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Showing papers from CyberAgent, Inc. Show all papers

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NeurIPS 2026KobeNTTCyberAgent, Inc.Kyushu University RIKEN AIPPhysics-informed ML & PDEs

Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems

Symplectic Neural Operators preserve Hamiltonian PDE structure, yielding long-term stability and superior energy behavior versus non-structure-preserving neural operators.

Makara Yeang, Yusuke Tanaka, Takashi Matsubara, Takaharu Yaguchi

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 3/5
medium 2/10
strict 0/5
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NeurIPS 2026NAISTNara Institute of Science and TeCyberAgent, Inc.Probing & representation analysis

StructLens: A Structural Lens for Language Models via Maximum Spanning Trees

StructLens analyzes language-model representations via maximum spanning trees, revealing middle-layer local-span organization and progressive pretraining of token units.

Haruki Sakajo, Frederikus Hudi, Yusuke Sakai, Hidetaka Kamigaito and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 0/5