Topic
multi-agent
Researchers Propose QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks
A new arXiv paper introduces a QoS-aware token scheduling and private data valuation framework for decentralized multi-modal agentic networks. The approach embeds multi-modal data in a shared semantic space and uses differentially private prototypes to balance utility and privacy, showing improved fairness and QoS in simulations.
StateGen Platform Generates Synthetic Training Data for Tool-Augmented LLMs with 9.66/10 Hallucination Score
Researchers introduce StateGen, a synthetic data generation platform that produces scored, reasoning-trace-rich training conversations for tool-augmented LLMs. The platform uses a four-role LLM loop and an authoritative state manager to eliminate tool-call hallucinations, achieving a 9.66/10 score across 64,698 evaluated conversations.
AgentLeak Benchmark Reveals Internal Channel Privacy Leaks in Multi-Agent LLM Systems
A new benchmark called AgentLeak evaluates privacy leakage in multi-agent large language model (LLM) systems, finding that inter-agent messages leak at 68.8% compared to 27.2% for final outputs. Across 1,000 scenarios and five models, total system exposure reaches 68.9%, highlighting risks invisible to standard output-only audits.
Synthetic Counteradaptation: A New Framework for Human-AI Co-evolution in Enterprise Systems
A new research paper introduces synthetic counteradaptation, a principle describing how humans and AI systems co-evolve by adapting to each other's strategies. The paper analyzes examples from the game of Go, mixed-motive social interactions, and geopolitical simulations, providing a framework for understanding recursive human-AI dynamics in multi-agent environments.