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arXiv:2602.20574v1 Announce Type: cross Abstract: We study self-distillation in settings where supervision is unreliable: there are no ground truth labels, verifiable rewards, or external graders …
arXiv:2602.20610v1 Announce Type: cross Abstract: Specifications are vital for ensuring program correctness, yet writing them manually remains challenging and time-intensive. Recent large language model (LLM)-based …
arXiv:2602.20735v1 Announce Type: cross Abstract: This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS~2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), …
arXiv:2602.20994v1 Announce Type: cross Abstract: Report-supervised (RSuper) learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., …
arXiv:2602.20995v1 Announce Type: cross Abstract: Pre-ranking is a critical stage in industrial recommendation systems, tasked with efficiently scoring thousands of recalled items for downstream ranking. …
arXiv:2602.21009v1 Announce Type: cross Abstract: Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due …
arXiv:2602.20175v1 Announce Type: new Abstract: We present an application of the tensor network generator-enhanced optimization (TN-GEO) framework to address the traveling salesman problem (TSP), a …
arXiv:2602.20194v1 Announce Type: new Abstract: Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance constraints. …
arXiv:2602.20197v1 Announce Type: new Abstract: Reinforcement Learning with verifiable rewards (RLVR) has emerged as a primary learning paradigm for enhancing the reasoning capabilities of multi-modal …
arXiv:2602.20199v1 Announce Type: new Abstract: Class imbalance, overlap, and noise degrade data quality, reduce model reliability, and limit generalization. Although widely studied in binary classification, …
arXiv:2602.20207v1 Announce Type: new Abstract: Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired …
arXiv:2602.20208v1 Announce Type: new Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model …