Good Papers

Showing papers from UC Davis Show all papers

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ContractBench: Can LLM Agents Preserve Observation Contracts?

Jicheng Wang, Yifeng He, Zili Wang, Hanwen Xing and 2 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
83%Must read
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Video Models Can Reason with Verifiable Rewards

VideoRLVR applies reinforcement learning with verifiable rewards to video diffusion models, improving rule-consistent visual reasoning and cutting training latency 40% via early-step optimization.

Tinghui Zhu, Sheng Zhang, James Yipeng Huang, Selena Song and 4 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 9 on Hugging Face · Code ★ 28

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5
91%Must read
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Rethinking Personalized Generation: Test-time Alignment via Factorized Ranking Models

Test-time alignment via million-parameter factorized ranking models exploits massive headroom for personalized generation, outperforming billion-parameter reward models with minimal overhead.

Qiyao Ma, Junshan Zhang, Zhe Zhao

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 11 on Hugging Face

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18/20 AI panelreviewers recommend it

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AI panel: 18 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 3/5
76%Highly rated
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Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models

Multimodal LLMs suffer spurious cross-modality interference that distorts decisions, and a unified finetuning framework with perturbation augmentation and consistency regularization improves robustness and generalization.

Rui Cai, Bangzheng Li, Xiaofei Wen, Muhao Chen and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 8

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5
78%Highly rated
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Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement

Linear self-attention transformers provably implement in-context policy-improvement via explicit constructions, with gradient flow converging exponentially to optimal RL update parameters under distribution richness conditions.

Haodong Liang, Lifeng LAI

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · 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 3/5
medium 5/10
strict 3/5
89%Must read
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ModelLens: Finding the Best for Your Task from Myriads of Models

ModelLens learns a latent space over model-dataset-metric tuples from noisy leaderboard data to rank unseen models on unseen datasets without target evaluation, improving routing by up to 81%.

Rui Cai, Wenjie Mo, Xiaofei Wen, Qiyao Ma and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 14 on Hugging Face · Code ★ 130

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5