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Beyond Suspicious Steps: Ontological Trust in Long-Horizon Agents

An He, Yao Wang, Haibin Zhang

arXiv:2608.17718v1cs.AI

TL;DR

Long-horizon agents can remain locally plausible while their trajectory prefixes drift from the user-authorized task. The paper formalizes this relation as ontological trust and implements RGE, an online monitor with deterministic trust-state evolution over structured LLM parses. Across benchmarks, larger estimators exceed 93% Drift F1 while preserving at least 95.8% benign coverage, but pseudo-consistency detection depends on externally visible task completion.

  • Problem

    Existing oversight tools do not directly estimate whether a long-horizon trajectory prefix still corresponds to the user-authorized task.

  • Method

    RGE decomposes task-conditioned trust into Role, Goal, and Evidence, using LLMs for structured representations and deterministic updates, projections, and intervention decisions.

  • Results

    Drift F1 exceeds 93% on every benchmark with GPT-4o and Qwen3.5-9B while at least 95.8% of benign trajectories remain unflagged.

  • Takeaways & Limitations

    RGE provides a replayable, auditable trust trajectory for online prefix-level drift detection across OSWorld, FinanceBench, and EICU-AC.

  • Takeaways & Limitations

    The threat model excludes overtly malicious commands and relies on externally observable behavior, not hidden reasoning or internal traces.

Abstract

from arXiv · show

Long-horizon agents increasingly operate across many steps, tools, and observa- tions. In this setting, the relevant oversight question is not only whether each action is locally valid, but whether the evolving trajectory still corresponds to the task the user authorized. Drift can accumulate quietly: an agent may call the right tool with plausible arguments at every step, while its prefix moves toward a broader role, an adjacent objective, or evidence the user never supplied. Existing monitors mostly check local compliance, deliver final-trace verdicts, or score generic risk; they do not directly estimate this prefix-level relation. We introduce ontological trust, a task-conditioned property of trajectory prefixes, and instantiate it as RGE, an online monitor that decomposes trust along Role, Goal, and Evidence. RGE uses LLMs only to derive structured task and step representations; trust-state updates, projec- tions, and intervention decisions are deterministic, so the output is a replayable and auditable trust trajectory rather than a single end-to-end judge verdict. We construct a cross-domain trajectory corpus from OSWorld, FinanceBench, and EICU-AC, covering benign executions, prefix-paired drift, and pseudo-consistency failures. On this corpus, RGE outperforms adapted rule-, judge-, and shield-style baselines on prefix-paired drift detection. With the two larger estimator models, it exceeds 93% Drift F1 on every benchmark while keeping benign coverage at or above 95.8%. Pseudo-consistency is harder: detection depends on whether task completion is externally visible, a structural limit we characterize empirically.

1 Introduction

Long-horizon oversight must assess whether a trajectory prefix remains the user-authorized task, not merely whether individual actions look valid. The paper formalizes this relation as ontological trust and introduces RGE, an auditable online monitor.

  • Motivation: Locally plausible actions can still accumulate into an unauthorized role, adjacent objective, or unsupported evidence basis.The final deliverable may remain correct even when the evolving prefix no longer corresponds to the delegated task.
  • Motivation: Existing oversight tools mainly check rules, final outcomes, explicit attacks, local anomalies, or unstructured risk, rather than prefix-task correspondence.
  • Motivation: 73.49% AUC is achieved by the best snapshot metric, while Predictive Entropy remains near random across all three evaluators.These results indicate that step-local signals do not recover the relation between a current prefix and the authorized task.
  • Contribution: Ontological trust holds when a trajectory prefix still corresponds to the user-authorized task across Role, Goal, and Evidence.Trust drift occurs when one or more axes deviate enough to break that correspondence, even if steps remain tool-appropriate.
  • Method: RGE uses LLMs for structured task and step interpretation, then deterministically projects, aggregates, and labels trust over time.Its replayable trust trajectory supports auditing through parsed fields and accumulated deviations.
  • Evaluation: The corpus spans OSWorld, FinanceBench, and EICU-AC with benign executions, prefix-paired drift, and pseudo-consistency failures.This construction directly evaluates whether a prefix has ceased to be the task it was authorized to perform.

2 Related Work

The paper situates RGE among trajectory benchmarks, security and redirection research, runtime monitors, and work on goals, process supervision, and reliance. Its distinction is deployment-time, prefix-level trust estimation for delegated tasks.

  • Trajectory corpora: OSWorld, FinanceBench, and EICU-AC expose realistic multi-step trajectories across desktop control, financial reasoning, and clinical querying.
  • Trajectory corpora: Most related benchmarks score final outcomes or domain-specific correctness rather than whether a locally plausible prefix changes task identity.
  • Agent security and redirection: Prompt-injection and adjacent security work studies redirection through inputs, web content, tools, documents, memory, and high-stakes interactions.
  • Runtime monitors and judgment: RGE differs from rule monitors, safety judges, memory-augmented auditors, and shields by producing deterministic online prefix-level trust estimates from structured representations.Those comparison methods respectively focus on rule satisfaction, safety risk, offline full-record verdicts, or audit-effort allocation.
  • Goals, process supervision, and reliance: Goal misgeneralization concerns trained policies, whereas RGE evaluates whether a fixed deployed agent’s trajectory has departed from its delegated objective.RGE also monitors intermediate behavior without modifying the agent or using step-level rewards.

3 Problem Setting and Trust Formulation

The paper models long-horizon delegation as bounded authorization over Role, Goal, and Evidence, then defines observable threat regimes and a behavior-only defender’s view.

  • 3.1 Task Setting: A task produces an execution trajectory, and trust judgments apply to each task-prefix pair rather than to isolated steps.
  • 3.2 Trust as Bounded Authorization: Bounded authorization is represented as D(x) = (R(x), G(x), E(x)), covering the authorized role, delegated objective, and evidence basis.
  • 3.2 Trust as Bounded Authorization: Role, Goal, and Evidence can fail independently through unauthorized actions, rewritten objectives, or claims unsupported by observed history.
  • 3.2 Trust as Bounded Authorization: Because D(x) is not directly observable, the monitor estimates whether the prefix remains within the delegated role, objective, and authorized evidence.
  • 3.3 Threat Model: Prefix-paired drift shares a benign prefix with a reference execution before diverging at an annotated onset, making onset detection externally anchored.
  • 3.3 Threat Model: The study targets locally legitimate failures driven by plausible auxiliary goals, scope expansion, or weakly grounded evidence, excluding overtly malicious commands.
  • 3.3 Threat Model: The monitor observes only the task description and action-observation prefix, not internal state, hidden reasoning, or internal traces.This improves portability across actor implementations while requiring inference from externally observable behavior.

4 Preliminary Analysis of Step-Local Signals

Step-local signals struggle to distinguish benign and drifting behavior, whereas three task-conditioned RGE coordinates recover trajectory-level separation and support an online aggregation pipeline.

