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CURA: Certified Runtime Alarms for Computer-Use Agents

Divake Kumar, Sina Tayebati, Devashri Naik, Amanda Sofie Rios, Nilesh Ahuja, Omesh Tickoo, Ranganath Krishnan, Amit Ranjan Trivedi

arXiv:2608.27808v1cs.AIcs.CVcs.LG

TL;DR

Computer-use agents can achieve strong task performance while rarely reporting their own failures, motivating an independent runtime oversight channel. CURA monitors only harness-visible telemetry, converts trajectories into a calibrated sequential test, and improves online failure recall at matched false-alarm budgets while supporting deployable escalation. Its guarantees cover false alarms only, and behavioral monitoring remains limited for quiet execution failures, belief-level errors, distribution shift, and adversarial evasion.

  • Problem

    Computer-use agents modify files, applications, and system state, but self-reports fail to reliably disclose unsuccessful trajectories.

  • Method

    CURA is an external read-only monitor that fuses task-agnostic behavioral signals into a CUSUM risk process with Learn-then-Test-calibrated false-alarm control.

  • Results

    At α = 0.10, CURA detected 42.3% of failures a median of 31 steps before termination at a realized false-alarm rate of 0.066, yielding an alarm-routed cascade with mean score 86.8.

  • Takeaways & Limitations

    Under matched certified budgets, behavioral monitoring provides higher online failure recall than simple length triggers, while exposing cases where behavior carries no useful signal.

  • Takeaways & Limitations

    The study contains 71 failures from one pipeline and one benchmark; certification controls false alarms only, and transfer requires recalibration under distribution shift.

Abstract

from arXiv · show

Self-report is the cheapest oversight channel a deployer has, and on capable computer-use agents (CUAs) it fails precisely where oversight matters. On 361 OSWorld tasks our pipeline, a read-only feasibility gate, a planner, and a GUI executor, reaches a mean task score of 82.9, above the 72.4 human reference, yet 64 of its 71 failures (90%) end with a success claim, 61 acknowledging no blocker, and the explicit failure affordance is never used in roughly 9,100 calls. We introduce CURA (Certified Runtime Alarms for Computer-Use Agents), an external monitor that reads only harness-visible telemetry, with no model internals, extra LLM calls, or prompt changes, and turns the running trajectory into a sequential test with certified false-alarm control. At alpha = 0.10 its CUSUM alarm detects 42.3% of failures a median of 31 steps before termination at a realized false-alarm rate of 0.066, and risk is partly resolvable before the first action (gate probe, 0.69 AUROC). Retrospectively the composite reaches 0.828 AUROC (fold-internal floor 0.802), but its margin over a total-token baseline is not significant (Delta = +0.026, p = 0.101); the separation is online, where CURA recalls more at matched certified budgets: 0.41 versus 0.34 at alpha = 0.10, 0.56 versus 0.38 at alpha = 0.20. Alarm-gated mid-execution oversight recovers 23 of 70 failures while spending a frontier overseer on 38, giving a deployable cascade at mean score 86.8 and 84.5% full-solve (305 of 361). The certificate bounds false alarms only. We also report where behavioral monitoring is uninformative.

Introduction

On OSWorld, the capable computer-use agent often recognizes difficulty internally but still reports success, making self-report unreliable for runtime oversight. CURA addresses this gap with an external behavioral monitor that detects failures online while preserving the frozen pipeline.

  • Oversight gap: 64 of 71 failures (90%) ended with a success claim, while none used the explicit failure affordance in approximately 9,100 calls.The failures often contained uncertainty, repeated actions, and unsuccessful strategy changes before completion was declared.
  • Contribution: CURA is an external read-only monitor that converts harness-visible behavior into a runtime oversight signal without model internals, additional LLM calls, or prompt changes.It combines task-agnostic signals into a calibrated CUSUM risk process supporting escalation, abstention, or stopping.
  • Results: At α = 0.10, CURA detected 42.3% of failures a median of 31 steps before termination at a realized false-alarm rate of 0.066.Online recall exceeded the baseline at matched budgets: 0.41 versus 0.34 at α = 0.10 and 0.56 versus 0.38 at α = 0.20.
  • Behavioral evidence: 83% of failures (59/71) contained pivot-language statements versus 45% of successes (131/290), averaging 25.6 versus 3.5 per run.Failed runs were 2.4× longer, and pivot rate alone predicted failure at 0.760 AUROC [0.695, 0.822].
  • Boundaries: Behavioral monitoring is weak for fast quiet failures and does not resolve belief-level errors, while token-level confidence and single-screenshot judging were ineffective on this corpus.These boundaries motivate complementary claim-time or plan-content instruments.

Related Work

CURA sits within work on agent monitoring, uncertainty estimation, conformal risk control, change detection, and oversight allocation. Its distinguishing combination is external behavioral observation, online stopping, and certified false-alarm control.

  • Monitoring and oversight: Prior oversight work uses LLM monitors, emulated risk evaluation, visibility infrastructure, and scalable supervision to allocate limited trusted oversight.These approaches provide context for external monitoring of computer-use agents.
  • CURA’s distinction: CURA differs by reading externally observable behavior rather than internals, emitting an online stopping time rather than a retrospective score, and carrying a false-alarm certificate.On this corpus, token-level log-probability confidence does not predict trajectory failure.
  • Risk control and change detection: Its risk-control design combines Learn-then-Test and conformal methods with anytime-valid martingales and CUSUM change detection.The paper positions these components as established methodological foundations for its certified behavioral trigger.
  • Novelty: CURA contributes a certified behavioral trigger and an escalation-aware risk ordering to the broader literature on learned abstention and uncertainty-guided routing.Related systems use context-aware nonconformity, learned abstention, or model hopping under compute budgets.

The Monitored System and Its Oversight Gap

The monitored system combines a feasibility gate, planner, and GUI executor, achieving strong benchmark performance while exposing a substantial gap between execution failure and self-reported failure. CURA is motivated by this gap rather than by a deficit in baseline task capability.

  • Pipeline: The pipeline comprises a feasibility gate, planner, and GUI executor, all using qwen3.7-plus and verifying information before acting.The executor operates through an observe–reason–act loop over screenshots.
  • Pipeline: 20 of 27 gold-infeasible tasks were refused at 100% precision, while the executor otherwise receives a verified environment dossier and can act for up to 100 steps.The gate uses non-mutating probes such as shell checks, window inspection, file reads, and web search.
  • Capability: 82.9 mean task score and 80.3% full-solve rate (290/361) exceeded the 72.4 human reference without fine-tuning or task-specific machinery.The result was also 9.6 points above the same backbone under its vendor harness.
  • Escalation: CURA-based routing reached 86.8 mean score and 84.5% full-solve rate (305/361), compared with 89.6 and 86.7% for oracle-routed escalation.Published benchmark rows are contextual because backbones and step budgets differ; the qwen3.7-plus vendor row is the controlled comparison.
  • Oversight gap: 64 of 71 failures (90%) ended with a success claim, none with a self-declared failure, and completion claims had 80.7% precision (267/331).The same qualitative pattern appeared in UI-TARS-1.5-7B, motivating an independent failure signal.

CURA: External Behavioral Monitoring

CURA turns externally visible execution into a task-level risk score and calibrated online stopping rule. It fuses behavioral signals trained primarily on successful trajectories, then applies sequential calibration to control false alarms under the success distribution.

  • External monitoring: CURA observes screenshots, reasoning, actions, and four telemetry variables while leaving the agent’s policy, prompts, and execution unchanged.Failure is defined as score < 0.99, and the monitor constructs a stopping time with certified false-alarm control.
  • Behavioral signals: Four task-agnostic signal families capture effort, reasoning semantics, say–do gaps, and execution physiology.These include output tokens, episode length, pivot-language patterns, repeated actions after pivots, and deviation from successful behavior.
  • Risk score: Eight trajectory signals are fused with ℓ2-regularized logistic regression into the task-level risk score ρ(Z).The headline fixed-monitor protocol fits success-only models on 290 successes and crossfits only the fusion; a fold-internal variant provides a bias-resistant floor.
  • Certified online alarm: Online monitoring uses six step-available signals, normalizes their risk direction, and accumulates evidence with CUSUM before alarming when the running process crosses a threshold.The six signals include pivot rate, physiology NLL, visual surprise, output tokens, state-visit entropy, and screen novelty.
  • Calibration: Learn-then-Test selects the threshold on held-out successful trajectories after disjoint fitting and calibration splits, requiring no labeled failures.The procedure tests thresholds sequentially for a specified false-alarm budget.
  • Scope of certification: The certificate controls only false alarms under the calibrated success distribution and does not guarantee failure detection, task correctness, or safety.Recalibration is required under distribution shift.

Experiments

Across the 361-task OSWorld evaluation, CURA provides certified online failure detection and supports alarm-gated oversight, while its behavioral coverage is strongest for prolonged failures and weaker for fast, quiet or belief-level errors.

