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Showing papers from Stanford University & LMU Munich Show all papers

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NeurIPS 2026Stanford University & LMU MuU Munich, Ludwig-Maximilians-UniStanfordU BolognaLMU MunichRL for LLMs

Tool Verification for Test-Time Reinforcement Learning

T³RL uses external tool verification to correct false-majority rewards in test-time RL, improving math reasoning across benchmarks.

Ruotong Liao, Nikolai Röhrich, Xiaohan Wang, Yuhui Zhang and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 7 on Hugging Face · Code ★ 2

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 0/5
74%Highly rated
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NeurIPS 2026LMU MunichStanford University & LMU MuCenter for Information and LanguImage editing

Edit the Bits, Diff the Codes: Bitwise Residual Editing for Visual Autoregressive Models

BitResEdit guides bitwise-residual VAR generators via bit-level source-negative guidance and masked multi-scale residual code injection for localized edits with preserved backgrounds.

Shengqiang Zhang, Ruotong Liao, Volker Tresp, Barbara Plank and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 2/5
medium 7/10
strict 0/5