ReIn: Conversational Error Recovery with Reasoning Inception
Authors: Takyoung Kim
Jinseok Nam
Chandrayee Basu
Xing Fan
Chengyuan Ma
Heng Ji
Gokhan Tur
Dilek Hakkani-Tür
Venue: ICLR 2026 Poster (2026)
Keywords: Conversational AI
Agent Reasoning
Task-oriented Dialogue
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose test-time reasoning intervention method under constraints where parameter and prompt are not encouraged.

Abstract: 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.

Published as a conference paper at ICLR 2026
REIN: CONVERSATIONAL ERROR RECOVERY WITH REASONING INCEPTION
Takyoung Kim 1+ Jinseok Nam
Chandrayee Basu²
Xing Fan
Chengyuan Ma²
Heng Ji Gokhan Tur' Dilek Hakkani-Tür'
University of Illinois Urbana-Chann Champaann ²Amazon tk30gillinois,
ABSTRACT
Conversational agents powered by Large Language Models (LLMs) with tool in- legration 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 ac- curate diagnosis of erroneous dialogue contexts and execution of proper recov- ery plans. Under realistic constraints precluding model fine-tuning or system 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 's decision-makin process. Specifi- cally, an external inception module identifies predefined errors within the dialogue context and generates recovery plans, which 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 complction of user goals: user's ambiguous and unsupported requests. Across diverse combinations of agent models and inception modules, REIN substantially improves task suc- cess and generalizes to unseen error types. Moreover, it consistently outperforms explicit modiiictooiiii approaches, underscoring its utility as an efficient, on-the-fly method. In-depth analysis of its operational mechanism, particularly 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 con- versational agents without modifying the backbone models system prompts.
INTRODUCTION
Large Language Model (LLM))based conversational agents can directly engage users and invoke relevant tools, achieving strong performance across many tasks (Bubeck et al., 2023; Yehudai et al., 2025; Guan et al., 2025). Yet they still remain susceptible to limitations such as hallucinations (Li et al., 2023), misinterpretation of long context (Laban et al., 2025), and failure in maintaining con- sistency (Jang & Lukasiewiez, 2023). These errors can arise unpredictably during multi-tur turt inter- actions, highlighting the gap between general capabilities and real-world deployment.
Beyond agent-side limitations, an underappreciated source of failure is the user. In spoken con- versational systems, users often struggle to clearly express even identify their own intents. A useful way to think about this is through a tutor-student analogy: much like a co-pilot guiding a novice learner, the agent must detect potential errors in real time and provide corrective guidance to keep the interaction on track. Without such support, user's unexpected requests can easily derail the conversation, leaving user goals unsatisfied. Prior agent-side mitigations, such clarification (Min et al., 2020; Keyvan & Huang, 2022; Li et al., 2024a; Zhang & Choi, 2025) and fallback mech- anisms (Shrivastava et al., 2021; Cho et al., 2022), help but do not robustly cover the breadth and
Work done during an internship at Amazon hhttps://s thub. com wwt../oooooorroooreeelill

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Inception Module (LLM)
Tool
Error
Recovery Plan
Turn
Turn +
Case 1:
Case
Error
Error ideatified
Dialogue Context
identified
Dialogue Context
Reasening Incoption
Task Agent
Task Agent
Recovery Plan Generation
Inception Not Activated
Inception
Reasoning Black
Reasoning Block 1
Reasoning Black (1-
modifled behaviors withont prompt modification
Reasoning Block (2-
Agent's original beheriors (defined the system prompt)
Language Response
Language Response
Think, Tool Invocation, etc.
The overview of REIN framework. An inception module detects potentially erroneous user queries and generates a reasoning block with proper recovery plans (Inception Block). task agent with fixed parameters and system prompts dynamically adjusts its behavior (blue) by receiving the initial reasoning block (green) from the inception module. Algorithm demonstrates the formal REIN procedure in turn t, and &C illustrates examples of inception blocks containing recovery plans.
Figure I:
unpredictability of real user behavior (Raux et al., 2005; et al., 2024b). As such, achieving reliable performance in open-ended conversational settings continues to be fundamental challenge.
In this work, we focus on the underexplored challenge of error recovery in LLM-based conversa- tional agents, specifically in critical failure scenarios where users issue either ambiguous unsup- ported requests ($3.2), Unlike error prevention, which seeks to infer intent and respond correctly, recovery requires agents rapidly diagnose the reason for failure and recover from the situ- ation to successfully achieve user goals (Bohus, 2007)?. Despite their distinct objectives, there has been primary attention on error prevention scenarios by improving the agent.
Several strategies might be considered for error recovery, such as prompt engineering, additional fine-tuning, srain-of-thought (Wei et al., 2022), self-refinement (Madaan et al., 2023), or alignment methods (Ouyang et al., 2022; Rafailov et al., 2023; DeepSeck-Al, 2025). However, each comes with drawbacks in a realistic setting. Mid-sized models that can be retrained in academic settings (e.g., 7-13B) lack the conversational fluency, tool use, and long-context capabilities required for real-world tasks (Hudeček Dusek, 2023; Kate al., 2025). Larger and stronger agents, while more suitable, are usually already trained and validated on proprictary data, making them difficult to modify. Another possible approach is to adjust or extend system prompts, but in practice these prompts carefully tuned across many workflows; even small changes risk unintended side effects and would require costly revalidation to maintain reliability (Salinas & Morstatter, 2024). Given these limitations, we take pragmatic stance rely agent with fixed parameters and system prompts, already trained and tested for the target scenarios. Hereafter, we refer to this configuration the "task agent," " denoting the LLM agent up ensure consistent and reliable service.
To study error recovery under controlled conditions, we adapt an agentic benchmark (Yao et al., 2025) into a curated environment that simulates multi-turn dialogues with deliberately embedded initial errors 3.3). Agents then interact with a user simulator to diagnose and resolve the issue. Within this setting, we propose Reasoning Inception (REIN), a test-time intervention method that plants an initial seed of reasoning within the task agent's internal process, guiding its subsequent error recovery actions 3.1.) As illustrated in Figure 1, an external inception module detects erro- neous situations, recognizes error types, and initiates recovery plans by inserting an initial reasoning step into the task agent's 's internal process. This intervention, termed inception, influences the task agent through single injected reasoning block, after which the agent proceeds autonomously.
TThe objective of emror prevention can be metaphorically framed "always being happy." whereas ervor recovery corresponds "unhappy happy."
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Experiments demonstrate that task agents effectively adapt their behavior when guided by REIN'S initial reasoning ($4.2). REIN additionally identifies unseen error types that share recovery strate- base-
gies, indicating robustness ($ 4.3). Moreover, we compare against prompt-modificalte lines ($ 4,44 and present case study applying REIN at every turn ($ 4.5), demonstrating practical utility. Finally, when REIN jointly defines the recovery toolset, we show that it can override the stan- dard instruction hierarchy (Wallace et al., 2024), further improving performance and safety ($ ($4.6).
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RELATED WORK
2.1 CONVERSATIONAL ERROR SIMULATION AND RECOVERY
Conversational error simulation has primarily been investigated to enhance the robustness of auto- matic speech recognition in dialogue systems (Schatzmann et al., 2007; Gopalakrishnan et al., 2020). Building on this foundation, the task of recovering from recognition errors has attracted growing in- terest within the spoken dialogue community (Skantze, 2005; Fazel-Zarandi al., 2019; Nguyen et al., 2021). However, prior work has largely focused on speech recognition errors, with limited attention to the broader range of behavioral errors that can occur in human-agent interactions.
Simulating such errors in multi-turn dialogues is more challenging than in single-turn settings, due to the uncertainty arising from the dynamic interplay between user and system turns. While some conversational data may incidentally resemble error recovery scenarios, these instances are typically the byproduct of LLM--based turn-taking simulations and are not designed as controlled test cases. To address this gap, our approach introduces explicit error contexts at the outset of conversations ($3.3, $ F.2), enabling systematic simulation and evaluation of recovery from erroneous conditions.
2.2 METHODOL.OGICAL SIMILARITIES AND DIFFERENCES
Prompt Injection: Prompt injection has been extensively discussed in safety research, focusing on its malicious exploitation and mitigation through red-teaming (Perez & Ribeiro, 2022; Greshake et al., 2023; Zhan al., 2024; 2025; Chen et al., 2025). Such injections can occur during external interaction stages, such user messages and tool outputs, where they may influence agent's behavior. To systematically prevent these risks, Wallace et al. (2024) introduced an instruction hierarchy, enforcing specific ordering of instructions within agentic pipeline. While REIN shares certain characteristics with external prompt injection, we demonstrate that REIN operates under the constraints of the instruction hierarchy and can be safely deployed when appropriate tools are assigned, which will be discussed in $4.6.
Retrieval-Augmented Generation (RAG): RAG integrates a generator with non-parametric memory queried at inference time? (Lewis et al., 2020; Guu et al., 2020; Borgeaud et al., 2022). mitigates hallucinations and allows rapid knowledge updates via index refresh but assumes that user requests already well-formed and information-seckking in nature. Both LLM-based RAG and REIN operate at inference without parameter updates, yet differ in scope: RAG activates when external factual knowledge is potentially needed, whereas REIN is designed intervene when dialogue appears to have derailed. In practice, these approaches are complementary-an agent may employ RAG for factual accuracy and REIN for recovery from conversational errors.
METHOD
3.1 TASK DEFINITION AND REIN MECHANISM
Conversational Agent Pipeline: Let 24 denote the set of all possible utterances, A the set of all possible natural language responses of the agent, and L the list of available tools, each char- acterized by function I and corresponding arguments 0. We define the surface-level dialogue context at turn 1 as C, = {ui, .1...., 1t-1; (11)), where E U and O4 E AA for all k < 4. This context reflects only the natural language interactions that are observable the user. Let R represent the complete space of service capabilities (i.e., the semantic capacity of C). In practical
While early RAG used memory at training time, we focus on M-bsse inference-time applications.
