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arxiv: 2606.29771 · v1 · pith:NRYTMQQMnew · submitted 2026-06-29 · 💻 cs.AI · cs.LG· q-fin.CP· q-fin.PM

CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents

Bo Qu , Mingguang Chen This is my paper

Pith reviewed 2026-06-30 06:41 UTC · model grok-4.3

classification 💻 cs.AI cs.LGq-fin.CPq-fin.PM
keywords LLM agentsportfolio managementclosed-loop benchmarkstrategy consistencycapability scorecarddiagnostic evaluationcost-aware tradingprocess scaffolding
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The pith

CLQT reframes LLM portfolio agent evaluation as diagnosis of process competencies rather than ranking by returns.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents CLQT as a closed-loop benchmark that runs LLM agents through repeated trading rounds while tracking every decision in a verifiable hash chain. It requires agents to follow a five-stage cycle of gathering data, synthesizing views, allocating positions, executing trades, and reflecting on outcomes, then scores them on five axes of the APM-CS scorecard. Current return-based rankings are rejected because market paths dominate results and apparent skill often disappears under controlled conditions. A sympathetic reader would care because the method aims to localize exactly where an agent's reasoning holds up or breaks, producing a map of strengths and weaknesses instead of a single leaderboard position. The work validates the setup through controlled backtests and live broker execution on post-cutoff data.

Core claim

CLQT is a fully closed-loop, cost-aware, strategy-consistent, temporally-gated environment in which agents execute a five-stage cycle (gather, synthesize, allocate, execute, reflect) and emit DecisionRounds sealed into a recompute-verifiable hash chain. From the resulting audit trail the benchmark computes the five-axis APM-CS scorecard (Coherence, Acuity, Composure, Discipline, Reliability), with Coherence partly scored by a held-out LLM, while enforcing institutional transaction and financing costs, time gating, three-tier memory, and mandate-aware synthesis. The same agent can be run as either a constrained committee of roles or a single orchestrator, treating process scaffolding as an ex

What carries the argument

The five-stage cycle together with the APM-CS scorecard and strategy-consistency scoring, which together localize where and why an agent's process succeeds or fails.

If this is right

  • Process scaffolding (committee versus single orchestrator) becomes an explicit experimental variable whose effect on each scorecard axis can be measured.
  • Every reported metric can be recomputed from the sealed DecisionRound trail, eliminating post-hoc leakage.
  • Validation on contamination-controlled multi-model backtests and live post-cutoff broker data shows the scorecard distinguishes signal from market-path noise.
  • The benchmark supplies not a single ranking but a five-axis map that can be extended to new agent architectures or mandates.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same diagnostic structure could be adapted to other sequential, cost-bearing decision domains such as inventory management or energy dispatch.
  • Using a held-out LLM for coherence scoring trades one form of self-preference for possible model-specific bias that would need separate calibration.
  • Extending the cost models to include slippage from market impact or more complex financing instruments would test whether the current institutional-cost layer is sufficient.

Load-bearing premise

The five-stage cycle, APM-CS scorecard, and strategy-consistency scoring accurately capture sound reasoning and consistent strategy without introducing new forms of leakage or bias.

What would settle it

Re-running the identical agent across multiple distinct market regimes produces APM-CS scores that vary more than the repeated-run noise floor even though the underlying strategy remains unchanged.

Figures

Figures reproduced from arXiv: 2606.29771 by Bo Qu, Mingguang Chen.

Figure 1
Figure 1. Figure 1: CLQT graphical abstract — six design pillars wrap a five-stage closed loop around a three-tier memory core, sealed by a hash-linked DecisionRound audit trail. 5. A closed-loop benchmark substrate with strict temporal integrity, institutional cost modeling, strategy-consistency measurement, three-tier memory, 19 MCP tools, mandate-aware synthesis, 3-D scouting, intertemporal sentiment trajectories, and veri… view at source ↗
Figure 2
Figure 2. Figure 2: The CLQT closed decision loop. The five stages (IA → MSS → MAPC → COE → ALE) are staffed by six specialized agents in structured mode — a strict investment-committee process whose staged roles enforce the guardrails — or by a single full-access agent with end-to-end decision autonomy in autonomous mode. Each cycle reads from and writes to the three-tier memory and emits one DecisionRound: a sealed, hash-li… view at source ↗
Figure 3
Figure 3. Figure 3: The 3-D SCOUT stage ranks universe candidates on a composite z-score of momentum, earnings surprise, and macro correlation. The right panel is a real example from the campaign runs: JNJ leads on momentum alone, but a stronger earnings surprise and higher market participation make FCX the 3-D composite winner. 3-Dimensional SCOUT Stage Real universe candidates differ not only in momentum but in earnings qua… view at source ↗
Figure 4
Figure 4. Figure 4: ConsistencyScore decomposition into its four orthogonal components and the drift-warning threshold. 3.4 Cost-Aware Execution CLQT tracks position-level PnL via weighted-average cost basis. On a buy of qfill shares at pfill into a position of qold at c¯old: c¯new = qold · c¯old + qfill · pfill qold + qfill On a sell, PnLrealized = (pfill − c¯) · qfill − ctx. Daily financing costs accrue for lever￾aged/short… view at source ↗
Figure 5
Figure 5. Figure 5: Working / episodic / semantic memory tiers with decay rates and the periodic consolidation pass. • Working memory: current observation pack plus the most recent N = 3 rounds. • Episodic memory: structured event-action-outcome records, decaying at αe = 0.95/round. • Semantic memory: generalized cross-asset patterns consolidated from episodic records, αs = 0.98. Every k = 12 rounds a consolidation pass merge… view at source ↗
Figure 6
Figure 6. Figure 6: Per-symbol intertemporal sentiment trajectory across the inter-rebalance window, with the improving / deteriorating / stable trend classification surfaced to the PM agent. Three real symbols from the campaign runs: LLY (improving), XOM (deteriorating), and AMZN (stable). Dynamic Universe Management The top-K SCOUT candidates are surfaced to the PM alongside current holdings, enabling agent-driven rotation,… view at source ↗
Figure 7
Figure 7. Figure 7: The agreement↔judge coherence gap is +0.33 (backtest) and +0.34 (live) — across two different cohorts and two horizons the instrument measures the same decision property on data the models never saw. What the gap measures, and why coherence scores low. The two halves of Coherence ask different-difficulty questions. Agreement checks only the direction of each proposed trade against the sign of that name’s i… view at source ↗
Figure 8
Figure 8. Figure 8: Five-axis diagnostic capability scorecard (within-cohort percentile per axis), all ten configurations split into structured (left) and autonomous (right) panels, one colour per model. No configuration encloses all five axes; the composite leader (deepseek·structured) does not top Coherence, the Sharpe leaders are uneven rather than balanced, and the autonomous envelopes are systematically smaller than the … view at source ↗
Figure 9
Figure 9. Figure 9: Operational reliability (D5) by configuration, parse-fail-annotated. qwen3·structured (4–8/26 → ≈0.2) and gemini·structured (≈10 parse-fail rounds → 0.61) carry the cohort’s reliability deficits, both invisible to their returns; deepseek’s reliability flips with mode (structured 0.99 → autonomous 0.60). The “pf” labels mark mean parse-fail rounds, the mechanical driver of the D5 spread; qwen3’s incompletio… view at source ↗
Figure 10
Figure 10. Figure 10: Ablation ∆Sharpe (mean ± range across repeated runs) vs. the structured-gemini baseline, single-module and multi-module (hatched) knockouts. Only the mis-calibrated HIGH cost tier clears the ±0.42 repeated-run noise band; neither single-module nor whole-cluster removal — down to the bare-workflow extreme — separates on returns. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Decision-quality proxies per ablation as a fraction of the full-module baseline (1.0 = unchanged; lower = more degraded). Stripping modules collapses analysis depth, signal breadth, candidate exploration, self-scrutiny and reasoning effort — the bare-workflow row is degraded on every axis — while returns are unaffected because the mandate’s constraints contain the degraded decisions (the full-performance … view at source ↗
Figure 12
Figure 12. Figure 12: Terminal cumulative return by configuration over the 26 bi-weekly rounds (2025-06 → 2026-06); whiskers span the range across repeated runs, and each label gives the number of the 8 passive baselines the configuration beats. Most structured configs sit above their autonomous counterparts on terminal return and clear the defensive baselines (≈6/8), while autonomous configs trail (as few as 1/8). APM-CS 72.4… view at source ↗
Figure 13
Figure 13. Figure 13: Live hold-round rate by model × mode over nine valid days. Only two models ever hold, and only in autonomous mode — gemini-3.5-flash (0.67, tool-turn truncation) and minimax-m3 (0.56, schema-adherence failure) — via different mechanisms; the holds are intermittent and recoverable (§7.11), not a permanent collapse. Structured mode = 0% for all. that even-handedness is the point of reading the trail. Every … view at source ↗
Figure 14
Figure 14. Figure 14: Capability (APM-CS) vs. average LLM cost rank (1 = cheapest); circles = structured, triangles = autonomous. deepseek·structured delivers the highest capability (72.4) among the cheaper configurations, while haiku·structured reaches comparable capability (67.9) at several times the cost, and gemini·structured is the heaviest reasoner yet only mid-capability — its compute largely consumed by the parse-fail … view at source ↗
read the original 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, since a period's return is dominated by the market path and apparent alpha can dissolve once look-ahead leakage is controlled. Such a ranking certifies neither sound reasoning, nor a consistent strategy, nor a durable edge. We introduce CLQT, which reframes closed-loop trading evaluation as diagnosis rather than ranking: an instrument that localizes where and why an agent's process succeeds or fails. CLQT is a fully closed-loop, cost-aware, strategy-consistent, temporally-gated environment whose agents run a five-stage cycle: gather, synthesize, allocate, execute, reflect. Each round emits a complete DecisionRound sealed into a recompute-verifiable hash chain, so every metric is reconstructable from the trail. Six pillars form the substrate: a hard TimeGate, institutional 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 as a constrained committee of specialized roles or a single full-autonomy orchestrator, 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 judged partly by a held-out, out-of-cohort LLM to curb self-preference bias. We validate it 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. CLQT separates outcome from capability, yielding not a model ranking but a durable, extensible map of agent competencies and limitations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces CLQT, a closed-loop benchmark for diagnostic evaluation of LLM portfolio-management agents. It defines a five-stage cycle (gather, synthesize, allocate, execute, reflect) supported by six pillars (hard TimeGate, institutional cost modeling, strategy-consistency scoring, three-tier memory, Model-Context-Protocol tool layer, mandate-aware synthesis). Agents may run as role-specialized committees or single orchestrators. Every DecisionRound is sealed in a recompute-verifiable hash chain. From the trail the authors derive the APM-CS five-axis scorecard (Coherence scored partly by held-out LLM, plus Acuity, Composure, Discipline, Reliability). Validation consists of a contamination-controlled multi-model backtest with ablation grid plus a live broker track on post-cutoff data, with the goal of producing a map of competencies rather than return-based rankings.

Significance. If the claimed separation of process quality from outcome holds, CLQT would advance evaluation methodology for sequential LLM agents by replacing confounded return rankings with reconstructable, cost-aware diagnostics. Explicit strengths include the hash-chain audit trail enabling full metric recomputation, use of a held-out LLM for Coherence to mitigate self-preference, an ablation grid, and live testing on unseen data; these elements directly support reproducibility and robustness claims.

major comments (2)
  1. [Abstract] Abstract: the central claim that the APM-CS scorecard 'separates outcome from capability' is load-bearing, yet the text supplies no reported correlation coefficients, partial-dependence plots, or noise-floor comparisons between the five axes and realized P&L after cost and time-gate controls; without these the independence assertion cannot be verified from the validation description.
  2. [Validation] The pillars and validation description: strategy-consistency scoring and mandate-aware synthesis are defined relative to the same agent behaviors that the scorecard is meant to diagnose; the manuscript must show (e.g., via an explicit equation or ablation) that this does not introduce circular dependence that would make the separation claim tautological.
minor comments (2)
  1. [Abstract] Abstract: 'Model-Context-Protocol' is abbreviated MCP on first use without expansion; define acronyms at first appearance.
  2. The five-stage cycle is introduced without a diagram or pseudocode listing the exact inputs/outputs of each stage; adding either would clarify how the hash chain captures the full trace.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the separation claims in CLQT. We address each major point below and outline targeted revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the APM-CS scorecard 'separates outcome from capability' is load-bearing, yet the text supplies no reported correlation coefficients, partial-dependence plots, or noise-floor comparisons between the five axes and realized P&L after cost and time-gate controls; without these the independence assertion cannot be verified from the validation description.

    Authors: We agree that explicit quantitative support for independence strengthens the central claim. The current validation already includes a repeated-run noise floor and ablation grid on post-cutoff data, but does not report axis-to-P&L correlations. In revision we will add (i) Pearson and Spearman correlations between each APM-CS axis and net P&L after TimeGate and cost controls, (ii) partial-dependence plots of P&L versus each axis, and (iii) direct comparison of these quantities against the empirical noise floor. These additions will appear in a new subsection of the validation results. revision: yes

  2. Referee: [Validation] The pillars and validation description: strategy-consistency scoring and mandate-aware synthesis are defined relative to the same agent behaviors that the scorecard is meant to diagnose; the manuscript must show (e.g., via an explicit equation or ablation) that this does not introduce circular dependence that would make the separation claim tautological.

    Authors: Strategy-consistency scoring is a deterministic, hash-recomputable function applied to the sealed DecisionRound trail that quantifies deviation from the pre-declared mandate; it is not derived from the APM-CS axes. Mandate-aware synthesis is an input constraint on the gather/synthesize stages, not an output metric. To eliminate any appearance of circularity we will insert an explicit equation in Section 3.4 that defines each APM-CS axis as a function of the trail variables excluding the consistency score itself, together with an ablation that recomputes the five-axis scorecard after removing the consistency pillar. The revised text will also state that the remaining axes remain stable under this removal. revision: yes

Circularity Check

0 steps flagged

No circularity: framework defines new process-based metrics without reduction to inputs by construction

full rationale

The paper introduces CLQT as a diagnostic benchmark built around an explicit five-stage cycle and the APM-CS scorecard computed from the audit trail, with Coherence using a held-out LLM. These components are presented as definitional design choices for separating process from outcome, not as derived predictions or fitted parameters renamed as results. No equations, self-citations, or uniqueness theorems are invoked that would make the claimed separation equivalent to the inputs by construction. The derivation chain is self-contained as an instrument definition, with the independence claim resting on the proposed structure rather than circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only; limited visibility into parameters or axioms. The framework assumes the five-stage cycle and scorecard capture capability without circularity in scoring rules.

axioms (1)
  • domain assumption The five-stage cycle models agent decision processes without introducing new leakage
    Invoked as the substrate for all evaluation in the abstract description of the agent cycle.
invented entities (1)
  • APM-CS five-axis scorecard no independent evidence
    purpose: To produce diagnostic capability map from audit trails
    New metric introduced by the paper; no independent evidence of validity provided in abstract.

pith-pipeline@v0.9.1-grok · 5877 in / 1257 out tokens · 32720 ms · 2026-06-30T06:41:17.350048+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

26 extracted references · 23 canonical work pages · 14 internal anchors

  1. [1]

    Chen, Y., et al. (2025). StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets? arXiv:2510.02209

    work page arXiv 2025

  2. [2]

    Yu, H., Li, F., & You, J. (2025). LiveTradeBench: Seeking Real-World Alpha with Large Language Models. arXiv:2511.03628

    work page arXiv 2025

  3. [3]

    Fan, T., et al. (2025). AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets. arXiv:2512.10971

    work page arXiv 2025

  4. [4]

    Li, C., Shi, Y., Luo, Y., & Tang, N. (2025). Will LLMs be Professional at Fund Investment? DeepFund: A Live Arena Perspective. arXiv:2503.18313

    work page arXiv 2025

  5. [5]

    Li, H., et al. (2024). InvestorBench: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent. ACL 2025. arXiv:2412.18174

    work page arXiv 2024

  6. [6]

    Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?

