Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System
arXiv:2610.10650v1 Announce Type: new Abstract: Work zones are critical yet hazardous components of transportation infrastructure, requiring carefully designed Transportation Management Plans (TMPs) to ensure safety and mobility. However, TMP preparation remains labor-intensive and heavily dependent on practitioner expertise.
arXiv:2610.10650v1 Announce Type: new Abstract: Work zones are critical yet hazardous components of transportation infrastructure, requiring carefully designed Transportation Management Plans (TMPs) to ensure safety and mobility. However, TMP preparation remains labor-intensive and heavily dependent on practitioner expertise. This paper proposes a Large Language Model (LLM)-assisted framework to a
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EmbeddingGemma 2: Text, Code, Images, Video and Audio in One Vector Space
EmbeddingGemma 2 launched on October 6, 2026 under Apache 2.0. It is a sub-1B model built on Gemma 4 that maps text, code, images, video and audio into one 768-dimensional space.  This article covers the architecture, the benchmarks, and runnable scripts to provide measured results.  Specifications Specification EmbeddingGemma 2 Base model Gemma 4 License Apache […] The post Embedd
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Freeze the Decoder, Heal the Encoder: Parameter-Efficient Adaptation for SVD-Based KV-Cache Compression
arXiv:2610.10552v1 Announce Type: new Abstract: Comparing parameter-efficient fine-tuning recipes under a single, shared learning rate is a common but flawed practice: when the arms being compared have very different trainable-parameter counts, a shared rate can simultaneously depress the larger arms' means and inflate their variance, manufacturing a large, seemingly multi-seed-significant advanta