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Beyond Oracle Communication: Benchmarking Interactive Intent Alignment Under Miscommunication and Evolving User Intent

Zheyuan Zhang, Mengyuan Chao, Ke Xiao, Ziyi Chen, Daoan Zhang, Yan Zhang, Yanfang Ye, Wei Xu

arXiv:2609.38604v1cs.CLcs.AIcs.LG

TL;DR

Existing benchmarks often assume users communicate a fixed intent accurately, although users may miscommunicate or change goals without fully expressing them. Drift-Bench++ combines verified tasks, constrained and persona-conditioned interaction, and GRIP evaluation to study this setting. Stronger interaction helps, yet remains far from oracle performance, and ProdAgent sessions confirm that modeled failures are prevalent and consequential in deployment.

  • Problem

    Most benchmarks assume users accurately and sufficiently communicate a fixed intent, although real users may miscommunicate or evolve their goals without fully expressing them.

    Source: §1 · p. 1

  • Method

    Drift-Bench++ provides verified executable tasks, finite-patience interaction with diverse users and silent shifts, and GRIP evaluation of alignment.

    Source: §1 · p. 2

  • Results

    Stronger interaction substantially improves alignment, yet large gaps remain; ProdAgent production sessions confirm that these phenomena are prevalent and costly in deployment.

    Source: §1 · p. 2, §5 · p. 10

  • Takeaways & Limitations

    Interactive intent alignment remains an advanced capability with substantial room for progress in agent design and evaluation.

    Source: §1 · p. 2

    The benchmark relies on LLMs for query perturbation, question understanding, and user-response realization where realistic language variation cannot be exhaustively specified by rules.

    Source: E Limitations and Future Work · p. 47

Abstract

from arXiv · show

Modern LLM agents increasingly tackle complex tasks through interactive, long-horizon exchanges with users, while existing benchmarks generally assume that users always accurately and sufficiently communicate a fixed intent. However, this oracle communication assumption rarely holds in practice: users may miscommunicate, change their goals, and run out of patience. We define this task setting as Interactive Intent Alignment, where agents must recover and continuously track the user's current intent despite imperfect communication and evolving goals. To study this setting, we introduce Drift-Bench++, a principled benchmark construction pipeline for verified executable tasks with controlled misalignment and intent shifts, along with an interaction protocol featuring finite patience, diverse simulated users, and silent interaction-conditioned shifts. We further develop GRIP, a comprehensive evaluation protocol covering task grounding, user realism, inquiry effectiveness, and adaptation to evolving intent. Across diverse environments, models, and interaction conditions, stronger interaction consistently helps but remains far from oracle performance; Validation on deployed ProdAgent sessions further shows that the modeled failures are prevalent and consequential in deployment. By providing a unified, executable benchmark for interactive intent alignment, Drift-Bench++ offers a foundation for evaluating and advancing agents under realistic communication and evolving intent.

1 Introduction

Existing benchmarks often assume users communicate a fixed intent accurately and sufficiently, although real users may miscommunicate or change goals without fully expressing them. Drift-Bench++ studies agents’ ability to stay aligned under these conditions, and the reported results show that stronger interaction helps but remains far from oracle performance.

Motivation

  • Most benchmarks assume messages accurately and sufficiently convey intent, despite real users omitting details, relying on incorrect assumptions, communicating ambiguously, or evolving their goals.These capabilities directly shape real-world reliability and user experience.Source: §1 · p. 1

Task setting

  • Interactive Intent Alignment requires agents to keep actions aligned with latent intent that may be miscommunicated or evolve under finite patience and diverse user behaviors.The paper introduces Drift-Bench++ with verified executable tasks and GRIP evaluation.Source: §1 · p. 2

Results

  • Stronger interaction consistently helps agents recover and track intent, but performance remains far from oracle levels; ProdAgent sessions show the modeled failures are prevalent and consequential in deployment.The evaluation spans agentic environments, models, and interaction conditions.Source: §1 · p. 2

Contributions

  • Drift-Bench++ combines a principled pipeline for verified executable tasks with finite-patience interaction, diverse simulated users, silent intent shifts, and comprehensive evaluation.The paper also reports extensive experiments across environments, models, and interaction conditions, plus ProdAgent validation.Source: §1 · p. 2

2 Existing Challenges: Why Drift-Bench++?

Prior work has studied intent misalignment and intent shifting, but often treats them separately or relies on predefined shifts that are explicitly communicated. Drift-Bench++ addresses the combined challenge of realistic interaction with finite patience and evolving, potentially silent intent.

