Yu Su (@ysu_nlp) / X

Yu Su

2,035 posts

Yu Su

@ysu_nlp

co-founder @NeoCognition | prof. @osunlp | sloan fellow | building towards abundance of specialized intelligence

Columbus, OH

Joined March 2013

Pinned
Yu Su
@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.
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Yu Su
@ysu_nlp
Dec 7, 2023
Hi @emilymbender, I'm one of the lead authors of MMMU. I can certify that 1) Google didn't fund this work, and 2) Google didn't have early access. They really like the benchmark after our release and worked very hard to get the results. It doesn't take that long to eval on a
@emilymbender.bsky.social
@emilymbender
Dec 7, 2023
Replying to @emilymbender
Returning to transparency, I see that they point to MMMU, which was published on arXiv (not peer reviewed) on November 27, 2023. Google must have had early access to this work, which I suspect means that Google funded it, but the paper doesn't acknowledge any funding source. /12
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Yu Su
@ysu_nlp
Jan 3, 2024
Generalist web agents may get here sooner than we thought---introducing SeeAct, a multimodal web agent built on GPT-4V(ision). What's this all about? > Back in June 2023, when we released Mind2Web (osu-nlp-group.github.io/Mind2Web/) and envisioned generalist web agent, a language agent
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Yu Su
@ysu_nlp
Dec 7, 2023
Replying to @emilymbender
(this will be the last response just for the record; this type of engagement is not why I use this app) 1. Dataset was released along with the paper. again, eval on a dataset of this scale really doesn't take long, especially for google 2. this was an one-off project
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Yu Su
@ysu_nlp
Sep 28, 2025
> working on semantic parsing in PhD > didn't even have its own track at ACL > it's a dead area, people say > had ~100 citations when graduating > but natural language programming is always the dream > 'Let machines understand human thinking. Don’t let humans think like machines'
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Yu Su
@ysu_nlp
Nov 13, 2024
Sharing the slides of my talk at Princeton yesterday--"A holistic and critical look at language agents": ysu1989.github.io/resources/lang… LLM-based language agents are exciting, but it's also undeniably a quite chaotic space: are agents the next big thing, or are they just thin wrappers
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Yu Su
@ysu_nlp
Oct 10, 2024
People into agents, let me pitch something to you: 🌟 An agent that works across every platform (web, desktop & mobile) 🌟 Visual perception only, no messy & often incomplete HTML or a11y tree 🌟 SOTA performance across 6 agent benchmarks Sounds too good to be true? Continue ⬇️
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Yu Su
@ysu_nlp
Dec 11, 2023
Introducing BioCLIP: A Vision Foundation Model for the Tree of Life imageomics.github.io/bioclip/ A foundation model that strongly generalizes on the tree of life (2M+ species), outperforming OpenAI CLIP by 18% in zero-shot classification, and supports open-ended classification over
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Yu Su
@ysu_nlp
Nov 18, 2024
We have received many requests for slides and recordings for the language agent tutorial, and have been working hard on the release. The slides are now available! tinyurl.com/language-agent… We are still working on releasing the recording. The tutorial website will be the one-stop
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Yu Su
@ysu_nlp
Feb 20, 2024
Q* from OpenAI and tree-of-thought reasoning triggered a lot of enthusiasm on augmenting LLMs' reasoning/planning capabilities with search. But is search really the panacea for LLMs? Answer from our new study @osunlp: Not quite yet. TLDR: For advanced planning methods like tree
Ziru Chen
@RonZiruChen
Feb 20, 2024
LLM planning methods, such as tree search, are critical for complex problem solving, but their practical utility can depend on the discriminator used with them. Check out our new findings: arxiv.org/abs/2402.10890 (1/6)
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Yu Su
@ysu_nlp
Jan 1, 2025
My 2024 predictions (agents, multimodality, synthetic data, sparsity) all came true, but those were generic and somewhat obvious in hindsight. So I'll make some bolder, more concrete predictions about AI in 2025: 🤖 On agents 1. We will surely see a rapid improvement in AI
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Yu Su
@ysu_nlp
Jun 11, 2025
📈 Scaling may be hitting a wall in the digital world, but it's only beginning in the biological world! We trained a foundation model on 214M images of ~1M species (50% of named species on Earth 🐨🐠🌻🦠) and found emergent properties capturing hidden regularities in nature. 🧵
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Yu Su
@ysu_nlp
Nov 21, 2024
Personally I think planning is the biggest bottleneck for language agents. So I'm super excited to introduce model-based planning, a new planning paradigm for LLM-based language agents––better than ReAct while safer and faster than tree search. ReAct-style planning is easy to
Yu Gu
@yugu_nlp
Nov 21, 2024
❓Wondering how to scale inference-time compute with advanced planning for language agents? 🙋‍♂️Short answer: Using your LLM as a world model 💡More detailed answer: Using GPT-4o to predict the outcome of actions on a website can deliver strong performance with improved safety and
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Yu Su
@ysu_nlp
Oct 23, 2025
This is huge mistake and loss for Meta
Yuandong Tian
@tydsh
Oct 23, 2025
Several of my team members + myself are impacted by this layoff today. Welcome to connect :)
56K