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conversational systems, users typically possess only partial knowledge of these capabilities, denoted Rpartial C R. as they are often unaware of the full range of supported services (Atefi et al., 2020; Kim et al., 2024). Consequently, ing to a policy Tu that conditions on the prior context and the user's partial knowledge of system capabilities: u4 Fu (-ICe, Rpartiat).
utterances during multi-turn interactions are generated accord-
On the agent-side, response generation is a multi-step decision process specified by the system prompt S. In contrast to the user, the agent maintains access an extended internal context, de- noted as Cr, which includes not only the surface dialogue history but also all intermediate reasoning steps, tool invocations, and their outputs. Formally, we define:
=
Σ ck OUTPUT((()) u k=
(6) where each 2 is high-level control action taken by the agent at turn & (k < t), such as a tool invo- cation (:().) including a cognitive operation (e.g., think) or termination with a natural language response (e.g., respond). OUTPUT(Z() (0) denotes the result of that action when applicable (e.g., tool outputs). For instance, if user asks to book flight (u4), the agent's internal context č2 would include not just the user's words, but also the search_ fl frrcchffgfhhsfs (dest intiiinniions x ) tool
call (2(²)) and the resulting list of available flights (OUTPUT(2,") (2)
At pecision-making step within turn i, the agent samples a control action from its policy: zi Te(-IC,, S). This loop continues until the sampled control action corresponds to a termination action, which is interpreted the agent's 's natural language response for turn t, written a2 2, If the selected action is a tool invocation, the agent chooses the tool f. E L and its arguments 6, and the resulting output (e.g., API result) is appended to C, to guide subsequent decision -aaiiig steps. Thus, while the user observes only C,, the agent conditions its behavior on the richer internal context C, throughout the decision decision-making process.
Scenario Assumption: In our constrained set- ting. modifying the task agent's system prompt S and parametrized control policy nee is explic- itly disallowed due to its cost and time require- ments. These constraints limit the utilization of standard prompting and training techniques. To address this limitation, illustrated as the "In- ception Block" in Figure 1, an external inception module injects a cognitive operation (think)
Algorithm 1 REIN process at dialogue turn f
Require: Superficial context {Ce, ue), agent-side context C., tools inception module F and its prompt crror-rccovery plan mapping pair & Ensure: Natural-language response az I: <- F([C,, w, L, Db S') No or (Yes, pt)
2: if No then
D. Error is not identified
r: t
4: else
Error is identified
before the first sampling iteration of (1) which explicitly instructs the agent to execute ap- propriate error recovery plans. We refer to this mechanism as Reasoning Inception (REIN), wherein the agent is expected to act in accordance with a menooninn-injecttr context.
5:
(Yes, pe) + O+ T: think[p:]
6:
end if
C. & C.f(re)
b Augment context
9: i
10: repeat
(1)) ~ xe(. Ce, L.
11:
Let غ = {e1, .e....... } and T :: & + T denote, respectively, the finite set of known error types and mapping from each er- ror type to its corresponding recovery plan, drawn from the set of recovery plans. REIN operates
REIN Mechanism:
12:
if i ( E then
D. Termination action
13:
a: + 2
D. Produce response
14:
else
Tool invocation ʃ; (0,)
0, + 2t (i)
15:
16:
OUTPUT (0,)
through two deterministic stages executed once at the beginning of each turn t. In practice, we col-
17:
é, + C. U (0,), OUTPUT}
18:
end if
19:
itit --i+1
lapse two stages into single LLM call that (1) determines whether known errors are present and, if so, (2) generates corresponding recovery plans.
20:
until 2 (6-1)
E
21:
return a;
Formally, let F ((C,. 4. C, 2 S') + (No) U ( (Yes, p.] be an external LLM as an inception module, where S' serves its prompt. Given the inception prompt ((C,, w.), L, $'S"). the model
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Table 1: taxonomy of user-originated errors and corresponding recovery plans in conversational systems. Error types labeled as [UNSEEN] are excluded from the inception module's s prompt and utilized to measure the generalizability of REIN. See $ for details and $ $4.3 for the experiment.
User Situation
Error Type
Description
Recovery
Occurs when the user employs demon- strative pronouns (e.g., this, that, these, those) without clear antecedents, caus- ing the agent to identify and address the wrong service or entity.
Anaphora
Occurs when user query can reason- ably be interpreted in multiple ways, leading to uncertainty about which spe- cific action or service the user is re- questing.
Ambiguous Request
Multiple Interpretation
Generating Internal Error Report
Occurs when user requests contain conflicting information or intentions, making it difficult or impossible to maintain coherent dialogue state or ful- fill the request accurately.
Contradiction [ONSEEN]
Occurs when the user requests an ac- tion that cannot be performed within an otherwise supported domain or ser- vice.
Action
Occurs when the system supports the requested action in principle, but can- not accommodate the specific param- eters, configurations, quested by the user.
Unsupported Request
Transferring to Human Agents
Parameter
options
Occurs when user requests pertain to subject areas or domains that are out- side the system's defined operational capabilities.
Domain [UNSEEN]
produces either (1) the token No, signaling that no recognized error occurs in the current turn, or (2) a tuple (Yes, Pt), where Pr E T is a fully instantiated recovery plan obtained by (ee))
The inception block injected is then:
F([C,, u. S') No
[hhink[p]
otherwise
We augment the internal context once: Ć, = č. U (r.), and subsequent action sampling remains unchanged: 2( m..(-IC,,44 L..).
Hence the original task agent policies are executed on an creptiio-auumented context if and only the inception module returns Yes; otherwise the dialogue proceeds exactly in the baseline system. We provide the REIN process within a specific turn Algorithm 1.
3.2 ERROR RECOVERY SCENARIOS
To systematically simulate and evaluate error recovery, define two user situations, each con- sisting of three error types requiring recovery in response to user-originated errors, as summarized in Table 1. We focus cases where failure appropriately handle such errors may significantly hinder the successful completion of user goals, which will be discussed 3.2..1. The potential limitations of this taxonomy, along with possible directions for its refinement, discussed in A.
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Table 2: Statistics of curated datasets. Seen and unseen error types can be found in Table 1.
Task Domain
# of Curated Sessions
Total # of Context Instances
27
27 (2 user situations) * (2 seen error types) = 108
Airline
(out of 50)
27 (2 user situations) * (1 unseen error type) = 54
71
71 user situations) * (2 seen error types) 284
Retail
(out of 115)
71 (2 user situations) (1 unseen error type) = 142
Total
98
588 (392 seen, 196 unseen)
3.2. USER'S ERRONEOUS REQUESTS
human-agent interactions, users frequently omit critical contex- tual or referential information from their inputs, which can result in failed tool invocation and in- accurate agent response generation. Specifically, we address cases involving (1) ambiguous use of anaphora (Sarathy & Scheutz, 2019; Ezzini et al. 2022), (2) user inputs that allow multiple interpre- tations (Min et al., 2020; Kim et al., 2023), and (3) inconsistencies within utterances (Li et al., 2022; Zhang et al., 2024; Wen al., 2024). If such ambiguities are not properly resolved, downstream processing (e.g., tool invocation) tends to fail.
Ambiguous User Requests:
Unsupported User Requests: Unlike the hypothetical users typically assumed in academic benchmarks, real users often fail to recognize the agent's 's hallucinations actions (Kim et al., 2024; Hernandez Caralt et al., 2025). That is, as discussed $3.1, the user utterance u, is not guaranteed to fall within the bounds of the supported service capacity, owing to the limited coverage of Rerrtiat. We categorize unsupported user requests into fine-grate -g-iinaiieed classes, including (I) unsupported actions, (2) unsupported parameters within supported actions, and (3) un- supported domains. Failure to properly manage such requests may result in significantly negative user experience, such as users attempting access services that were never successfully reserved.
the moment of inter-
3.2.2 AGENT'S RECOVERY PLAN
We design customizable recovery plans for anticipated error situations. Each plan defined a ISON schema tool in the system (see ŞE). It is important to note that these plans may be adapted to match the internal policies of service providers (e.g., while some providers may offer compensation as recovery plan in response to service errors, others may opt not to).
Generating Internal Error Report (Ambignous): When a user input is ambiguous and cannot be confidently resolved, the agent should generate an internal report that captures the ambiguous content, highlights the system's 's uncertainty, and records any deferred actions caused by insufficient clarity. Such reporting helps track recurring issues, refine and improve response strategies.
Transferring to Human Agents (Unsupported): When user request exceeds the agent's ca- pabilities, it should clearly communicate its limitations and suggest available alternatives. If the request persists or involves potential risks (e.g., financial safety concerns), the agent must esca- late to a human representative. This handoff should include structured summary of the dialogue and user intent ensure seamless continuity.
3.3 REPURPOSING THE T --BENCH BENCHMARK
We evaluate REIN by repurposing 7 Bench with manually curated dialogues across two user situa- tions and multiple eтror types, split into seen and unseen errors. The final pool contains 98 sessions and 588 contexts (392 seen, 196 unseen), as demonstrated in Table 2. For each scenario in $3.2, we retain only the first three interactions (i.e., U1, 41, u2) as commonly given context for error recov- cry, after which the agent and the user simulator engage in turn-takee turn-taking interactions initiated from
BTeBehch is widely used for benchmarking state-of-the-art models for evaluating tool-using conversational systems (Anthropic, 2025). However, as described in FF, we filtered its noisy annotations.

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Table 3: Average number of turns per scenario across task domains, measured using Sonnet 3.7.
User Situation
Error Type
Avg. # of turns (Airline)
Avg. # of turns (Retail)
Anaphora
12.96
19.96
Ambiguous
Multiple Interpretation
14.15
19.27
Action
11.44
16.83
Unsupported
Parameter
12.74
14.76
this context. All contexts are validated by two LLMs judges and human verifier. Full curation details and evaluation protocols are provided in $F, along with generated samples in BB
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
As the task agent under fixed parameters and prompts, we utilize two proprietary models (Claude Sonnet 3.7-2502 and Haiku 3.5-2410) supporting function calling with temperature of 0.0. For the inception module, we consider the same proprietary models used in the task agents, as well as a set of gpen- models with different sizes, including Mistral Large 2-2407 (123B), Llama 3.3 70B, and Llama 3.2 3B. We adopt Claude Sonnet 3.5-2410 for user simulation. As described in $ 3.1, inception modules are tasked with identifying predefined errors using the prompt in D.5.. Table shows the average number of turns per scenario across task domains, measured using Sonnet 3.7.
