The paper introduces Adaptive Context Management (AdaCoM), an external context manager that uses reinforcement learning to improve the performance of frozen LLM agents on long-horizon tasks by intelli…

The study compares agentic data retrieval using unstructured web data versus structured, semantically-annotated datasets, concluding that semantic metadata remains essential for high-precision, reliab…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The paper proposes a layered, server-side isolation architecture to secure Retrieval-Augmented Generation (RAG) and agentic AI systems in multitenant enterprise environments, ensuring that retrieval a…

The paper introduces a systematic framework and defense mechanisms to analyze and mitigate autonomous LLM agent worms that propagate through persistent agent state and cross-platform multi-agent syste…

Agent libOS introduces a library-OS-inspired runtime substrate that treats LLM agents as schedulable processes, providing explicit capability control and robust auditing for long-running, stateful age…

The paper introduces a 'Privacy Guard' framework that simultaneously reduces operational costs and eliminates data leakage risks when using LLMs by optimizing prompts and routing queries to secure mod…

AgenTEE is a system that enables the secure, confidential execution of complex LLM agent pipelines directly on edge devices by using isolated confidential virtual machines.

This paper analyzes the security of LLM-based autonomous agents by drawing parallels to operating system security, finding that while some vulnerabilities are inherent, many can be mitigated using est…

The paper introduces a Contextual Integrity (CI) framework and a new benchmark (DelegateCI-Bench) to rewrite user queries sent to cloud LLMs, ensuring only task-essential information is retained while…

This paper introduces AgentREVEAL, a diagnostic framework showing that the utility of web retrieval in LLM agents creates a safety-utility trade-off, as relevance itself can degrade safety alignment a…

This paper introduces AgentREVEAL, a diagnostic framework that demonstrates that the utility of web retrieval in LLM agents creates a safety-utility trade-off, as relevance itself can degrade safety a…

The paper introduces MCP-Persona, a novel benchmark designed to evaluate LLM agents' performance on real-world, personalized applications using the Model Context Protocol (MCP), revealing that current…

The paper introduces a unified framework to fairly evaluate LLM agentic capabilities by standardizing diverse benchmarks and separating the effects of the LLM model from the surrounding framework and…

The paper introduces AGENTREDBENCH, a dynamic redteaming benchmark that significantly measures indirect prompt injection threats in LLM agents using third-party integrations, and releases AGENTREDGUAR…

The paper introduces AGENTREDBENCH, a dynamic redteaming benchmark that significantly measures indirect prompt injection threats in LLM agents using SaaS integrations, and releases AGENTREDGUARD, a su…

The paper proposes an organization-scoped LLM agent runtime architecture designed to provide an auditable, model-agnostic platform for regulated cybersecurity operations, integrating deeply with exist…

The paper proposes a novel, organization-scoped LLM agent runtime architecture designed specifically for regulated cybersecurity operations, ensuring auditable context and integration with existing se…

This study benchmarks token-optimized formats (TOON and TRON) against JSON in end-to-end agentic AI systems, finding that TRON significantly reduces token overhead with minimal performance degradation…

The paper introduces Unified Context Evolution (UCE), a gradient-free framework that externalizes and manages agent experience into a typed, evolving library, significantly improving performance on mu…