We propose PIP (Perturbation-based Iterative Pruning), a method that iteratively prunes parameters based on the distinction between unperturbed and perturbed views. Experimental results show that PIP reduces parameter count by approximately 20% while retaining over 85% of the original accuracy.

We propose TIV, an innovative framework that compresses token-level reasoning into compact vectors without sacrificing performance. Rather than generating explicit thoughts, TIV injects learnable vectors into the post-attention hidden states of the final token across Transformer layers, enabling implicit and lightweight reasoning.

We propose LLM4MS, a method leveraging expert knowledge from large language models to generate discriminative spectral embeddings for improved compound identification. Experimental results show a 13.7% improvement in accuracy over existing methods on a million-scale library, with a query speed of nearly 15,000 queries per second.

We propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. By partitioning a single UMM into three collaborative roles: Proposer, Solver, and Judge, UniCorn generates high-quality interactions via self-play and employs cognitive pattern reconstruction to distill latent understanding into explicit generative signals.