13 papers
SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution
Jiachen Jiang, Huminhao Zhu, Zhihui Zhu
LLM-driven program evolution has emerged as a powerful tool for automated scientific discovery, yet existing frameworks offer no principled guide for designing their individual com…
LLMs Struggle with Abstract Meaning Comprehension More Than Expected
Hamoud Alhazmi, Jiachen Jiang
Understanding abstract meanings is crucial for advanced language comprehension. Despite extensive research, abstract words remain challenging due to their non-concrete, high-level…
Learning to Adapt: In-Context Learning Beyond Stationarity
Zhen Qin, Jiachen Jiang, Zhihui Zhu
Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key capability underlying their s…
DeltaEvolve: Accelerating Scientific Discovery through Momentum-Driven Evolution
Jiachen Jiang, Tianyu Ding, Zhihui Zhu
LLM-driven evolutionary systems have shown promise for automated science discovery, yet existing approaches such as AlphaEvolve rely on full-code histories that are context-ineffic…
Improving Visual Discriminability of CLIP for Training-Free Open-Vocabulary Semantic Segmentation
Jinxin Zhou, Jiachen Jiang, Zhihui Zhu
Extending CLIP models to semantic segmentation remains challenging due to the misalignment between their image-level pre-training objectives and the pixel-level visual understandin…
In-Context Learning for Non-Stationary MIMO Equalization
Jiachen Jiang, Zhen Qin, Zhihui Zhu
Channel equalization is fundamental for mitigating distortions such as frequency-selective fading and inter-symbol interference. Unlike standard supervised learning approaches that…