Enhancing Pathological VLMs with Cross-scale Reasoning
arXiv:2606.17412v2 Announce Type: replace-cross Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at…
arXiv:2606.17412v2 Announce Type: replace-cross Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at…
arXiv:2606.18271v1 Announce Type: new Abstract: As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has…
arXiv:2606.17872v1 Announce Type: cross Abstract: Large language models (LLMs) outperform earlier architectures on generative inference and long-context tasks, but their…
arXiv:2606.17209v1 Announce Type: new Abstract: Test-time scaling for agentic search typically increases depth (i.e., more turns and tokens per trajectory)…
arXiv:2606.17867v1 Announce Type: cross Abstract: Despite increasing adoption of multimodal approaches in Alzheimer’s Disease (AD) research — aimed at integrating…
arXiv:2606.15903v2 Announce Type: replace-cross Abstract: Where an LLM sits in an agent memory pipeline — between the recall plane that…
arXiv:2606.16590v2 Announce Type: replace-cross Abstract: Exploration in deep reinforcement learning (RL) is commonly implemented as temporally uncorrelated white noise. However,…