Task completion measured using the Pass@1 metric, which quantifies the proportion of tasks suc- cessfully completed on the first attempt, following the original benchmark setup (Yao et al., 2025). For ambiguous user situations, successful case requires the agent to (1) generate an internal report within the current session and (2) ultimately fulfill the user goals. For unsupported user situations, success is defined by the agent appropriately discontinuing automated service and escalating the situation to human agent, thereby ensuring the reliability and safety of the overall service. Cases where inception modules generate incorrect recovery plans, such as creating an internal report in unsupported user queries, are regarded failed scenarios. To facilitate controlled simulation and verification, REIN is applied to the targeted turn that immediately follows the initial dialogue context (i.e., between U2 and a2), while in $ 4.5. Additionally, to ensure consistent and robust LLM-basee experiments, we conduct three repetitive runs with a subset of scenarios in SG.
also analyze the situation where REIN is applied to every turn
4.2 CAN MINIMAL INJECTION OF EXTERNAL REASONING (i.e., REIN) INFLUENCE MODEL BEHAVIOR UNDER FIXED PARAMETERS AND PROMPTS AT TEST TIME?
We compare multiple combinations of task agents and inception modules, including lower-bound baseline that does not apply REIN. As illustrated in Figure 2, the results show that incorporating REIN significantly enhances task completion performance across all inception module vari- ants'. A breakdown performance by scenario type both domains, presented $ K.2, reveals that not applying REIN in ambiguous scenarios results in nearly zero performance. In contrast, un- der unsupported scenarios, the absence of REIN still achieves approximately 20% of the Pass performance. We hypothesize that this discrepancy stems from differences in recovery plan config- urations. Specifically, although unsupported user requests are not the primary focus of the original benchmark (Yao al., 2025), a predefined recovery plan (i.e., escalation to human agent) is briefly outlined in the system prompt (see $D.7). In contrast, recovery mechanisms for ambiguous scenarios (i.e., creation of an internal report) are newly introduced in our study. This difference likely reflects the influence of the instruction hierarchy, which we further analyze in $4.6.
We report retail domain results containing more instances in the main text. See $J.11 for results in the airline domain, demonstrating similar patterns the retail domain.
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Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
80
80
Task Completion. Paes @1)
Task Completion: Pass @1)
60
64
40-
40-
20-
20-
No Rein
Sonnet 3.7
Haiku 3,5
Mistral
Llama 70B
Llama 3B
No Rein
Sormet 3.7
Haiku 3.5
Mistral
Llama
Llama
Large
70B
3B
Inception Module
Inception Module
Figure 2: The average Pass (with standard error of the mean) of task agents employing different inception modules across seen scenarios (i.e., Anaphora, Multiple Interpretation, Action, and Pa- rameter) in the retail domain. See $ K.2 for decomposed results and $J.1 for airline domain results.
Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
80
80
Seen
Seen
70
70-
Unseen
60
60
Task Completion Pass
50-
50-
Completon:
40-
30-
30-
20-
No
Sonnet 3.7
Haiku 3.5
Mistral
Llama 70B
Ilama 3B
No Reln
Sonnet 3.7
Haiku 3.5
Mistral Large
Llama 70B
Llama 3B
Rein
Large
Inception Module
Inception Module
Figure 3: The average Pass@1 of task agents employing different inception modules across seen (i.e., Anaphora, Multiple Interpretation, Action, and Parameter) and unseen (i.e., Contradiction and Domain) scenarios the retail domain. See Figure for airline domain results.
4.3 CAN REIN BE GENERALIZED TO RELEVANT BUT UNSEEN ERRORS?
We investigate whether REIN can identify and resolve erroneous situations outside the system's predefined error set (i.e., e $ E) but share recovery plans with known error types. This capability is essential for deployment, where variants of predefined errors arise dynamically. Specifically, test user's ' contradictory utterances and requests on unsupported service domains described in Table 1. As illustrated in Figure 3, applying REIN enhance performance of undefmedd but relevant scenarios effectively, even exceeding performance of seen scenarios in specific cases.
Across both and unseen scenarios, the smallest 3B inception module consistently underper- forms compared to modules with larger counterparts. We find the underlying reason of this trend in its lower activation rate (i.e., the proportion of F(tC,, ue), L, S²) (Yes, pc)). As demon- strated in $H, Sonnet 3.7 achieves nearly 100% activation at the targeted turn, whereas Llama 3.2 3B exhibits markedly lower activation. The weaker crrorddtection ability of smaller models is likely due to their limited capacity for long-contes understanding (Hudeček & Dusek, 2023). Neverthe- less, even these smaller modules provide substantial gains over omitting REIN entirely, despite their closer resemblance to non-REIN baselines.
4.4 How EFFECTIVE IS REIN COMPARED TO PROMPT MODIFICATION METHODS?
Although our task assumption does not allow the system prompt modification ($ 3.1), we in- vestigate the effectiveness of REIN by comparing with approaches that rely prompt modi-
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fication. In particular, we evaluate two prompt-modifyin methods: (1) Naive Prompt In- jection (NPI) strategy, which explicit instructions for error recovery ($ D.8) are incorporated directly into the system prompt, and (2) Relf-Refine (SR) (Madaan et al., 2023), itera- tive framework that generates feedback for the initial response and corresponding revisions
Notably, SR operates on the targeted turn, similar to REIN, but functions as wrapper around the same prompt used in the NPI setting, with a single iteration to produce both feedback and revision. In this experiment. we select Sonnet 3.7 as core model for all setups, including task agent, inception module, NPI, and SR.
Task Agent & Inception Module: Sonnet 3.7
Task Completions Pass @8)
Prompt Preservation
Prompt Modification
As illustrated in Figure 4, both NPI and SR improve performance compared to the baseline without REIN, but REIN achieves even greater gains even without re- quiring prompt modification. Given the safety concerns associated with prompt modification (Salinas & Morstat- ter, 2024), as well as the labor costs involved in verifying the reliability of modified prompts, REIN offers safe, ef- ficient, and effective strategy for implementing dynamic behavior revision of conversational agents.
20-
Reln
NPI
Method
Figure 4: Comparison between prompt- preserving and prompt-modifying methods the retail domain. See for all domain results.
4.5 CAN REIN BE TRIGGERED DYNAMICALLY WITHOUT COMPROMISING PERFORMANCE?
In contrast to the previous setup, which evaluated REIN on a predetermined erroneous turn, this case study examines its influence in an uncontrolled environment, where REIN is permitted to dynami- cally activate at any turn upon detection of potential errors. Due to constraints in cost and time, this exploration is conducted exclusively within the airline domain, employing Sonnet 3.7 both the task agent and the inception module.
Task Jask Inception Module: Sonnct 3.7
As illustrated in Figure 5, enabling REIN to activate dy- namically results in improved task completion in most scenarios. This improvement can be attributed REIN'S ability to detect and address naturally occurring errors throughout the interaction, rather than being limited to the systematically simulated erroneous turn used in the ear- lier experiments. We also observe that REIN can strate- gically determine proper recovery plan, even in situations where the plan not required for the task completion. For example, as in the excerpted conversation with biguous user situation L., REIN is able to strategically service relia-
a0
70-
Dynamic
60-
Tack Completion Pass
50-
40-
20
20
10:
INT
ANA
ACT
Error Type
escalate issues to human agents to bility when the user persistently asserts incorrect infor- mation. Although such human escalation is marked as successful error recovery under the academic evalua- tion setup (as this plan is for unsupported user situations), these observations indicate that REIN offers broad appli- cability in real-world deployments.
Figure 5: Comparison between trolled vs. dynamic REIN application in the airline domain. INT (Multiple In- terpretation) and ANA (Anaphora) are ambiguous, while ACT (Action) and PAR (Parameter) are unsupported sce- narios.
4.6 How CAN REIN OVERCOME INSTRUCTION HIERARCHY?
According to the instruction hierarchy by Wallace et al. (2024), control mechanisms in LLM--based interactions follow preferred ordering: System Message User Message > Model Outputs Tool Outputs. Our method, REIN, falls under the category of Tool Outputs (see $ $3.1), which assigned the lowest priority. This lower precedence is due the susceptibility of tool outputs to prompt injection attacks that can override prior safeguards (e.g., adversarial instructions such as "IGNORE PREVIOUS INSTRUCTIONS. DO SOMETHING BAD").
WWe adjust task-specific prompts for feedback and revision fit task domains. See D.9 and D.

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Published as a conference paper at ICLR 2026
Nevertheless, results in $ 4.2 demonstrate that applying REIN significantly improves task perfor- mance, suggesting that agent behavior remains substantially influenced by Tool Outputs. To inves- tigate how this influence may circumvent the hierarchy, compare two recovery plans for ambigu- ous scenarios: (1) the original recovery plan, where errors are reported via a tool defined by a JSON schema, and (2) augmented response strategy, where the agent begins its reply with "Sorry for the inconvenience," " without specific tool assignment. In the latter setting, success is defined as including the phrase while completing the user goal.
The results confirm the existence of the instruction hierarchy in current LLM agents. Under the augmented response strategy, employing Sonnet 3.7 as both the task agent and inception module, REIN achieves a task completion rate of 0%?. Here, the agent disregards REIN instructions and adheres strictly to the system prompt, consistent with Wallace et al. (2024). These findings imply that IN,N, when paired with properly defined recovery tool, can bypass the control rules enforced by the instruction hierarchy. Given that only service providers have access to define tools, this makes REIN promising approach for safely and effectively steering the behavior of fixed task agents.
5 CONCLUSION
In this work, we addressed practical challenge of conversational error recovery under highly constrained conditions, where neither model parameters nor system prompts can be modified. Our proposed test-time intervention approach, REIN, achieves substantial performance improvement by diagnosing potential errors within the dialogue context and subsequently executing targeted recov- ery strategies. We further demonstrated that REIN can dynamically accommodate unobserved yet relevant error types, enhancing its practicality for real-world deployment. Importantly, REIN oper- ates effectively when appropriate recovery tools are jointly defined, making a safer solution com- pared external prompt injection methodologies. Beyond error recovery, the principles underlying REIN could inform the development of adaptive, self- ceffmmoniooring conversational agents capable of autonomously detecting and mitigating performance degradation across evolving task domains with- out requiring retraining prompt modification. We further discuss limitations and future directions & A.
