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arXiv:2604.02778v1 Announce Type: new Abstract: Real-world multimodal knowledge graphs (MMKGs) are dynamic, with new entities, relations, and multimodal knowledge emerging over time. Existing continual knowledge …
arXiv:2604.02795v1 Announce Type: new Abstract: Rubric-based Reinforcement Learning (RL) has emerged as a promising approach for aligning Large Language Models (LLMs) with complex, open-domain instruction …
arXiv:2604.02608v1 Announce Type: new Abstract: Function vectors (FVs) -- mean-difference directions extracted from in-context learning demonstrations -- can steer large language model behavior when added …
arXiv:2604.02511v1 Announce Type: new Abstract: Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited …
arXiv:2604.03216v1 Announce Type: new Abstract: Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, …
arXiv:2604.02617v1 Announce Type: new Abstract: Scientific and Technical Intelligence (S&TI) analysis requires verifying complex technical claims across rapidly growing literature, where existing approaches fail to …
arXiv:2604.03127v1 Announce Type: new Abstract: Automated annotation of pedagogical dialogue is a high-stakes task where LLMs often fail without sufficient domain grounding. We present a …
arXiv:2604.02339v1 Announce Type: new Abstract: Natural language context-such as instructions, knowledge, or feedback-contains rich signal for adapting language models. While in-context learning provides adaptation via …
arXiv:2604.02545v1 Announce Type: new Abstract: The preservation of intangible cultural heritage is a critical challenge as collective memory fades over time. While Large Language Models …
arXiv:2604.03110v1 Announce Type: new Abstract: Knowledge distillation is an effective technique for pre-trained language model compression. However, existing methods only focus on the knowledge distribution …
arXiv:2604.02361v1 Announce Type: cross Abstract: Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study …
arXiv:2604.02353v1 Announce Type: cross Abstract: We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents' decisions in discrete, causally …