Good Papers

Showing papers from University of Edinburgh, United Kingdom Show all papers

45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Polytechnic Institute of BariIndependentU Edinburgh, United KingdomU Edinburgh, University of EdinbU EdinburghVision-language models

Do Composed Image Retrieval Benchmarks Require Multimodal Composition?

Matteo Attimonelli, Alessandro De Bellis, Aryo Gema, Rohit Saxena and 7 more

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

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
91%Must read
?Must readVote to see the score
NeurIPS 2026U EdinburghU Edinburgh, United KingdomMohamed bin Zayed University of University College LondonAlignment & preference optimization

SCOPE: Self-Play via Co-Evolving Policies for Open-Ended Tasks

SCOPE co-evolves a task-generating challenger and retrieval solver with rubric-based self-judging to improve open-ended and QA performance without curated data.

Wai-Chung Kwan, Aryo Gema, Joshua O Leang, Pasquale Minervini

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 25 on Hugging Face · Code ★ 2

– ReadersNo votes yet
18/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 18 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 4/5
78%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026U Edinburgh, United KingdomJagiellonian University, ul. GołJagiellonian University in KrakoJagiellonianExplainable AI

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

B-XAIC benchmark evaluates explainable AI for graph neural networks on real molecular tasks with ground-truth rationales, revealing major limitations in current explanation methods.

Magdalena Proszewska, Tomasz Danel, Antoni Antoszek, Dawid Damian Rymarczyk

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

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5