Good Papers

Showing papers from Oak Ridge National Laboratory Show all papers

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NeurIPS 2026OralU TübingenStanfordEberhard-Karls-Universität TübinTübingen AI Center, University oUniversity College London, UniveComputational neuroscience

Evaluating Neural Data Tokenizers: A Framework for Assessing Learned Representations of Spiking Activity

Federico D'Agostino, Alex Gilbert, Susanne Keller, Jaivardhan Kapoor and 16 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U FloridaU OklahomaOak Ridge National LaboratoryArgonne National LaboratoryUnited States Military AcademyMulti-agent LLM systems

Unified Resource-Grounded Coordination Protocol for Orchestrator-Free Heterogeneous Multi-Agent Systems

Vishal Pramanik, Maisha Maliha, Olivera Kotevska, Arvind Ramanathan and 3 more

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

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NeurIPS 2026U FloridaU OklahomaOak Ridge National LaboratoryUnited States Military AcademyDARPAJailbreaks & red teaming

Harmless in Pieces, Harmful in Motion: Detecting Multi-Agent Jailbreaks

Vishal Pramanik, Maisha Maliha, Olivera Kotevska, Nathaniel D Bastian and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Oak Ridge National LaboratoryUMNVision backbones

Evaluating Deployable Inference-Time Error Prediction in Vision MoEs

Aristeidis Tsaris, JangHyeon Lee, Abhishek Potnis, Philipe Dias and 4 more

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

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NeurIPS 2026George MasonThomas Jefferson High School forOak Ridge National LaboratoryML systems & compilers

HPC-Bench: A Comprehensive Benchmark for High Performance Computing Codes

Bowen Cui, Junyu Yin, Tejas Ramesh, Leo Lim and 2 more

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

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76%Highly rated
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NeurIPS 2026Oak Ridge National Laboratory, University of Minnesota - TwinUMNU Maryland Baltimore CountyUnified multimodal models

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired dataset for Earth Sciences

MOSAIC-CONUS introduces a point-indexed multimodal Earth observation dataset over the contiguous U.S. with cross-sensor alignment tables and benchmarks for geospatial AI embeddings.

Abhishek Potnis, Youssef Hussein, Waqwoya Abebe, JangHyeon Lee and 6 more

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

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 1/5