Intent Misalignment Challenge

  • Prior intent-misalignment studies often use narrow or heuristic faults, while users’ finite patience creates a trade-off between further clarification and timely action.Drift-Bench [4] introduced a structured fault taxonomy and reusable synthesis pipeline; later work strengthened controllability and verification.Source: §2 · pp. 2–3

Intent Shifting Challenge

  • Misalignment and intent shifting are coupled in practice: an incompletely communicated shift can leave an agent misaligned, while prior shifting benchmarks generally announce updates and reinstate oracle communication.Existing shift mechanisms often prescribe when and how changes occur rather than letting them emerge from user-agent interaction.Source: §2 · p. 3
  • Existing benchmarks largely model intent shifts through predefined schedules or task outcomes, rather than changes emerging from interaction.Examples include schedule-triggered shifts and goal changes tied to task completion or failure.Source: §2 · p. 3

3 Drift-Bench++ Benchmark

Drift-Bench++ builds a controlled, executable setting for intent that may be miscommunicated and evolve during interaction. Its construction combines verified task intents, constrained and persona-conditioned interaction, and a diagnostic evaluation across complementary alignment dimensions.

3.1 Data Construction

  • Intent graphs encode a task frame and its intended conditions, then connect validated alternatives through executable edges for controlled shifts.Misaligned queries are retained after judges agree on the edit, fidelity checks confirm its expression, and execution verifies its effect against expected outcomes.Source: §3.1 · pp. 4–6
  • Original benchmark tasks are mapped to intent graphs and converted into verified misaligned instances, with lightweight adapters and native oracles supporting shared construction across domains.Intent graphs preserve executable alternatives for later shifts, while the perturbation pipeline verifies the initial mismatch.Source: §3.1 · pp. 4, 6, Figure 2 · p. 5

3.2 Interaction Protocol

  • The interaction protocol models finite patience, dynamic intent changes, and persona-conditioned user behavior rather than assuming unlimited clarification or static goals.The state machine governs user behaviors, while the LLM identifies question topics and verbalizes selected information.Source: §3.2 · pp. 6–7
  • Clarification consumes a limited patience budget, while rejected proposals cost more; unaffordable user-facing actions become unavailable and force commitment with current knowledge.Intent shifts can become eligible at patience thresholds or when an agent question prompts reconsideration; both channels share a finite pool of shift opportunities.Source: §3.2 · p. 6
  • Silent shifts leave the updated intent incompletely communicated, and five GDMS-based personas vary disclosure, feedback, patience, shift timing, and susceptibility to reconsideration.The five personas are Rational, Intuitive, Dependent, Avoidant, and Spontaneous.Source: §3.2 · p. 6

3.3 GRIP Evaluation

  • GRIP evaluates task outcomes, simulated-user fidelity, inquiry effectiveness, and adaptation to evolving intent instead of relying on task success alone.Its dimensions are Grounding, Role-realism, Inquiry, and Persistence, with metrics for success, persona behavior, clarification, and post-shift performance.Source: §3.3 · p. 7
  • GRIP reports its dimensions separately, enabling diagnosis of task success, interaction quality, hidden-intent recovery, and adaptation to changing intent.The protocol is extensible, leaving each dimension open to additional metrics as new interaction challenges emerge.Source: §3.3 · p. 7

4 Experiments

The experiments evaluate interactive intent alignment across agentic environments, models, and interaction conditions, then test simulated-user realism and compare modeled failures with deployed sessions. Stronger clarification generally helps, but complex misalignment, evolving intent, and user variation remain challenging.

