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Showing papers from Northeastern University, China Show all papers

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NeurIPS 2026NortheasternU MunichShanghai Jiao TongNortheastern University, ChinaCenter for Information and LanguUnified multimodal models

DiM$^3$: Bridging Multilingual and Multimodal Models via Direction- and Magnitude-Aware Merging

Zijing Wang, Mingyang Wang, Ercong Nie, Yongkang Liu 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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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 2026SpotlightNortheasternTsinghuaNortheastern University, ChinaU International Business and EcoSichuanGraph neural networks

CLEAR: Complementary Tripartite Play with Bayesian Calibration for Semi-Supervised Edge Classification

Zhipeng Sun, Fanchun Meng, Jiazhen Huang, Yongpeng Zhang and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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NeurIPS 2026NortheasternCarnegie MellonMicrosoft ResearchNortheastern University, ChinaFudanRL for LLMs

What are Key Factors for Updates in RL for LLM Reasoning?

Peidong Wang, Demi Wang, Xufang Luo, Jiahang Xu and 4 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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medium 0/10
strict 0/5
74%Highly rated
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NeurIPS 2026NortheasternNortheast University at QinhuangShandongNortheastern University, ChinaNorthersternInstruction tuning

SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning

SMoA modulates spectra via block-diagonal Hadamard low-rank branches to expand representational coverage under small parameter budgets, outperforming LoRA on multiple tasks.

Yongkang Liu, Xing Li, Mengjie Zhao, Shanru Zhang and 6 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 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 3/5
medium 5/10
strict 1/5
80%Must read
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NeurIPS 2026NortheasternShanghai Jiao TongU MunichShandongNorthersternInstruction tuning

ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning

ChunkFT enables memory-efficient full-parameter fine-tuning via dynamically activated sub-tensors, cutting 7B model memory to 13.72GB and outperforming baselines.

Yongkang Liu, Zijing Wang, Mengjie Zhao, Ercong Nie and 6 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · 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 7/10
strict 0/5