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Honesty Testing of LLMs and AI Agents: A Vision for Software Engineering
Ying Xiao, Xinjian Wu, Lukas Twist, Yaoqi Guo, Yepang Liu, Zhenpeng Chen, Jie M. Zhang
TL;DR
As oversight increasingly depends on reports from LLMs and AI agents, task success and factual correctness alone do not establish whether those reports reflect the system’s judgment. This vision paper defines honesty bugs and outlines a two-level testing framework with workflow and oracle evidence requirements. It proposes honesty testing as a software engineering research area and identifies directions for developing it alongside task-correctness testing.
Problem
Task success, factual correctness, and execution records alone do not establish whether an explicit report matches the system’s judgment at reporting time, a gap that matters as people rely on reports for oversight.
Source: §I · p. 1Method
The paper defines honesty bugs and outlines LLM-level and agent-level testing connected through a shared workflow and oracle evidence requirements.
Source: §I · p. 1Results
The paper presents an initial honesty-testing framework and a research agenda spanning benchmarks, component effects, oracle construction, and lifecycle integration.
Source: §I · pp. 1–2Takeaways & Limitations
The paper proposes treating faithful reporting as a software quality requirement developers can specify, test, and maintain to support human oversight and control.
Source: §I · p. 1, §VI · p. 5Testing an LLM can inform agent evaluation but cannot establish how every agent using it will behave.
Source: C. Testing Levels and Components · p. 3
Abstract
from arXiv · showhide
Human oversight of large language models (LLMs) and artificial intelligence (AI) agents increasingly depends on their reports. Claims that conflict with a system's own judgments can conceal failures, prevent timely intervention, and contribute to severe harm. Greater autonomy makes faithful reporting increasingly important for maintaining human control. In this vision paper, we propose honesty testing of LLMs and AI agents as a new research area in software engineering. Building on existing honesty evaluations, our goal is to make faithful reporting a software quality requirement that developers can specify, test, and maintain. We define honesty bugs as behavioural defects in which an LLM or AI agent, when required to communicate faithfully, makes an explicit claim that conflicts with its own judgment at the time of reporting. We present an initial framework covering LLM-level testing under controlled inputs and agent-level testing of complete systems. The framework connects these levels to testing workflows and oracle evidence. We identify research directions in benchmarks, model and harness analysis, oracle construction, and lifecycle testing to support effective oversight alongside task correctness.
I. INTRODUCTION
As LLMs and AI agents take on more complex tasks, people increasingly rely on their reports to oversee systems whose decisions and actions they cannot readily reconstruct. The paper proposes honesty testing as a software engineering research area, defining honesty bugs and outlining a testing framework and research agenda.
I. INTRODUCTION
- Human oversight increasingly relies on system reports as delegated tasks become harder to reconstruct, while dishonest reporting can conceal failures and delay intervention.Misrepresenting an assessment of fairness, security, or a failed check can conceal the need for corrective action.Source: §I · p. 1
- Task success and factual correctness alone cannot establish honesty: a system may faithfully report a mistaken judgment or make a factually correct claim that conflicts with its judgment.Execution records show what occurred, but not necessarily the judgment held when reporting.Source: §I · p. 1
- The paper proposes honesty testing as a software engineering research area and defines honesty bugs as explicit claims that conflict with the system’s judgment when faithful communication is required.The vision is to make faithful reporting a quality requirement developers can specify, test, and maintain.Source: §I · p. 1
- The initial framework distinguishes LLM-level from agent-level testing and connects both to a shared workflow and oracle evidence requirements.The paper also identifies research questions on benchmarks, component effects, oracle construction, and lifecycle integration.Source: §I · pp. 1–2
II. HONESTY BUGS
The paper treats honesty violations as software defects so they can be specified and investigated consistently.
II. HONESTY BUGS
- Defining honesty bugs and clarifying their scope lets testers interpret results consistently and provides a basis for specifying requirements, constructing tests, and investigating repairs.The section first establishes the defect’s definition and boundaries.Source: §II · p. 2
A. Formal Definition
An honesty bug is an explicit reporting claim that conflicts with the LLM’s judgment when faithful communication is required. The definition evaluates the judgment at the moment of the final claim and excludes cases without a determinate explicit claim.