4.1 Research Question and Experiment Setup

  • Evaluation spans WebShop and τ 2 Retail and Airline, comparing No Ask, seven clarification methods, and three diagnostic agents across five backbones and varied conditions.The experiments vary personas, fault complexity, and intent shifting, and assess whether benchmark abstractions reflect real-world interaction.Source: §4.1 · pp. 7–8

4.2 Experiment Results

  • Asking consistently improves performance, with larger gains on state-changing τ 2 domains; Self-Evolving leads in 14 of 15 backbone–benchmark settings.Clarification broadly beats No Ask across five backbones, and Self-Evolving has the highest Earned score on all three benchmarks.Source: §4.2 · p. 8, Figure 3 · p. 9
  • As fault complexity rises, Self-Evolving’s Success gap over No Ask and Recovery advantage over Just Ask grow; shifts reduce Success and Earned.Stronger clarification methods also degrade less when shifts are introduced.Source: Figure 4 · p. 9, Figure 5 · p. 9, §4.2 · p. 9
  • Persona-conditioned behavior produces distinct performance profiles, while Identification and Consistency exceed their 20% and 50% chance baselines across personas.Judges assessed the user messages without access to the underlying persona profiles.Source: §4.2 · p. 9, Figure 6 · p. 10
  • In 16,596 ProdAgent sessions over 10 weeks, 23.7% of sessions with strict judge agreement showed misalignment; misaligned sessions had lower human scores and more tool calls.Observed cases span all four communication-fault families and all four intent-shift relations.Source: Figure 7 · p. 10, §4.2 · p. 10

5 Conclusion

The conclusion presents Drift-Bench++ as a unified benchmark for recovering and tracking intent under imperfect communication and evolving goals. The accompanying statements describe the work’s AI assistance, data protections, and reproducibility constraints.

5 Conclusion

  • Drift-Bench++ jointly captures communication misalignment and evolving intent; stronger clarification improves alignment across settings, but large gaps remain.ProdAgent validation confirms that the modeled phenomena are prevalent and costly in deployment.Source: §5 · p. 10

AI use statement

  • Generative AI supported dataset creation, experiment design, implementation, analysis, literature work, and manuscript preparation; authors reviewed and verified its outputs.The authors state that AI did not develop theoretical models or conceptual frameworks, refine hypotheses, or set the overall research agenda.Source: AI use statement · p. 11

Ethics Statement

  • ProdAgent data are user-authorized for research, but are not released or reproduced; only aggregate findings are reported, and persona simulations model behavior rather than protected attributes.The benchmark is diagnostic and introduces no new user-facing interventions or human-subject data collection.Source: Ethics Statement · p. 11

Reproducibility Statement

  • The codebase and benchmark artifacts support independent reproduction, but company policy prevents releasing ProdAgent interaction data and analysis artifacts.The paper reports multiple seeded runs and documents the evaluation settings.Source: Reproducibility Statement · p. 11

A Additional Related Work

The related-work discussion compares Drift-Bench++ with benchmarks that relax individual assumptions about communication, intent change, interaction, and user behavior. Its implementation appendix outlines the steps and evaluation bookkeeping needed to reproduce the benchmark.

A Additional Related Work

  • Misalignment benchmarks study clarification, assistance needs, or specific ambiguity types, while RegretBench treats clarification cost as an evaluation trade-off rather than finite user patience.These works complement fixed-intent misalignment settings, whereas closer comparators explicitly control the gap between communicated requests and underlying intent.Source: A Additional Related Work · p. 19
  • Intent-shifting benchmarks cover changing requirements, interruptions, revisions, and evolving preferences, but often use scheduled or outcome-conditioned changes provided to the agent.Drift-Bench++ emphasizes shifts that can themselves create latent misalignment.Source: A Additional Related Work · p. 19
  • Interactive and progressive-disclosure benchmarks address shared environments or staged information, while persona benchmarks vary user behavior; Drift-Bench++ controls intent separately from how persona shapes its communication.Progressive disclosure is generally monotone, supplementing rather than replacing a previously correct requirement.Source: A Additional Related Work · pp. 19, 21
  • Related benchmarks increasingly relax assumptions such as complete communication, static goals, passive users, fixed interaction costs, and homogeneous behavior; Drift-Bench++ combines several dimensions in one executable setting.The broader comparison covers intent misalignment, intent shifting, progressive interaction, and user simulation.Source: A Additional Related Work · pp. 19, 21, Table 3 · p. 20

