Apple Silicon dominates local LLM inference, yet nearly every published optimization is designed and benchmarked on NVIDIA server GPUs under batched serving workloads. This paper asks which speedups survive when moved to single-request, unified-memory Apple machines, evaluating four families—KV-cache pruning, speculative decoding, ported CUDA kernels, and post-training quantization—across four Apple devices with matched RTX 4090 controls. The central finding is that a reported speedup is not an intrinsic property of a technique but a joint function of technique, runtime, hardware contract, and workload regime.

Federated clustering under non-IID data must minimize communication rounds while preventing leakage of local data properties. This paper proposes Evolving Federated Density Peak Clustering (eFDPC), where each client incrementally learns diagonal-covariance Gaussian micro-clusters from private mini-batches, selects local density peaks, and transmits masked peaks through a shuffler-based anonymization layer. A server-side second density-peak step reconstructs unlinkable global centers from additive shares, aiming for accurate one-shot clustering without centralizing raw samples.

JointServe targets multi-model LLM agent serving on memory-constrained GPUs, where heterogeneous expert weights and ever-growing KV caches physically contend for HBM bytes. It unifies weight shards and KV blocks as competing residency objects under one budget, exploits the agent's own tool-call stream to prefetch destination experts during caller generation gaps, and streams only incremental layer groups when a swap occurs. The work is motivated by the architectural shift from single-model tool use to planner-orchestrated pools of heterogeneous experts—a pattern visible in SWE-bench, $\tau$-bench, and OSWorld traces—and targets the private and edge deployments where full co-residency is arithmetic impossible.

Configuring distributed LLM training over hundreds of GPUs involves a combinatorial explosion over tensor, pipeline, expert, context, and data parallelism plus micro-batch size, recomputation, and sharding, where a single byte of activation accounting can separate a legal run from an OOM crash. This paper proposes Primus Projection, a hybrid engine that benchmarks compute kernels on as few as one GPU and analytically reconstructs out-of-scope communication and pipeline schedules at the target scale, coupled with an LLM-driven tuning agent that searches the space in minutes instead of days. The authors validate across dense and MoE models on two AMD GPU generations, reporting projection errors around $5\%$ at up to 1,024 GPUs and a $+27\%$ throughput gain discovered in under 30 minutes of single-node exploration.

Choosing a parallelism configuration for distributed LLM inference is challenging because the prefill and decode phases impose conflicting demands on time-to-first-token (TTFT) and inter-token latency (ITL). This paper develops a systematic framework that defines parallelism operating regions via TTFT-ITL Pareto frontiers and formulates SLO-aware selection using goodput $G_p(w,T_s)=\lambda_p(w)F_p(T_s|w)$. The core finding is a workload-dependent transition: tensor-parallel (TP) configurations excel for decode-heavy workloads and relaxed latency requirements, while pipeline-parallel (PP) configurations dominate for prefill-heavy workloads, stringent TTFT targets, and long contexts. The work matters because it moves parallelism selection from a static deployment choice to a workload- and SLO-dependent operating decision.

This paper tackles privacy, energy, and infrastructure constraints in smart buildings by proposing a decentralized cloud-fog-edge architecture that treats the building itself as a data center. Its core idea couples the KOptim container orchestrator with FIWARE for dynamic SLA management, while running lightweight online ML algorithms such as GHA-PCA and K-means directly on Arduino-class microcontrollers. The humanitarian motivation—bringing autonomous AI to underserved communities—is compelling, although the current evaluation remains largely preliminary.

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.

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.

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."

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).