aiqbench analysis
This article introduces CLQT, a rigorous benchmark framework designed to diagnose the specific capabilities and failure modes of LLM agents acting as autonomous portfolio managers rather than simply ranking them by financial returns. By integrating cost modeling, temporal gating, and strategy-consistency scoring into a closed-loop environment, it provides a more robust method for evaluating agent reliability in sequential trading tasks.
CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
arXiv:2606.29771v2 Announce Type: replace Abstract: LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading. Yet most still rank agents by returns over a fixed window, a weak proxy: the market path dominates a period's return, and apparent alpha can dissolve once look-ahead leakage is controlled. We introduce CLQT, which reframes closed-loop trading evaluation as diagnosis before ranking: an instrument that localizes where and why an agent's process succeeds or fails. CLQT is a closed-loop, cost-aware, strategy-consistent, temporally-gated environment whose agents run a five-stage cycle: gather, synthesize, allocate, execute, reflect. Every round emits a DecisionRound sealed into a recompute-verifiable hash chain. Six pillars form the substrate: a TimeGate, transaction- and financing-cost modeling, strategy-consistency scoring, three-tier memory, a Model-Context-Protocol tool layer, and mandate-aware synthesis. The same agent runs structured or fully autonomous, making process scaffolding an experimental variable. From the audit trail we compute a five-axis capability scorecard (APM-CS: Coherence, Acuity, Composure, Discipline, Reliability), with Coherence partly judged by a held-out, out-of-cohort LLM. We validate CLQT on a contamination-controlled multi-model backtest with an ablation grid and a live broker track on unseen, post-cutoff data, against a repeated-run noise floor. Three findings illustrate the diagnosis-first read: the capability leader is not the Sharpe leader (the nominal winner is a 5-of-26-round reliability artifact); signal-action agreement exceeds a held-out judge's coherence score by a stable margin on both tracks (+0.30 backtest, +0.23 live); and module value registers on the capability axes when returns cannot separate it. CLQT yields not a model ranking but a durable, extensible map of agent competencies and limitations.
Abstract:LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading. Yet most still rank agents by returns over a fixed window, a weak proxy: the market path dominates a period's return, and apparent alpha can dissolve once look-ahead leakage is controlled. We introduce CLQT, which reframes closed-loop trading evaluation as diagnosis before ranking: an instrument that localizes where and why an agent's process succeeds or fails. CLQT is a closed-loop, cost-aware, strategy-consistent, temporally-gated environment whose agents run a five-stage cycle: gather, synthesize, allocate, execute, reflect. Every round emits a DecisionRound sealed into a recompute-verifiable hash chain. Six pillars form the substrate: a TimeGate, transaction- and financing-cost modeling, strategy-consistency scoring, three-tier memory, a Model-Context-Protocol tool layer, and mandate-aware synthesis. The same agent runs structured or fully autonomous, making process scaffolding an experimental variable. From the audit trail we compute a five-axis capability scorecard (APM-CS: Coherence, Acuity, Composure, Discipline, Reliability), with Coherence partly judged by a held-out, out-of-cohort LLM. We validate CLQT on a contamination-controlled multi-model backtest with an ablation grid and a live broker track on unseen, post-cutoff data, against a repeated-run noise floor. Three findings illustrate the diagnosis-first read: the capability leader is not the Sharpe leader (the nominal winner is a 5-of-26-round reliability artifact); signal-action agreement exceeds a held-out judge's coherence score by a stable margin on both tracks (+0.30 backtest, +0.23 live); and module value registers on the capability axes when returns cannot separate it. CLQT yields not a model ranking but a durable, extensible map of agent competencies and limitations.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Portfolio Management (q-fin.PM)
I.2.11; I.2.1; J.1
arXiv:2606.29771 [cs.AI]
(or arXiv:2606.29771v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.29771 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 366 words
