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The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
cs.LGcs.DC Huamin Chen, Xunzhuo Liu, Bowei He et al. · Mar 22, 2026

This vision paper from the vLLM Semantic Router project proposes the Workload-Router-Pool (WRP) architecture, a three-dimensional framework for LLM inference optimization. The authors synthesize two dozen prior publications into a structured matrix, arguing that workload characteristics, routing policy, and pool architecture are coupled dimensions that must be co-optimized. The paper maps existing work onto a $3\times3$ interaction matrix and proposes twenty-one concrete research directions tiered by maturity.

Over the past year, the vLLM Semantic Router project has released a series of work spanning: (1) core routing mechanisms -- signal-driven routing, context-length pool routing, router performance engineering, policy conflict detection, low-latency embedding models, category-aware semantic caching, user-feedback-driven routing adaptation, hallucination detection, and hierarchical content-safety classification for privacy and jailbreak protection; (2) fleet optimization -- fleet provisioning and energy-efficiency analysis; (3) agentic and multimodal routing -- multimodal agent routing, tool selection, CUA security, and multi-turn context memory and safety; (4) governance and standards -- inference routing protocols and multi-provider API extensions. Each paper tackled a specific problem in LLM inference, but the problems are not independent; for example, fleet provisioning depends on the routing policy, which depends on the workload mix, shifting as organizations adopt agentic and multimodal workloads. This paper distills those results into the Workload-Router-Pool (WRP) architecture, a three-dimensional framework for LLM inference optimization. Workload characterizes what the fleet serves (chat vs. agent, single-turn vs. multi-turn, warm vs. cold, prefill-heavy vs. decode-heavy). Router determines how each request is dispatched (static semantic rules, online bandit adaptation, RL-based model selection, quality-aware cascading). Pool defines where inference runs (homogeneous vs. heterogeneous GPU, disaggregated prefill/decode, KV-cache topology). We map our prior work onto a 3x3 WRP interaction matrix, identify which cells we have covered and which remain open, and propose twenty-one concrete research directions at the intersections, each grounded in our prior measurements, tiered by maturity from engineering-ready to open research.
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Reasoning Provenance for Autonomous AI Agents: Structured Behavioral Analytics Beyond State Checkpoints and Execution Traces
cs.AIcs.DCcs.SE Neelmani Vispute · Mar 23, 2026

As AI agents move from human-supervised copilots to fully autonomous infrastructure, organizations face a critical observability gap: existing systems capture computational state and execution traces but lack structured records of the agent's reasoning. This paper introduces the Agent Execution Record (AER), a schema-level primitive that captures intent, observation, and inference as first-class queryable fields at execution time. The core claim is that reasoning provenance cannot be faithfully reconstructed from state checkpoints due to fundamental non-identifiability (intent multiplicity, observation ambiguity, inference volatility). If validated, AERs would enable population-level behavioral analytics—systematic comparison of reasoning patterns across thousands of investigations, confidence calibration against expert judgments, and counterfactual regression testing via mock replay—that existing tooling achieves only through fragile post-hoc extraction.

As AI agents transition from human-supervised copilots to autonomous platform infrastructure, the ability to analyze their reasoning behavior across populations of investigations becomes a pressing infrastructure requirement. Existing operational tooling addresses adjacent needs effectively: state checkpoint systems enable fault tolerance; observability platforms provide execution traces for debugging; telemetry standards ensure interoperability. What current systems do not natively provide as a first-class, schema-level primitive is structured reasoning provenance -- normalized, queryable records of why the agent chose each action, what it concluded from each observation, how each conclusion shaped its strategy, and which evidence supports its final verdict. This paper introduces the Agent Execution Record (AER), a structured reasoning provenance primitive that captures intent, observation, and inference as first-class queryable fields on every step, alongside versioned plans with revision rationale, evidence chains, structured verdicts with confidence scores, and delegation authority chains. We formalize the distinction between computational state persistence and reasoning provenance, argue that the latter cannot in general be faithfully reconstructed from the former, and show how AERs enable population-level behavioral analytics: reasoning pattern mining, confidence calibration, cross-agent comparison, and counterfactual regression testing via mock replay. We present a domain-agnostic model with extensible domain profiles, a reference implementation and SDK, and outline an evaluation methodology informed by preliminary deployment on a production platformized root cause analysis agent.
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arXivPDF
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ARYA: A Physics-Constrained Composable & Deterministic World Model Architecture
cs.AIcs.DC Seth Dobrin, Lukasz Chmiel · Mar 22, 2026

