Quality follows upgrading
Ever a pioneer, ever a student — learning through our mistakes and failures
Academic
arXiv:2602.18291v1 Announce Type: new Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination. Crucially, enhancing policy expressiveness is pivotal for …
arXiv:2602.17671v1 Announce Type: cross Abstract: As students increasingly rely on large language models, hallucinations pose a growing threat to learning. To mitigate this, AI literacy …
arXiv:2602.17672v1 Announce Type: cross Abstract: Technology-facilitated abuse (TFA) is a pervasive form of intimate partner violence (IPV) that leverages digital tools to control, surveil, or …
arXiv:2602.17675v1 Announce Type: cross Abstract: Enterprise conversational UIs increasingly need to orchestrate heterogeneous backend agents and tools across project and account boundaries in a secure …
arXiv:2602.17684v1 Announce Type: cross Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from …
arXiv:2602.17686v1 Announce Type: cross Abstract: Distilling Chain-of-Thought (CoT) reasoning from large language models into compact student models presents a fundamental challenge: teacher rationales are often …
arXiv:2602.17687v1 Announce Type: cross Abstract: AI systems have achieved remarkable success in processing text and relational data, yet visual document processing remains relatively underexplored. Whereas …
arXiv:2602.17689v1 Announce Type: cross Abstract: Medical vision-language models show strong potential for joint reasoning over medical images and clinical text, but their performance often degrades …
arXiv:2602.17690v1 Announce Type: cross Abstract: Graphic design generation demands a delicate balance between high visual fidelity and fine-grained structural editability. However, existing approaches typically bifurcate …
arXiv:2602.17692v1 Announce Type: cross Abstract: In this paper, we introduce \textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents …
arXiv:2602.17693v1 Announce Type: cross Abstract: Post-Training Quantization (PTQ) is crucial for efficient model deployment, yet its effectiveness on Ascend NPU remains under-explored compared to GPU …
arXiv:2602.17694v1 Announce Type: cross Abstract: With the rapid development of large language models (LLMs), an increasing number of applications leverage cloud-based LLM APIs to reduce …