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Continuous p-adic Optimization

Julian Salazar, Dimitri Kanevsky, Matt Harvey, Pascal Getreuer and 1 more

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

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0/20 AI panelreviewers recommend it

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
71%Highly rated
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The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality

The FACTS Leaderboard benchmarks large language model factuality across multimodal, parametric, search, and grounding tasks via automated judges.

Aileen Cheng, Alon Jacovi, Amir Globerson, Ben Golan and 36 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 8 on Hugging Face

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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 2/10
strict 1/5
76%Highly rated
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Evolutionary Feature Engineering for Structured Data

Evolutionary Feature Engineering uses LLM-based evolution to discover structured-data preprocessing transformations, reducing forecasting errors up to 19% and improving tabular predictions.

Ege Onur Taga, Yilin Zhuang, Muhammed Emrullah Ildiz, Petros Mol and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 1/5
74%Highly rated
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WAXAL: A Large-Scale Multilingual African Language Speech Corpus

WAXAL introduces an open 1,250-hour ASR and 235-hour TTS speech corpus for 24 African languages to advance inclusive speech technology.

MohamedElfatih MohamedKhair, Emmanuel Asiedu Brempong, Subhashini Venugopalan, Abdoulaye Diack and 8 more

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

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 1/5
88%Must read
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Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration

Uncertainty-guided tree search decouples exploration from policy optimization to bypass RL during exploration, then distills discovered trajectories into deployable policies achieving state-of-the-art sparse-reward results.

Zakaria Mhammedi, James Cohan

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

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 3/5