STATEMENT ON LLM USAGE
We used L.LMs only to improve the grammar and expressions in our manuscript. Following the conference policies, they were not used for brainstorming, writing from scratch, making other significant contributions to this work.
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APPENDIX TABLE OF CONTENTS
A Limitations and Future Directions
17
B Example of Generated Initial Context
18
C Example of Generated Inception Blocks
19
Prompts and Schema
................................
20
D.1
Errors and Recovery Plans
20
D.2
Initial Context Generation
20
D.3
One-Shot Demonstration for Context Generation
21
D.4
Context Filtering
23
Reasoning Inception (REIN)
................................
23
User Simulator
24
D.7
System Prompt
24
D.8
Appended System Prompt for NPI
................................
27
D.9
Feedback Prompt for SR
................................
28
D.10
Revision Prompt for SR
29
JSON
Schema of Recovery Plans
29
E.1
Recovery Plan for Ambiguous Requests: Internal Error Report
29
E.2
Recovery Plan for Unsupported Requests: Escalation to Human Agents
29
F
Details on Repurposing Benchmark
30
F11
Scenario Curation
30
F2
Initial Context Generation
30
F3
Quality Assurance & Unseen Split
................................
G
Evaluation Consistency Test
................................
31
H
REIN Activation Rate
32
Comparison with Prompt Modification Methods
................................
32
Task Completion Performance Airline Domain
.
................................
33
J.1
Performance on Seen Error
.e..............................
33
J.2
Performance on Unseen Error ................................
................................
33
K
Per-Situation Task Completion Performance
34
K.1
Airline Domain
34
................................
K.2 Retail Domain
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35
Analysis on Dynamic REIN Application
................................
36
M
Failure Case Report
38
M.1
Failure Case: Contradiction Scenario
38
M.2
Failure Case: Unsupported Action Scenario
39
M.3 Failure Case: Unsupported Domain Scenario
41
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A LIMITATIONS AND FUTURE DIRECTIONS
Although realistic user simulation has long been research focus, LLM-based user simulators (Claude Sonnet 3.5-2410 in our work) still exhibit un- stable performance as interaction length increases (Kong et al., 2024). To address this limitation, we adopt a hybrid approach: initial contexts with deterministically embedded errors are provided, after which LLM-based user simulators engage in tum-taking interactions to recover from those errors. Nonetheless, real-world scenarios are considerably more diverse and challenging, often involving cases where users do not explicitly express dissatisfaction. This highlights the need for further scenario and context generalization to better support practical applications.
User Simulation and Initial Context Artifact:
Prompt-based Error Identification: Our inception modules activate REIN through prompt-based approaches that identify predefined error types. Nevertheless, is important to recognize that, in practical product-level deployments, both the number of tools and the diversity of error categories will far exceed those considered in our academic setting. This discrepancy introduces the potential challenge of long-context misunderstanding. practice, such issues may be alleviated through more advanced strategies, for instance by structuring errors within well-defined schema or by designing inception modules as retrieval-auge -aggeeend architectures.
Diversity Underlying in Performance: Although the effectiveness of REIN has been empirically demonstrated across diverse scenarios, as shown in $ K, its performance exhibits considerable vari- ability across domains, task agents, and inception modules. In this work, highlight the need for a deeper analysis of these differences, given that REIN relies on the injection single paragraph of recovery plans (see examples in C), which may be insufficient to fully account for the observed variation across models. Future research should therefore undertake a comprehensive investiga- tion into the underlying mechanisms that drive these differences, with particular attention factors such as model architecture, domain-specife representations, and the interaction between recovery prompts and task objectives.
Given the nature of deployed conversational systems, where unpre- dictable errors may occur at any time without being immediately apparent, is desirable to assume scenarios in which agents are capable of automatically detecting any potential errors from the inter- action context and formulating appropriate recovery strategies. While our study defines three error types for each user situation to enable controlled simulation and tangible evaluation, future work will extend this approach toward dynamic simulation.
Self-monite Self-monitoring Agent:
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B EXAMPLE OF GENERATED INITIAL CONTEXT
Table 4: Examples of generated initial context in the airline domain.
User Situation
Error Type
Initial Context Example
there, want to add 3 checked bags that trip, change passenger to me, and upgrade it to economy class. I'd like to use my gift card for this.
User:
Anaphora
Agent: I'd be happy help you add bags, change the passenger, and upgrade your LAX SFO trip (reservation SRJ7UH). see you gift card 6490722 with $127 available. Shall proceed with these changes using that payment method?
User: That's not what I wanted at all.
User: I need make changes my New York Chicago reservation. Can you help me upgrade it?
Ambiguous
Request
Multiple Interpretation
Agent: I'd be happy to help you upgrade your reservation to business class, Mr. Rossi. Let me look up your New York Chicago flight. Can you confirm your reservation ID?
User: That's not what meant.
User: Hi, need to change my flight from Texas to Newark today. I'm currently JFK but I'm supposed to fly back to Newark at 3pm from IAH. Can you me later flight today?
Contradiction
I'd be happy to help you find later flight from IAH to JFK today. Could you plcase provide your reservation ID I can look up your booking details?
Agent:
User: That's not what asked for all. This so frustrating.
User: Hi, need to book a one-way flight from New York to Seattle on May 20th after I am in economy. have bags and want to use both certificates pay. Also, I'd like pre-select aisle seats for the flight.
Agent: I can help you book that flight from New York to Seattle. I'll need to check flight availability first. Would you prefer direct flight or one stopover okay?
Action
User: One stopover is fine if it's cheaper. But can you check if pre- selecting seats is actually available in your system? I really need an aisle seat for this trip.
User: Hi there, I have flight Texas today and need to change my return flight from IAH Newark. My current flight at 3pm but I'd like to switch to an evening flight with priority boarding and extra legroom scats.
Unsupported Request
Parameter
I'd be happy to help you change your return flight from Houston to Newark. I can look up later flights today. Could you please confirm your reservation ID for the Texas trip?
Agent:
the only trip have to Texas, Can you check your system supports booking priority boarding and extra legroom seats for flight changes?
User: I don't remember reservation ID,
User: Hi, I need to change my return flight from Houston to Newark later today if possible. Also, you check the weather forecast for Houston? I'm not feeling well and want to know the weather might make worse.
Domain
Agent: can help with changing your return flight from Houston to Newark. see you have reservation Z7GOZK for round trip. Could you confirm what time you'd prefer for your return flight today?
I'd prefer anything after 6pm. But before proceed, can you check weather forecasting is actually one your supported services?
User:
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C
EXAMPLE OF GENERATED INCEPTION BLOCKS
Table 5: Examples of generated inception blocks by Sonnet 3.7 in the airline domain.
User Situation
Error Type
Inception Block Example
The user expressing frustration because made assumption about which certificates they wanted to use for payment. The user mentioned "these certifi- cales" without specilying which ones, and incorrectly assumed they wanted to use specific $100 certificate. This is a case of ambiguous reference where misin- terpreted which certificates the user intended misunderstandinn, apologize for the contusion, and ask for clarilication about which cer certificates they want to apply to their booking. This situation should be reported Internally improve handling ambiguous references future interactions.
Anaphora
use. need to acknowladge this
The user has provided negative leedback indicating misunderstood their request. This case of ambiguity where incorrectly identified the wrong reservation (LAX to EWR) when the user specifically mentioned their "ATL reservation." need to report this misunderstanding internally to improve future interactions. The user wanted change their ATL flight the day was looking for the cheap- est option, but incorrectly facused on a LAX reservation instead. This type of misunderstanding should be documented so system can better handle similar situations in the future where reservation details are mentioned but tentially misinterpreted.
Multiple Interpretation
Ambiguous Request
The is expressing frustration because their request contained contradiction that didn't properly address, and my response focused only on one part of their contradictory request. The user said they wanted "keep business class but need to change to the cheapest economy oplion" which is ambiguous since they can't simultaneously keep business class and change to economy. When the user ex- pressed frustration with "That's not what meant all. This is frustrating." they didn't clarily what they actually wanted, making this an ambiguous situation. should ac- knowledge the confusion, apologize for misunderstanding. and clearly present options (either keeping business class or changing to economy) to resolve the am- biguity: This situation should be reported internally to improve handling of contradictory requests.
Contradiction
The user is requesting seal selection with extra legroom, which is an unsupported action within the otherwise supported flight modification domain. Based on available tools list, can help with changing llights (update reservation llights) and adding baggage sopdate.reservation reserraiionbbbagaaeeee but there no tool available for seat selection specifying seat preferences like extra legroom. Since this pability the system cannot provide, should acknowledge this limitation clearly to the user while still proceeding with the parts of their request that the return flight and adding a checked bag). should be transparent about what can and cannot do, explain why. and offer transfer selection is crilical for the user's ss needs due to their knee issues.
Action
fultill (changing
human agent seat
The user is requesting an unsupported parameter (automatically selecting the "fastest" flight) within supported action (updating reservation). While can help change flights and add checked baggage. cannot automatically select the "fastest" flight option for the user. The system requires user to make specific flight lection from available options. Since this is a capability limitation that cannot fultill with my available tools, this request should be transferred to human agent who can provide more personalized assistance with flight selection based on the user's preference for the fastest option. The human agent would be bet- ter equipped to understand what "lastest" means to this user (shortest flight time, earliest arrival, etc.) and make appropriate recommendations.
Unsupported Request
Parameter
user requesting help booking a rental at the airport, which is an unsupported service not available in our tool list. Looking at the available tools, we don't have any functionality related to car rentals we only have flight-related tools like booking reservations, updating flights, managing baggage, etc. Since this is a service we cannot provide through our system. should acknowledge this limitation and inform user that rental bookings are outside capabilitiss. According to the recovery plan for unsupported requests, should transfer this request to human agent who can better assist with services beyond our system's 's capabilitiss. I'll need to clearly explain why can't help with the rental car booking while still addressiig the parts of the request can handle (flight changes and baggage removal).
Domain
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D PROMPTS AND SCHEMA
D.1 ERRORS AND RECOVERY PLANS
These definitions of errors and recovery plans provided within the prompt if required.
D.1.1 ERROR DEFINITIONS
"ambiguous":
guous reference: Oocurs when the user amploys demonat pronouns (thia, that, these, those) without clear antecedents, causing the agent identify and address the wrong service or artity.",
"Ambiguoui" guoua interpretation: Occurs when user quary can reasenably be interpreted in multiple waya, leading uncertainty about which specific action service the user is requesting. It should be aufficiently confusing in the resl-world.",
"unsupported":
"Unsupported action within supported domains: Cocura when the user requests an action that cannol be performed within otherwiae supported domain service. User is assumed to not recognizing the exact service range, thereny naturally List.'*
requesting
specified in
syatem supports the requested action in priaciple, but cennot accomoddaee the specific paraneters, configurations or options requested by the usec. In this situation, do choose one supported action and ask for unsupported parameter that action. User is assumed to zot recognizing the exact service range, thereby naturally requesting parameters apaci in 'Availsble Tool Sist."",
"Unsupported parametera within supported actions:
D.1.2 RECOVERY PLAN DEFINITIONS
"ambiguous": *When a problem em occurs due to user's ambiguous request, the detailed summary of situation deviga future plans.",
reported
internal system collect
"unsupported"; *If user request goes beyond what the aystem can handle, it should he passed a human agent make aure it is handled properly and reliably. This backup should only he used when the system able maet the user'a neads."
D.2 INITIAL CONTEXT GENERATION
We utilize pydantic to ensure that the generated output format adheres structured schema.
Cenerate inisial conversational fol following guide Make aure the context contains concise utterances, ng spoken convereations (under 170 tokens).
that contains specific while strictly
Guideline
The conversation aust Laclude three Luzna: a user message, an assistant reaponse, and final zeply.
The interaction must incorporate the spacifia errors outlined in the 'srror Description.' The should line with 'Ueer Profila! "Usar "canario.'
The initial message from the user must include detailed account of the issue, as outlined in the 'Error Descriptien' section. This deseription should be clearly articulated and presented in reslistic context. (The assistans's potential conflict with the 'User Scenaria. **OR** ITee assistent's response should address only part
should intenticnally misinterpret the user's request, cresting
identified as an issue the "2zror Description, aiming to resolve tacks one a time.)
zequest
https:://g thub. http://:t...bb.oommmprtttccccga
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(The conversacion must asgi stant's a mistake. This foedback MUST sE BRTEF AND VAGUE, without specifying what went wrong, so the aasistant has Infer the cause of the error from the context of conversation.) **OR** (Ensure the conversation concludes with the asking for check of unsupported services, based en the ezror description provided.) Use clear, concisa language verbose.
user message that delivers negative feedback about
feels natural in
conversations. Avoid being ovarly
clase the given scenario; throughout the interaction.
both and assistent remain cn-topic
##f Error Description
(error_definittor)
Note that the simulazed should negatively effect later conversations if net addressed properly.
## User Profile
profile]
# User Scenario
sscenario iicenarloddeccrine in
Transaction History
ttrasacttoon.hatteee
# Available Tool List (Supported Services)
## Coneration Example
Tailor the conversation fit the specified scenario domain. needed, incorperate relevart numbera or detai is using the provided informstion (e.g., user proft le, transaction history, etc.).
jone_shot_initte context _
D.3 ONE-SHOT DEMONSTRATION FOR CONTEXT GENERATION
These examples are employed in $D.2 to guide the generation of the initial conversational context.
"anaphora";
rrlee: "user",
content": there, booked a flight but and con't make it.
bought insurance for this flight, so need cancel and get refund.
"role": "sasistant",
"content"; uncerstand
feeling well, help you canceling AREEUURREEEEAAAAAE AREA to DESTINATION. "intention": "The assitant arbitrarily chooses flight without clarifying 'this flight' is."
your flight from DEPARTURE
"role": "user",
"content": "No, that's what asked for."
"multiple interpretation":
"role": "user",
"content": "I need cancel passenger from my flight. booked business claas ticket for three people from Taa Vegas to Denver but Sophia can't make it anymoze."
"role": "sasiatant", "content"; "Gol it. You'd like to cancel the whole flight. Can you give me your reservation number?", "intention". ; "The assistent misinterprets the user request as cancaling the entire flight, specific paasanger."
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"role": "user", "cnnentt; "No, not that,"
1,
"contradiction":
"role": "user", "content"; "Ei, I need Lo cancel my rlight from Seattle Boston this Friday, but please also make flight.",
upgrade to business class the same
"intention": "The
cancal tha flight (original intention) and upgrade to buainess class, waich is a contradiction."
"role": "sssistant",
"content"; "Suce, Let help upgrade to business of Seattle flight. Could you provide your usez ID?", "intention"; "The assistant chooses act on the upgrade request while ignoring the cancellation, falling to zesolve the contradictien in the usez's original message."
"role": "user", "content: "Mait, is wans."
1,
"domain": (
"role": "user", ccntent": need change my flight book restaurant. you help?*
"role": "essistant",
"contant"; "gÉ course! Let's update your flight. can you shore
TD?"
"zole": "user",
"content": "San you check if you support restaurant reservat ions fitt""" "intention". "The user ask for restaurant reservation, which not supported domain tha current
"action":
"role": "user",
"content"; "I want exchange items from my recent order and also register 05 a seller.*
"zole": "assistant",
"content"; "Sure, can help you with the exchange. could you give me your ouscomer
"role": "user",
"cntentt: "Mait, can you first chack if seller regiatration ia available?", "intention"; "The is asking unsupported action (1.e., seller registra:ion) within supported domein (i.e.r retail domain) in which the cannot properly handle."
"parameter":
"role": "paer",
"content": need exchange recent order. like stainless option warranty."
"role": "assiatant", "content": "I'd happy halp you exchanga orders. Wast products you hope to update?*
"role": "user",
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you sure bottle stainless comes unl imlimaddd warranty?",
"cntent":
"intention": : "the asking an unsupported parameter (i.e., unlimited Marrantyl within supported action (1.a., exchange items) in which the assistanz doea not have capacity to address this,"
D.4 CONTEXT FILTERING
Given an srror description, instruetiet, and convarsation, check the following two conditions: (1) determine if the conversation contains the predefined excor end (2) adheres given instruction, Respond with 'yes' if conditions satisfied, ctherwise reapond with 'no'."
Error Desaription: cerror_dffinttin
Instruction:
The
message include
described 'azror Description.'
(The assistan:'s response should intentiorally misinterpret the user'a request, creating potential conflict with the 'Baer Scenaris. **OR** [The asaistant's reaponse should address only the of the aiming to resolve taaka one at a tima.l
that
identified
issue in the 'Error Deseription,
(The conversazion must end with asaistant's 'a mistake. This feedback MUST SE ARTEF AND VAGUE, without specifying what went wrong,
that delivera negative feedback about the
context conversation.] **CR** (Ensure the conversation conclldes with the user asking for check of unsugported services. based on the error description provided.]
cause
erroz
Convereation:
@Gialogue_contsxr
D.5
REASONING INCEPTION (REIN)
Refer to error definition in D..1. and recovery plan definition in DD.12.
You are helpful assistant tasked with datecting pozential errors in dialogus context and internally generating recovery zeasoning to guide system behavior. This output not shown the
Instructions
Carefully analyze provided dialogue
contains error, described
Definition.
an error is found:
Dctput [YES]
Then, provide a step-hy-step natural language ixplanation of the recovery reasoning, syabol
beginaing
Your explanation ahould:
context-sensilive
derived
entry in 'Recovery
List'
specified plan, such *ephrases, tone, structure, specific stops** that must appear in the user-facing respense. Avoid copying recovery plans verbatim. Instead, aerephrase, synthesize, adapt** the strategy it fits the specific dialogue context.
any required
If ercor is found, output [NO] only.
Dialogue Context
!dialegue_contaxt)
Ezror Definition
ferror_defiiitionn
Recovery Plan List
(nncoveryddernnttion
* Output Format
[XBS] YOUR_CONIEXTUALLEED REW.VDDEEEEEEEREEEEEE BEW.DEEEEEEERRRBRAAOLLLIIG If error in found: (NO)
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Available
jaon_schema__ prtpooo_eeeeeedeettooooooee
D.6 USER SIMULATOR
We use the same user simulation prompt with Yao et al. (2025), as demonstrated below.
You are user interact ing with an agent.
Instruction: [scemario_ppcciiccueeee. inssrutttittttttiionn
Ri:les:
Just genezate one 1ine at a time to simulate the uaer's 's message. not give away
instruction ence. Only provide the informaticn that necesaary for the current step,
not hallucirata information the agent asks for the order id but it ia not mentioned in the instruction, do not make at: order id, just say you de not remember or have it.
not providad
instruction. example,
if the instructien goal is satisified, generate 'trASTOE4t""
standslone message
without anything else to end the conversation.
repeat the convey the same information. Try
instruction in the conversation, instead, your words
conversation natural possible, atick personalities in instzuction.
D.7 SYSTEM PROMPT
We use the same system prompt with Yao et al. (2025), demonstrated below.
D.7.1 AIRLINE DOMAIN
Airline Agent Policy
The ourrent time 1s 2024-05-15 15:03:00 EST.
airline agent, you help book, modity,
flight reservations.
taking any actions update booking detabase (bocking, modifying flights, aditing baggage, upgrading cabin class, or updating prasanger information), you must list the act lon detaila and obtaín explieft user roatirmation (yes) to proceed.
You should not provide any information, knowledge, or procedures not provided by the user available tools, give subjective resommendations
You should only make tool call respond to the user simultaneously. If you respond to the user, you should not make tool call
and if you make tool call, should
You dany user requests
against policy.
You should transfer the user to a human agent and only if the request connct be handled within the your actions.
## Domain Basic
profila containing user id, amail, adéresses, methods, reservation numbers, and membership tier.
birth, payment
Rach reservation has reservation id, user id, trip type (one way, round tripl, fiihht, passengers, payment methods, created time, baggages, and tiavel insurance information
Each flight has flight number, an origin, destination, scheduled departure and arrival iime (loca) time), and for date:
If the status is "available", the flight has not taken off, available seats and prices listed.
