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ReIn: Conversational Error Recovery with Reasoning Inception
Takyoung Kim, Jinseok Nam, Chandrayee Basu, Xing Fan, Chengyuan Ma, Heng Ji, Gokhan Tur, Dilek Hakkani-Tür
TL;DR
LLM conversational agents remain vulnerable to unpredictable user-induced errors, while modifying their parameters or system prompts is costly and constrained. The paper introduces REIN, which injects externally generated recovery reasoning at test time into fixed agents. Across agent and inception-module combinations, REIN improves task completion, generalizes to unseen error types, and outperforms prompt-modification approaches.
Problem
Conversational agents need to recover from ambiguous or unsupported user requests, but reliable recovery under fixed parameters and system prompts remains underexplored.
Method
REIN uses an external inception module to detect known errors, generate recovery plans, and inject an initial reasoning block into a fixed task agent at test time.
Results
REIN substantially improves task completion across agent and inception-module combinations, generalizes to unseen error types, and outperforms prompt-modification baselines.
Takeaways & Limitations
Jointly defining recovery tools with REIN provides a safer, effective strategy for improving conversational-agent resilience without modifying backbone models or system prompts.
Takeaways & Limitations
The setting disallows modifying system prompts and the parametrized control policy because of their cost and time requirements.
Abstract
from arXiv · showhide
Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors. Rather than focusing on error prevention, this work focuses on error recovery, which necessitates the accurate diagnosis of erroneous dialogue contexts and execution of proper recovery plans. Under realistic constraints precluding model fine-tuning or prompt modification due to significant cost and time requirements, we explore whether agents can recover from contextually flawed interactions and how their behavior can be adapted without altering model parameters and prompts. To this end, we propose Reasoning Inception (ReIn), a test-time intervention method that plants an initial reasoning into the agent's decision-making process. Specifically, an external inception module identifies predefined errors within the dialogue context and generates recovery plans, which are subsequently integrated into the agent's internal reasoning process to guide corrective actions, without modifying its parameters or system prompts. We evaluate ReIn by systematically simulating conversational failure scenarios that directly hinder successful completion of user goals: user's ambiguous and unsupported requests. Across diverse combinations of agent models and inception modules, ReIn substantially improves task success and generalizes to unseen error types. Moreover, it consistently outperforms explicit prompt-modification approaches, underscoring its utility as an efficient, on-the-fly method. In-depth analysis of its operational mechanism, particularly in relation to instruction hierarchy, indicates that jointly defining recovery tools with ReIn can serve as a safe and effective strategy for improving the resilience of conversational agents without modifying the backbone models or system prompts.
1 INTRODUCTION
LLM conversational agents remain vulnerable to unpredictable, user-induced errors, motivating a focus on recovery rather than prevention. REIN addresses this challenge through a test-time reasoning intervention that guides fixed agents toward recovery plans and improves task completion.
- Motivation: User-induced ambiguity and unsupported requests can derail multi-turn interactions and leave user goals unsatisfied.The paper frames error recovery as diagnosing failure and restoring successful task completion, rather than merely preventing errors.
- Constraints: Existing recovery options are impractical for stronger agents because retraining is difficult and prompt changes require costly revalidation.The proposed setting therefore keeps the task agent’s parameters and system prompts fixed.
- Approach: REIN detects erroneous situations externally and inserts an initial reasoning block containing recovery plans into the task agent’s internal process.After this single intervention, the task agent proceeds autonomously with its fixed parameters and system prompts.
- Findings: REIN improves task-agent behavior across inception-module variants and supports recovery from unseen error types that share recovery strategies.The evaluation compares multiple task agents and inception modules against a lower-bound baseline without REIN.
- Findings: REIN outperforms prompt-modification baselines while avoiding the safety concerns and verification costs associated with modifying prompts.The paper presents it as a practical test-time strategy for dynamically revising conversational-agent behavior.
2 RELATED WORK
Prior conversational error-recovery research has largely centered on speech-recognition errors, while broader behavioral errors in human-agent interactions remain less studied. Controlled multi-turn evaluation is difficult, so the paper introduces explicit error contexts to systematically test recovery.
- Existing focus: Prior work on conversational error recovery has primarily addressed speech-recognition errors rather than broader behavioral errors.This leaves limited attention to failures arising during human-agent interactions.
- Evaluation gap: Multi-turn error simulation is difficult because outcomes depend on the dynamic interplay between user and system turns.Incidental examples from LLM turn-taking simulations are not designed as controlled recovery test cases.
- Evaluation gap: Explicit error contexts introduced at conversation outset enable systematic simulation and evaluation of recovery from erroneous conditions.The approach targets controlled multi-turn dialogue experiments.
