Good Papers

Showing papers from UNC-Chapel Hill Show all papers

45%Niche pick
?Niche pickVote to see the score

See, Read, Compare: Candidate-Aware Verification for Agent Test-Time Scaling

Xinyu Ye, Yongliang Wu, Xingyu Zhu, Peng Xia and 6 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · 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
86%Must read
?Must readVote to see the score

DriveHierarchy: A Benchmark for Diagnosing VLM Driving Capabilities from Open-Loop Understanding to Closed-Loop Execution

DriveHierarchy hierarchically benchmarks VLM driving across four ranks from perception to closed-loop execution, linking open-loop understanding to embodied performance for diagnosing 15 models.

Chengkai Xu, Jiaqi Liu, Yicheng Guo, Peng Hang and 1 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026 · ▲ 1 on Hugging Face

– ReadersNo votes yet
14/20 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: 14 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 0/5
89%Must read
?Must readVote to see the score

When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning

AVIC adaptively scales test-time visual imagination via world models for spatial reasoning, matching fixed strategies with fewer calls while exceeding GPT-4o.

Shoubin Yu, Yue Zhang, Zun Wang, Jaehong Yoon and 3 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 9 on Hugging Face · Code ★ 20

– ReadersNo votes yet
16/20 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: 16 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 1/5
80%Must read
?Must readVote to see the score

SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

SkillRL evolves agents via recursive skill-augmented reinforcement learning with automatic skill discovery and hierarchical library co-evolution, cutting token use while achieving state-of-the-art results across complex tasks.

Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu and 9 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 75 on Hugging Face · Code ★ 998

– ReadersNo votes yet
12/20 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: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5
74%Highly rated
?Highly ratedVote to see the score

The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy

Binary acyclic SCMs with constant observational description length require Θ(n²) additional bits for interventional answers, and interventional oracles leave Θ(n) counterfactual gaps, matching ambiguity bounds.

Seyedmorteza Emadi

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
9/20 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: 9 of 20 reviewers recommend it
lenient 1/5
medium 4/10
strict 4/5