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AmalthAI is presented, an open-source CV platform that bridges this gap, enabling non-ML CH experts to independently produce and validate archaeologically meaningful findings, and its self-hostable deployment ensures sensitive data is kept within premises.
The findings suggest that purpose-built AI study assistants can support academic learning when integrated into regular classroom instruction, however, short-term exposure and predominantly task-focused interactions may limit their influence on motivational and engagement-related outcomes.
A function-centric framework based on Transcoders, sparse approximations of MLP sublayers that act as a causal proxy for layer-wise computation yields interpretable and predictive accounts of multimodal computation in VLMs.
Results show that scaling consistently improves performance, while multitask learning acts as a beneficial regularizer primarily for smaller-capacity models.
The Contrastive Routing Mechanism (CoRM) is proposed, which scores each expert by the gap between its affinity for the incoming token and its affinity for this shared reference state, interpreted through a distinct per-expert projection.
A lifecycle model for LLM systems is proposed that supports security analysis by structuring it around security-relevant boundaries rather than workflow optimisation, and is supported by a 12-stage LLMOps pillar and a 9-category governance pillar.
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