Good Papers

Showing papers from ELLIS Institute Finland Show all papers

45%Niche pick
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Axiomatic Reinforcement Learning for Open Multi-Agent Systems from Shapley Axioms

Jianhong Wang, Yang Li, Samuel Kaski, Jonathan Lawry

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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
80%Must read
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Amortized Bayesian Experimental Design with In-Context Knowledge Conditioning

IMBUE enables amortized Bayesian experimental design to incorporate external deployment knowledge via in-context tokens and a reliability filter, accelerating early information gain with reliable inputs while maintaining baseline performance otherwise.

Zhanghu Zhao, Daolang Huang, Xinyi Wen, Samuel Kaski

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · 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 4/5
medium 7/10
strict 1/5
88%Must read
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Diversity Combining for Multi-Path LLM Reasoning

Multi-path LLM reasoning is modeled as diversity combining, showing path correlation limits majority-vote gains and that adaptive sampling retains near-peak accuracy.

Guangsheng Yu, Litianyi Zhang, Qin Wang, Xu Wang and 4 more

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

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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 9/10
strict 2/5
74%Highly rated
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Frozen Memory Is Not Enough: Rethinking External Memory as Extraction

Cross-model frozen-memory extraction shows target-aligned readers matter more than frozen tables, with dual-layer readers nearly closing reuse gaps and compatible interfaces enabling direct utility.

Mingyuan Li, Guangsheng Yu, Xu Wang, Shaoxiong Ji

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 8 on Hugging Face · Code ★ 3

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 1/5
74%Highly rated
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In-Context Black-Box Optimization with Unreliable Feedback

FICBO pretrains a feedback-aware transformer to condition on both optimization history and unreliable auxiliary feedback, estimating source reliability in context to improve black-box query selection.

Nicolas Samuel Blumer, Julien Martinelli, Samuel Kaski

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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