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arXiv:2604.04171v1 Announce Type: new Abstract: I propose a model of systematic understanding, suitable for machine learning systems. On this account, an agent understands a property …
arXiv:2604.03525v1 Announce Type: new Abstract: We study adversarial online learning of real-valued functions on $\mathbb{R}$. In each round the learner is queried at $x_t\in\mathbb{R}$, predicts …
arXiv:2604.03820v1 Announce Type: new Abstract: Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We …
arXiv:2604.03815v1 Announce Type: new Abstract: Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties in modelling …
arXiv:2604.03877v1 Announce Type: new Abstract: Analogical reasoning is a core cognitive faculty essential for narrative understanding. While LLMs perform well when surface and structural cues …
arXiv:2604.03422v1 Announce Type: new Abstract: All languages have a noun category, but its realisation varies considerably. Depending on the language, semantic and/or morphosyntactic differences may …
arXiv:2604.03571v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) generate structured chains of thought (CoTs) before producing final answers, making them especially vulnerable to knowledge …
arXiv:2604.03527v1 Announce Type: new Abstract: Modern agentic workflows decompose complex tasks into specialized subtasks and route them to diverse models to minimize cost without sacrificing …
arXiv:2604.04131v1 Announce Type: new Abstract: Large language model agents that use external tools are often implemented through reactive execution, in which reasoning is repeatedly recomputed …
arXiv:2604.03350v1 Announce Type: new Abstract: Systematic exploration of Agent-Based Models (ABMs) is challenged by the curse of dimensionality and their inherent stochasticity. We present a …
arXiv:2604.03672v1 Announce Type: new Abstract: Government agencies worldwide face growing volumes of citizen appeals, with electronic submissions increasing significantly over recent years. Traditional manual processing …
arXiv:2604.03583v1 Announce Type: new Abstract: This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance …