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XYEval: Agents say yes to bad advice

Zhengxuan Wu, Yuxuan Li, Oyvind Tafjord, Been Kim

arXiv:2609.23939v1cs.CL

TL;DR

The XY problem arises when users describe an attempted solution rather than their actual goal, yet how agents handle plausible misleading user suggestions remains an open question. XYEval adapts existing benchmarks to test this problem. Across six benchmark suites, agents show substantial performance drops, reaching 46.7%, and pedantic-user interactions add communication challenges.

  • Problem

    The paper asks whether agents can resist plausible but misleading user suggestions and communicate the underlying problem, a communication challenge where users present attempted solutions instead of actual goals.

    Source: §1 · pp. 1–2

  • Method

    XYEval transforms existing benchmarks by appending plausible but incorrect suggestions to task instructions and evaluates performance changes and execution traces.

    Source: §1 · p. 2

  • Results

    Across six benchmark suites, agents suffer substantial performance drops under XY mutation, reaching 46.7%, and pedantic users who demand detailed explanations produce further drops.

    Source: §1 · p. 2, §6 · p. 13

  • Takeaways & Limitations

    Mitigating the XY problem requires agents to recognize misleading suggestions and communicate the underlying problem rather than passively complying; system-instruction warnings offer only partial mitigation.

    Source: §1 · p. 2, §5.3 · p. 8

    Most benchmark oracles return binary pass/fail judgments, although the formulation generalizes to partial-credit scoring.

    Source: §3 · p. 3

Abstract

from arXiv · show

Effective communication between users and AI agents is essential for human-AI collaboration. The XY problem is a well-known communication pitfall where a person asks about their attempted solution rather than their actual problem. We extend prior sycophancy evaluation to the XY problem in agentic settings, evaluating whether agents can resist plausible but misleading suggestions from users and communicate their reasoning. We introduce XYEval, a meta-evaluation framework that can transform an existing benchmark into an XY problem evaluation. We evaluate five models across six diverse benchmark suites. Agents suffer large XY drops under XY mutation across benchmarks, with relative drops reaching up to 46.7%. With $\tau^2$-bench, we further show that agent performance drops more when encountering a pedantic user who requires detailed explanations before approving a better solution. Our findings suggest that current agents lack the ability to effectively reason and communicate when facing misleading suggestions. A simple system instruction baseline that encourages awareness of XY problems only offers partial mitigation. Extensive trace analyses provide behavioral insights into how and why these XY drops occur across execution trajectories. Our results show that mitigating the XY problem remains challenging, requiring agents to both recognize user misdirection and clearly communicate the underlying problem.

1. Introduction

The XY problem occurs when a user presents an attempted solution instead of their actual goal, creating a communication challenge for AI agents. XYEval evaluates whether agents resist plausible misleading suggestions and communicate the underlying problem.

1. Introduction

  • The XY problem is a semantic breakdown in which a user presents an attempted solution X instead of the actual goal Y, and effective collaboration depends on communicating the underlying issue.The introduction frames this as a human-AI communication problem as well as a general communication pitfall.Source: §1 · p. 1
  • The open question is whether agents behave and communicate effectively when users offer suggestions that appear correct but are misleading.Source: §1 · pp. 1–2
  • XYEval transforms existing benchmarks by appending plausible incorrect suggestions and uses performance drops and execution traces to assess susceptibility and communication.The framework examines whether agents recognize and attribute the misdirection and communicate the underlying problem to users.Source: §1 · p. 2
  • Across six benchmarks, tested models show XY-mutation performance drops reaching 46.7%, while pedantic users and generic system instructions expose further communication challenges and only partially mitigate drops.The authors conclude that agents need to recognize misleading suggestions and communicate the underlying problem rather than passively comply.Source: §1 · p. 2

2. Related work

Prior work studies sycophancy, false or ambiguous premises, and proactive communication, but the paper focuses on a cooperative, realistic case: plausible user advice that misleads about the root cause.

2. Related work

  • Sycophancy studies find that models may echo user opinions over facts and fold under conversational pressure, while agent evaluations must account for multi-step interaction and verification.Source: §2 · pp. 2–3
  • Related evaluations examine adversarial instructions, false premises, missing or ambiguous premises, and requests to prove false claims; XYEval instead targets plausible suggestions about the underlying root cause.Source: §2 · p. 3
  • Proactive communication work studies clarifying questions and user feedback, while the XY problem additionally requires agents to evaluate plausible advice, identify misconceptions, and convince users of a better solution.Source: §2 · p. 3
  • Unlike prior adversarial agent benchmarks focused on attacks, poisoning, perturbations, deceptive feedback, or synthetic games, XYEval adapts existing benchmarks to evaluate communication failures across real-world tasks.Source: §2 · p. 3

3. XYEval

XYEval starts from tasks with instructions, environments, and verifiable oracles, then mutates instructions with plausible incorrect suggestions while keeping the environment and oracle unchanged. It measures score changes under misleading advice.

3. XYEval

  • An agentic benchmark comprises tasks with natural-language instructions, environments, and verifiable oracles that score the agent’s responses; most oracles are binary, though the formulation allows partial credit.Source: §3 · p. 3
  • XY mutation rewrites only each task instruction, preserving its environment and oracle, and appends a generator-produced plausible but incorrect suggestion to the original instruction.Source: §3 · p. 4
  • The suggestion generator has access to the full task specification, including the ground-truth solution, and frames its incorrect suggestion confidently rather than as a hard request.Preserving the original task and its golden solution lets the authors attribute performance drops to following bad advice rather than reduced task solvability.Source: §3 · p. 4
  • XY drop is the relative score change under misleading advice; a negative value indicates a drop, and the paper reports it on the full benchmark and on tasks solved correctly without mutation.The full-benchmark measure mixes capability loss and XY drop, whereas the solved-task subset isolates the effect of misleading advice.Source: §3 · p. 4

4. Benchmarks and models

The evaluation spans six benchmark suites, from multi-turn tool use and software engineering to terminal automation and expert reasoning. Each suite receives misleading suggestions tailored to its task format, alongside the five-model evaluation setup.

4.1. 𝜏2-bench

  • 𝜏2-bench tests multi-turn service requests across airline, retail, and telecom, with domain-aware misleading suggestions excluded from the golden action trace.The simulated user is instructed to accept agent pushback, making this variant less difficult than a pedantic-user version; suggestions are expert-designed because constrained tool use limits plausible distractors.Source: §4.1 · pp. 4–5

4.2. SWE-bench Verified and Pro

  • SWE-bench Verified pairs real-world GitHub issues with developer patches, while SWE-bench Pro targets longer-horizon tasks requiring more code changes and multi-file patches.For primary Verified scores, the prompt is decomposed and the misleading suggestion replaces the user direction, avoiding conflicting signals from the original prompt.Source: §4.2 · pp. 5–6

4.3. Terminal-Bench

  • Terminal-Bench 2.0 evaluates terminal automation through verification tests, with appended suggestions targeting wrong configurations, root causes, or command flags.Suggestions are generated by the model under test for the Gemini models and by Gemini 3.5 Flash for Claude Opus 4.8 and GPT 5.5.Source: §4.3 · p. 6

4.4. MCP-Atlas

  • MCP-Atlas tests tool discovery, multi-step workflows across servers, and answer synthesis, with suggestions targeting wrong tools, premises, search directions, or interpretations.Correctness is mean claims coverage, and a task passes only when all ground-truth claims are covered.Source: §4.4 · p. 6

4.5. Humanity’s Last Exam (HLE)

  • Humanity’s Last Exam uses single-turn expert-curated questions, with correctness graded by exact match and a judge fallback; misleading hints suggest a wrong reasoning direction without stating a wrong answer.The hint is appended to the question, and the reported metric is accuracy.Source: §4.5 · pp. 6–7

4.6. Models

  • The evaluation covers Gemini 3.1 Pro, Gemini 3.5 Flash, Gemini 3.7 Flash, Claude Opus 4.8, and GPT 5.5, using public harnesses for three suites and internal systems for others.The public harnesses are used for 𝜏2-bench, Terminal-Bench, and MCP-Atlas; the remaining configuration details are specified in Section C.Source: §4.6 · p. 7

5. Results

XY mutations substantially reduce agent performance across benchmarks, while pedantic user pressure deepens the communication challenge. System-instruction defenses offer incomplete recovery, and trace analyses link failures to off-track reasoning, inappropriate compliance, and disagreement left unexpressed to users.

5.1. XY drops

  • Relative performance drops reach −46.7% across benchmarks; Claude Opus 4.8 is generally most resistant, while Gemini models show large drops on interactive tasks.Gemini 3.7 Flash is an exception on SWE-bench Pro (+2.4%).Source: §5.1 · p. 7

5.2. Pedantic users

  • Pedantic insistence increases performance drops across most domains, and aggressive emotional escalation can still prompt an unhelpful human transfer despite proactive instructions.The setting requires agents to explain why the misleading suggestion is unhelpful before the simulated user accepts pushback.Source: §5.2 · p. 8, Figure 3 · p. 9

5.3. Mitigation baselines

  • Generic XY warnings reduce drops on most benchmarks but leave substantial gaps; Gemini 3.7 Flash eliminates drops on Terminal-Bench and HLE while retaining notable drops on interactive tasks.The Golden defence narrows drops across most benchmarks, and on HLE can push performance above the original baseline.Source: §5.3 · pp. 8–9

5.4. Trace analyses

  • Average turn counts remain nearly identical under XY mutations, indicating that performance drops reflect off-track reasoning rather than longer execution.Figure 4 compares control and XY conditions across models.Source: §5.4 · p. 9, Figure 4 · p. 10
  • Under XY mutations, disagreement and compliance surge; disagreement concentrates in correct traces, while compliance is almost confined to incorrect runs.Prompt defenses increase disagreement, and compliance drops to almost zero under the Golden defence.Source: §5.4 · p. 10, Figure 5 · p. 10, Figure 6 · p. 11
  • Lack of reasoning is the dominant inappropriate-compliance failure mode across models, while appeals to user authority and policy overrides are less frequent.Incorrect traces show rapid, early compliance; correct traces largely resist initial compliance or accumulate tool evidence before disagreeing.Source: §5.4 · pp. 11–12, Table 5 · p. 12

5.5. How agents communicate the XY problem

  • On τ²-bench, agents often recognize a flaw internally but fail to express disagreement in dialogue; prompt defenses substantially reduce this communication gap.Internal disagreement averages 92.1% of traces.Source: §5.5 · pp. 12–13, Table 6 · p. 12

6. Conclusion

XYEval measures how agents respond to misleading user suggestions, and the conclusion reports substantial performance drops and further difficulty communicating under pedantic pressure. The paper frames the XY problem as a failure to convey and achieve the user’s latent goal, while noting that measured drops can reflect communication failures or capability deficits.

6. Conclusion

  • Across six diverse domain suites, agents suffer relative performance drops up to 46.7% under misleading advice, and pedantic users cause further drops when demanding detailed explanations.The conclusion says system-instruction improvements provide only partial mitigation; addressing the problem remains an open challenge requiring agents to recognize misdirection and communicate the underlying problem.Source: §6 · p. 13

A. A historical note on communication theory and the XY problem

  • The XY problem is a semantic and effectiveness failure: an agent may execute the stated attempted solution X while missing the user’s latent goal Y.The paper distinguishes this from technical transmission, in which the prompt is received faithfully.Source: A. A historical note on communication… · p. 17

A.1. When is the XY drop a communication problem vs. a capability problem?

  • An XY performance drop can reflect either communication failure or capability deficit; the paper considers external dialogue and internal chain-of-thought as communication channels.The taxonomy is grounded in empirical findings, but the supplied table caption does not specify its individual regimes.Source: §A.1 · p. 17, Table 7 · p. 17

B. XY mutation and evaluation details

Appendix B describes how misleading suggestions are generated or injected across benchmarks, alongside evaluation and hyperparameter details. The procedures tailor the mutation to each benchmark’s task format.

B. XY mutation and evaluation details

B.1. τ2-bench

  • For τ2-bench, six distractors across three domains are selected from golden action traces and injected as affirmative suggestions; the simulated user is instructed to accept agent pushback.Source: Table 8 · p. 18, §B.1 · p. 18

B.2. SWE-bench Verified and Pro

  • For SWE-bench Verified and Pro, a generator explores the repository to propose a plausible wrong direction; prompt decomposition replaces existing user suggestions to avoid conflicting directions.The decomposition separates objective bug information from subjective user direction.Source: §B.2 · p. 18

B.3. Terminal-Bench

  • For Terminal-Bench, generated suggestions target incorrect configurations, root causes, or command flags and are appended to the task instruction.Source: §B.3 · p. 19

B.4. Humanity’s Last Exam (HLE)

  • For HLE, a generated misleading hint suggests a wrong line of thought without stating a wrong final answer; responses are graded by exact match with a judge fallback.The configuration caption specifies hybrid exact-match and Gemini 3.5 Flash judging.Source: §B.4 · p. 19, Table 12 · p. 20

B.5. MCP-Atlas

  • For MCP-Atlas, generated suggestions target wrong tools, premises, search directions, or interpretations; correctness uses claims-based coverage scoring.The configuration caption specifies a 100-turn tool-call budget.Source: §B.5 · p. 19, Table 13 · p. 20

C. Hyperparameters

  • Hyperparameters follow standard evaluation procedures, with decoding parameters defaulting to provider values unless noted otherwise; settings were checked against reported baselines.Source: C. Hyperparameters · p. 19

D.1. Compliance on SWE-bench Verified

The appendix tests explicit compliance with misleading directions and describes system-instruction defenses across a limited benchmark set. The supplied passages provide the experimental setup and table scopes, but not the outcome values.