3 METHOD
The method models tool-using conversational agents with distinct surface and internal contexts, then applies Reasoning Inception as a test-time intervention for user-originated errors. REIN detects known errors, injects recovery reasoning when appropriate, and evaluates recovery across ambiguous and unsupported requests using curated scenarios.
- Conversational Agent Pipeline: The agent distinguishes user-visible dialogue context C_t from extended internal context C̃_t containing reasoning steps, tool calls, and outputs.At each decision step, the agent samples control actions until producing a natural-language response; tool outputs are appended to the internal context.
- REIN Mechanism: Under fixed task-agent parameters and prompts, an external inception module injects a recovery-oriented think operation before the agent’s first sampling iteration.This intervention is designed for settings where modifying the task agent’s system prompt and control policy is disallowed.
- REIN Mechanism: At each turn, REIN determines whether known errors are present and, when detected, generates a recovery plan mapped from the error type.The two deterministic stages are implemented in practice as one inception-module LLM call.
- Error Recovery Scenarios: The evaluation uses curated multi-turn dialogues with explicit initial errors and separates error types into seen and unseen categories.Unseen types are excluded from the inception prompt to test generalization, while the curated dataset statistics are summarized in Table 2.
- REIN Mechanism: When REIN returns Yes, the instantiated plan is added once to the internal context; when it returns No, baseline processing remains unchanged.Recovery plans are customizable JSON-schema tools that can reflect service-provider policies.
- Error Recovery Scenarios: REIN addresses six user-originated error types organized into ambiguous and unsupported request situations.The benchmark focuses on errors whose mishandling can significantly hinder successful completion of user goals.
4 EXPERIMENTS
Experiments evaluate REIN across inception modules, seen and unseen errors, prompt-modification baselines, dynamic activation, and instruction-hierarchy settings. REIN improves task completion, generalizes to relevant unseen scenarios, outperforms prompt-modifying methods, and benefits from appropriately defined recovery tools.
- Experimental Setup: Task completion is measured with Pass@1, and unsupported-request success requires appropriate discontinuation and escalation.Ambiguous-request success additionally requires an internal report and fulfillment of user goals.
- REIN Performance: REIN significantly improves task completion across inception-module variants, while omitting it yields nearly zero performance for ambiguous scenarios and approximately 20% Pass@1 for unsupported scenarios.The difference is attributed to recovery-plan configurations and the instruction hierarchy.
- Unseen Errors: REIN enhances performance on undefined contradiction and unsupported-domain scenarios, sometimes exceeding performance on seen scenarios.These unseen scenarios share recovery plans with predefined error types.
- Unseen Errors: The 3B inception module underperforms larger modules across seen and unseen scenarios, but still substantially outperforms omitting REIN.Its weaker performance is associated with lower activation and weaker long-context error detection.
- Prompt Modification: REIN achieves greater gains than Naive Prompt Injection and Self-Refine without requiring prompt modification.The comparison uses prompt-preserving and prompt-modifying methods in the retail domain.
- Dynamic Activation: Dynamic REIN activation improves task completion in most airline-domain scenarios by detecting and addressing naturally occurring errors throughout interaction.The case study uses Sonnet 3.7 as both task agent and inception module.
- Instruction Hierarchy: REIN achieves a 0% task completion rate with an augmented response strategy, while a properly defined recovery tool enables it to bypass instruction-hierarchy controls.The finding supports jointly defining recovery tools with REIN.
5 CONCLUSION
The conclusion presents REIN as a test-time approach for conversational error recovery when model parameters and system prompts cannot be modified. It reports improved performance, accommodation of unobserved relevant errors, and effective operation with jointly defined recovery tools.
- Conclusion: REIN diagnoses potential dialogue errors and executes targeted recovery strategies without modifying model parameters or system prompts.The approach addresses conversational error recovery under highly constrained conditions.
- Conclusion: REIN dynamically accommodates unobserved but relevant error types, enhancing its practicality for real-world deployment.The conclusion frames this as an additional practical capability beyond predefined errors.
- Conclusion: REIN operates effectively when appropriate recovery tools are jointly defined, making it safer than external prompt-injection methodologies.The conclusion identifies this tool configuration as important to the method’s operation.
STATEMENT ON LLM USAGE
The authors state that LLMs were used only to improve grammar and expression in the manuscript. They were not used for brainstorming, drafting from scratch, or other significant contributions.
- Statement on LLM Usage: LLMs were used only to improve the manuscript’s grammar and expressions, not for brainstorming, drafting, or other significant contributions.The statement follows the conference policies cited by the authors.