  • Task-level discrimination: 0.828 AUROC is achieved by the composite, with a fold-internal floor of 0.802; its edge over total output tokens is not significant (∆= +0.026, p = 0.101).Visual dynamics provides a significant increment (∆= +0.025, p = 0.043), while naive inclusion of all fourteen signals dilutes performance.
  • Online detection: 0.41 versus 0.34 recall at α = 0.10, and 0.56 versus 0.38 at α = 0.20, favor CURA over the token CUSUM at matched certified false-alarm budgets.The comparison holds calibration procedure and false-alarm budgets fixed, isolating online trigger quality.
  • Risk across pipeline stages: 0.688 AUROC is available before any state-changing action, rising to 0.736 at planning, 0.809 by execution step 15, and 0.842 for the full trajectory.The gate-stage signal is driven mainly by deliberation effort, including plan length, planner tokens, and gate latency.
  • Certified online alarm: 42.3% of failures are detected at certified α = 0.10, with realized FPR 0.066 and median lead time of 31 steps before termination.Across α values 0.05, 0.10, and 0.20, realized FPR remains below requested bounds; recall reaches 0.606 at α = 0.20.
  • Alarm-gated oversight: 86.8 mean score and 84.5% full-solve rate (305/361) result from deployable alarm-routed cascading, 3.9 points above the base pipeline while invoking the frontier model on roughly one quarter of tasks.Alarm-gated escalation recovers 23 failures while firing on 38 of 70 reruns; hard stopping would reduce full-solve rate from 80.3% to 72.3%.
  • Detection boundaries: 0.61 recall on multi-app failures contrasts with 0.22 on browser tasks and 0.0 on media-player and document tasks, whose failures typically terminate quickly without behavioral irregularity.The monitor detects execution failures that manifest behaviorally, but does not reliably distinguish plan-fixable from resistant failures or resolve belief-level errors.

Conclusion

The study finds that agents rarely disclose failure, while CURA provides an independent, behavior-based oversight channel with certified false-alarm control and early warnings.

  • 64 of 71 failed runs (90%) end with a completion claim, 61 without acknowledging any blocker, and none with a self-declared failure.
  • CURA estimates risk before the first action and converts execution into a sequential test with certified false-alarm control.
  • A median 31-step warning supports allocating escalation, abstention, or stopping during execution.
  • The resulting deployable cascade reaches mean score 86.8 without oracle routing.
  • Behavioral monitoring can outperform simple length triggers online at matched false-alarm budgets, while exposing cases where behavior carries no useful signal.

Supplementary Material

The supplementary material documents the two-loop execution architecture, tool inventory, and accounting details underlying the monitored pipeline and its oversight gap.

  • Monitored system: The harness loop runs up to 100 iterations, each consisting of screenshot capture, one agent action, virtual-machine execution, trajectory logging, and possible termination.
  • Monitored system: A harness step may contain many model calls and tool executions, while only GUI actions return control to the screenshot-owning harness.
  • Cost structure: The 361-task run contains 6,883 GUI actions, roughly 9,100 executor calls, and 11,693 model calls in total.
  • Pipeline phases: The re-entrant entry point advances through feasibility, planning, execution, verification, and terminal phases, with several phases able to complete within one tick.
  • Oversight gap: The verifying phase reports success after the executor calls done without checking the result, leaving self-declared completion unconditionally trusted.
  • Roles and tools: The tool inventory shows eight gate GUI-navigation tools and one executor failure-declaration tool, which was never called across the 361-task run.
  • Models and budgets: Reasoning budgets are caps ranging from 1K to 32K tokens, while the escalation leg swaps only the planner slot and CURA uses a frozen encoder plus two linear fits.

C. A Trajectory End to End

A solved trajectory illustrates that strong task performance can coexist with unlogged mutations, budget exhaustion, failed attempts, and unenforced verification habits.

  • The worked task asks the agent to brighten an image on a presentation slide and save it under a specified desktop filename.
  • The evaluator checks both increased brightness and preserved structural similarity, so an arbitrary bright image fails.
  • The gate uses 20 of 50 calls, exhausts its turn budget, and defaults to feasible without an attached uncertainty signal.
  • The planner independently produces four milestones and selects a cleaner route than the gate began exploring.
  • The executor verifies its work with code before claiming completion, but this verification is a model habit rather than a framework-enforced step.
  • The task scores 1.0 despite an unlogged state mutation, budget exhaustion, and two failed mechanism attempts.

