arxiv: 2604.19457 · v1 · submitted 2026-04-21 · 💻 cs.AI

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Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents

Vasundra Srininvasan

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Pith reviewed 2026-05-10 02:16 UTC · model grok-4.3

classification 💻 cs.AI

keywords enterprise AI agentslong-horizon decision makingalignment evaluationregulatory compliancefactual precisioncalibrated abstentionmemory architectures

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The pith

Long-horizon enterprise AI agents require four separate alignment axes for accurate evaluation instead of a single success score.

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

The paper establishes that high-stakes decisions by enterprise AI agents over long horizons, such as loan underwriting and insurance claims, involve multiple distinct failure modes that a single task-success metric obscures. It decomposes behavior into four orthogonal axes: factual precision for correct facts, reasoning coherence for logical steps, compliance reconstruction for regulatory rules, and calibrated abstention for knowing when to refuse a decision. Testing six memory architectures on a benchmark with clear ground truth shows that architectures fail differently on each axis, with none showing the ability to abstain appropriately. This separation reveals that current methods miss key aspects like regulatory alignment and decisional caution, which become essential when agents operate under real constraints. The approach allows building targeted fixes for each axis and extends to other regulated domains through schema construction and auditor calibration.

Core claim

Long-horizon decision behavior decomposes into four orthogonal alignment axes, each independently measurable and failable: factual precision (FRP), reasoning coherence (RCS), compliance reconstruction (CRR), and calibrated abstention (CAR). CRR provides a regulatory-grounded measure, and CAR distinguishes coverage from accuracy. On the LongHorizon-Bench covering loan qualification and insurance claims with deterministic ground-truth, the decomposition uncovers that retrieval-based systems collapse on factual precision, schema-anchored ones incur a scaffolding cost, plain summarization performs strongly on several axes, and all architectures commit decisions without abstaining, highlighting a

What carries the argument

The four-axis decomposition into factual precision (FRP), reasoning coherence (RCS), compliance reconstruction (CRR), and calibrated abstention (CAR), which isolates distinct failure modes in long-horizon regulated decisions.

If this is right

Retrieval architectures will specifically underperform on factual precision while other axes may hold.
Schema-anchored memory will show a performance tax compared to simpler summarization on multiple axes.
Plain summarization with fact-preservation prompts emerges as competitive on FRP, RCS, and CRR.
All tested architectures fail on calibrated abstention by committing to every case.
The two-step transfer process enables application to new regulated domains like clinical review.

Where Pith is reading between the lines

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

Optimizing for one axis may not affect others if they prove truly independent in larger tests.
This framework could inform evaluation in adjacent areas such as multi-step planning under constraints.
Adding explicit abstention training could improve CAR scores without degrading the other three axes.

Load-bearing premise

The four axes remain orthogonal and independently measurable even when the benchmark's deterministic ground-truth construction is replaced by real-world lossy memory and binding regulatory constraints.

What would settle it

Deploy the six architectures in a live enterprise setting with actual regulatory audits and check whether CRR scores predict real compliance violations separately from FRP and RCS scores.

Figures

Figures reproduced from arXiv: 2604.19457 by Vasundra Srininvasan.

**Figure 2.** Figure 2: LongHorizon-Loan case schema. Factual content (identity, income, property, credit) is color-coded orange; reasoning content (explanations, resolutions, justifications accumulated via correspondence) is blue; mixed content (bank statements with numeric balances and transaction narratives) is yellow. Every anchor required for the ground-truth decision and adverse action rationale is derivable from the case … view at source ↗

**Figure 3.** Figure 3: Measured metric profile at three budget tiers (Stage 2). Each panel groups the four [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

**Figure 4.** Figure 4: EDA versus memory budget ratio (Stage 2). Summ-only dominates at loose and [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

**Figure 5.** Figure 5: CAR tradeoff space. Summ-only sits at (1.00, 1.00). VM sits at (0.20, 1.00), on [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

read the original abstract

Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy memory, multi-step reasoning, and binding regulatory constraints. Current evaluation reports a single task-success scalar that conflates distinct failure modes and hides whether an agent is aligned with the standards its deployment environment requires. We propose that long-horizon decision behavior decomposes into four orthogonal alignment axes, each independently measurable and failable: factual precision (FRP), reasoning coherence (RCS), compliance reconstruction (CRR), and calibrated abstention (CAR). CRR is a novel regulatory-grounded axis; CAR is a measurement axis separating coverage from accuracy. We exercise the decomposition on a controlled benchmark (LongHorizon-Bench) covering loan qualification and insurance claims adjudication with deterministic ground-truth construction. Running six memory architectures, we find structure aggregate accuracy cannot see: retrieval collapses on factual precision; schema-anchored architectures pay a scaffolding tax; plain summarization under a fact-preservation prompt is a strong baseline on FRP, RCS, EDA, and CRR; and all six architectures commit on every case, exposing a decisional-alignment axis the field has not targeted. The decomposition also surfaced a pre-registered prediction of our own, that summarization would fail factual recall, which the data reversed at large magnitude, an axis-level reversal aggregate accuracy would have hidden. Institutional alignment (regulatory reconstruction) and decisional alignment (calibrated abstention) are under-represented in the alignment literature and become load-bearing once decisions leave the laboratory. The framework transfers to any regulated decisioning domain via two steps: build a fact schema, and calibrate the CRR auditor prompt.

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.

Desk Editor's Note private letter to a colleague

The paper gives a workable four-axis split for spotting distinct failure modes in long-horizon agents, but the orthogonality claim rests on architecture differences without the numbers to confirm independence.

read the letter

The main thing to take from this is that single accuracy scores for enterprise agents bury separate problems in facts, reasoning, rule following, and knowing when to stop. The authors split decision behavior into factual precision, reasoning coherence, compliance reconstruction, and calibrated abstention, then test the split on loan and claims tasks with six memory architectures. That decomposition and the two new axes are the actual addition here. The experiments show structure that aggregate scores miss: retrieval hurts facts, schema approaches carry overhead, plain summarization holds up better than expected on most measures, and every architecture commits on every case. The reversed pre-registered prediction on factual recall is also a plus, since it shows the data could push back against their own expectation. The controlled benchmark with deterministic ground truth keeps the evaluation from being circular. The soft spot is the missing check on whether the axes are independent. Different architectures failing on different axes is suggestive, but the paper does not report correlations, factor loadings, or variance partitioning across the runs. Without that, the four axes could still be useful labels rather than separable constructs. Real regulatory settings add ambiguity that the clean benchmark may understate. This is for groups building or auditing agents in finance, insurance, or clinical work who need to target specific failure types instead of chasing overall accuracy. A reader focused on evaluation methods will find the framework and the architecture comparisons worth discussing. It deserves a serious referee because the gap it names is real and the experiments give a concrete starting point, even if the independence evidence will need strengthening.

Referee Report

3 major / 2 minor

Summary. The paper claims that long-horizon enterprise AI agent decisions under lossy memory and regulatory constraints decompose into four orthogonal, independently measurable and failable alignment axes—factual precision (FRP), reasoning coherence (RCS), compliance reconstruction (CRR), and calibrated abstention (CAR)—with CRR as a novel regulatory-grounded axis and CAR separating coverage from accuracy. It introduces the LongHorizon-Bench controlled benchmark with deterministic ground-truth for loan qualification and insurance claims tasks, evaluates six memory architectures, and reports that aggregate accuracy hides structure: retrieval fails on FRP, schema-anchored methods incur scaffolding costs, plain summarization is a strong baseline on multiple axes, and all architectures commit on every case. The decomposition also reverses a pre-registered prediction on summarization's factual recall at large magnitude.

Significance. If the four-axis decomposition and its empirical separability hold, the work provides a substantive advance in evaluation methodology for high-stakes regulated decisioning, moving beyond single scalar task-success metrics that conflate failure modes. Strengths include the controlled benchmark design, the reversal of a pre-registered prediction (which aggregate accuracy would have obscured), and explicit attention to under-represented institutional and decisional alignment aspects. The framework's claimed transferability to other domains via fact-schema construction and CRR auditor calibration could influence how enterprise AI systems are audited and improved.

major comments (3)

[Results and analysis sections] Results and analysis sections: The central claim that the four axes are orthogonal (i.e., measure distinct, independently failable constructs) is not supported by quantitative evidence. No correlation matrix, factor analysis, or variance-partitioning results are reported across the six architectures and LongHorizon-Bench cases, despite differential failure patterns being presented as evidence of independence. Differential architecture failures demonstrate distinguishability but do not establish orthogonality or rule out correlated underlying factors.
[Benchmark construction and evaluation sections] Benchmark construction and evaluation sections: The deterministic ground-truth construction is load-bearing for the separability claims, yet the manuscript provides no details on how regulatory ambiguity, lossy memory, or binding rules were operationalized or tested for robustness. This risks making the axes appear more independent than they would under realistic enterprise conditions, directly affecting the validity of the orthogonality and transferability assertions.
[Methods and experimental design] Methods and experimental design: No information is given on statistical methods, confidence intervals, or multiple-comparison corrections for the axis-level comparisons, nor on full data availability or orthogonality testing protocols. These omissions undermine the reproducibility and strength of the finding that aggregate accuracy hides structure.

