Orchestration Systems
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Related Articles from SNS
Channel Fracture: Architectural Blind Spots in Scheduled Cross-Agent Memory Injection for Multi-Agent Orchestration Systems
arXiv:2606.04896v2 Announce Type: replace Abstract: Multi-agent AI orchestration systems increasingly rely on persistent memory to maintain context across sessions, agents, and tasks. When one agent must inject knowledge into another agent's memory -- a common requirement in hierarchical team architectures -- the delivery mechanism must be architecturally sound. We report the discovery of a systematic failure mode we term channel fracture: a condition where scheduled (cron) agents in...
Channel Fracture: Architectural Blind Spots in Scheduled Cross-Agent Memory Injection for Multi-Agent Orchestration Systems
arXiv:2606.04896v1 Announce Type: new Abstract: Multi-agent AI orchestration systems increasingly rely on persistent memory to maintain context across sessions, agents, and tasks. When one agent must inject knowledge into another agent's memory -- a common requirement in hierarchical team architectures -- the delivery mechanism must be architecturally sound. We report the discovery of a systematic failure mode we term channel fracture: a condition where scheduled (cron) agents in...
Federated Formal Verification: Cross-Backend Citation, Cross-Axis Convergence, and AI-Orchestrated Proof Dispatch for Production Systems
Announce Type: new Abstract: We propose a federated architecture for production formal verification. Rather than forcing all obligations into a single proof-assistant kernel, the architecture treats a verification campaign as a polyglot proof system composed of three mechanisms: cross-backend citation discharges a TLA+ obligation by citing an equivalent theorem in a structurally distinct kernel, with build- system-level drift-resistance enforced through kernel-level closure-assertion...
Audio-Oscar: A Multi-Agent System for Complex Audio Scene Generation, Orchestration, and Refinement
arXiv:2606.07397v1 Announce Type: new Abstract: In recent years, audio generation has made significant progress in tasks such as text-to-speech (TTS), text-to-audio (TTA) and text-to-music (TTM). However, generating long-form and controllable audio from complex audio scene descriptions remains a significant challenge, as such scenes often require coordinated speech, sound effects, music, songs, temporal structure, and post-production. In this work, we introduce \textbf{Audio-Oscar}, a...
Recognize Your Orchestrator: An Entropy Dynamics Perspective for LLM Multi-Agent Systems
new Abstract: The transition from single-turn models to Multi-Agent Systems (MAS) promises enhanced problem-solving capabilities, yet the centralized orchestration topology remains a critical point of fragility. To analyze this, we propose a Mean-Field Entropy Dynamics framework, modeling the orchestration process as a system governed by the competing forces of task resolution and cumulative context loading. To facilitate validation, we introduce Inverse Workflow Generation (IWG), a...
POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems
Announce Type: new Abstract: Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and hallucinations that resist characterisation block their deployment in safety-critical domains -- a gap made legally untenable by emerging AI regulation. Existing evaluation paradigms share a common flaw: centralised judgment creates single points of failure and demands domain-specific expertise. Here we present POIROT, a...
PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting
Announce Type: new Abstract: Real-world LLM applications are moving beyond single-agent workflows toward orchestrated multi-agent systems, yet current models still struggle to determine what each sub-agent needs to know. To measure this, we introduce PerspectiveGap, a benchmark for evaluating LLMs' ability to compose orchestration prompts for multi-agent systems. PerspectiveGap contains 110 scenarios, each evaluated through two distractor-mixed task formats: role-fragment assignment and...
Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems
Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory, and recovery. In these systems, failures arise not only from model errors, but also from orchestration-level issues such as tool timeouts, malformed arguments, stale context, contradictory evidence, retry loops, and unverified intermediate outputs. This paper presents a self-healing agentic orchestrator that...
Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration
Announce Type: new Abstract: Enterprise agent systems increasingly need to connect large language models to private tools, internal knowledge, and Model Context Protocol (MCP) interfaces. In this setting, raw task capability is insufficient: organizations also require policy enforcement, tenant-scoped isolation, and execution that remains within explicit operational boundaries. We present Queen-Bee, a governed multi-agent architecture in which a Queen control plane retrieves capabilities,...
vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models
arXiv:2603.04444v3 Announce Type: replace Abstract: As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- selecting the right model for each query at inference time -- has become a critical systems challenge. We present vLLM Semantic Router, a signal-driven decision routing framework for Mixture-of-Modality (MoM) model deployments. The central innovation is composable signal orchestration: the system...