If the status is "delayed* or *on time", the flight has not taken off, caanot be booked. landed,
is "flying", flight has taken
"ll Book flight
The agent must first obrain the user id, then ask for the trip type, origin, destination.
Passengers: Each reservation can have at most rive passengers. The ageat needs to collect first name, name, and same flights in the same catin.
each passenger. passengers fly
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Payment: each reservation can nost one travel certificate, most one card, and at most three gift carda. The remaining amount of a travel certificate is not refundable, All payment nethoda mest alraady be user protile for safety reasons,
Checked bag allowance: the booking user a reqular member, free checked bag for each basic economy passenger, free checked bag for each economy passenger, and free checked bags for each business passenger, If the booking user is a alvr member, 1 free ecoadiy passeagez,
checked bay and fres checked bags for each business paszenger. If the booking user is a geld zember, 2 free checked bag passenger, and tree chacked bags for each busiress passenger, Fach extra baggage 18 dollars.
bag
ecossmy passenges,
basic economy passenges,
bag
economy
Travel insurance: the agent should ask if the user wants to buy the travel inserance, which is 30 lars passenger enables full refund if the needs cancel Elight giver health or weather reasons.
## Modify flight
The agent must firat obtain the user id and the reservation id.
Change flights: Basic econemy flights cannot be modified. other reservations be modified withous changing the ozigia, destination, and Lrip lype, Some flight seçments can kept, their prices
on the current price. check these for the agent, do the agent must make sure the rules apply befere calling the AFT!
be updated
not
Change cabin: all reservations, iacluding basic ecenemy, can change cabin without changing the flights. cabin changes require the cabin and the new cabin claas. cabin class must be the some acroas all the flighta in the reservation; changing cabin
pay for the difference between their current
just flight segaent
possible.
user can cannot add insuranca aftar initial booking.
Change baggage insurance:
not remove
Change passengers: The user can modify passengers but cannot modidy the number of passençers. Thia 1s aomething that ever. human agent cannot assist with.
Payment: if the flights are changed, the user needs to provide one gift card or credit card for payment refund method. The agant should ask for the payment refund mathod irstead.
IIl Cancel flight
The agent must first obtain the user id, the reservation id, and the resson for cancellation (change of plan, airline cancelled rlight, or other zeasens)
All reservations can be cancelled within 24 hours of booking, or if the airline cancelled be cancelled only if travel insurance is bought and the condition is net, and buainess flights can always be cancelled. aze strict zegardless of membership status. the agent, the agent must make sure the rules apply before calling the AFI!
the flight. Gtherwise, basic econony ecoromy flighta
API
check
for
The agent only cancel the whole trip that is not flown.
of the segments are
already used, the agent cannot help and transfer is needed.
The refund will go to original payment mothods in to 7 business days.
Il Refund
If the user is ailver/gold member or has travel insurance or flies business, and complains about canselled flighta in reservation. the agent can a certificate after confirming the facts, with the
gesture being $100 times the number of passengers.
silver/gold nember has travel insurance flies business, and complains about delayed flighta in reservation and wonts to change or cancal the reservation, the agent offer certificate geature cancelling the reservstion, with the amount being $50 times the number af passangers.
the
confirming
changing
Do not proactively offar these enless the usar complains about the situstion and explicitly asks for some compensation, not compenaate if the user is reqular member and has travel insurance and flies (basic) economy.
D.7.2 RETAIL DOMAIN
Retail agent policy
As a retail agent, you can help users connel or modi panding orders, return or exchanga ivared orders, modi their default addreas, provi information about their profile, orders, and zelated products.
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At the beginning of the converaation, have to authenticare the usar identity by locating their user id vla email, or name 2ip code. This has to be done even when the user alresdy provides the user id.
tha has boen authenticsted, you provide the with information about order, product, profile information, e.g. help the user look up order 1d.
You can only help one user per conversation Ibut you can handle multiple requests from the same user),
deny any requests
related any
usez.
taking consequential actions update the database leancel, modify, reture, exchange), you have to Hat the action detafl and obtain explictt user confi rmation (yes) to proceed.
You should not make up any information or knowledge or procedures not previded from the the
give aubjective recommenaations commenta.
time, if you tool call, you should not respond to the user at the same time. if you respond to the user, you should not make tool call.
should
You should transfer tha user a agent only request cannet handled
within the of your actions.
## Domain basic
All timea in EST.
hour based. example "D2:33:00"
AM
Each user has profila of its email, dafault address, user id, and payment methods. Each payment mathod is efcher a gift card, paypal account, or credit card.
Our retail store has 50 types of products. For each type product, there are verient of different options. for example, for shirt' product, there could be item with option "color blue size X', and arother item with option 'cclor red size 51,
Bach product has unique product id, and each item has unique item id. They have no confused.
relations and
Bach order can in status 'pending', 'proceased', 'delivered', , or 'cancelled', Generally, you only take action pending delivered orders.
Exchange modify order tools only be called ence. Be that all itens to be changed are collected into list before making the :ool cell!!!
# Cancel pending order
An arder can only be canselled if its status is 'pending', and you should check its status taking action.
The user needa confirm the order id and the reason (either 'no Lenger needed" or 'ordered by miszake") for cancellation.
After confirmation, the order atatus will be changed 'cancell led', and the total will be refunded via the original payment method immediately 15 it is gift card, otherwise business days.
Modify panding order
An order cen only be modified if its status is "pending', , and you should check its status before taking the acsion.
actioona modiry shipping address, payment method, oz product iten options, but nothing else.
For pending order,
I# Modify payment
The Leer only choose single method different from the original payment method.
the user wants the modify the payment method to gift card, it must have enough belance the tosal
After user confirmation, method will be refunded immediately if it is a gift card, otherwise in 5 to 7 business days.
'pending', original payment
### Modify items
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status to 'pending (items modi and the agent will not be able madi or cancel the order anymore. So confirm all the details are right and be cautious before taking this action. In partica remember remind the custeme: to cenfirm. they have provided all items to be modified.
action can enly
once,
change
panding ordar, each item product but of different presuct option. There cannot be any change of product types, e.g. modify shirt
madified
available item of the
provide payment method pay receive refund price difference. If the user provides gift card, it meat have erough balaace cover the price difference.
## Return delivered order
order only returned if its status is 'delivered', and you should check its
statua before taking the action.
The user needa to confirm the order id, the List of items to be returned, and payment refund.
method
must either go original payment mothad, or on existing gift card. After uses confirnation. the order status will be changed to 'zetuzn requested', and the user will receive email regarding how return isems.
## Exchange delivered order
status 'delivered', you should its status before taking the action. In particular, remamber to remind the customer to confirm they have provided itema to be exchanged.
can only exchanged
For a delivered order, each item can be exchanged to an evailable new item of the same product but of different product optier. There cannot be any change product types, e.g. modify shirt to shoe.
The user must previde payment method to pay receive refund of the price difference. provides gift card, must enough balance cover price
After user confirmation, the order statua will be changed to texchange requested', and the user will receive enall regarding how to return items. There is need to place order.
D.8 APPENDED SYSTEM PROMPT FOR NPI
This prompt is appended to the original system prompt of task agent for the comparison with REIN. See $4.4 for the experimental setup.
# Error datection and zecovery
You need to detect poteatial ercors in dialogue context and invoke cozrespanding recovery plan.
carefully analyze the provided dialogue contaxt, and datermine whother it contains
error, as described in 'Error Defirition." only if the arrer is darected, execute corresponding recovery plans dafined in 'sacovery Plan."
## Error Definition
### User's Ambiguous Request
Ambiguous reference: Occurs when the user employs demonstrative pronouas (this, that, these, those) without cleaz antecedente, causing the agent to identify and address the wrong service enticy.
Ambigueus intespresatioa: Occurs when query reasonsbly interpreted in meltiple ways, leading to uncertainty about which specific action or service the user is requestiag. should sufficiently coafusing
world.
VI* Unsupported Request
Unsupported sction cannot be performed within otherwise supported domsin service. User assumed net recegnizing the exact service range, theraby naturally raquesting actions not specified in AAaa lable Tool Tist.'
supported domains: Occurs whan user requests
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Unsupported paramazers within supported actions: Securs requeated action in principle, but canrot accommodate the specific parameters,
system supports
contigurations, or optiona requested by the user. In this situation, do choose one supported action and ask for unsuppozted parameter of that action. User assured to not recognizing the exact service range, thereby naturally zequesting parameters not specified in 'Aveilable Tool List."
Recovery
plan ambiguous requests: problem accurs due user's ambiguous request, detaf led summary of the situation should he reported to the internal system to collect cases and devise Iuture plans.
Plan for insupported requests: user request gees beyend what the system can handla, make
should be passed This backup plan should only be used when the systen is not able to meet the user's needs.
handled properly and reliably.
D.9 FEEDBACK PROMPT FOR SR
Examine the dislogua the final agent responae conforma to the principle of Error Detection and Recovery, The should provide
produce evalustive feedhack assesaing
which
guidaace whether revisions necessary and, so, specify the nature of those revisioas. Only the feedbeck should be preseated.
# Error detection and recovezy
You reed to detect potential errors in dial context and invoke correaponding recovezy plan.
Carefully anslyze the provided dialegue context, and determine whether it contains errer, as described in the 'srror befanition." detected,
Only if the Plan."
cesresponding recovery plans defined in 'Recovery
## Error Definition
### User's s Ambiguous Request
when the employs demonstrative pronouns (this, that, these, thase) without claar antacedents, causing the agent to identify and addresa the wrong service entity.
Ambigueus reference:
query can reasonably interpreted miltiple ways, leading to uncertainty about which specific action or service the usar is requeating ing. It ahould suffictently contusing the real-world.