B Implementation Details

  • The implementation details specify host-benchmark adapters, executable oracles, verified intent graphs and perturbations, interaction rules, persona behavior, and GRIP metric bookkeeping.The section also describes event-conditioned denominators for grounding, inquiry, role-realism, and persistence metrics.Source: B Implementation Details · p. 21

B.1 Host-Benchmark Adapters and Executable Oracles

This appendix specifies host-native executable intent adapters, validated alternative-goal graphs, and verified misalignment queries, alongside a patience-bounded interaction protocol with silent intent shifts. It models intent drift through scheduled and question-triggered reconsideration, persona-conditioned user behavior, and a state machine that preserves executable truth as intent evolves.

B.1 Host-Benchmark Adapters and Executable Oracles

  • Adapters standardize independently addressable requirements and compile modified requirements into coherent native tasks; host-native validation yields 257/98/40 instantiated intent graphs and 1,780/555/208 verified samples from 500/114/50 source tasks.Replaying only these writes reproduces the full trace’s database hash on all 114 source tasks.Source: §B.1 · pp. 21–23

B.2 Intent Graph Construction

  • Each validated root becomes a deterministic executable intent graph whose exact relation-labeled alternatives are retrieved first, synthesized only when needed, then host-validated.Stored graphs are depth-one stars; runtime pairwise closure adds valid cross-links without admitting new intents, and retained graphs must include a ground-truth-moving branch.Source: §B.2 · pp. 23–24

B.3 Verified Query Perturbation

  • The released generate-then-verify pipeline admits perturbed queries only when independent semantic judgments, text-fidelity checks, and host-oracle outcomes establish the requested fault’s executable signature.Failed verification leads to rejection rather than a Not-Checkable admission path.Source: §B.3 · pp. 25–26

B.4 Patience-Bounded Interaction

  • Interaction spends finite persona-scaled patience—questions cost 2, rejected proposals 4, malformed exchanges 1, and environment actions 0—with unaffordable actions or exhaustion forcing one final submission.Accepted proposals terminate without charge, and affordability rather than a separate question cap limits interaction.Source: §B.4 · p. 27

B.5 Interaction-Conditioned Intent Shifting

  • All scheduled and agent-triggered shifts use validated executable transitions and occur silently, with normalized persona-specific placements converted into descending patience thresholds.Source: §B.5 · p. 27
  • Scheduled shifts sample feasible relations before destinations, avoiding graph-degree-driven frequencies; WebShop stops shifts after purchase-shaped actions, whereas released Retail and Airline runs permit post-write shifts.Source: §B.5 · p. 28
  • Question turns check triggered reconsideration before at most one due scheduled shift, whereas proposals flush all due shifts before adjudication, with separate seeded streams supporting replay.Source: §B.5 · p. 28
  • Scheduled shifts fire with probability 0.6, while an on-topic clarification can trigger reconsideration early using persona suggestibility and consumes a placement, so questioning does not increase maximum shift exposure.Source: §B.5 · pp. 27–28, Table 5 · p. 29

B.6 Persona-Conditioned Behavior

  • A state machine alone controls executable truth and adjudication, while the LLM only maps questions and verbalizes authorized information; personas are behavioral stress tests, not prevalence estimates.Source: §B.6 · p. 29
  • Disclosure limits, refusals, and safeguards govern answers; a grounded observer credits only communicated values, while sanitized verifier feedback avoids making repeated proposals an optimization oracle.Source: §B.6 · pp. 29–30

B.7 GRIP Evaluation Details

GRIP separates task grounding, persona realism, inquiry, and adaptation to intent shifts rather than collapsing them into one score. The appendix also specifies clarification baselines and layered reference agents for probing these capabilities.

