NTILC replaces in-context tool registry lookup with learned latent retrieval using a signature-aware composite loss, reducing context consumption by over 95% and latency by up to 74%.
Efficient solutions for an intriguing failure of llms: Long context window does not mean llms can analyze long sequences flawlessly
5 Pith papers cite this work. Polarity classification is still indexing.
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ADAM uses personality-guided LLM augmentation and cross-lingual attention distillation to raise balanced accuracy on multilingual personality recognition to 0.6332 on Essays and 0.7448 on Kaggle, outperforming standard BCE loss.
SURGENT is a multi-agent surgical assistance system with novel memory management that outperforms baseline LLMs on case analysis, plan simulation, safety monitoring, risk assessment, and rehabilitation guidance.
Fine-tuned small language models trained on a synthetic Windows event log dataset with remediation steps outperform larger models in issue detection and solution generation with lower computational cost.
Develops a conceptual distinction between human-cognitive and artificial-stochastic error architectures in code generation, drawing on Dennett, Rescher, and Floridi to explore implications for AI-human collaboration.
citing papers explorer
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NTILC: Neural Tool Invocation via Learned Compression
NTILC replaces in-context tool registry lookup with learned latent retrieval using a signature-aware composite loss, reducing context consumption by over 95% and latency by up to 74%.
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Cross-Lingual Attention Distillation with Personality-Informed Generative Augmentation for Multilingual Personality Recognition
ADAM uses personality-guided LLM augmentation and cross-lingual attention distillation to raise balanced accuracy on multilingual personality recognition to 0.6332 on Essays and 0.7448 on Kaggle, outperforming standard BCE loss.
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SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflow
SURGENT is a multi-agent surgical assistance system with novel memory management that outperforms baseline LLMs on case analysis, plan simulation, safety monitoring, risk assessment, and rehabilitation guidance.
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Fine-Tuning Small Language Models for Solution-Oriented Windows Event Log Analysis
Fine-tuned small language models trained on a synthetic Windows event log dataset with remediation steps outperform larger models in issue detection and solution generation with lower computational cost.
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Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation
Develops a conceptual distinction between human-cognitive and artificial-stochastic error architectures in code generation, drawing on Dennett, Rescher, and Floridi to explore implications for AI-human collaboration.