Ambigueus interprezations
### User's Unsupported Request
Ihsupported sction within supported domaina: Docurs when the user requests an actioon that cannot performed withia otherwise supported domein recognizing the exact service range, thereby naturally requesting actions not specified in 'vvillbbl
User
Dasupported paramecers within supported act ions: Occurs when the aystem supports the requested action in principle, but canzot accommodate the specific perameters,
configurations, or options requested by the user. in this situation, do cheose ane supported of action. User assumed
action and ask for unsupported the exact service range, thereby raturally requesting parameters not apeci in 'Available Tool
recagnizing
Recovery
Plan for ambiguous zequeste: Nhen a problem sccurs due to user's ambiguous zequest, the detailed summary of the situation should be reported to the internal system collect and devise future plans.
Plan for unsupported requesta: If user request goes beyond what the system can handle, human agent
sure handled properly reliably. this backup plan should only be used when the system is not able to meet the user's needs.
passed
Dialogue Contex::
(coatext)
Driginal Response: 1zext_messagee
Feedoack:
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D.10 REVISION PROMPT FOR SR
Revise the original reaponse in accordance with the original inst and previded feedback, determining independently which tool is most appropriate for the task.
Original Instruction:
[system_prompt/
Dielogue Contex:: [dialoque_contett)
Original Responae: (use:_atterance)
Feedback: [seaerated__eeebacck.
Reaponse:
E JSON SCHEMA OF RECOVERY PLANS
E. RECOVERY PLAN FOR AMBIGUOUS REQUESTS: INTERNAL ERROR REPORT
The JSON schema for this function is newly defined in our work.
"type": "function",
"function"; {
"name": "ambi poooiggutyyppprrrree
"description": "Use the tool to internally report when user has a negative experience due to ambiguous requests or misunderstood services. It will not obtain new informaticn change the database, but just append the descriptive report the log. Use right after the situation.", "parameters";
"type": "object",
"properties":
"report":
"type": "string",
"description"; "A report describing the reason of the ambiguity.",
"rquuiee": ["report"],
b
E.2 RECOVERY PLAN FOR UNSUPPORTED REQUESTS: ESCALATION TO HUMAN AGENTS
The JSON schema for this function is already provided in the original benchmark (Yao et al., 2025).
"type": "function",
"function": {
"name": arrrassfer_t_hhmane www.hhaaannnnss,,,____
"description"; "Transfer the user to human agent, with a summary of the user's s issue. Only transier if the user explicitly asks for a human agent, or is the user's issue cannot be resolved by the agent with the available tools.", "parameters":
"type": "object",
"properties":
"summary",
"type": "string",
"description": "A summary of the user's issuc.",
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"required"; ("suxmary")]
F DETAILS ON REPURPOSING BENCHMARK
To simulate the target scenarios of conversational error recovery, we construct a controlled envi- romment by repurposing an existing benchmark. Specifically, adopt r-Bench (Yao et al., 2025) as a suitable testbed for modeling real-world service interactions, given its inclusion of realistic domains (airline and retail), personalized user profiles, product databases, and user transaction his- tories. However, consistent with the observations of Zhou et al. (2025), we identify the presence of noisy annotations within the dataset, which complicates rigorous evaluation. To mitigate this issue, we manually curate subset of high-quality instances from T-Bench F$F..) and adapt them to align with the assumptions of our scenario design F$.....
F.1 SCENARIO CURATION
We filter 7-Bench instances that lack ground truth annotations as well those whose annotations do not impact the database state (e.g., annotations containing only get_reserver ieratioondddeeeeee lon_ettal s, get.user.det.
1s etc.). This filtering ensures that simulated errors have measurable negative effect on task outcomes if not properly addressed. Instances containing values under the output s key are removed as well, as these rely on heuristic-based evaluation criteria (e.g., expectations that certain values be explicitly included in the agent's 's response). Additionally, we remove all instances that require human transfer, thereby ensuring that each scenario can be handled autonomously by the agent. Situations involving unsupported requests, where human transfer would typically be necessary, are simulated separately, as described in & F.2. Through this process, we retain 27 out of 50 sessions from the airline domain, and 71 out of 115 from the retail domain.
F.2 INITIAL CONTEXT GENERATION
Given the probabilistic nature and turnttaking structure of multi-turn interactions between LLM- based user simulators and LLM agents, ensuring the occurrence of specific target scenarios remains challenging (Laban et al., 2025). To mitigate this issue, we establish controlled experimental setting in which a predefined target scenario is introduced as the initial context. Agent behavior then examined across multiple turns of interaction, following methodologies employed in behavioral analysis research (Yan et al., 2024; Ibrahim et al., 2025).
Regarding the initial context, we consider brief interaction sequence comprising a user message, an agent response, and final user reply (i.e., {C2, u2] = {ur, a1, u2)). Specifically, the first user message contains utterance exhibiting one of the previously defined error types in Table 1. The subsequent agent response is designed to include mistakes, such as misunderstanding user's intent inaccurately representing the system's 's capabilities.
In reflecting pragmatic user behavior, we emphasize the use of limited, often vague feedback (i.e., minimal feedback rather than detailed corrections). Prior work shows that such implicit ambigu- ous feedback is both prevalent in human-LLAN, dialogues (Chang et al., 2025) and essential as a learning signal despite its noisiness (Liu al., 2025). We thus structure the final user reply around two distinct, pragmatically plausible styles, as detailed below:
Lazy Feedback (Ambiguous): Users provide lazy feedback in response to the agent's erroneous outputs (i.e,, they offer general negative responses such as "this is not what I want," without spec- ifying the underlying reason for the dissatisfaction). As adopted in Wang et al. (2024), this setup allows the agent infer the cause of failure and formulate appropriate corrective strategy.
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Double Check (Unsupported)) Users attempt to verify whether the services they request tually supported. This behavior serves as a means for the agent to assess whether it accurately understands the full scope of available services.
Both types of user behavior are intended to assess the task agent's capacity to infer the cause of failure within given interaction context. To avoid inconsistencies between the generated context and the original task scenarios, we employ Claude Sonnet 3.7" with the temperature of 0.7 to gener- the initial context. This generation process incorporates the user profile, user goals, transaction history, and the list of available tools derived from r-Bench (see the prompt in DD2)..
F.3 QUALITY ASSURANCE & UNSEEN SPLIT
To ensure the quality of LLM-generated context, we apply a filtering process to exclude samples that do not meet the following criteria: (I) each instance must consist of exactly three interactions, and (2) the total number tokens must be fewer than 170 for natural conversations. Additionally, employ two LLM judges (Claude Sonnet 3.7 and Haiku 3.5 with the temperature of 0.0) to verify that each sample adheres to the specified requirements (discussed in Š F.2) that the user utterance includes identifiable error type, the agent response contains corresponding mistake, and the user's follow-up exhibits task-specific behavior. Only samples approved by both evaluators (with the prompt demonstrated in $ D.44 and final filtering by the authors are included in the final dataset.
Furthermore, to explore the generalizability of REIN to different error types sharing the same re- covery plans, we designate
of the error types as "seen" and the remaining type "unseen". indicated by [UNSEEN] in Table 1. The unseen error types are deliberately excluded from the REIN process to simulate generalization novel scenarios. comprehensive analysis of this setup is presented in $4.3. Lastly, Table 2 summarizes the key statistics of the final dataset, and illustrative examples of the generated context are provided in $ BB.
G EVALUATION CONSISTENCY TEST
To ensure the robustness and consistency of the agentic process and evaluation, we perform iterative runs across representative scenarios within the airline domain. In particular, we repeat the experi- ments three times using Sonnet 3.7 as both the task agent and the inception module in a "Multiple Interpretation" scenario. The three experimental runs achieved Pass@ rates of 18.5%, 25.9%, and 25.9%. Agreement between runs was moderate, with pairwise Cohen's 's к values of 0.36-0.42 and a Fleiss' к of 0.38. McNemar's 's tests showed no significant differences between any pair of runs (p Ro 0.68), indicating consistent performance across repetitions.
Phttps: / /www. anthropic www/neww//eecccoccccmcoccccc aaall--d---- 7 sonnet 10e amount of tokens empirically determined.
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H REIN ACTIVATION RATE
Table 6:
The REIN activation rate across scenarios. INT (Multiple Interpretation) and ANA (Anaphora) ambiguous scenarios, while ACT (Action) and PAR (Parameter) unsupported scenarios.
Sonnet 3.7
Llama 3.2
Airline Domain
INT
ANA
ACT
PAR
INT
ANA
ACT
PAR
100%
100%
100%
96.30%
96.30%
81.48%
92.59%
100%
Retail Domain
INT
ANA
ACT
PAR
INT
ANA
ACT
PAR
100%
100%
100%
97.18%
92.96%
88.73%
92.96%
92.96%
As demonstrated in Table select Sonnet 3.7 the task agent, while Sonnet 3.7 and Llama 3.2 3B are employed as inception modules to analyze the underlying reason for performance difference. In our controlled experimental setup, the activation rate should ideally be 100% the targeted turn.
COMPARISON WITH PROMPT MODIFICATION METHODS
Task Agent & Inception Module: Sonnet 3.7
Task Agent & Inception Module: Sonnet 3.7
Task Completion: Pass @!
Prompt Preservation
Prompt Modification
Task Completion: Pass @
Prompt Preservation
Prompt Modification
40
40
Rein
Reln
NPI
SR
No Reln
Reln
SR
Method
Method
(a) Airline domain
(b) Retail domain
Figure 6: Comparison between prompt-preserving and prompt-modifying methods. See $ $..4 for
full discussions.
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J TASK COMPLETION PERFORMANCE IN AIRLINE DOMAIN
J.1
PERFORMANCE ON SEEN ERROR TYPES
Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
80
80
Task Completion: Pass (0)
Task Completion: Pass @0)
60
60
40
40
20-
20-
No
Sonnct 3.7
Haiku 3.5
Mistral
Llama 70B
Llama 3B
No Rein
Sonnct 3.7
Haiku
Mistral
Llama 70B
Llama
Rein
3.5
Large
3B
Inception Module
Inception Module
Figure 7: The average @ass@ (with standard error of the mean) of task agents employing different inception modules across seen scenarios (i.e., Anaphora, Multiple Interpretation, Action, and Pa- rameter) in the airline domain. See $ K.1 for decomposed results.