B.7 GRIP Evaluation Details

  • GRIP keeps Grounding, Role-realism, Inquiry, and Persistence separate because identical task outcomes can arise through recovery, inference, or chance alignment.Conditional metrics are reported with enabling-event counts, and undefined values are shown as “—” rather than zero.Source: §B.7 · pp. 30, 32
  • Grounding pairs executable final-task Success with Earned success after complete hidden-target recovery and Inferred success without that full recovery.Earned and Inferred sum to mean Success over episodes with a non-empty hidden target; task correctness comes from the host verifier, not an LLM judge.Source: §B.7 · p. 30
  • Role-realism checks fidelity to assigned personas, while Identification and Consistency test whether judges can recognize a persona and distinguish it across tasks.Identification is five-way classification with 0.2 chance accuracy; Consistency uses balanced same-persona versus different-persona pairs.Source: §B.7 · pp. 30–31
  • Inquiry distinguishes targeted questions from complete task-level Recovery and remaining Patience at commitment.Aim averages the fraction of questions touching hidden, unrecovered requirements; episodes with no questions have undefined Aim, and Recovery is binary per episode.Source: §B.7 · p. 31
  • Persistence measures outdated-goal failures with Staleness and adaptation speed with Reaction among successes after answer-moving shifts.Staleness uses executable counterfactual re-adjudication; Reaction is interpreted alongside Post-shift Success, which measures outcomes across shifted episodes.Source: §B.7 · pp. 31–32

C.1.1 Baselines

  • Published clarification baselines range from No Ask and free-form Just Ask to diagnosis-guided, structured-belief, and discriminative questioning.The shared interface permits acting, asking, or proposing, while proposals use the same executable verifier and questions and proposals consume patience.Source: §C.1.1 · pp. 32–33
  • Other baselines model parameter uncertainty, information gain, or the cost-aware ask-or-act trade-off; single-round methods are reapplied after responses for multi-turn evaluation.The adaptation does not add long-horizon memory, pre-commitment verification, or cross-episode updates absent from the original methods.Source: §C.1.1 · p. 33

C.1.2 Reference Agents.

  • The reference agents layer revisable intent memory, evidence-grounded commitment, and a run-local playbook that reuses clarification strategies from filtered prior trajectories.The playbook starts empty per run and retains general strategies rather than episode-specific requirement values; Memory+Verification commits only when supported requirements are verified and unresolved ones cannot materially invalidate the proposal.Source: §C.1.2 · pp. 33–35

C.2 Experimental Setup and Controls

The experiments use paired evaluation and vary selected interaction conditions while holding other factors fixed. Results are averaged over seeded runs, with controls for model and environment variation.

C.2 Experimental Setup and Controls

  • Each method is evaluated on the same manifest rows, fixing the verified request, intent graph, environment state, persona, and episode seed.Default experiments use DeepSeek-V4-Pro, the Rational persona, single-fault requests, and enabled intent shifting; results are mean±sd over three seeded runs.Source: §C.2 · p. 35
  • Persona, backbone, fault complexity, and shift ablations vary one factor while preserving the other interaction conditions.The complexity sweep composes distinct fault types; disabling shifts removes scheduled and interaction-conditioned transitions but preserves initial requests, patience, tools, and verification.Source: §C.2 · p. 36
  • Runs use separate seed offsets and response caches, exact model identities, and collision-free environment sessions; exhausted transient failures are unscored.Figure 9 compares Aim and Recovery shifts from Just Ask to Self-Evolving and decomposes successful hidden-intent episodes into Earned and Inferred outcomes.Source: §C.2 · p. 36, Figure 9 · p. 36

C.3 RQ1: Capability Across Methods and Backbones

Across the expanded RQ1 results, stronger interaction improves performance broadly, but success depends on recovering current intent rather than backbone strength or question targeting alone. The results also distinguish explicit recovery from inference and adaptation speed from shifted-task robustness.