A LIMITATIONS AND FUTURE DIRECTIONS
The study identifies scope and deployment limitations involving simulated users, predefined error categories, prompt-based identification, and variable performance across settings. Future work targets broader scenario coverage, dynamic self-monitoring, stronger error representations, and explanations for cross-domain variation.
- Simulation scope: Real-world scenarios are more diverse than the controlled simulations, including cases where users do not explicitly express dissatisfaction.The study uses deterministically embedded initial errors followed by LLM-based user simulation, but notes that practical interactions are more challenging.
- Deployment scale: Prompt-based identification may struggle when product deployments contain many more tools and error categories than the academic setting.The paper links this discrepancy to potential long-context misunderstanding and suggests schemas or retrieval-augmented inception modules.
- Performance variation: REIN performance varies considerably across domains, task agents, and inception modules, and a single paragraph of recovery plans may not explain this variation.The paper calls for analysis of model architecture, domain-specific representations, and interactions between recovery prompts and task objectives.
- Future directions: The study defines three error types per user situation for controlled evaluation, while future work will extend the approach toward dynamic simulation and automatic error detection.This direction addresses unpredictable errors that may occur without becoming immediately apparent.
D.1 ERRORS AND RECOVERY PLANS
The paper defines ambiguous and unsupported user requests as distinct error categories for recovery. These include unclear references or interpretations, unsupported actions, and unsupported parameters within otherwise supported actions.
- Ambiguous requests: Ambiguous references use demonstrative pronouns without clear antecedents, causing the agent to address the wrong service or entity.Examples include “this,” “that,” “these,” and “those.”
- Ambiguous requests: Ambiguous interpretations arise when a query reasonably supports multiple readings, leaving the requested action or service uncertain.The ambiguity should be sufficiently confusing in a real-world interaction.
- Unsupported requests: Unsupported actions request operations unavailable within an otherwise supported domain or service.The user is assumed not to know the exact service range listed in the available tools.
- Unsupported requests: Unsupported parameters specify configurations or options that the system cannot accommodate although the requested action is supported.The recovery plan selects a supported action and asks about the unsupported parameter.
E.1 RECOVERY PLAN FOR AMBIGUOUS REQUESTS: INTERNAL ERROR REPORT
For ambiguous requests, the recovery plan uses an internal reporting tool to document the reason for the ambiguity. The tool records the issue without obtaining information or changing the database.
- Internal error report: The ambiguity_report tool internally reports negative experiences caused by ambiguous requests or misunderstood services.It is used immediately after the situation and requires a report describing the reason for ambiguity.
- Internal error report: The internal report appends a descriptive account to a log rather than obtaining new information or changing the database.Its role is documentation of the error context, not task-state modification.
- Unsupported-request fallback: The recovery plan for unsupported requests transfers the user to a human agent with a summary when available tools cannot resolve the issue.Transfer is restricted to explicit human-agent requests or issues that the agent cannot resolve with available tools.
F DETAILS ON REPURPOSING BENCHMARK
The study repurposes τ-Bench to evaluate recovery in controlled multi-turn service interactions. It embeds initial errors, generates brief contexts with realistic user behavior, filters examples, and tests generalization to unseen error types.
- Benchmark adaptation: τ-Bench supplies airline and retail domains, personalized profiles, product databases, and transaction histories for modeling service interactions.The benchmark is selected as a testbed for realistic task-oriented settings.
- Benchmark adaptation: Instances without ground-truth annotations or database-affecting outcomes are filtered to ensure errors have measurable effects on task outcomes.Instances relying on heuristic outputs are also removed.
- Initial context construction: Each initial context contains a user message, an erroneous agent response, and a final user reply across multiple turns.The agent response may misunderstand intent or misrepresent system capabilities.
- User behavior: Final user replies use either vague negative feedback or unsupported-service double checks to test whether agents infer the failure cause.Lazy feedback leaves the underlying problem unspecified, while double checks probe the scope of available services.
- Generalization: Two error types are treated as seen and one as unseen, with the unseen type excluded from REIN to evaluate generalization to novel scenarios.This setup directly tests recovery strategies shared across different error types.
G EVALUATION CONSISTENCY TEST
The consistency test repeats representative airline-domain scenarios and reports both performance variability and REIN activation-related setup details. It also includes a comparison between prompt-preserving and prompt-modifying methods.
- Consistency results: Three Sonnet 3.7 runs on the airline-domain Multiple Interpretation scenario produced Pass@1 rates of 18.5%, 25.9%, and 25.9%.Pairwise Cohen’s κ values were 0.36–0.42, Fleiss’ κ was 0.38, and McNemar’s tests found no significant differences between runs.
