Good Papers

Showing papers from University of Roma "La Sapienza" Show all papers

45%Niche pick
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The Panel Complexity of Sortition: Is 12 Angry Men Enough?

Johannes Brustle, Simone Fioravanti, Tomasz Ponitka, Jeremy Vollen

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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RL-Guided Contraction of Symbolic Tensor Networks for Quantum Circuit Equivalence

Suhaib Al-Rousan, Christian Schilling, Max Tschaikowski, Kim Larsen

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · 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
78%Highly rated
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Metaphor Is Not All Attention Needs

Poetic jailbreaks bypass LLM safety not via specific devices or format confusion, but through accumulated stylistic irregularities altering processing independently of harm detection, requiring style-aware defenses.

Olga Sorokoletova, Francesco Giarrusso, Giacomo De Luca, Piercosma Bisconti and 5 more

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

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
80%Must read
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HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations

HOPSE replaces higher-order message passing with Hasse-graph encodings to scale linearly on combinatorial domains while matching or exceeding state-of-the-art performance.

Guillermo Bernárdez, Marco Montagna, Louis Van Langendonck, Martin Carrasco and 6 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 4/5
medium 8/10
strict 0/5
76%Highly rated
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DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention

DashAttention uses adaptive α-entmax to select variable key-value blocks per query, enabling fully differentiable hierarchical sparse attention that matches full-attention accuracy at 75% sparsity with faster inference than FlashAttention-3.

Yuxiang Huang, Nuno Gonçalves, Federico Alvetreti, Lei Li and 4 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5