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arXiv:2604.02605v1 Announce Type: new Abstract: Audio-Visual Large Language Models (AVLLMs) are emerging as unified interfaces to multimodal perception. We present the first mechanistic interpretability study …
arXiv:2604.03058v1 Announce Type: new Abstract: LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing …
arXiv:2604.02733v1 Announce Type: new Abstract: Reasoning benchmarks typically evaluate whether a model derives the correct answer from a fixed premise set, but they under-measure a …
arXiv:2604.02369v1 Announce Type: cross Abstract: Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other …
arXiv:2604.02881v1 Announce Type: new Abstract: Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While …
arXiv:2604.02346v1 Announce Type: cross Abstract: Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research …
arXiv:2604.02371v1 Announce Type: cross Abstract: Visual long-document understanding is critical for enterprise, legal, and scientific applications, yet the best performing open recipes have not explored …
arXiv:2604.02653v1 Announce Type: new Abstract: Empirically, modern deep learning training often occurs at the Edge of Stability (EoS), where the sharpness of the loss exceeds …
arXiv:2305.18915v1 Announce Type: cross Abstract: In this work we build upon negative results from an attempt at language modeling with predicted semantic structure, in order …
arXiv:2604.02335v1 Announce Type: new Abstract: Modeling groundwater flow in three-dimensional fractured crystalline media requires accounting for strong spatial heterogeneity induced by fractures. Fine-scale discrete fracture-matrix …
arXiv:2604.03192v1 Announce Type: new Abstract: We study multiteacher knowledge distillation for low resource abstractive summarization from a reliability aware perspective. We introduce EWAD (Entropy Weighted …
arXiv:2604.02834v1 Announce Type: new Abstract: Longitudinal health agents must reason across multi-source trajectories that combine continuous device streams, sparse clinical exams, and episodic life events …