Quality follows upgrading
Ever a pioneer, ever a student — learning through our mistakes and failures
Academic
arXiv:2603.04873v1 Announce Type: new Abstract: Accurate time series forecasting underpins decision-making across domains, yet conventional ML development suffers from data scarcity in new deployments, poor …
arXiv:2603.04885v1 Announce Type: new Abstract: Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. …
arXiv:2603.04894v1 Announce Type: new Abstract: Vision-language models are increasingly applied to sensitive domains such as medical imaging and personal photographs, yet existing differentially private methods …
arXiv:2603.04896v1 Announce Type: new Abstract: The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value …
arXiv:2603.04900v1 Announce Type: new Abstract: LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging due to delayed …
arXiv:2603.04904v1 Announce Type: new Abstract: In perpetrator treatment, a recurring observation is the dissociation between insight and action: offenders articulate remorse yet behavioral change does …
arXiv:2603.04920v1 Announce Type: new Abstract: Bid optimization in online advertising relies on black-box machine-learning models that learn bidding decisions from historical data. However, these approaches …
arXiv:2603.04949v1 Announce Type: new Abstract: The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the …
arXiv:2603.04951v1 Announce Type: new Abstract: While RAG has greatly enhanced LLMs, extending this paradigm to Time-Series Foundation Models (TSFMs) remains a challenge. This is exemplified …
arXiv:2603.04981v1 Announce Type: new Abstract: Dynamic data selection accelerates training by sampling a changing subset of the dataset while preserving accuracy. We rethink two core …
arXiv:2603.05016v1 Announce Type: new Abstract: Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language …
arXiv:2603.05024v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) methods (SHAP, LIME) are increasingly adopted to interpret models in high-stakes businesses. However, the credibility of …