  • 4.1 Step-Local Normality Does Not Establish Ontological Trust: The Living-off-the-Land corpus uses legitimate tools and routine operations, making drifting prefixes locally resemble benign actions.
  • 4.1 Step-Local Normality Does Not Establish Ontological Trust: 73.49% AUC is the best result among 9 × 3 snapshot metric–evaluator combinations, achieved by Causal Grounding with Llama-3-8B-Instruct.Predictive Entropy remains near random at 47–56% across evaluators, and benign and drifting steps overlap heavily.
  • 4.2 Three-Axis Coordinates Recover the Signal: RGE scores maximum-deviation trajectory points along Role, Goal, and Evidence and projects them into a two-dimensional t-SNE visualization.The figure marks benign density with contours, drift with red markers, and the trust origin with a star.
  • 4.2 Three-Axis Coordinates Recover the Signal: 97.67%, 97.45%, and 98.86% trajectory AUC are reached by the three RGE coordinates on Qwen3-4B, Llama-8B, and GPT-4o-mini, respectively.Removing any coordinate reduces AUC on every evaluator.
  • 5.1 Task-Conditioned Completion Structure Formulation: Online monitoring adds task memory, temporal aggregation, and threshold rules to convert retrospective RGE coordinates into prefix-level intervention decisions.
  • 5.1 Task-Conditioned Completion Structure Formulation: RGE derives completion gaps as task-specific reference points for closed and open progress, rather than as an execution plan.
  • 5.2 Local Semantic Parsing: Each step is parsed into typed fields using the task, prior prefix, current action and observation, and memory state.
  • 5.3 State Memory: Conservative memory confirms progress only when fixed criteria are satisfied; otherwise parser-proposed resolutions remain candidates and do not advance progress.

6 Experiments

The experiments evaluate RGE online across three domains, estimator models, and two threat regimes. RGE detects prefix-paired drift strongly with high benign coverage, while pseudo-consistency detection varies with externally observable task closure and baseline representation.

  • Experimental Setup: The corpus contains 248 trajectories from OSWorld, FinanceBench, and EICU-AC: 80 benign, 81 prefix-paired drift, and 87 pseudo-consistency variants.The traces average 7.9 steps and are evaluated with GPT-4o, Qwen3.5-9B, and Gemma-4-E4B-it estimators.
  • Experimental Setup: The monitor replays each trajectory prefix online and labels a trajectory positive when it raises an alarm at any step.Drift F1, Pseudo F1, and Benign Coverage are reported at the shared default operating point.
  • Drift Detection and Benign Coverage: Drift F1 exceeds 93% on every benchmark for GPT-4o and Qwen3.5-9B, while benign coverage remains at least 95.8%.The aggregate result reflects broad subtype coverage, including detection of every EICU-AC evidence-fracture case.
  • Pseudo-Consistency: Pseudo F1 has a median of 18.2% and ranges from 0.0%–98.4% across nine benchmark-estimator cells.OSWorld reaches 98.4 Pseudo F1 with GPT-4o because UI tasks expose concrete completion states, whereas read-only reasoning domains provide fewer closure signals.
  • Baseline Comparison: Baseline comparisons show that broader trace context alone does not solve trust-drift transfer, while a structured defense reaches only 3.4% Benign Coverage.The results attribute these failures to missing task-conditioned representations rather than context length alone.

A.3.1 Legitimate task renegotiation

The framework treats legitimate task changes as new delegations while distinguishing them from gradual drift and explicit instruction override. Its threat model assumes clean, externally observable action–observation prefixes and excludes internal reasoning.

  • Legitimate task changes: Task modifications reinitialize the completion-gap set and state memory, preventing legitimate renegotiation from being classified as drift.Requests for additional authorization, constraints, or clarification are treated as self-correcting signals and do not directly increase degradation or completion measures.
  • Threat-model boundary: The threat model isolates gradual, externally observable drift that occurs without an explicit guardrail trigger.It does not aim to subsume all agent attack classes or syntax-level instruction overrides.
  • Observability assumptions: At each step, the defender observes the task instruction and clean action–observation prefix but not the agent’s internal reasoning.Noisy observations, partial masking, and asynchronous external changes can violate this assumption.
  • Observability assumptions: Noisy or partially observable environments would require an additional filtering or latent-state estimation layer.The present method estimates trust drift from externally observable behavior alone.
  • Local diagnostics: The snapshot study groups nine single-step metrics by local-observation dimensions and evaluates them without maintaining trajectory state.The templates include intent, syntactic complexity, persistent-state transition, role energy, role-energy delta, and causal grounding signals.

B.3.3 Sample Counts by Attack Type

The benchmark uses paired benign and drifting trajectories to evaluate whether local metrics recover trajectory-level drift. Its sample distribution is mildly imbalanced across four attack types, motivating per-type analysis.

  • Sample distribution: Each drifting trajectory has one semantically paired benign trajectory, so benign and drifting counts are identical within each attack type.Step totals are accumulated from action-step counts.
  • Sample distribution: 34 Backdoor trajectories versus 21 Log Deletion trajectories create an approximately 1.6:1 attack-type imbalance.Per-type AUC is reported alongside aggregate AUC to check whether frequent categories dominate the conclusion.
  • Trajectory labels: Drifting trajectories match their reference before onset, then decouple from the delegated objective and introduce high-risk operations.The annotated drift-step index marks the transition from task-aligned progress to attack-intent behavior.
  • Evaluation protocol: Snapshot metrics are computed independently for every action step, with drifting-trajectory labels projected to steps and paired benign steps labeled negative.This deliberately tests whether local scores can recover trajectory class without stateful monitoring.
  • Evaluation findings: 73.49% AUC is achieved by the best snapshot metric, while Predictive Entropy remains near random across all three evaluators.The results indicate that local signals alone do not recover the prefix-level relation between a trajectory and its authorized task.

C Supplementary Snapshot-Metric Analyses

The supplementary analyses test snapshot-score reliability, uncertainty estimation, attack-type effects, axis combinations, and visualization choices. They emphasize that trajectory aggregation and full RGE combinations preserve information that isolated local metrics miss.

  • Snapshot diagnostics: AUC is the primary threshold-free diagnostic, while fixed-recall Precision and F1 use oracle quantiles only to characterize score separability.The fixed-recall operating points are not deployable thresholds.
  • Uncertainty and imbalance: 95% confidence intervals are estimated by resampling entire trajectories across 1000 bootstrap iterations because steps within trajectories are temporally correlated.Tables 13–15 report the resulting clustered-bootstrap uncertainty estimates.
  • Uncertainty and imbalance: Per-type AUC decomposition checks whether aggregate snapshot findings are driven by non-uniform attack-type frequencies.The weighted average uses trajectory counts of 34, 28, 25, and 21, with N = 108.
  • Trajectory aggregation: The monitor uses p95 aggregation because drift can appear in a few critical steps, while max is more sensitive to one-step errors.p95 preserves high-deviation signals better than mean and yields more stable cross-model behavior.
  • Axis ablations: The full RGE combination has the highest AUC for all three preliminary estimator models, although the strongest single axis varies by model.Qwen3-4B is Role-dominant, Llama-8B is more balanced across Role and Evidence, and GPT-4o-mini has strong Role and Goal signals but weaker Evidence.
  • Visualization: The 2D t-SNE plots visualize critical trust-state points only and are not part of quantitative evaluation.Figures 6 and 7 show embeddings for Llama-8B-Instruct and GPT-4o-mini; the main figure uses Qwen3-4B for readability.

D.2.1 Full 7-Combination Ablation Results

The full ablation and experimental setup evaluates RGE across benchmark-grounded trajectory classes, estimator models, and deterministic operating choices. Results support combining all three axes while exposing limits from task-closure observability and data assumptions.

