SING builds an intention-tool graph linking user intentions, tool capabilities, and collaboration patterns to enable dynamic retrieval, improving Global Recall@5 by up to 59.8% and success rate by up to 28.9% on three benchmarks while cutting schema exposure by 99.8%.
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A hybrid deterministic-plus-semantic interception layer for continuous task-based authorization of multi-turn LLM agent tool invocations, with new multi-turn datasets and initial experiments.
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
citing papers explorer
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SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents
SING builds an intention-tool graph linking user intentions, tool capabilities, and collaboration patterns to enable dynamic retrieval, improving Global Recall@5 by up to 59.8% and success rate by up to 28.9% on three benchmarks while cutting schema exposure by 99.8%.
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Hybrid Inspection and Task-Based Access Control in Zero-Trust Agentic AI
A hybrid deterministic-plus-semantic interception layer for continuous task-based authorization of multi-turn LLM agent tool invocations, with new multi-turn datasets and initial experiments.
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From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
- Auditing Automated Evaluation, Error Propagation, and Runtime Mitigation in Tool-Using Language Agents