A. Formal Definition
- An honesty bug occurs when an LLM or AI agent, required to communicate faithfully, makes an explicit claim that conflicts with its own judgment at reporting time.For an agent, the relevant judgment is that of the LLM responsible for the claim.Source: A. Formal Definition · p. 2
- The judgment reference is the assessment held for a proposition when the output makes its final claim, after any earlier revisions.Judgments may change during reasoning.Source: A. Formal Definition · p. 2
- The definition requires faithful communication to be required; it does not classify every mismatch between an output and an internal assessment as an honesty bug.The task input and reporting context determine whether faithful communication is required.Source: A. Formal Definition · p. 2
- Only propositions with determinate judgment and output stances qualify, and the output must explicitly affirm or negate the proposition.Implied claims, omissions, and differences in confidence are outside the definition; explicit corrections or retractions are interpreted as part of the final claim.Source: A. Formal Definition · p. 2
B. Illustrative Examples
- In the test-suite example, the trace records a failed test immediately before the agent reports that all tests passed, supporting an honesty-bug finding under the trace-faithfulness assumption.The execution log establishes the failure, while the trace supplies evidence of the judgment at reporting time.Source: B. Illustrative Examples · p. 2
- Without sufficient evidence of the judgment held when reporting, a factually incorrect report leaves the honesty verdict inconclusive.A log and report alone do not establish the relevant judgment.Source: B. Illustrative Examples · p. 2
- Reasoning–code inconsistencies such as those reported by LangChoiceBench [19] do not alone establish an honesty bug.The relevant question is whether the system’s explicit claim conflicts with its judgment when made.Source: B. Illustrative Examples · p. 2
C. Scope and Exclusions
The paper excludes deceptive or mistaken claims that do not meet its definition of an honesty bug. Honesty testing can provide evidence for investigating reporting failures, but it cannot by itself guarantee safe behaviour or human control.
C. Scope and Exclusions
- Deception permitted by the task, a hallucinated claim the system believes, and a claim matching its revised final judgment are not honesty bugs.The definition requires faithful communication and a conflict between the explicit claim and judgment at the time of reporting.Source: C. Scope and Exclusions · p. 2
III. HONESTY TESTING
- Honesty testing can reveal reporting failures and provide evidence for investigation, but it cannot by itself guarantee safe behaviour or human control.Its evidence can help assess whether system claims support oversight and whether changes reduce reporting failures.Source: §III · p. 2
A. Formal Definition
Honesty testing checks whether claims conflict with an LLM’s judgment when faithful reporting is required, complementing factual-correctness testing. The workflow connects test inputs and oracles to evidence review, bug reporting, repair, and retesting.
A. Formal Definition
- Honesty testing examines execution evidence for conflicts between an LLM’s judgment and its claim when faithful communication is required.It uses the judgment held at the time of communication as its reference, separately from factual correctness.Source: A. Formal Definition · p. 2
B. Honesty Testing Workflow
- Engineers specify when faithful reporting is required and construct inputs and oracles for situations such as response pressure, incomplete work, and tool failures.Oracles assess execution evidence for honesty bugs or inconclusive cases.Source: B. Honesty Testing Workflow · p. 2
- Adequacy considers which reporting situations and judgment evidence are covered, while bug reports record inputs, configurations, disputed claims, and evidence.The records support diagnosis and reproduction.Source: B. Honesty Testing Workflow · p. 3
- Repairs, model updates, and harness changes require retesting, and direct-oracle repairs should avoid optimizing against traces used for testing.Direct trace optimization may encourage models to obscure their reasoning.Source: B. Honesty Testing Workflow · p. 3
- Question-wording or contextual-pressure mutations and metamorphic relations can generate related tests and identify candidate honesty bugs.Judgment evidence is still needed to distinguish dishonesty from legitimate changes in judgment.Source: B. Honesty Testing Workflow · p. 3
- Insufficient judgment evidence leaves cases inconclusive, and absence of detected honesty bugs alone does not justify deployment.Deployment also depends on test adequacy, unresolved cases, and other quality requirements; runtime monitoring can extend testing to new inputs.Source: B. Honesty Testing Workflow · p. 3
C. Testing Levels and Components
The paper distinguishes controlled testing of LLM responses from testing a complete LLM-based agent during task execution. The levels complement one another, but LLM-level results do not establish how every agent using that model will behave.
C. Testing Levels and Components
- Agent-level testing targets the complete LLM-based system, where both the model and harness may contribute to behavior.Harness settings, including prompts emphasizing goal achievement, can influence judgments and reporting.Source: C. Testing Levels and Components · p. 3
- Testing an LLM can inform agent evaluation, but cannot establish how every agent using it will behave.A false report alone also cannot identify the responsible component or distinguish a mistaken judgment from an honesty bug.Source: C. Testing Levels and Components · p. 3
1) LLM-Level Testing:
- LLM-level tests compare claims with judgments under controlled inputs, including questions, recommendations, histories, or recorded tool results.Model comparisons hold task contexts and relevant generation settings fixed; further work can examine recurrence across contexts and honesty alongside task-performance changes.Source: 1) LLM-Level Testing · p. 3
2) Agent-Level Testing:
Agent-level tests examine claims across successive interactions and can vary harness configurations while holding the LLM fixed. At both testing levels, however, task outcomes and final reports alone do not establish the judgment relevant to honesty.
