Li Nanbo

Postdoc Fellow at @AI_KAUST (with @SchmidhuberAI) | Previously PhD at @EdinburghUni | Working on Machine Learning and World Models

[Li Nanbo @nbl_ai]

Come and check this out 🤖！！！

[Piotr Piękos @PiotrPiekosAI]

Hey #ICLR2026, We're in Rio!

Today we're presenting Huxley-Gödel Machine on the Oral 1C session (10:30 am-12, room 202 A/B.

Apart from that, we also have a poster session later at 3:15 pm in Pavilion 3!

[Li Nanbo @nbl_ai]

@braza_faouzi Hi - Yes. SlotSPE can uncover structural pathological patterns, not restricted to cancers, so we believe that it’d generalizes to other pathologies

[Li Nanbo @nbl_ai]

Excited to share our ICLR 2026 paper, SlotSPE! 🎉

Modular programming for healthcare to unlock efficiency, reveal interpretable structures, identify prognostic events, and advance trustworthy survival analysis.

Honored to work on research with a higher purpose.
See u in Rio🇧🇷

[Li Nanbo @nbl_ai]

Honored to work with:
Yilan Zhang, @ChangchunYangCS, @SchmidhuberAI, Xin Gao
Arxiv: https://t.co/o4Ab8AoPIu
Github: https://t.co/l3z2Y1tuQ2

[Li Nanbo @nbl_ai]

[3/3] More accurate, more interpretable, and more structured: not just risk prediction, but event-level morphology–molecular correspondence.

[Li Nanbo @nbl_ai]

We code only the agents that code themselves🤖🤖🤖!
Wenyi Wang, @PiotrPiekosAI, @nbl_ai, Firas Laakom, @Beastlyprime, @MatOstasze, @MingchenZhuge, @SchmidhuberAI

[Piotr Piękos @PiotrPiekosAI]

🚨Time to let agents code themselves! Meet Huxley-Gödel Machine (HGM), a game changer in coding agent development🚨
[🤖vs.🧑‍💻]HGM evolves by self-rewrites to match the best officially checked human-engineered agents on SWE-Bench Lite despite being optimized on a different dataset.

[Piotr Piękos @PiotrPiekosAI]

Authors: Wenyi Wang, @PiotrPiekosAI , @nbl_ai , Firas Laakom, @Beastlyprime , @MatOstasze, @MingchenZhuge , @SchmidhuberAI
ArXiv: https://t.co/jpQ1JQjhO4
Github: https://t.co/bZbSf0b3Ap

[Piotr Piękos @PiotrPiekosAI]

🚨Time to let agents code themselves! Meet Huxley-Gödel Machine (HGM), a game changer in coding agent development🚨
[🤖vs.🧑‍💻]HGM evolves by self-rewrites to match the best officially checked human-engineered agents on SWE-Bench Lite despite being optimized on a different dataset.

[Li Nanbo @nbl_ai]

Dear #ICLR2025 attendees, pls drop by our poster tomorrow if you’re interested in RNNs, world models, or some random chats. Looking forward to seeing you!

Sat 26 Apr 15:00-18:00
At Hall 3 + Hall 2B #317.

[Li Nanbo @nbl_ai]

@SchmidhuberAI [3/3] This design offers sophisticated state-space expressiveness, adaptability to data modularity, robustness to input permutation changes, and supports parallel linearisation. Check our paper below:

https://t.co/VnYJegT4T3

[Li Nanbo @nbl_ai]

@SchmidhuberAI [2/3] FACTS advances further: It introduces attentive routing to augment input-memory selectivity, enabling permutable memory structures such as graphs and sets.

[Li Nanbo @nbl_ai]

@SchmidhuberAI [1/3] LSTMs utilise input-memory interactions (the famous gating mechanism) for selective history compression. Mambas simplify such compression with input-only selectivity and linearised recurrence for enhanced parallelisation.

[Li Nanbo @nbl_ai]

@CianEastwood Thx man