    Li, W. W., Kim, H., Cucuringu, M., & Ma, T. (2025). Can LLM-based Financial In- vesting Strategies Outperform the Market in Long Run? (FINSABER). arXiv:2505.07078

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  7. [7]

    Zhao, Y., Chen, S., & Su, N. (2026). PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management. arXiv:2605.27887

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  8. [8]

    Li, X., et al. (2025). Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents. arXiv:2510.07920

    work page arXiv 2025

  9. [9]

    Zhu, T., et al. (2026). From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets. arXiv:2605.28359

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  10. [10]

    Xiao, Y., Sun, E., Luo, D., & Wang, W. (2024). TradingAgents: Multi-Agents LLM Financial Trading Framework. arXiv:2412.20138

    work page arXiv 2024

  11. [11]

    Yu, Y., et al. (2023). FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design. arXiv:2311.13743. 45

    work page arXiv 2023

  12. [12]

    Cao, H., Driouich, I., & Thomas, E. (2026). Beyond Task Completion: Revealing Corrupt Success in LLM Agents through Procedure-Aware Evaluation. arXiv:2603.03116

    work page arXiv 2026

  13. [13]

    Zheng, L., et al. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.NeurIPS Datasets & Benchmarks. arXiv:2306.05685

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  14. [14]

    Liang, P., et al. (2022). Holistic Evaluation of Language Models (HELM).TMLR (2023). arXiv:2211.09110

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  15. [15]

    Yao, S., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR. arXiv:2210.03629

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  16. [16]

    Shinn, N., et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning.NeurIPS. arXiv:2303.11366

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  17. [17]

    Generative Agents: Interactive Simulacra of Human Behavior

    Park, J. S., et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior.UIST. arXiv:2304.03442

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  18. [18]

    Packer, C., et al. (2023). MemGPT: Towards LLMs as Operating Systems. arXiv:2310.08560

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  19. [19]

    Sumers, T., Yao, S., Narasimhan, K., & Griffiths, T. (2024). Cognitive Architectures for Language Agents (CoALA).TMLR. arXiv:2309.02427. [20]Anthropic(2024). ModelContextProtocol(MCP):AnOpenStandardforConnecting AI Assistants to Tools and Data. https://modelcontextprotocol.io

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  20. [20]

    Qin, Y., et al. (2024). ToolLLM: Facilitating LLMs to Master 16000+ Real-World APIs.ICLR. arXiv:2307.16789

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  21. [21]

    Hong, S., et al. (2024). MetaGPT: Meta Programming for a Multi-Agent Collabora- tive Framework.ICLR. arXiv:2308.00352

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  22. [22]

    Liu, X., et al. (2024). AgentBench: Evaluating LLMs as Agents.ICLR. arXiv:2308.03688

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  23. [23]

    $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

    Yao, S., et al. (2024).τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains. arXiv:2406.12045

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  24. [24]

    Anderson, J. R. (1983).The Architecture of Cognition.Harvard University Press. (ACT*; declarative vs. procedural knowledge, compilation through practice.)

    1983

  25. [25]

    Boyd, J. R. (1987).A Discourse on Winning and Losing.Air University.(The OODA decision cycle.)

    1987

  26. [26]

    Almgren, R., & Chriss, N. (2001). Optimal execution of portfolio transactions. Journal of Risk, 3(2), 5–39. 46 Appendix A: Cost Tier Parameterizations Tier Spread (bps) Commission (bps) Slippage (bps) Borrow (ann bps) Impact Model zero 0 0 0 0 none low(default)2.0 0.5 1.0 30 square_root medium 3.0 1.0 2.0 50 square_root high 8.0 3.0 5.0 100 almgren_chriss...

    2001