Good Papers

Showing papers from Department of Computer Science, University of Illinois at Urbana-Champaign Show all papers

45%Niche pick
?Niche pickVote to see the score

Advancing Affordance-Grounded Creative Tool Use in Large Multimodal Models

Cheng Qian, Hyeonjeong Ha, Jiayu Liu, Jeonghwan Kim and 8 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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
80%Must read
?Must readVote to see the score

Trimming the Long-Tail of Visual World Modeling Evaluation

Tailor-Bench evaluates visual world models on rare physical interactions via regular, unconventional, and impossible scenarios, revealing long-tail performance gaps and superficial visual-pattern reliance.

Bingxuan Li, Yining Hong, Cheng Qian, Hyeonjeong Ha and 5 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 40 on Hugging Face · Code ★ 1

– 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
78%Highly rated
?Highly ratedVote to see the score

MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels

MemReward propagates rewards through a heterogeneous rollout graph to enable LLM reinforcement learning using only 20% ground-truth labels and achieves over 96% of oracle performance.

Tianyang Luo, Tao Feng, Zhigang Hua, Yan Xie and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 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 6/10
strict 0/5
80%Must read
?Must readVote to see the score

MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents

MemSkill learns and evolves reusable memory skills for extracting and revising agent memories via selection, execution, and design loops, improving long-context task performance.

Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 64 on Hugging Face · Code ★ 582

– 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 8/10
strict 0/5