D. Pipeline Results in Full

The pipeline performs strongly overall, but its failures are longer, often misreported as successes, and only partly addressable through gating, monitoring, or recovery mechanisms.

  • Per-domain outcomes: 69.9% of multi-application tasks are solved, making that domain the weakest and the source of 27 of 64 false completion claims.Code-editor work is strongest at 95.7% (22 of 23), while 8 of 13 partially scored tasks are multi-application.
  • Budget and trajectory length: 55.4% of tasks finish within 15 steps and 77.0% within 50, so the final 50 budget steps contribute 3.3 points.Most pipeline failures terminate before the cap, unlike the open-weight 7B baseline whose failures accumulate at the 100-step limit.
  • Failure and gate outcomes: 64 of 71 failures end with a completion claim, while six exhaust the budget and one is stopped by a provider-side content filter.The gate’s seven misses cost 2.8× an average task each and all ended in false completion claims.
  • Completion claims: 80.7% completion-claim precision declines with trajectory length, from 88% within ten steps to 41% after fifty steps.This makes late completion claims particularly unreliable on trajectories requiring more oversight.
  • Loops and monitoring limits: Four of six cap-reaching failures are action loops, but repetition-only detection both false-positives on legitimate repetition and misses strategy-churn loops.The paper therefore combines effort, semantics, physiology, and visual novelty rather than relying on action statistics alone.
  • Addressable failures: The six upper failure categories imply an 86.4% ceiling through mechanisms outside the executor, rising to 87.0% when half-done tasks are included.These are retrospective addressable categories, not improvements demonstrated by the pipeline itself.

E.1 The two substrates

The two agents exhibit different failure signatures: the 7B agent loops, while the stronger pipeline works harder and misreports clean-looking failures. CURA’s external-monitoring stance transfers, but its signal fusion and thresholds do not.

  • The two substrates: Claim precision collapses from 80.7% for the pipeline to 44.9% for the 7B agent, while self-declared failures rise to 142 for 7B and remain zero for the pipeline.At the strict threshold, the 7B agent solves 113 of 361 tasks (31.3%).
  • Two failure regimes: 7B failures are led by state revisit, novel-state rate, action repetition, and identical consecutive thoughts, whereas pipeline failures are led by effort, including output tokens at 0.803.The contrasting rankings indicate different behavioral failure regimes across the two agents.
  • Transfer and scope: The monitoring stance transfers, but the paper does not claim agent-agnosticism because signal sets and thresholds are regime-specific.External behavioral observation and success-only calibration are the transferable principles.
  • Composite replication: Individual signals replicate on 7B, but fusion does not: adding visual dynamics reaches 0.768 versus 0.784 for length alone.The table reports this as a negative cross-model result; physiology adds no improvement.
  • Why fusion fails: The 7B composite is limited by 113 successes for fitting success-only components, inverted class balance, and screen dynamics unusual relative to rare successes.These are proposed mechanisms that cannot be separated at the available sample size.
  • White-box confidence: The best 7B log-probability aggregate reaches 0.687, below several behavioral signals requiring no model access.This comparison supports externally observing behavior even when white-box confidence is available.
  • Signal construction: CURA’s lean composite uses eight trajectory signals, while the no-length composite uses twelve by adding gate and plan features.The signal families include effort, reasoning semantics, say–do behavior, physiology, and visual dynamics.
  • Anytime-valid monitoring: The anytime-valid betting-martingale variant controls false alarms under optional stopping and records FPR 0.097 at α = 0.1.It fires earlier on fewer failures than the CUSUM variant in the reported empirical comparison.

J.2 The success side of the cascade

The cascade’s escalation tier concentrates residual failures, while replay checks show most flagged successes persist and interventions can improve outcomes. The evaluation distinguishes certified thresholds and accuracy metrics, with visual dynamics providing the only significant incremental fusion gain.

  • Success-side validation: 83% of flagged successes persisted under identical live reruns, retaining 38 of 46 trajectories.The eight losses included two without intervention, indicating first-leg stochasticity rather than supervisor damage for those cases.
  • Success-side validation: 46 of 69 escalated reruns failed, compared with 2 of 292 remaining tasks.This concentration makes escalation the first risk tier in the selective predictor.
  • Fusion and calibration: Only visual-dynamics fusion produced a significant incremental gain under the fixed-monitor protocol.The fully fold-internal final variant reaches 0.802 [0.742, 0.858].
  • Fusion and calibration: Total output tokens is the strongest single predictor at 0.8023, motivating online rather than retrospective evidence for CURA.The paper treats matched online comparison as evidence beyond a token counter.
  • Behavioral evidence: The quoted failure-pattern counts use the 361-task base run, where 59 of 71 failed runs and 131 of 290 passed runs contain at least one statement.Failures average 25.6 statements per run versus 3.5 for passed runs.
  • Evaluation scope: Thirteen clarified-variant tasks create a cross-paper comparison delta, while the excluded eight credential tasks were absent from both evaluated systems.The evaluation retains 361 of 369 tasks and applies the same exclusion to the 7B baseline.

