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4th Workshop on Practical Deep Learning (Practical-DL 2025):
Toward Robust Compressed Foundation Models in the Real World


Workshop at IJCAI 2025



Overview

Compressed foundation models, particularly compressed large language models (LLMs), are increasingly deployed in real-world applications due to their efficiency advantages. Techniques such as quantization, pruning, and distillation significantly reduce memory consumption and latency, making it possible to run these models on resource-constrained devices, such as edge computing platforms and mobile applications. However, compression can also introduce critical robustness and security vulnerabilities, affecting both model reliability and trustworthiness. Compressed models are often more susceptible to adversarial attacks, hallucinations, jailbreak exploits, and degradation in out-of-distribution generalization, raising concerns about their safe deployment in high-stakes applications such as healthcare, finance, and content moderation.

This workshop places a particular emphasis on the robustness challenges of widely deployed compressed foundation models. While existing workshops may discuss general robustness issues in foundation models or focus on efficiency-driven compression, our workshop uniquely targets the intersection of model compression and robustness vulnerabilities. This perspective is crucial because compressed models are now widely used in practice, yet their resilience under real-world threats remains poorly understood and insufficiently addressed. For instance, quantized LLMs are more vulnerable to adversarial prompts, while pruned models may suffer from increased susceptibility to factual errors and hallucinations. Addressing these challenges is not just a theoretical exercise but a pressing need for ensuring secure and reliable deployment.



Call for Papers

Compressed large language models (LLMs) are increasingly adopted in practice due to their efficiency advantages. However, these models pose new challenges in robustness and security, raising concerns for their reliable deployment in real-world scenarios. We welcome research contributions related to the following (but not limited to) topics:
  • Robustness of Compressed Foundation Models
  • Strategies for Jointly Enhancing Compression Efficiency and Robustness
  • Compression for Robust Foundation Models
  • Efficient and Robust Architectural Designs
  • Robust and Efficient Inference Strategies
  • Privacy and Ethical Considerations in Compressed Models
  • Real-World Case Studies on Compressed Model Security and Privacy
Submission Format: Submissions papers (.pdf format) must use the IJCAI Article Template and be anonymized and follow IJCAI 2025 author instructions. The workshop considers two types of submissions: (1) Long Paper [7 pages]; (2) Extended Abstract [4 pages], including figures, tables and references.
Important: Our workshop will feature a Best Paper Award, and certificates will be presented during the workshop.
Submission Site: https://openreview.net/group?id=ijcai.org/IJCAI/2025/Workshop/Practical-DL
Submission Due: 30th May, 2025 GMT
Date and Location: August 29, as part of the IJCAI 2025 Satellite Event in Guangzhou, China.

Workshop Schedule

Schedule

Room: Ballroom FG

Keynote Speakers

Aaron Gilad Kusne



NIST
Yawei Li



ETH Zürich
Jie Zhang


Center for Frontier
AI Research
(CFAR), A*STAR
Haidong Kang



Northeastern University

Organizer

Xingyu Zheng


Beihang University
Haotong Qin


ETH Zürich
Aishan Liu


Beihang University
Jie Zhang


Center for Frontier
AI Research
(CFAR), A*STAR
Jiakai Wang


Zhongguancun Laboratory

Yulun Zhang


Shanghai Jiao Tong University
Olivera Kotevska


Oak Ridge National Laboratory
Xianglong Liu


Beihang University
Michele Magno


ETH Zürich
Dacheng Tao


Nanyang Technological University

Accepted Papers

Accepted Long Paper

  • Detection of Deepfake Videos and Audios on Social Media Platforms [Paper]
    Charles Fross (Sam Houston State University); Qingzhong (Frank) Liu (Sam Houston State University)
  • Scalability and Noise Resilience in Q-Transformer [Paper]
    Jun Wang (Hangzhou Dianzi University)
  • ASA: Adaptive Subquadratic Attention with Dynamic Complexity Adjustment [Paper]
    Xinyuan Liu (Northwestern University)
  • Dialectical Chain Distillation: Transferring Dialectical Reasoning from Teacher–Student Interactions to Small Language Models [Paper]
    Hairong Chen (Beijing Jiaotong University); Ruitao Li (Beijing Jiaotong University); Yuzhe Guo (Beijing Jiaotong University); Renshuai Tao (Beijing Jiaotong University); Huihui Bai (Beijing Jiaotong University); Yao Zhao (Beijing Jiaotong University) — Best Paper Award
  • Leveraging LLM-based Sentiment Analysis for Portfolio Optimization with Proximal Policy Optimization [Paper]
    Kemal Kirtac (University College London); Guido Germano (University College London) — Outstanding Paper Award

Accepted Short Paper

  • Towards Leaner Architectures in Multimodal Language Models [Paper]
    Prasanth (Affiliation TBD)
  • The Fault in Our LLM Leaderboards [Paper]
    Pegah Jandaghi (University of Southern California); Kian Ahrabian (University of Southern California)

Awards

Best Paper Award: Dialectical Chain Distillation: Transferring Dialectical Reasoning from Teacher–Student Interactions to Small Language Models

Outstanding Paper Award: Leveraging LLM-based Sentiment Analysis for Portfolio Optimization with Proximal Policy Optimization

Best Paper Award Outstanding Paper Award

Conference Photos

Opening Session

Opening

Speaker Haidong Kang

Speaker: Haidong Kang

Best Paper Award

Best Paper Award

Organizers

Organizers

Organizing Committee

Kewei Liao



Beihang University
Shenghao Jin


Beihang University
Xudong Ma


Beihang University
Wei Huang


The University of Hong Kong
Mingyuan Zhang


Nanyang Technological University
Zixiang Zhao


Xi'an Jiaotong University
Renshuai Tao


Beijing Jiaotong University

Previous Workshops

3rd Workshop on Practical Deep Learning: Towards Efficient and Reliable LLMs @ IEEE CAI 2024

2nd International Workshop on Practical Deep Learning in the Wild @ AAAI 2023

1st International Workshop on Practical Deep Learning in the Wild @ AAAI 2022