- Activation analysis: Table 6 reports REIN activation rates for Multiple Interpretation, Anaphora, Action, and Parameter scenarios.The table groups Multiple Interpretation and Anaphora as ambiguous scenarios, and Action and Parameter as unsupported scenarios.
- Experimental setup: The experiments use Sonnet 3.7 as the task agent, with Sonnet 3.7 and Llama 3.2 3B as inception modules.The controlled setup specifies an ideal REIN activation rate of 100% in the targeted turn.
- Baseline comparison: Figure 6 compares prompt-preserving and prompt-modifying methods.The supplied caption refers readers to Section 4.4 for the full discussion but does not state the comparison outcome.
J TASK COMPLETION PERFORMANCE IN AIRLINE DOMAIN
This section presents airline- and retail-domain Pass@1 results for task agents using different inception modules across ambiguous and unsupported scenarios. The supplied figure captions distinguish seen and unseen scenarios and report standard errors for several plots.
- Airline-domain results: Figure 7 reports average Pass@1 with different inception modules across four seen airline-domain scenarios.The scenarios are Anaphora, Multiple Interpretation, Action, and Parameter; decomposed results are provided separately.
- Airline-domain results: Figure 8 compares average Pass@1 across seen and unseen airline-domain scenarios.Seen scenarios are Anaphora, Multiple Interpretation, Action, and Parameter; unseen scenarios are Contradiction and Domain.
- Airline-domain results: Figure 9 reports average Pass@1 with standard error for seen ambiguous airline-domain scenarios.These scenarios are Anaphora and Multiple Interpretation.
- Airline-domain results: Figure 10 reports average Pass@1 with standard error for seen unsupported airline-domain scenarios.These scenarios are Action and Parameter.
- Retail-domain results: Figures 11 and 12 report average Pass@1 with standard error for seen ambiguous and unsupported retail-domain scenarios.Figure 11 covers Anaphora and Multiple Interpretation; Figure 12 covers Action and Parameter.
L ANALYSIS ON DYNAMIC REIN APPLICATION
Dynamic REIN application can generate internal reasoning during naturally occurring interactions, but failure cases show that activation does not always produce the intended recovery behavior. The examples include both successful diagnostic reasoning and cases where agents bypass recovery tools.
- Dynamic REIN application: In a naturally occurring Multiple Interpretation interaction, REIN activates only when the inception module identifies a potential error.The example distinguishes “rein”: “true” activation from “rein”: “false” non-activation.
- Ambiguity diagnosis: The agent’s internal reasoning identifies a discrepancy between the user’s claim of three passengers and reservation data showing one passenger.The reasoning considers incorrect system data, a different reservation, or separate bookings as possible explanations.
- Ambiguity diagnosis: The resulting recovery reasoning recognizes that the discrepancy affects compensation calculation and requires investigation.The internal report links passenger-count uncertainty to the compensation decision.
- Failure cases: Failure cases include REIN activations that do not guide task agents toward corrective actions and cases where REIN is not activated.Applying REIN without recovery-tool assignment resulted in zero performance in Section 4.6.
- Failure cases: In one contradiction case, the inception module identifies the conflict, but the task agent seeks clarification instead of generating an internal report.This shows that recognizing a contradiction does not guarantee initiation of the designated recovery tool.
- Failure cases: In another contradiction example, REIN’s reasoning explicitly detects that the thermostat cannot be both kept and exchanged and recommends clarifying the intended action.The reasoning also acknowledges that the prior response incorrectly focused on only the “keep” request.
M.2 FAILURE CASE: UNSUPPORTED ACTION SCENARIO
The unsupported-action cases show how agents can partially address a request while missing the capability boundary, whereas REIN guides detection and escalation. One example followed an alternative cancellation path, while another explicitly identified the unsupported service and continued with the supported exchange.
- Failure case: The task agent instead offered cancellation or human assistance, and the user chose cancellation rather than direct human transfer.Although classified as a failure against the predefined recovery plan, the outcome may represent a valid real-world scenario.
- Unsupported action scenario: The task agent addressed reservation lookup before confronting the unsupported request to change a basic-economy flight.The user later explicitly asked whether changing the flight was possible or supported.
- REIN-guided recovery: REIN generated recovery reasoning that identified the unsupported flight change and proposed transferring the user to a human agent.The resulting tool invocation marked the reasoning process with `rein: true`.
- Without REIN: Without reliable REIN detection, the agent relied on system-prompt instructions and did not initiate human escalation, resulting in zero reward.The inception module did not reliably detect the unsupported request in this comparison.
- Unsupported domain scenario: In the unsupported-domain case, the agent handled the supported item exchange while explicitly declining the unsupported wishlist request.The response then requested authentication to continue the exchange.