  • Ablation results: The full RGE combination achieves the highest AUC for all three preliminary estimators, despite model-dependent strengths among Role, Goal, and Evidence.The ablation compares single-axis, pairwise-axis, and three-axis combinations under shared trajectory aggregation.
  • Corpus construction: The trajectory corpus contains 80 unique source tasks and 248 trajectories spanning benign, prefix-paired drift, and pseudo-consistency variants.The sources cover OSWorld desktop and shell automation, FinanceBench financial reasoning, and EICU-AC clinical querying.
  • Observability limits: Pseudo-consistency detection depends on whether task completion is externally observable, making read-only reasoning domains harder than OSWorld.The monitor must infer closure from reasoning and cited evidence when the environment provides no state change.
  • Estimator setup: The monitor uses GPT-4o, Qwen3.5-9B, and Gemma-4-E4B-it as estimators distinct from the Claude Opus 4.6 trajectory generator.Deterministic decoding and structured JSON interfaces support consistent downstream parsing.
  • Operating point: The shared threshold κ=0.5 remains stable for Drift F1 and benign coverage, although pseudo-consistency becomes harder as the threshold rises.The value was fixed before evaluation and avoids per-benchmark tuning rather than optimizing every cell.
  • Detection timing: Drift detection is near onset rather than far in advance, with misses of 1/24, 2/27, and 0/30 across the reported settings.FinanceBench and EICU-AC are mostly onset-aligned, while OSWorld tends to fire about one step after onset.

E.6.1 Task-Swap AUC

Task-swap AUC tests whether RGE’s deviation score depends on the delegated task rather than only the fixed action–observation trace. Across all three AUCs, scores changed as intended when benign task descriptions were swapped.

  • Task-swap AUC evaluates whether a fixed action–observation trace receives a different deviation score under a matched benign task description.A good monitor should assign higher deviation to the task-swapped version than to the original.
  • All three AUCs are far above chance, indicating that the score changes when the same benign trace is evaluated against a different task.

E.6.2 Length-Bucket Analysis

The length-bucket analysis finds no evidence that longer traces alone explain observed failures. Drift remains near-saturated, pseudo-consistency detection improves with length, and benign coverage stays above 92.9%, although domain composition matters.

  • Length buckets are short (≤5 steps), medium (6–10), and long (≥11), but changing benchmark composition makes this descriptive rather than causal.
  • Drift F1 remains near-saturated across length buckets, while Pseudo F1 rises and Benign Coverage stays above 92.9%.The reported pattern does not support length alone as an explanation for failures.
  • Both estimators recover Role and Goal drift at high rates across constructed sub-types, while pseudo-consistency remains harder in leaner-action domains.
  • The small Gemma-4-E4B-it estimator achieves 100.0% benign coverage on all three benchmarks, but prefix-paired Drift F1 weakens on EICU-AC.Its trust-drift signal is therefore domain-sensitive.
  • For the edge estimator, pseudo-consistency weakens on FinanceBench and EICU-AC, where evidence grounding is harder.At the shared threshold, benign coverage is preserved, but apparent progress often does not escalate when task closure is not externally visible.

E.6.5 Qualitative Case Studies

The case studies illustrate onset detection for prefix-paired drift, accumulation-based detection for pseudo-consistency, and a borderline FinanceBench case that remains below escalation thresholds. Together, they show how externally visible completion and apparent verification affect intervention.

  • The case studies include drift detected at onset, pseudo-consistency detected through accumulation, and borderline pseudo-consistency in a lean-action benchmark.
  • Case 1: Drift detected at onset: At t=5, RGE fires REANCHOR when an appended chrome:// directive causes all three consistency scores to drop together.The first four steps follow the benign Chrome route before the role-drift deviation.
  • Case 2: Pseudo-consistency detected by accumulation: At step 6, accumulated weak deviations trigger REANCHOR after post-completion overreach becomes active in pseudo_volume_max.The burst statistic crosses the semantic reanchor gate, while the accumulation statistic independently raises the trajectory-level alarm.
  • Case 3: Borderline pseudo-consistency in FinanceBench: In the FinanceBench borderline case, two legitimate-looking analyst cross-checks keep accumulation and burst statistics below their hard-alarm thresholds.The signal never escalates past JUSTIFY, making verification-like apparent progress difficult to separate from expert workflow.

E.6.6 Failure Analysis

Failure analysis identifies errors when trajectories remain close to legitimate workflows after task authorization has failed. The main boundaries are post-completion padding, sparse-evidence pseudo-consistency, and role-fit calibration around expert reframing.