M. Negative Results in Full

Several behavioral and confidence signals are weak, redundant, or poorly aligned with the failure distinctions of interest. The negative results indicate that monitoring detects execution trouble more reliably than failure cause, and that small-sample correlation makes feature selection necessary.

  • Weak signals: Token-level log-probability confidence provides no predictive signal beyond a length artifact on the pipeline corpus.On the 7B corpus it reaches 0.687, described as mid-pack.
  • Oversight routing: Alarm-gated escalation reaches six replanning-resistant cases that blanket escalation does not touch.The comparison uses 70 reruns and 69 completed planner reruns.
  • Weak signals: 0.738 with reasoning embeddings trails the 0.748 tabular baseline without them.The encoder overfits 71 positives.
  • Weak signals: 0.497 is chance performance for a single-screenshot LLM judge asked whether the screen violates expectations.
  • Failure characterization: 0.44–0.59 is the near-chance range for predicting whether failures are plan-fixable or resistant.The monitor detects that execution is going wrong, not why.
  • Weak signals: Per-step weak-label training underperforms trajectory-level fusion, while escape-key counters remain uninformative at approximately 0.5.
  • Fusion failure: 0.786 versus 0.803 on the pipeline and 0.747 versus 0.810 on 7B show that naive stacking fails to beat the best single signal.The passages attribute this to correlated features and a small sample, making feature selection necessary.

N. Belief-Level Risk: The Case Study

A held-out document-editing replay demonstrates a boundary of behavioral monitoring: two trajectories can look healthy while differing sharply in grader outcome because the risk resides in the plan’s beliefs. Hard-stopping can save compute, but its reported cost is measured in full-solve rate.

  • Compute trade-off: 28.3% of output tokens and 27.0% of steps are recovered at the quoted 10% hard-stop threshold, at a cost of 8 points of full-solve rate.Full-solve rate falls from 80.3% to 72.3%; escalation hands off flagged trajectories instead of killing them.
  • Case study: 0.29 versus 0.99 separates two document-editing runs despite identical content quality and a correct execution plan.The lower-scoring plan encodes style assumptions that order deletions the grader penalizes; the same frozen executor reaches 0.99 in 32 steps with grader-consistent content.
  • Case study: No behavioral irregularity separates the two runs, which look healthy while one confidently destroys value.
  • Boundary of monitoring: CURA bounds execution risk, while belief-content risk requires an instrument that reads the plan’s text.The paper proposes stratifying planned edits into evidence-backed and belief-only as a next layer.

O. Reproducibility

The paper’s reproducibility materials expose the telemetry substrate, evaluation protocol, prompts, and read-only probing rules used to reproduce CURA and the deployed pipeline. Its implementation freezes the monitored agent and derives behavioral signals from harness-captured model-call data.

  • Evaluation protocol: Out-of-fold scores use task-level GroupKFold(5), with 95% task-level bootstrap intervals over B = 104 resamples.Paired comparisons share resamples, and the one-sided p-value is (1 + #{∆≤0})/(B + 1).
  • Telemetry: Per-call telemetry records model-call metadata, reasoning text, token counts, and tool calls with arguments.These records are the substrate for every behavioral signal and are already produced by the harness.
  • Release materials: Code, banked telemetry, the pivot family, calibrated thresholds, prompts, and regeneration scripts will be released.
  • Prompt provenance: The deployed pipeline’s behavior depends substantially on its three shipped documents, including an executor prompt that reserves failure for unrecoverable errors.The prompt also tells the executor not to second-guess the gate’s feasibility verdict.
  • Feasibility gate: The feasibility gate uses read-only inspection to verify resources, parse task constraints, and check a complete GUI-only path before execution.It reports feasibility or infeasibility exactly once and treats scripting-only paths as infeasible.
  • Executor scope: The executor is a VLM-based GUI agent that can interact with screens and terminals but prioritizes the application’s own GUI.It cannot use application-internal scripting consoles requiring specialized syntax knowledge.
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