D.1. Compliance on SWE-bench Verified

  • On SWE-bench Verified, agents were additionally instructed to follow misleading user directions exactly, with Pass@1 compared against a no-defense baseline.Source: §D.1 · p. 21, Table 14 · p. 21

D.2. System instruction defenses

  • The defense analysis compares XY and Golden system-instruction defenses across four benchmarks, excluding MCP-Atlas and SWE-bench Pro; Gemini 3.7 Flash was not tested under defense on SWE-bench Verified.The XY defense warns about the XY problem without naming the distractor; the Golden defense names it and serves as an upper bound on prompt mitigation.Source: §D.2 · p. 21, Table 15 · p. 21

D.3. Performance drop on solved tasks

Across the six benchmarks, the reported solved-only results show performance drops for every listed model. The appendix also reports τ2-bench domain breakdowns and qualitative pedantic-user cases.

D.3. Performance drop on solved tasks

  • Every listed model shows a solved-only performance drop across all six benchmarks after misleading guidance.The metric is restricted to tasks each model solved in the control condition, isolating previously solved tasks that fail after misleading advice.Source: §D.3 · p. 22, Table 16 · p. 22

D.4. τ2-bench per-domain performance

  • τ2-bench reports original performance, misleading-suggestion performance, and relative change separately for Airline, Retail, and Telecom.Source: §D.4 · p. 22, Table 17 · p. 22

D.5. Pedantic user

  • In the Retail case, the agent accepts canceling an order without checking whether that resolves the original need; in Telecom, it concedes to a human transfer after troubleshooting fails.Both cases concern Gemini 3.1 Pro despite a defense instruction for pedantic users.Source: §D.5 · p. 22, Figure 8 · p. 23, Figure 9 · p. 23

D.6. Non-decomposed SWE-bench Verified results

In the injection-only SWE-bench Verified variant, misleading directions consistently reduce performance, while restructuring the prompt alone has a similar result to the original condition.

D.6. Non-decomposed SWE-bench Verified results

  • Misleading directions consistently lead to performance drops in the injection-only variant.The suggestion is appended to the original problem statement alongside existing user directions.Source: §D.6 · p. 24
  • The rewrite control shows similar performance to the original condition.The control decomposes and reassembles the problem while preserving the faithful user direction.Source: §D.6 · p. 24
  • Only 298/500 instances had non-empty user direction in the original prompt, partly accounting for the difference between original and rewrite conditions.Source: Table 18 · p. 24

D.7. Additional trace analysis results

For Claude Opus 4.8 and GPT 5.5, disagreement is strongly correlated with correct outcomes, while compliance is concentrated in failure cases.

D.7. Additional trace analysis results

  • The judge pipeline consolidates each execution trajectory into a turn-by-turn transcript with explicit step boundaries.Source: §D.7 · p. 24
  • The judge extracts behavioral steps including disagreement, inappropriate compliance, self-blame, key insight, key result, risk, and pivot.It also assigns importance ratings, short summaries, and verbatim supporting quotes.Source: §D.7 · p. 24
  • Disagreement is strongly correlated with correct outcomes, while compliance is overwhelmingly concentrated in failure cases for Claude Opus 4.8 and GPT 5.5.Source: §D.7 · p. 24

D.8. Attribution analyses

These attribution analyses examine whether agents recognize misleading suggestions as originating from users, using a prompt that distinguishes explicit reference to or adoption of a suggestion from generic problem-solving or debugging. Under XY mutations, first-person attribution more than doubles across both models, with traces illustrating this behavior.

D.8. Attribution analyses

  • Agents can internalize misleading user advice and frame it as their own hypothesis rather than crediting the user.The observed first-person framing includes formulations such as “I think” and “My proposed fix,” rather than “The user suggested.”Source: §D.8 · p. 24
  • A two-stage LLM-as-a-Judge pipeline extracts authorship-framed claims, classifies them as first- or third-person, and checks whether they adopt or evaluate the injected suggestion.Identical tasks under Control, without an injected suggestion, calibrate for judge noise and domain keyword overlap.Source: §D.8 · p. 24
  • First-person attribution of misleading suggestions more than doubles under XY mutations across both models on SWE-bench Verified.Figure 11 reports the fraction of extracted claims referencing the misleading suggestion under Control and XY conditions.Source: §D.8 · p. 25, Figure 11 · p. 26
  • Qualitative examples from clean filtered trajectories show agents adopting misleading user premises as their own ideas or intuitions.The authors suggest future work could examine how this internal verbalization affects faithfulness and self-correction.Source: §D.8 · p. 25

E. Prompt templates

  • The evaluator receives the specific misleading suggestion and extracted thought quotes, marking a quote related only when it specifically refers to or adopts that suggestion.Generic problem-solving, unrelated code, and standard debugging are marked false; each decision includes a one-sentence rationale.Source: E. Prompt templates · p. 33
  • The attribution prompt requires a strict JSON array with each quote’s index, suggestion-related boolean, and one-sentence rationale.The required object fields are quote_idx, is_suggestion_related, and rationale.Source: E. Prompt templates · p. 33

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