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arXiv:2603.15634v1 Announce Type: new Abstract: Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational …
arXiv:2603.15633v1 Announce Type: new Abstract: Answering complex first-order logic (FOL) queries on knowledge graphs is essential for reasoning. Symbolic methods offer interpretability but struggle with …
arXiv:2603.16002v1 Announce Type: new Abstract: Radiology report annotation is essential for clinical NLP, yet manual labeling is slow and costly. We present RadAnnotate, an LLM-based …
arXiv:2603.16017v1 Announce Type: new Abstract: Large language models (LLMs) increasingly participate in morally sensitive decision-making, yet how they organize ethical frameworks across reasoning steps remains …
arXiv:2603.16070v1 Announce Type: new Abstract: Hate speech detection relies heavily on linguistic resources, which are primarily available in high-resource languages such as English and Chinese, …
arXiv:2603.16073v1 Announce Type: new Abstract: Scientific papers do more than report results $-$ they advance $\textit{claims}$ that later work supports, extends, or sometimes refutes. Yet …
arXiv:2603.16091v1 Announce Type: new Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, …
arXiv:2603.16105v1 Announce Type: new Abstract: Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several …
arXiv:2603.16112v1 Announce Type: new Abstract: Adapting large language models (LLMs) to specialized financial reasoning typically requires expensive fine-tuning that produces model-locked expertise. Training-free alternatives have …
arXiv:2603.16120v1 Announce Type: new Abstract: Deep Research (DR) tools (e.g. OpenAI DR) help researchers cope with ballooning publishing counts. Such tools can synthesize scientific papers …
arXiv:2603.16127v1 Announce Type: new Abstract: We investigate the role of learning rate scheduling in the large-scale pre-training of large language models, focusing on its influence …
arXiv:2603.16128v1 Announce Type: new Abstract: As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research …