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arXiv:2602.21947v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate remarkable breadth of knowledge, yet their ability to reason about computational processes remains poorly understood. …
arXiv:2602.21950v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. …
arXiv:2602.21951v1 Announce Type: new Abstract: Knowledge graph reasoning (KGR) infers missing facts, with recent advances increasingly harnessing the semantic priors and reasoning abilities of Large …
arXiv:2602.21978v1 Announce Type: new Abstract: Recent work has examined language models from a linguistic perspective to better understand how they acquire language. Most existing benchmarks …
arXiv:2602.22014v1 Announce Type: new Abstract: Diversity has been gaining interest in the NLP community in recent years. At the same time, state-of-the-art transformer models such …
arXiv:2602.22045v1 Announce Type: new Abstract: We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from …
arXiv:2602.22072v1 Announce Type: new Abstract: Theory of Mind (ToM) refers to an agent's ability to model the internal states of others. Contributing to the debate …
arXiv:2602.21276v1 Announce Type: new Abstract: Neural networks are trained by optimizing multi-dimensional sets of fitting parameters on non-convex loss landscapes. Low-loss regions of the landscapes …
arXiv:2602.21297v1 Announce Type: new Abstract: The standard way to evaluate language models on subjective tasks is through pairwise comparisons: an annotator chooses the "better" of …
arXiv:2602.21307v1 Announce Type: new Abstract: Symbolic distillation replaces neural networks, or components thereof, with interpretable, closed-form mathematical expressions. This approach has shown promise in discovering …
arXiv:2602.21317v1 Announce Type: new Abstract: Large Language Models (LLMs) are converging towards a singular Artificial Hivemind, where shared Nature (pre-training priors) result in a profound …
arXiv:2602.21319v1 Announce Type: new Abstract: Accurate and uncertainty-aware trajectory prediction remains a core challenge for autonomous driving, driven by complex multi-agent interactions, diverse scene contexts …