Tool Adapter Layer
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LayerRoute: Input-Conditioned Adaptive Layer Skipping via LoRA Fine-Tuning for Agentic Language Models
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Adaptive Minds: Empowering Agents with LoRA-as-Tools
Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke. We hypothesize that, when adapters are trained to provide strong domain-specific gains and are exposed with clear metadata, a base model can reliably route queries to the appropriate expert, effectively aggregating the benefits of many specialized adapters within a single framework. We introduce Adaptive Minds, a...
Universal Memory Protocol – a shared format for agent memory
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SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation
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An Organization-Scoped LLM Agent Runtime Architecture for Regulated Cybersecurity Operations
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Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data
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The ways we contain Claude across products
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PictSure: Pretraining Embeddings Matters for In-Context Learning Image Classifiers
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