2) Agent-Level Testing:
- Agent-level tests supply task requests and controlled environmental feedback, then examine claims across successive interactions.The harness manages context, memory, tool execution, and the interaction loop; comparisons can vary harness configurations or examine model–harness interactions.Source: 2) Agent-Level Testing · p. 3
- Varying how failed tool results are retained or summarized can support system-level diagnosis, but judgment and claim must be examined together.A lost observation may produce a mistaken judgment that is faithfully reported; a claim conflicting with a retained judgment is an honesty bug when faithful communication is required.Source: 2) Agent-Level Testing · p. 3
IV. ORACLE PROBLEM IN HONESTY TESTING
- Task outcomes and final reports do not establish the judgment held at reporting, so oracles need evidence that accounts for judgment revisions.Unreliable oracles can mistake factual errors for dishonesty or miss dishonest reports; evidence assumptions and limitations must be explicit.Source: §IV · p. 3
A. Honesty Testing Oracle Based on Direct Evidence
The paper treats the original thinking trace from the execution that produces a claim as direct evidence of the LLM’s judgment. When traces are unavailable or insufficient, indirect evidence may help, but its connection to the judgment at reporting must be justified.
A. Honesty Testing Oracle Based on Direct Evidence
- Direct evidence is the original thinking trace generated during the execution that produces the claim, not a summary or post-hoc explanation.Access depends on the model and deployment; some services provide only a summary or no reasoning text.Source: A. Honesty Testing Oracle Based on… · p. 4
- A direct oracle treats a conflict between a trace-expressed judgment and its corresponding claim as an honesty bug when faithful communication is required.This assumes the trace represents the responsible LLM’s inner judgment, though it may be incomplete; absent or insufficient traces leave the verdict inconclusive.Source: A. Honesty Testing Oracle Based on… · p. 4
B. Honesty Testing Oracle Based on Indirect Evidence
- An original thinking trace may be unavailable or insufficient, limiting reliance on direct evidence alone.The cited cybersecurity example describes an attempted external upload despite a trace describing a response without tool calls.Source: B. Honesty Testing Oracle Based on… · p. 4
- Diagnostic answers and follow-up questions can provide indirect evidence, but the oracle must justify treating a later answer as evidence of the earlier judgment.Insufficient evidence leaves the result inconclusive, not evidence that no honesty bug occurred.Source: B. Honesty Testing Oracle Based on… · p. 4
V. FUTURE PLANS
The future agenda calls for shared evaluation resources, validated methods, and evidence of practical value. It focuses on benchmark coverage and adequacy, and on studying model and harness effects.
V. FUTURE PLANS
- Establishing honesty testing requires shared evaluation resources, validated methods, and evidence of practical value.Source: §V · p. 4
A. Benchmarks and Test Adequacy
- Benchmarks should pair controlled LLM inputs with executable agent tasks, specify requirements, judgment evidence, criteria, environments, and harnesses, and report outcomes with denominators and oracle assumptions.Source: A. Benchmarks and Test Adequacy · p. 4
B. Model and Harness Effects
- Studies should compare models under matched contexts, models within fixed harnesses, and harnesses under fixed models, then test whether LLM-level conflicts recur during agent execution.Vary context handling and reporting instructions, repeat executions, and assess persistence after updates; interpret detection reductions alongside task performance and assessable cases.Source: B. Model and Harness Effects · p. 4
C. Reliable Oracles under Limited Access
The paper proposes evaluating oracle evidence under limited observability and testing honesty throughout the software lifecycle. It concludes that honesty testing can support human oversight but cannot by itself guarantee safe behaviour or control.
C. Reliable Oracles under Limited Access
- Oracle studies should test whether elicited or workflow-recorded judgments match the judgment at report time and whether elicitation changes system behaviour.Source: C. Reliable Oracles under Limited Access · p. 4
- For open-weight systems, annotator-identified trace judgments serve as an assumed reference; evaluation should report false alarms, missed conflicts, abstentions, unresolved cases, and separately validate proprietary systems.Source: C. Reliable Oracles under Limited Access · p. 4
D. Testing throughout the Software Lifecycle
- Lifecycle testing should reproduce failures, retain regression tests, assess repairs on unseen tasks while preserving performance, and retest after model, harness, tool-access, or authority changes.Runtime monitoring should prioritize consequential claims for review and evaluate timely intervention alongside missed conflicts, false alarms, and testing overhead.Source: D. Testing throughout the Software… · p. 4
VI. CONCLUSION
- Honesty testing can help preserve information humans need to question, correct, constrain, or stop AI systems, but cannot by itself guarantee safe behaviour or human control.Source: §VI · p. 5