Very Large Scale Integration (VLSI) global routing is an NP-hard combinatorial optimization problem requiring signal net assignment across capacity-constrained 3D grids while minimizing congestion, wirelength, and via transitions. Because traditional heuristics rely on static penalty schedules that fail on complex congestion topologies, we present AlphaRoute: a multi-objective adaptive search framework reformulating rip-up and reroute (R&R) into a dynamic optimization system. We introduce SHAP-based overflow decomposition to isolate per-net congestion, driving targeted subgraph extraction via 3D Dijkstra maze routing and an adaptive PathFinder policy. Crucially, AlphaRoute employs Large Language Models (LLMs) as semantic policy optimizers. Bounded by a deterministic knowledge graph, the LLMs interpret congestion metrics to dynamically adjust penalty parameters. Evaluated on ISPD 2025 benchmarks, AlphaRoute reduces overflow by 98.6% on MEMPOOL. On the constrained ARIANE design, we achieve an overflow of 146,109 (a 29.8x reduction in overflow over the state of the art), yielding a penalized score of S_orig = 0.0538 versus the State-of-the-art (SOTA) 1.780. These results demonstrate that superior algorithmic search geometry can overcome the latency of interpreted Python implementations.
A Directed Graph-Guided Automated Algorithm Design framework, termed DGA$_2$D, which structures the open-ended program space as a directed graph, where each node represents a functional operator that can be instantiated using one of multiple candidate code implementations, while directed walks constitute complete algorithmic pipelines.
Jiale Zhao, Zimu Chen, Sirui Mao et al.· 0 citations
Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA evaluation, lacks systematic calibration optimization for open-set scenarios, and employs training objectives that dilute multi-positive signals via softmax normalization without incorporating cost. We address these limitations with three contributions: (1)VLM-ExecRouterBench, the first execution-oriented VLM routing benchmark covering Code, Agentic, and Search domains with 11 candidate models spanning nearly two orders of magnitude in pricing; (2)SCOPE-Router, a dual-tower router that matches queries to model behavior profiles constructed via hybrid calibration (random/diagnostic/diversity sampling), enabling new models to join routing without retraining; (3)CRM+RCCR, an architecture-agnostic cost-aware objective that encodes cost preference into continuous relevance targets through per-pair independent scoring, eliminating multi-positive dilution while regularizing queries with similar routing preferences to be closer in the routing space. Empirically, SCOPE-Router achieves the best Rank Score on all three benchmarks, surpassing the runner-up by 1.84 points under OOD settings and by 6.75 points under doubly OOD open-set evaluation. When applied to four diverse routers, CRM+RCCR improves Rank Score by 1.25--6.21 points.
Empirical results on RouterBench and SWE-Bench demonstrate that WR-Offline surpasses existing baselines in performance under a fixed budget and adheres more closely to budget constraints, and WR-Online achieves comparable performance to the baselines, while using substantially less exploration data.
We present $\texttt{bucket-graph-spprc}$ ($\texttt{bgspprc}$ for short), an open-source, header-only C++23 library for the shortest path problem with resource constraints (SPPRC), the pricing subproblem at the heart of branch-cut-and-price for vehicle routing and related problems. The library implements the bucket-graph labelling algorithm of Sadykov, Uchoa and Pessoa (2021), with bidirectional labelling, across-arc concatenation, bucket fixing and arc elimination, and a structure-of-arrays label store with SIMD-accelerated dominance. Its central design feature is a compile-time resource concept: a new SPPRC variant is added by implementing a fixed seven-function interface, and resources compose into a label state with no runtime dispatch, the state layout fixed at compile time. Five resources ship built in: time/capacity, ng-path elementarity relaxation, rank-1 cuts, cumulative cost, and pickup-and-delivery. In a reproducible, head-to-head comparison on shared public instances at an identical bound, $\texttt{bgspprc}$ outperforms PathWyse (Salani, Basso and Giuffrida, 2024), the main open-source comparator, by $1.3\times$--$2.35\times$ in shifted geometric mean (and by $1.3\times$--$2.3\times$ even when itself run single-threaded), and runs within $1.9\times$--$2.4\times$ of parallel pull labelling (Petersen and Spoorendonk, 2025), a different labelling technique for the same problem. The library, benchmark scripts, and pinned instances are publicly available.
Large Language Model-based Multi-Agent Systems (LLM-MAS) have shown exceptional promise for complex tasks, including retrieval-augmented generation and autonomous data analytics. However, their deployment in resource-constrained industrial environments faces critical challenges, such as unpredictable end-to-end latency, low task success rates, and expensive operational costs. Existing orchestration strategies for LLM-MAS mainly rely on static heuristics, implicit LLM-based routing, or reinforcement learning, which suffer from brittleness under workload drift, prohibitive online exploration costs, and inherent governance risks. To address these issues, we propose G-STAR, a general graph-based scheduling framework that formalizes complex MAS pipelines as attributed Directed Acyclic Graphs (DAGs). Specifically, G-STAR decouples its learning pipeline into three critical stages to ensure both system stability and execution efficiency. First, we build a data layer that logs fine-grained signals for agent node (e.g., execution latency, token I/O, model IDs, resource metrics) and task-grounded outcomes (e.g., accuracy, cost, SLA), yielding a trace-driven dataset. Second, we train an offline GNN model to predict optimal dynamic routing decisions, including agent activation, concurrency-constrained Top-K selection, and execution ordering, conditioned on the evolving graph state. Finally, a lightweight dispatcher deploys the pre-trained GNN model for single-forward-pass routing with negligible computational overhead. Extensive experiments on two public benchmarks and our real-world production workload confirm G-STAR's superiority over static and LLM-driven baselines. It maintains competitive task quality while reducing latency variability and consistently improving end-to-end latency. Furthermore, we develop an industry-grade orchestration stack with asynchronous execution, resilient serving, and audit-friendly artifacts, offering a practical solution for optimizing web-scale deployments of complex MAS pipelines.
Jiabao Song, Yunsheng Xia, Beibei Kong et al.· Proceedings of the 32nd ACM...· 0 citations
The rapid proliferation of Large Language Models (LLMs) with varying capability profiles, context window limits, execution latencies, and financial costs presents a significant operational challenge for enterprise AI deployments. Monolithic deployment strategies wherein all requests are directed to a single high-capability frontier model result in substantial compute over-provisioning and excessive operational costs for routine queries. Conversely, relying solely on lightweight models degrades output accuracy on complex multi-step reasoning tasks. To resolve this trade-off, this paper introduces LLM-Advisor, an open-source, adaptive framework designed for intelligent query categorization, dynamic model evaluation, and constraint-aware request routing across heterogeneous multi-LLM pools. LLM-Advisor analyzes incoming prompt features, structural complexity, domain requirements, and user-defined constraints (e.g., maximum cost per request, latency thresholds) to route tasks to the optimal candidate model. We evaluate LLM-Advisor using a benchmark suite of 1,000 queries across code generation, general reasoning, and contextual retrieval tasks using both proprietary and open-weight models (including GPT-4o, Claude 3.5 Sonnet, Llama 3, and Mistral). Experimental results demonstrate that LLM-Advisor achieves a 42% reduction in overall inference expenditure and a 35% decrease in average response latency while retaining 94.6% task accuracy compared to static GPT-4o baseline routing. These findings highlight LLM-Advisor as an efficient, highly scalable middleware solution for production-grade AI system deployments.
Harshil Lodhiya· International Journal of Res...· 0 citations