arXiv:2607. 21426v1 Announce Type: new Abstract: Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations.
By Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Bho Matthiesen, Armin Dekorsy
arXiv:2607. 16554v1 Announce Type: cross Abstract: In multi-task learning (MTL) negative transfer is often considered as an optimization artifact, but it can also be viewed as a consequence of limited shared capacity and weak task redundancy.
By Asif Khan
The paper extends the Cooperative Multi-Task Semantic Communication (CMT‑SemCom) framework to jointly perform heterogeneous classification and regression tasks on the Cityscapes dataset, using an InfoMax principle to handle mixed discrete and continuous semantic variables. It compares the new framework against independent single‑task training, conventional task‑agnostic digital transmission, and single‑encoder multi‑decoder SemCom, and studies how the capacity of the common unit affects joint task performance. Extensive evaluations show that CMT‑SemCom outperforms all benchmarks.
By Ahmad Halimi Razlighi, Mohammad Siddiqur Rahman, Maximilian H. V. Tillmann, Edgar Beck, Armin Dekorsy
The paper extends the Cooperative Multi-Task Semantic Communication (CMT‑SemCom) framework to handle heterogeneous classification and regression tasks on the Cityscapes dataset. By incorporating the InfoMax principle, the system accommodates mixed discrete and continuous semantic variables, and it is benchmarked against single‑task training, task‑agnostic digital transmission, and single‑encoder multi‑decoder SemCom. Experiments show that CMT‑SemCom outperforms all baselines and provide insights into how the common unit capacity affects joint task performance.
arXiv:2608. 03573v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) exhibit fundamentally different behaviors in enhancing multi-task reasoning for large language models (LLMs).
By Kejian Zhu, Zhuoran Jin, Shangqing Tu, Hongbang Yuan, Yushi Bai, Kang Liu, Juanzi Li, Jun Zhao
arXiv:2509. 16959v5 Announce Type: replace-cross Abstract: When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, thereby potentially reducing the final model's performance.
By Santosh Patapati, Ian Noronha