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Semantic Homogeneity As Demonstration: Batch-Structured Semi-Supervised In-Context Learning for Natural Language Understanding
Cheng Chen, Yuangang Pan, Ivor Tsang
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
What Scalable Second-Order Information Knows for Pruning at Initialization
Ivo Gollini Navarrete, Nicolas Mauricio Cuadrado, Martin Takáč, Samuel Horváth
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Oral
- Readers: Everyone
Emergence of Auditory Receptive Fields based on Surprise
Yashaswini, Sneha Dash, Sharba Bandyopadhyay
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration
Mohammad Amanlou, Erfan Shafiee Moghaddam, Mahdi Nouri, Yasaman Amou Jafary, Farhan Farsi, Behnam Bahrak
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
SonoEdit: Null-Space Constrained Knowledge Editing for Pronunciation Correction in LLM-Based TTS
Ayush Pratap Singh, Harshit Singh, Nityanand Mathur, Akshat Mandloi, Sudarshan Kamath
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization
Ru Wang, Wei Huang, Selena Song, Haoyu Zhang, Qian Niu, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs
Erik Schultheis, Dan Alistarh
- Published: 22 Jan 2026, Last Modified: 09 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
A Stein identity for $q$-Gaussians with bounded support
Sophia Sklaviadis, Thomas Möllenhoff, Mario A. T. Figueiredo, Andre Martins, Mohammad Emtiyaz Khan
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
ROSE: Reordered SparseGPT for More Accurate One-Shot Large Language Models Pruning
Mingluo Su, Huan Wang
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Oral
- Readers: Everyone
Deep Neural Regression Collapse
Akshay Rangamani, Altay Unal
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Lattice-Based Vector Quantization for Low-Bit Quantization-Aware Training
Rishika Kohli, Soma S Dhavala, Shaifu Gupta, Manoj Singh Gaur
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Superclass-Guided Representation Disentanglement for Spurious Correlation Mitigation
Chenruo Liu, Hongjun Liu, Zeyu Lai, Yiqiu Shen, Chen Zhao, Qi Lei
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Analyzing and Mitigating Model Collapse in Reflow Methods
Huminhao Zhu, Fangyikang Wang, Tianyu Ding, Qing Qu, Zhihui Zhu
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Oral
- Readers: Everyone
GRAIL: Post-hoc Compensation by Linear Reconstruction for Compressed Networks
Wenwu Tang, Dong Wang, Lothar Thiele, Olga Saukh
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
Jose Marie Antonio Miñoza
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
FLIPR: FLexible and Interpretable Prediction Regions for time series
Eshant English, Christoph Lippert
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Learning of Discretized LSTMs
Nikolaus Kopp, Franz Pernkopf
- Published: 22 Jan 2026, Last Modified: 12 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Trainable Bitwise Soft Quantization for Input Feature Compression
Karsten Schrödter, Jan Stenkamp, Nina Herrmann, Fabian Gieseke
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Selective Collaboration for Robust Federated Learning
Nazarii Tupitsa, Samuel Horváth, Martin Takáč, Eduard Gorbunov
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Byzantine-Robust Optimization under $(L_0,L_1)$-Smoothness
Arman Bolatov, Samuel Horváth, Martin Takáč, Eduard Gorbunov
- Published: 22 Jan 2026, Last Modified: 13 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
MMA:Benchmarking Multi-ModalLarge Language Models in Ambiguity Contexts
Ru Wang, Selena Song, Yuquan Wang, Liang Ding, Mingming Gong, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Learning in the Null Space: Small Singular Values for Continual Learning
Cuong Anh Pham, Praneeth Vepakomma, Samuel Horváth
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Oral
- Readers: Everyone
FocusDC: Real-World Scene Infusion for Robust Dataset Condensation
Youbing Hu, Yun Cheng, Olga Saukh, Firat Ozdemir, Anqi Lu, Zhiqiang Cao, Min Zhang, Zhijun Li
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs
Ruichen Zhang, Mufan Qiu, Zhen Tan, Mohan Zhang, Xiaopeng Lu, Jie Peng, Kaidi Xu, Leandro Z. Agudelo, Peter Zhenghao Qian, Tianlong Chen
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone
Stochastic Unrolled Neural Networks
Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro
- Published: 22 Jan 2026, Last Modified: 06 Mar 2026
- CPAL 2026 (Proceedings Track) Poster
- Readers: Everyone

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