kaggle trajectories, labeled. Accepted at #NeurIPS2026 E&D Track and #COLM Workshop🎉 🔗jerryyan123.github.io/TraceML/
Late to the party, but happy to introduce our #ECCV2026 paper: On the Diffusibility of High-Dimensional Latents. We found that finetuning representation autoencoders (RAEs) for reconstruction can hurt diffusibility under standard velocity prediction in flow matching. Why?(1/10)
@Huangyu58589918 @rsen91 @sujaysanghavi @weihaokong0000 You may find more details in this thread: x.com/DeqingFu/statu…
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.
TabFM-Auto is a collaboration with @Huangyu58589918 @rsen91 Taman Narayan, @sujaysanghavi Abhimanyu Das and my amazing summer host @weihaokong0000. I’ll talk more in details in a separate thread coming soon.
We now release the technical report for TabFM: arxiv.org/abs/2609.37959. Together, we introduce TabFM-Auto: an LLM agent working on feature engineering on top of a frozen TabFM. You can find more details at arxiv.org/abs/2609.37989 and explore features LLMs found at deqingfu.github.io/tabfm-auto/.
Introducing TabFM, a foundation model designed specifically for tabular data classification & regression. This approach allows generation of high-quality predictions on previously unseen tables in a single forward pass. Learn more and try out the model →goo.gle/4eR7uku
This work is a collaboration with @Huangyu58589918 @rsen91 Taman Narayan, @sujaysanghavi Abhimanyu Das and my amazing summer host @weihaokong0000.
You can find more technical details in the paper arxiv.org/abs/2609.37989 and explore agents final data pipelines for all 59 datasets at deqingfu.github.io/tabfm-auto/.
Excited to share our new work: TabFM-Auto at @GoogleResearch & @GoogleDeepMind ! 🚀 Freeze the model. Evolve the pipeline. An LLM agent improves the data pipeline around a frozen tabular foundation model—gaining 228 Elo and ranking #1 overall across 51 TabArena datasets. Paper: arxiv.org/pdf/2609.37989 Project + interactive results: deqingfu.github.io/tabfm-auto/
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