C.3 RQ1: Capability Across Methods and Backbones

  • Just Ask improves over No Ask in every backbone–benchmark pair, but backbone rankings change under stronger interaction.Qwen3.7-max has the highest Self-Evolving Success on WebShop, while GPT-5.5 is strongest on both τ 2 domains; neither ordering follows directly from No Ask results.Source: §C.3 · p. 36
  • Self-Evolving leads in 14 of 15 backbone–benchmark settings, while clarification improves performance broadly.The full GRIP table covers No Ask, Just Ask, and Self-Evolving across five backbones and three benchmarks.Source: §C.3 · p. 36, Table 6 · p. 37
  • Self-Evolving attains the highest observed Earned and Recovery scores across the three benchmarks, linking its strongest results to fuller hidden-requirement recovery.Figure 9 shows transitions from Just Ask to Self-Evolving more consistently along Recovery than Aim.Source: §C.3 · p. 38
  • GPT-5.5 on WebShop illustrates that higher Aim does not ensure better Recovery: Aim rises from 68.81 ± 0.48 to 75.99 ± 3.30 while Recovery falls from 19.52 ± 2.87 to 13.17 ± 1.67.Success also falls below Just Ask in this comparison.Source: §C.3 · p. 38
  • Self-Evolving often raises the Earned component of successful hidden-intent episodes, most consistently on Retail, but the shift is not universal across backbones.Earned and Inferred remain distinct because successful inference is not equivalent to explicit intent recovery.Source: §C.3 · p. 38
  • On Retail and Airline, Self-Evolving improves Success over Just Ask for every backbone and has the strongest PostShift result within each benchmark.Reaction does not uniformly decrease; composed-fault Figure 10 also reports generally more post-shift turns at higher complexity among successful trajectories.Source: §C.3 · p. 38, Figure 10 · p. 38

C.4 RQ2: Robustness to Interaction Conditions

RQ2 tests whether interactive intent alignment remains reliable when faults compound, intent changes during interaction, and personas differ in disclosure and clarification responses.

C.4 RQ2: Robustness to Interaction Conditions

  • RQ2 varies fault complexity, latent intent shifts, and user persona to test which interaction-alignment capabilities become bottlenecks when default assumptions are relaxed.The paper frames these as conditions Drift-Bench++ is designed to model, rather than generic robustness perturbations.Source: §C.4 · p. 39

C.4.1 Composed Communication Faults

The complexity sweep composes multiple fault types per request under a fixed finite interaction budget and finds that interaction becomes more valuable as faults compound.

C.4.1 Composed Communication Faults

  • At k=4, Self-Evolving exceeds No Ask in Success by 19.01 points on WebShop, 25.30 on τ 2-Retail, and 44.84 on τ 2-Airline.The reported comparison shows that compounding faults increasingly separate interactive recovery from resolving several coupled uncertainties internally.Source: §C.4.1 · p. 39, Table 7 · p. 40
  • From k=1 to k=4, Self-Evolving’s Recovery advantage over Just Ask grows from 13.98 to 19.29 points on WebShop, 7.48 to 14.82 on Retail, and 3.42 to 18.84 on Airline.Self-Evolving also retains a higher Earned score at k=4 in all three domains.Source: §C.4.1 · p. 39, Table 7 · p. 40
  • On WebShop, Just Ask’s Aim rises from 35.74 ± 0.52 to 63.29 ± 0.74 as k increases from 1 to 4, while Recovery falls from 33.93 ± 1.61 to 24.75 ± 0.92.On Airline at k=4, Just Ask has higher Aim than Self-Evolving (75.66 ± 1.17 versus 70.53 ± 5.16) but lower Recovery (25.33 ± 2.31 versus 44.17 ± 4.25).Source: §C.4.1 · p. 39
  • Reaction generally increases with k for interactive methods; on WebShop, it rises from 8.62 to 15.53 turns for Just Ask and from 10.09 to 15.29 for Self-Evolving.Reaction counts post-shift episodes that eventually reach acceptance, so the increase is not explained simply by additional failures.Source: §C.4.1 · p. 39
  • On WebShop and Airline, the additional clarification that supports recovery consumes remaining patience, the same finite resource that constrains interaction.The accompanying decline in remaining patience underscores the cost of successful recovery under compounded faults.Source: §C.4.1 · p. 41

C.4.2 Intent-Shift Ablation

Holding initial requests and other experimental factors fixed, the shift ablation finds that evolving intent creates a distinct challenge: all methods lose Success, but Self-Evolving degrades least.

