Good Papers

Showing papers from Waseda University Show all papers

45%Niche pick
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ESENSC: A Polynomial-Time Axiomatic Alternative to SHAP

Kazuhiro Hiraki, Shinichi Ishihara, Takumi Kongo, Junnosuke Shino

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
69%Highly rated
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Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations

Junjue Wang, Weihao Xuan, Heli Qi, Pengyu Dai and 6 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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3/20 AI panelreviewers recommend it

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AI panel: 3 of 20 reviewers recommend it
lenient 2/5
medium 1/10
strict 0/5
57%Worth a look
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Efficient Collaborative LLM Fine-Tuning over Heterogeneous Mobile Devices via Many Backbones to One Side-Network Tuning

Xingke Yang, Liang Li, Sicong Li, Liwei Guan and 5 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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1/20 AI panelreviewers recommend it

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
80%Must read
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IRIS: Interpolative Rényi Iterative Self-play for Large Language Model Fine-Tuning

IRIS unifies self-play fine-tuning via adjustable Rényi divergence with adaptive schedules, surpassing supervised fine-tuning with fewer annotations across benchmarks.

Wenjie Liao, Like Wu, Liangjie Zhao, Shihui Xu 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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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 1/5
86%Must read
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AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models

AlloSpatial is an agentic framework that converts egocentric observations into allocentric spatial priors via cognitive mapping and reasoning harnesses, improving spatial reasoning by 5%-18% and outperforming larger general-purpose models.

Shouwei Ruan, Bin Wang, Zhenyu Wu, Qihui Zhu and 4 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 4 on Hugging Face · Code ★ 21

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 0/5
83%Must read
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When Noise Meets Long-Tail: Feature-Threshold Dual Calibration for Robust Pseudo-Labeling

FTC-Seg uses orthogonal prototype reconstruction and adaptive threshold calibration to break pseudo-label degradation cycles between imaging noise and long-tail class imbalance in semi-supervised semantic segmentation.

Ping Guo, Zhiqi Huang, Xinran Li

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 0/5