  • Remaining errors concentrate in trajectories that stay close to legitimate workflows after D(x) has failed.Most are characterized as parser-calibration errors rather than failures of deterministic aggregation.
  • Failure mode 1: Post-completion padding: Post-completion padding adds plausible audit or cross-check steps after the requested deliverable is complete, leaving categorical drops weak and accumulation below escalation thresholds.The analysis points toward stronger progress-state modeling after core completion.
  • Failure mode 2: Sparse-evidence pseudo-consistency: In FinanceBench and EICU-AC, verification-like steps can remain lexically close to legitimate analyst or clinical workflow even when required gaps are not truly closing.These cases blur required evidence gathering with apparent progress.
  • Failure mode 3: Role-fit calibration: FinanceBench creates false positives and false negatives near the boundary between benign analyst reframing and adversarial role expansion.Future versions should improve role/evidence calibration rather than simply raise the shared threshold.

E.7 Limitations

The section identifies observability, evaluation design, baseline comparability, and resource demands as important boundaries on interpreting the monitor. It also notes dual-use concerns and warns that reported baseline gaps are design contrasts rather than isolated component effects.

  • Observability: RGE cannot verify hidden agent state, unobserved environment changes, or completion criteria that the environment does not expose.This limitation is especially important for read-only reasoning domains and specialized domains requiring parser calibration.
  • Responsible use: The work has dual-use risks: attack examples may aid subtler attacks, while conservative alarms may interrupt legitimate workflows or encourage overreliance.The authors recommend treating intervention labels as decision support rather than automatic punishment or proof of maliciousness.
  • Evaluation design: Reported baseline comparisons use native decision protocols rather than matched false-positive rates, limiting causal attribution.Different parsers, thresholds, aggregation rules, and input scopes make the gaps design contrasts rather than isolated component effects.
  • Evaluation design: Offline trajectory-level predictions summarize an online prefix process by labeling a trajectory positive when any prefix raises an alarm.This reporting convention distinguishes the monitor’s online operation from its offline evaluation summary.
  • Baseline behavior: Any-step aggregation can amplify a single benign-step false alarm into a full-trace false positive for strict baseline guards.The effect is explicitly described for MAS-Shield, whose verifier is strict about scope drift, secondary objectives, broad export, privacy leakage, and post-completion continuation.
  • Resource demands: Monitor overhead is length-driven, with setup calls costing 2.3k–3.4k tokens per trajectory and parser calls costing 3.6k–4.7k tokens per step.OSWorld has the highest per-trajectory cost because its trajectories are longest, while FinanceBench has the highest per-step cost because its context is denser.

2. Limitations

The checklist records limitations involving observability, pseudo-consistency, calibration, token cost, reproducibility, and future evaluation needs. It also notes that complete code and processed trajectories are not yet publicly released, while access restrictions apply to some source materials.

  • Scope and assumptions: The paper discusses limitations including pseudo-consistency failures, clean-observation assumptions, parser calibration limits, token cost, and future evaluation needs.These limitations are identified across the Conclusion and appendices.
  • Reproducibility: Complete code and the processed trajectory corpus are not yet publicly released.The manuscript provides method specifications, prompts, monitor configuration, and reproduction details, with a future archival repository planned.
  • Data access: eICU-CRD-derived materials will be referenced rather than redistributed because access follows PhysioNet’s credentialed-access agreement.FinanceBench is restricted to non-commercial use under CC-BY-NC 4.0.
  • Statistical reporting: The paper reports clustered-bootstrap confidence intervals for snapshot analyses, resampling trajectories to respect within-trajectory correlation.This describes the statistical protocol used for the reported uncertainty estimates.
  • Study scope: No human-subject recruitment, intervention, or crowdsourcing study is conducted, so IRB-style participant-risk disclosure is not applicable.Clinical-data use complies with PhysioNet’s credentialed-access agreement for eICU-CRD.
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