Yu He

CS PhDing @stanford | BA+MEng @Cambridge_CL

[Learning on Graphs Conference 2026 @LogConference]

📢 Call for Papers – LoG 2026
Paper submissions are now open for LoG 2026!

Submit your work on any areas broadly related to learning on graphs and geometry

🔗 https://t.co/OAVHVYCSeg

📅 Key dates (AoE):
• July 29 – Abstract deadline
• Aug 1 – Full paper deadline

#LoG2026

[Yu He @yuhe441]

@YuanqiD @MSRNE Congrats!

[Ellen Vitercik @vitercik]

Can we evaluate if LLMs can reason structurally?

I discussed this question through the lens of data structures in a talk at @SimonsInstitute. In a joint ICML'26 work with @yuhe441, Yingxi Li, and @crwhite_ml, we introduce DSR-Bench, the data structure reasoning benchmark. (1/9)

[Yu He @yuhe441]

@rishabh16_ @crwhite_ml @vitercik @rishabh16_ unfortunately I cannot personally make it but do let me know if u come to the bay next time 😁

[Yu He @yuhe441]

Very happy to announce that it’s accepted to ICML’26! ✨thanks again to the most amazing collaborators Yingxi Li, @crwhite_ml , @vitercik

[Yu He @yuhe441]

[0/n] Can LLMs 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 reason about structure—order, hierarchy, connectivity, and how parts fit together?🧩

We introduce 𝗗𝗦𝗥-𝗕𝗲𝗻𝗰𝗵: a data-structure benchmark designed to test this 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗼𝗼𝗹𝘀. Even SOTA LLMs still struggle in the hardest settings.

[Yu He @yuhe441]

@PetarV_93 congrats!!!

[Yu He @yuhe441]

@kfountou @semihcanturk_en @DobrikG @ChendiQian @sirolfg @StefanieJegelka @AspectStalence @chrsmrrs thank you so much for the pointers!!

[Yu He @yuhe441]

📢 New position paper: “𝗡𝗲𝘂𝗿𝗮𝗹 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗮 𝗖𝗹𝗲𝗮𝗿 𝗦𝗰𝗼𝗽𝗲, 𝗧𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝗰𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀, 𝗮𝗻𝗱 𝗘𝗺𝗽𝗶𝗿𝗶𝗰𝗮𝗹 𝗥𝗶𝗴𝗼𝗿”

When and where should we use Neural Algorithmic Reasoning (NAR)? NAR is an exciting emerging area at the intersection of 𝙘𝙡𝙖𝙨𝙨𝙞𝙘𝙖𝙡 𝙖𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢 𝙙𝙚𝙨𝙞𝙜𝙣 and 𝙣𝙚𝙪𝙧𝙖𝙡 𝙘𝙤𝙢𝙥𝙪𝙩𝙖𝙩𝙞𝙤𝙣 — but its foundations are still underdeveloped.

In this paper, we argue that the field needs:
✨ a sharper 𝙙𝙚𝙛𝙞𝙣𝙞𝙩𝙞𝙤𝙣 of what NAR is (and is not), including clearer distinctions from neighboring paradigms,
📐 stronger 𝙩𝙝𝙚𝙤𝙧𝙮 for expressivity and generalization,
🧪 more rigorous 𝙗𝙚𝙣𝙘𝙝𝙢𝙖𝙧𝙠𝙨 and connection to high-impact 𝙖𝙥𝙥𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣𝙨.

[Yu He @yuhe441]

Check out our paper here: https://t.co/QwN0q1et4x. We’d love to hear the community’s thoughts!

This is a joint work by Robert R. Nerem, Timo Stoll, @semihcanturk_en, @DobrikG, @ChendiQian, Solveig Wittig, @sirolfg, @StefanieJegelka, @Vitercik, and Yusu Wang; co-led by @AspectStalence and @chrsmrrs.

[Yu He @yuhe441]

@chaitjo Congratulations! 🙌

[Yu He @yuhe441]

@crwhite_ml Tagging @vitercik !

[Yu He @yuhe441]

[0/n] Can LLMs 𝘢𝘤𝘵���𝘢𝘭𝘭𝘺 reason about structure—order, hierarchy, connectivity, and how parts fit together?🧩

We introduce 𝗗𝗦𝗥-𝗕𝗲𝗻𝗰𝗵: a data-structure benchmark designed to test this 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗼𝗼𝗹𝘀. Even SOTA LLMs still struggle in the hardest settings.

[Yu He @yuhe441]

[5/n] This is a joint work with my amazing collaborators Yingxi Li, @crwhite_ml, and Ellen Vitercik.

Paper: https://t.co/ium6fmz32p
Code: https://t.co/uBl55JXMbo
Data: https://t.co/40YUd3WNEG

[Yu He @yuhe441]

The thing about a more focused, in-person conference is the spontaneous research discussions and friendships that felt natural and intimate. Grateful to be part of this community and to everyone who made LoG 2025 happen. Phoenix is great and will always hold a place in my heart.

[Learning on Graphs Conference 2026 @LogConference]

🚨 Keynote lineup for #LoG2025! 🚨

Thrilled to welcome these amazing pioneers! 🚀

- Aditi S. Krishnapriyan @ask1729 (UC Berkeley)
- Haggai Maron @HaggaiMaron (Technion / NVIDIA)
- Melanie Weber @mweber_PU (Harvard)
- Michael Galkin @michael_galkin (Google)