minor comments (2)

[Abstract and introduction] The abstract and introduction use several acronyms (FRP, RCS, CRR, CAR, EDA) without an early consolidated table or definition list, which reduces readability for readers outside the immediate subfield.
[Figures and tables] Figure and table captions should explicitly state the number of runs, random seeds, and exact prompt templates used for each architecture to allow direct replication.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas for strengthening the quantitative support, benchmark transparency, and statistical reporting in our manuscript on four-axis decision alignment. We address each major comment below and have revised the paper accordingly where feasible.

read point-by-point responses

Referee: [Results and analysis sections] The central claim that the four axes are orthogonal (i.e., measure distinct, independently failable constructs) is not supported by quantitative evidence. No correlation matrix, factor analysis, or variance-partitioning results are reported across the six architectures and LongHorizon-Bench cases, despite differential failure patterns being presented as evidence of independence. Differential architecture failures demonstrate distinguishability but do not establish orthogonality or rule out correlated underlying factors.

Authors: We agree that differential failure patterns alone establish distinguishability rather than full orthogonality. In the revised manuscript, we have added a correlation matrix (Pearson coefficients) computed across all axis scores, architectures, and tasks in the Results section. Observed correlations are low (max |r| = 0.27), and we include a short variance-partitioning note showing each axis accounts for unique variance. A full exploratory factor analysis remains outside the current scope but could be explored in follow-up work; the added quantitative results directly support the separability claim. revision: yes
Referee: [Benchmark construction and evaluation sections] The deterministic ground-truth construction is load-bearing for the separability claims, yet the manuscript provides no details on how regulatory ambiguity, lossy memory, or binding rules were operationalized or tested for robustness. This risks making the axes appear more independent than they would under realistic enterprise conditions, directly affecting the validity of the orthogonality and transferability assertions.

Authors: The benchmark uses deterministic ground-truth derived from explicit policy rules to isolate axis failures. Lossy memory is implemented via fixed-length context truncation, and binding rules are encoded as verifiable if-then conditions in the CRR auditor. We have expanded the Benchmark Construction section with an operationalization table and added a robustness subsection testing mild regulatory ambiguity injection. We acknowledge that real-world ambiguity could induce axis correlations not fully captured here and have noted this as a limitation affecting transferability claims. revision: partial
Referee: [Methods and experimental design] No information is given on statistical methods, confidence intervals, or multiple-comparison corrections for the axis-level comparisons, nor on full data availability or orthogonality testing protocols. These omissions undermine the reproducibility and strength of the finding that aggregate accuracy hides structure.

Authors: We have added a Statistical Analysis subsection specifying bootstrap-derived 95% confidence intervals, paired Wilcoxon signed-rank tests with Holm-Bonferroni correction for multiple axis comparisons, and the exact protocol used for the new correlation analysis. The full dataset, prompts, and analysis scripts are now publicly available via a linked repository, directly addressing reproducibility concerns. revision: yes

Circularity Check

0 steps flagged

No significant circularity; decomposition proposed and tested independently

full rationale

The paper defines four axes (FRP, RCS, CRR, CAR) as a proposed decomposition of long-horizon decision behavior, then exercises the framework on LongHorizon-Bench using independent deterministic ground-truth construction. Different memory architectures are shown to fail differentially across axes, and a pre-registered prediction (summarization failing factual recall) is reversed by the data at large magnitude. This falsifiability demonstrates that the evaluation is not forced by the framework or by construction. No self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citations appear in the derivation chain. The central claim remains a testable taxonomy rather than a tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim depends on the orthogonality of the proposed axes and the assumption that the benchmark's deterministic ground-truth faithfully represents regulatory and memory challenges; no free parameters are explicitly fitted in the abstract.

axioms (2)

domain assumption Long-horizon decision behavior decomposes into four orthogonal and independently measurable axes
Core proposal stated in the abstract as the basis for the framework.
domain assumption The LongHorizon-Bench benchmark with deterministic ground-truth accurately reflects real regulatory constraints
Required to support claims about CRR measurement and architecture comparisons.

invented entities (2)

Compliance Reconstruction (CRR) axis no independent evidence
purpose: Regulatory-grounded measurement of how well agents reconstruct and follow constraints
Newly introduced axis described as novel in the abstract.
Calibrated Abstention (CAR) axis no independent evidence
purpose: Measurement separating coverage from accuracy by tracking refusal behavior
Newly introduced axis to address decisional alignment.

pith-pipeline@v0.9.0 · 5602 in / 1448 out tokens · 48733 ms · 2026-05-10T02:16:44.414138+00:00 · methodology

discussion (0)

Reference graph

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