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Showing papers from Stevens Institute of Technology Show all papers

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NeurIPS 2026U HoustonPeng Cheng LaboratoryBeijing University of Posts and Stevens Institute of TechnologyWasedaInstruction tuning

Efficient Collaborative LLM Fine-Tuning over Heterogeneous Mobile Devices via Many Backbones to One Side-Network Tuning

Xingke Yang, Liang Li, Sicong Li, Liwei Guan and 5 more

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

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57%Worth a look
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NeurIPS 2026Stevens Institute of TechnologyQueen's University BelfastUIUCDepartment of Computer Science, Federated learning

Bayes-pFCL:Bayesian Personalized Federated Continual Learning

qingyang yu, Yang Hua, Hao Wang, Yue Ning 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: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026U Southern CaliforniaStevens Institute of TechnologyWestlakepeking universityInstitute of automation, ChineseImage recognition

PhysGraphNet: Physical-State Scene Graphs via Latent Graph Reasoning and Counterfactual Supervision

Zhengtao Yao, Runhao Li, Yan Wen, Guang Yang and 6 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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57%Worth a look
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NeurIPS 2026Hong Kong BaptistBeijing NormalInstitute of automation, ChineseStevens Institute of TechnologyCity University of Hong KongTest-time compute

Not All Low-Confidence Tokens Are Equal: Calibrated Confidence for Efficient Test-Time Reasoning

Tangyu Jiang, Haodi Wang, Yuanbing Zhu, Xiaojiang Du and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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89%Must read
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NeurIPS 2026ShandongPekingXi'an JiaotongHeilongjiangSHENZHEN INSTITUTE of ARTIFICIALPrivacy

SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing

SMI replaces MIA-based unlearned model auditing with training-free statistical estimation of non-member mixture proportions in feature space, yielding reliable forgetting rates and bootstrap reliability ranges.

Jialong Sun, Zeming Wei, Jiaxuan Zou, Jiacheng Gong and 5 more

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

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 3/5
89%Must read
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NeurIPS 2026Stevens Institute of TechnologyLambdaColumbiaU Texas Health Science Center atStevens Institute of Technology;LLM pretraining & scaling laws

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation

RISE sketches LLM output-layer influence hotspots into compressed dual-channel sketches, reducing storage up to 112x versus gradient methods while scaling to 32B parameters for attribution and data valuation.

yide ran, Jianwen Xie, Minghui Wang, W. Jim Zheng and 3 more

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

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5
70%Highly rated
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NeurIPS 2026Stevens Institute of TechnologyAmazon Web ServicesAmazonLLM evaluation & benchmarks

s2n-bignum-bench: A practical benchmark for evaluating low-level code reasoning of LLMs

s2n-bignum-bench evaluates LLM theorem proving on verified industrial cryptographic assembly using HOL Light proof synthesis. It provides a challenging, practically relevant benchmark beyond competition mathematics.

Balaji Rao, Soonho Kong, Juneyoung Lee, Carlo Lipizzi

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 3 on Hugging Face · Code ★ 5

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AI panel: 5 of 20 reviewers recommend it
lenient 4/5
medium 1/10
strict 0/5