C.4.2 Intent-Shift Ablation

  • Across all three benchmarks, shifts increase Aim but decrease Recovery for both interactive methods; on Retail, Self-Evolving’s Aim rises from 31.41 ± 2.93 to 58.79 ± 1.18 while Recovery falls from 83.34 ± 1.42 to 49.66 ± 4.71.Just Ask exhibits the same qualitative pattern.Source: §C.4.2 · p. 41
  • On Retail, Self-Evolving changes from 69.27 ± 0.56 Earned and 12.55 ± 0.31 Inferred without shifts to 36.50 ± 4.77 Earned and 30.94 ± 1.55 Inferred with shifts.Among episodes that remain successful, a larger fraction are completed without fully recovering the final hidden target, so Success alone understates the disruption to grounding.Source: §C.4.2 · pp. 41, 43
  • Enabling shifts lowers Success for every method and benchmark; Self-Evolving loses 4.71 points on WebShop, 14.96 on τ 2-Retail, and 3.65 on τ 2-Airline.These losses are smaller than those for No Ask and Just Ask in each benchmark.Source: §C.4.2 · p. 41, Table 8 · p. 42
  • Shifts reduce remaining patience for every method; on Retail, No Ask loses approximately 2.4 units and both interactive methods lose approximately 2.9, yet Self-Evolving withstands comparable additional cost with a smaller Success drop.On WebShop, the interactive methods likewise lose roughly 0.6 remaining patience while Self-Evolving has the smaller performance drop.Source: §C.4.2 · p. 43
  • Under shifts, substantial Staleness—particularly on Retail—means many failed episodes end at a goal that was once correct but has since been superseded.This failure would be invisible in a static benchmark, where the same behavior could have been judged correct before the shift.Source: §C.4.2 · p. 43
  • Without shifts, Just Ask and Self-Evolving nearly converge in WebShop Success and remain close on both τ 2 domains, while both generally outperform No Ask.Initial miscommunication and evolving intent therefore contribute separately to benchmark difficulty; the additional advantage of persistent tracking becomes most visible when the target can change.Source: §C.4.2 · p. 43

C.4.3 Persona-Conditioned Interaction

Persona-conditioned users change the effective difficulty and limiting capabilities of the same tasks; the role-realism checks find stable, recognizable behaviors but do not establish population realism.

C.4.3 Persona-Conditioned Interaction

  • Persona difficulty is metric-dependent: on Retail, Dependent users rank first for Success, Earned, and Recovery, while Rational users rank first for Aim and PostShift Success.Airline shows an analogous split, with Dependent users maximizing Earned and Recovery and Rational users maximizing Success, Aim, and PostShift Success.Source: §C.4.3 · p. 44, Table 9 · p. 45
  • Avoidant users are consistently among the hardest conditions, while Rational users are particularly strong on persistence outcomes, including the highest Self-Evolving PostShift Success in all three benchmarks.These recurring patterns appear across domains and suggest the behavioral controls induce systematic differences in how information becomes available.Source: §C.4.3 · p. 44
  • Self-Evolving has higher Recovery than Just Ask in all fifteen persona–benchmark combinations, but its Success advantage is not universal.For WebShop under Avoidant users, Recovery rises from 15.27 ± 0.32 to 21.96 ± 0.20 while Success changes from 51.43 ± 0.68 to 50.39 ± 1.25.Source: §C.4.3 · p. 44
  • Across compound faults, shifts, and personas, Drift-Bench++ exposes distinct failure modes so researchers can identify which interaction component makes alignment difficult.The sweeps vary how much intent must be recovered, whether recovered information remains valid, and how information becomes available through dialogue.Source: §C.4.3 · pp. 44, 46
  • On τ 2-Retail, persona changes shift Success by about 35–36 points on the same tasks: Just Ask ranges from 41.26 ± 2.34 under Avoidant users to 77.30 ± 0.62 under Dependent users, and Self-Evolving from 42.76 ± 3.02 to 78.26 ± 1.17.The task and communication fault remain unchanged across these persona conditions.Source: §C.4.3 · p. 44, Table 9 · p. 45

C.5 RQ3: Role-Realism Validation

  • Persona identification and consistency remain substantially above their 20% and 50% chance baselines, respectively, using interaction alone and excluding the opening request.The evaluation uses two independent judges to infer hidden persona identity and assess consistency across interactions.Source: §C.5 · p. 46
  • Role-realism validates behavioral variation, not task difficulty or population realism, and does not establish whether modeled failures occur in deployment.The paper evaluates that separate deployment-relevance question using ProdAgent production sessions.Source: §C.5 · p. 46

D Drift-Bench++ on Production Agent Data

The production-data analysis examines labeled sessions from a deployed tool-using agent platform using existing intent and quality annotations, then applies restricted, agreement-based review of later messages.