ARYA presents a world model architecture using "nano models"—small specialized components orchestrated by an autonomous agent (AARA)—rather than monolithic neural networks. The system claims physics-constrained determinism, sub-20-second training cycles, and an "unfireable" safety kernel that cannot be bypassed. The authors position this as production-deployed across seven industry domains from aerospace to pharma, achieving state-of-the-art results on six of nine benchmarks with "zero neural network parameters."

This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models, composability, causal reasoning, determinism, and architectural AI safety. We demonstrate that ARYA satisfies all canonical world model requirements, including state representation, dynamic prediction, causal and physical awareness, temporal consistency, generalization, learnability, and planning and control. Unlike monolithic foundation models, the ARYA foundation model implements these capabilities through a hierarchical system-of-system-of-systems of specialized nano models, orchestrated by AARA (ARYA Autonomous Research Agent), an always-on cognitive daemon that executes a continuous sense-decide-act-learn loop. The nano model architecture provides linear scaling, sparse activation, selective untraining, and sub-20-second training cycles, resolving the traditional tension between capability and computational efficiency. A central contribution is the Unfireable Safety Kernel: an architecturally immutable safety boundary that cannot be disabled or circumvented by any system component, including its own self-improvement engine. This is not a social or ethical alignment statement; it is a technical framework ensuring human control persists as autonomy increases. Safety is an architectural constraint governing every operation, not a policy layer applied after the fact. We present formal alignment between ARYA's architecture and canonical world model requirements, and report summarizing its state-of-the-art performance across 6 of 9 competitive benchmarks head-to-head with GPT-5.2, Opus 4.6, and V-JEPA-2. All with zero neural network parameters, across seven active industry domain nodes spanning aerospace, pharma manufacturing, oil and gas, smart cities, biotech, defense, and medical devices.
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arXivPDF
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NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum
cs.DCcs.AIcs.LG Peihan Ye, Alfreds Lapkovskis, Alaa Saleh et al. · Mar 22, 2026

Modern AI services increasingly run across the computing continuum—from cloud to edge devices—yet fault management remains challenging due to resource constraints, noisy telemetry, and cascading failures. This paper proposes NeSy-Edge, a three-layer neuro-symbolic framework that performs local log parsing, causal graph construction, and root-cause analysis on edge nodes, invoking cloud LLMs only when local evidence is insufficient. The core idea is to combine lightweight symbolic caching and prior-constrained causal discovery with selective neural inference, trading off autonomy against accuracy under strict memory budgets ($\sim$1500 MB).

The computational demands of modern AI services are increasingly shifting execution beyond centralized clouds toward a computing continuum spanning edge and end devices. However, the scale, heterogeneity, and cross-layer dependencies of these environments make resilience difficult to maintain. Existing fault-management methods are often too static, fragmented, or heavy to support timely self-healing, especially under noisy logs and edge resource constraints. To address these limitations, this paper presents NeSy-Edge, a neuro-symbolic framework for trustworthy self-healing in the computing continuum. The framework follows an edge-first design, where a resource-constrained edge node performs local perception and reasoning, while a cloud model is invoked only at the final diagnosis stage. Specifically, NeSy-Edge converts raw runtime logs into structured event representations, builds a prior-constrained sparse symbolic causal graph, and integrates causal evidence with historical troubleshooting knowledge for root-cause analysis and recovery recommendation. We evaluate our work on representative Loghub datasets under multiple levels of semantic noise, considering parsing quality, causal reasoning, end-to-end diagnosis, and edge-side resource usage. The results show that NeSy-Edge remains robust even at the highest noise level, achieving up to 75% root-cause analysis accuracy and 65% end-to-end accuracy while operating within about 1500 MB of local memory.
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arXivPDF