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Showing papers from Caltech, Asari AI Show all papers

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NeurIPS 2026California Institute of TechnoloCaltech, Asari AIPhysics-informed ML & PDEs

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

FLAPS unifies stochastic-process regression and PDE inverse problems via function-space flow-matching priors for efficient, calibrated posterior sampling from sparse noisy observations.

Yaozhong Shi, Zachary Ross, Yisong Yue

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 7 of 20 reviewers recommend it
lenient 3/5
medium 3/10
strict 1/5
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NeurIPS 2026ETH ZurichETHZ - ETH ZurichU PennsylvaniaMila, McGillCaltech, Asari AIFlow matching

Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

Active Flow Expansion uses verifier-guided active exploration to grow a flow model's generable set, yielding theoretical guarantees and superior out-of-distribution molecule and protein design.

Riccardo De Santi, Bruce D Lee, Cristian Jensen, Kimon Protopapas and 5 more

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

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
76%Highly rated
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NeurIPS 2026PurdueCalifornia Institute of TechnoloCalifornia Institute of TechnoloU Illinois Urbana-ChampaignCaltech, Asari AIRL for LLMs

Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards

RLVR trains a 30B LLM buyer via verifiable economic rewards to negotiate, revealing four-phase strategic evolution and outperforming much larger frontier models in surplus extraction.

Shuze D Liu, Claire Chen, Jiabao S Xiao, Lei Lei and 3 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 2/5
80%Must read
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NeurIPS 2026California Institute of TechnoloMITETH ZurichNew YorkGenesis TherapeuticsMolecules & drug discovery

On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization

Online discrete diffusion adaptation for molecular optimization finds acquisition, reward shaping, and debiasing complementarily boost reward, with replay and validity control stabilizing exploration to outperform offline and search baselines.

Trevor Chen, Ariel Dai, Jason Yang, Riccardo De Santi and 7 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5