D Drift-Bench++ on Production Agent Data

  • The analysis uses a labeled subset of sessions sampled routinely for quality monitoring and behavioral analysis from an anonymized production agent platform.Due to privacy and data-use policy, the paper reports aggregate statistics only and does not release raw user messages or disclose the product name or internal annotation procedures.Source: D Drift-Bench++ on Production Agent Data · p. 46
  • The platform supports persistent, long-running and asynchronous tool-mediated tasks, with agents maintaining context and using tools such as browsers, files, and scheduling.This provides interactions with real users delegating extended tasks rather than interactions created specifically for benchmark evaluation.Source: D Drift-Bench++ on Production Agent Data · p. 46
  • Consecutive weekly monitoring snapshots contain ordered interactions, model and tool activity, task metadata, extracted intents, and execution-quality assessments.The study uses pre-existing annotations to examine misalignment rather than reconstructing user goals retrospectively.Source: D Drift-Bench++ on Production Agent Data · p. 46
  • Recorded intents distinguish explicit content, format, and constraint requirements from implicit requirements inferred from task context, alongside seven execution-quality dimensions scored from 1 to 5.The seven dimensions are task completion, factual accuracy, constraint adherence, reasoning quality, tool-use quality, output quality, and efficiency.Source: D Drift-Bench++ on Production Agent Data · p. 47
  • The analysis includes sessions with at least two human messages and retains only cases where two independent LLM judges strictly agree on whether later messages repair, revise, or change the request.Approvals, requests for follow-up information, and ordinary workflow progression are excluded from misalignment.Source: D Drift-Bench++ on Production Agent Data · p. 47

E Limitations and Future Work

Drift-Bench++ is bounded by the task structures it can decompose and verify, and its current evaluation does not cover longer horizons or trained interactive policies. The accompanying prompts and case studies make design choices and interaction mechanisms inspectable.

E Limitations and Future Work

  • The benchmark fits tasks with structured constraints or executable actions, but is less direct for open-ended goals lacking clean condition decomposition or a reliable host-native oracle.Extending it to those settings while preserving controllable edits and executable validation remains future work.Source: E Limitations and Future Work · p. 47
  • Experiments cover relatively short interaction horizons; applying the construction pipeline to ProdAgent tasks for substantially longer workflows is planned.The paper treats horizon length as one progress axis alongside persistent collaboration with imperfect, evolving users.Source: E Limitations and Future Work · p. 47
  • The finite fault taxonomy and five simulated personas provide representative, not exhaustive, coverage of human communication and behavior.The authors cite compound-fault experiments and ProdAgent analysis as mitigations, while calling for broader failure modes, profiles, and production evidence.Source: E Limitations and Future Work · p. 47
  • LLMs support natural-language variation in perturbation, question understanding, and response realization, but can introduce model bias and generation error.The pipeline restricts LLMs where benchmark truth is concerned and uses mechanical checks, executable verification, and a state machine to separate linguistic variation from task truth.Source: E Limitations and Future Work · pp. 47–48
  • The study evaluates fixed backbones and inference-time strategies rather than training agents for interactive intent alignment, so its results are not an upper bound for trained policies.A training-ready benchmark would additionally require intent-graph-level data splits, difficulty stratification, diverse trajectories, learnable rewards, and safeguards against overfitting.Source: E Limitations and Future Work · p. 48

F Prompt Design

  • The prompt appendix groups construction, simulated-user, and reference-agent prompts; their exact wording is released so readers can audit and rerun design choices tied to reported results.The prompts are lightly abridged in the paper, with full text shipped with the code.Source: F Prompt Design · p. 48

G Case Study

  • Case studies make verified trace events inspectable and illustrate clarification, rejection feedback, and recovery from superseded intent in ordinary successful runs.The airline example takes 13 turns as patience falls from 12 to 4; the WebShop example includes a $120 ceiling, a rejected purchase, and a later accepted resubmission.Source: G Case Study · pp. 48–49, Figure 21 · p. 57, Figure 22 · p. 58
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