RASP-QAOA, a per-instance selector over ten QAOA actions, is introduced, a per-instance selector over ten such actions that removes actions that cannot implement the requested QAOA semantics or execution requirements, then orders the remaining actions using instance features.
Abstract
Exact QAOA simulation spans several computational representations whose useful regions differ sharply across graph structure, circuit depth, precision, and available memory. Choosing only a backend name hides these differences: an executable choice also fixes the representation, adapter, precision mode, and memory policy. We introduce RASP-QAOA, a per-instance selector over ten such actions. It first removes actions that cannot implement the requested QAOA semantics or execution requirements, then orders the remaining actions using instance features; actions outside learned support are handled by analytical work estimates. On a content-disjoint 60-request H200 evaluation, RASP-QAOA succeeds on all 31 requests for which at least one admissible action completes and validates. Within this set it reaches 27/31 top-1 and 31/31 top-2 selection, with 1.051 geometric-mean regret. Its failure-penalized PAR10 score is 0.0396 times that of development-selected CUAOA (95% interval: 0.0085-0.1644). A separate 30-request crossover shows that graph structure changes 16 decisions and improves the paired penalized score, while a depth-1 stump matches gradient boosting. The evidence supports resource-aware representation selection at n<= 35, p<= 5, with gains driven by representation features rather than classifier complexity.
HACO is proposed, a runtime control scheme that treats each role request as a reliability-constrained selection problem over candidate agent instances, each coupling a role type, an LLM, and a concrete execution environment.
EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohibitively expensive. Prior LLM-assisted tuners mainly use the LLM as an external proposer with transient working context; we instead present \textbf{StateTune}, which reformulates LLM-assisted EDA tuning as a closed-loop, state-carrying process. Its optimizer state is a typed, evidence-gated \emph{persistent optimization memory} that is updated by every evaluation and shared between candidate generation and budget allocation. On top of this optimizer state, an expected hypervolume improvement (EHVI)-guided, runtime-aware promotion policy ranks quick-stage candidates by expected Pareto frontier gain per unit of runtime cost. Evaluated on a Cadence industrial flow across six benchmark blocks (two technology nodes \(\times\) three designs), against five baselines including LLM+retrieval-augmented generation (RAG) and preference-based Bayesian optimization (BO) tuners, StateTune achieves the strongest final hypervolume on all six benchmark blocks, showing a stable improvement in frontier quality across the full matrix; it also matches or surpasses the strongest baselines on worst negative slack (WNS), area, and power across the same set. Ablation shows persistent memory is the largest contributor: removing it costs 58.5\% of the hypervolume. Dedicated analyses of evidence-gating sensitivity, memory poisoning, cross-design transfer, and three-seed reproducibility (CV\,\(<\)\,7\% on five of six blocks) further validate the memory design.
Kunlong Li, Shangshang Yao, Su Zheng et al.· 0 citations
The Efficiency-Adjusted Success Rate (EASR) is reported - success weighted by resource consumption relative to declared budgets - revealing that high accuracy at unbounded cost is not production-viable.
Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju· 0 citations
A paired benchmark that tests whether LLM agents exhibit conscious allocation behavior under a fixed budget in two contexts: an abstract text-based formulation and a code-construction task finds that every frontier model testedacts near-optimally in the abstract framing but fails to transfer this ability to script-writing.
LLM-MetaAS is proposed, a semantic-statistical framework for AutoML execution policy selection that improves overall AutoML performance and selects policies closer to the oracle than fixed strategies, random selection, and the native Auto-sklearn 2.0 selector.
Zhihuan Peng, Pincheng Liu, Yong Li et al.· Applied Sciences· 0 citations
Cascade, an LLM serving system that estimates and continuously updates this per-request latency budget from request characteristics, KV-cache state, and current system load, and uses a single per-request budget to jointly coordinate request scheduling and KV-cache management across the memory hierarchy.
Muhammad Adnan, R. Mahapatra, Prashant J. Nair et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