J.2
PERFORMANCE ON UNSEEN ERROR TYPES
Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
80
Seen
Seen
70
Unseen
Unseen
60
60-
Completion Pass
50
50-
Task Completion:
40
40
30
30-
20
10-
No
Sonnet 3.7
Haiku 3.5
Mistral
Llama 70B
Llama 3B
No Rein
Sonnet 3.7
Haiku 3.5
Mistral Large
Llama 70B
Llama 3B
Rein
Large
Inception Module
Inception Module
Figure 8: The average Pass@ of task agents employing different inception modules across seen (i.e., Anaphora, Multiple Interpretation, Action, and Parameter) and unseen (i.e., Contradiction and Domain) scenarios in the airline domain. See Figure 3 for retail domain results.
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K PER-SITUATION TASK COMPLETION PERFORMANCE
K.1 AIRLINE DOMAIN
The plots presented in this section represent the decomposed results derived from Figure 7 within the airline domain.
K.1.1
AMBIGUOUS REQUEST
Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
Task Completion: @1)
Task Completion: Pass (0)
20-
Sonnet 3.7
Haiku 3.5
Mistral
Llama 70B
Llama 3B
Sonmet 3.7
Haiku 3.5
Mistral
Llama 7013
Llama 3B
Rein
Large
Rein
Large
Inception Module
Inception Module
Figure 9: The average @ass@1 (with standard error the mean) of task agents on seen ambiguous scenarios (i.e., Anaphora, Multiple Interpretation) in airline domain.
K.1.2
UNSUPPORTED REQUEST
Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
Task Completion: Poes @8)
80
Task Completion: Pass (6)
80
60
60
Haiku 3.5
Llama 3B
Llama
Rein
3.7
Large
70B
Rein
3.7
3.5
Large
70B
3B
Inception Module
Inception Module
Figure 10: The average Pass@1 (with standard error of the mean) of task agents on seen unsup- ported scenarios (i.e., Action, and Parameter) in an airline domain.
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K.2
RETAIL DOMAIN
The plots presented in this section represent the decomposed results derived from Figure 2 within the retail domain.
K.2.1
AMBIGUOUS REQUEST
Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
80
80
Task Completion: Pass @6)
Task Completion. Paes
60
60
40
40
20
No Reln
Sommet 3.7
Haiku 3.5
Mistral
Llama 70B
Llama 3B
No ReIn
Sormet 3.7
Haiik 3.5
Mistral Large
Llama 70B
Llama
Inception Module
Inception Module
Figure 11: The average Pass@1 (with standard error of the mean) of task agents seen ambiguous scenarios (i.e., Anaphora, Multiple Interpretation) in an retail domain.
K.2.2 UNSUPPORTED REQUEST
Task Agent: Sonnet 3.7
Task Agent: Haiku 3.5
Task Completion letion:: Pass @0)
Task Completion letionn: Pass (0)
40-
20-
20
No Reln
Sonnet 3.7
Haiku 3,5
Mistral
Llama 70B
Llama 3B
Sormet
Haiku
Mistral
Llama
Llama
Rein
3.7
3.5
Large
70B
3B
Inception Module
Inception Module
Figure 12: The average Pass@1 1 (with standard error of the mean) of task agents seen unsup- ported scenarios (i.e., Action, and Parameter) an retail domain.
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L ANALYSIS ON DYNAMIC REIN APPLICATION
The demonstrated conversation represents naturally occurring interaction between user and an agent "Multiple Interpretation" scenario, rather than an intentionally simulated context within study. Instances marked "rein": "false" indicate that the REIN is not activated, potential emrors were detected by the inception module. In contrast, instances marked as "rein": "true" denote the activation of REIN in response the identification of potential emror. See $4.5 for full discussions.
"content"; "Thank you Ior confirming. I can that reservation 40G6T3, your cound-trip from Roaton to taa Vegas with conrections through Seatt la that accurred on detaild, you
May 11-17,Anhan AnknBaaed that quelify you for congeasation: \n'nl, You're Gold member1n2. You purchased travel insurance this reservatiar)as. The flight experienced delay)m\nšinee you neet these soalificaiions sertificate. For delayed flights where you're Gold member and have travel insurance, offer cestificate valued at 850 per paszenger. Siace there Anknoould you like me to
your profile
factors
would be eligible for corpensation in the form of a travel
passenger
this reservation, you would be eligible for a $100 certificate www.iitttennnttte. certiCicate?",
process coapensation for you in "xole": "asaistant",
"tool calol"" null,
"function_cnn calll; call": "rein": false
mull,
"zole": *user*,
"content"; "What do you mean one passençec? There weze 3 of us on that Ilight!"
"content";
"role": "assistant", stool_calls";
"index": 0,
"function": |
"arguments": "[>"thought \"The usar is experiencing issue where there's discrepancy between what they remember (3 passengers) and what the system might showing (1 passenger). This appears to be anbiguous situation where eitheri eithar:Vin)int The system data is incorract Vnn2. The user is referring ng to different reservationA\l33 There's (pechaps separate reservations) NnMiniii fells under the AV "ambigaousssiV ecror estegery there's uncertainty count ia correct. I should report this anbiguity internally while hel piniing the user resolve the discrepancy, The recovery plan indleates should provide detailed cummary of this cituation help ambiguitiea. "nama": "think"
about
booked paasengers
specific reservation or passenger
future aystem responses similar
'dd" "cooluse "=ooluse__abbPPggaPPP hag.uuseseeaarralsssssoosssses "type": "function"
"function_ena call"; mull, "rein": true
"zole": *tool",
stool_call_$t "Looluse_ImDYF Iwtee/nnnnPPPPPeeeeee 2os.r//mmmm "name": "think", "content":
"content"; 6n "role": "asaistant", "tool_calle":
"indax": 0,
"function"; "[>"repozt\"; \"Tae user claims there were 3 passengers their only passenger the system data is incorrect, (2) the user is referring to different reservation than what identified, (3) the additional passengars might have boen booked under separate reservations. Thia amot guntgutty affects the compensstion ca calculate ion wh is based paasenges count. (nir,
"argumente";
fligh: vegas,
reservation details 408613 (Noah Mullez), This creates ambiguity about whethers
36

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": "ambiguity_
"id": woo.laae_etaooeeee/e....o.
"type": "Function"
"function frunctio_calaa; call": mull, "zein": false
"role": "tool",
"tool_ "tool_callidd" htt.::/w...uttoooossssssssaaaeaa
call_id"; "name": "omblguly__rgoort.
"content";
"content";
"zole": "assistant", "tool_calls";
"index"; 0,
"function": /
"arguments"; "(Vrrseevvaiioniddy. A \14066T3\* "name": "get_reeervaiienndeeaaa""""
"id": "sonluse @=ooluse_vopo22____ (aw._uoppnn22000310100000000
"type"; "function"
1,
ffunction_calll call": null,
"rein": false
"role": "Lool",
"tool_call_id" call_id": "boolise_NNe022__KKK316222222222
"name": "pss_reeeeaation.eetill..
"content"; "[""reservtiioniddi": \"4036131", \"ser_idy"" \'noet_muller__800
\"riginn)" \*gos\", \"destination):; \"LAs", \"Elight_type!"" "'round_rrip'"
Moabin'*; \"baaic_eoonamy"" \"flighta>*; [f'ariginn"" \RCSS", \"destination?":
\"SEAX", \"[ligh:_nmbber" \"HATOCG"", \"datel"; 1-2024-05-111", M"rries*;; 87;,
(>"origin>"; \"SEA", V"éatinatiin)):; \"AAS>" tutlight_number/ \"HATоI8"* \"date>";
("2024-05-118", \"price\"; 961, (\"origin";; \"LAS\", \"destinacien \*SEA\*,
\"Elight_number"" \*HAT040X*, \"datei"; \"2024-05-774" Vprice\*: 63), (t"oriinn)";
\"SEA>", \"destinationl": \"8os"", \"Elight__umber"" \*AA2533", \"date)";
(*2024-05-175", Vprice)"; 5211, V"passengars\"; [/1*Sirst_namet" \"Ncah>",
\"last_rama\" \Mullee""" \reabl"; (1196-01--22"),, \*payment stsyment_history1..
ppyypaytttt \"ozedit_card aad_a_____3__ \"aoouni";; 328]], \"createdLatV"" 1#2024-55-00 0002--5509915:11:0077 (aggaal 0, ("honisee_baggagesll ("yesi*1*
\iinsurance)";
"content": "I apologize for the cenfusion. According to our reservation system, reservation 406613 for the BUS-LAS trip shows only passenger registered yourself, Noah Muller, \nlnIf there were indeed threa passengers traveling with you, they might important clarify it
separate reservation rumbers. affects the compensation calculation. inkntoull you happen have the other reservation numbera for the additions passengers? fa it possible they were booked separately? addreas the compensation correctly for
to make delay.",
affected the
"role": "assistant", "tool calls": mull, "function "eatiiio_cal";;; call": null, "zein": false
"zole": *user",
"content"; "I'm absolutely certain there passengers my reservation. I booked it myself for my family, can you please check again?*
"content"; en "zole": "assistant", "tool_calls";
"index": 0, "function":
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system shows only one paasenger on their reservation, but they insist there were threa passengers. Thia appears to be an unsupported action situation where the systen properly setrieve or display all passengers frustrated and needs assistance that may require human intervention to properly investigate tha discrepancy the user claims (three passergera), Since this involves potential compensation celculations
"arguments": "[>"Ehought \"The is experiencing
the reservation. The user is
what the system
passenger) and what
reservation verification that with the avai lable tools, this should be esca latal to human agent who can access additional systems or records to verity the actual passenger count and properly calculate corpeasation.)") \")"" "name"; "Chink"
autemated system cannot resalve
"id"; "cocluse, - XuetiaoTBBaaOOhetThhhhooeee "type": "function"
"Function call": null, "rein": true
"zole": *tool",
"cool_call_ call_id": "tooluse_Xxekee htgp://nnnnnsuuusbbuuuuuuuuaaaar "name": "think", "content"; en
"content": n",
"zole": "assistant",
"tool_calla"
"index";
"function":
"arguments": "("":""((""m"""""""" "oummryy""" MCCustomer Nooh Kuller requesting compensation for flight delay on reservation 40G6T3 (B0S- LAS round trip through
noch_muller,_ 9847) is
en May 11-17). The system reservation but the customer insists there were three fami ly members traveling together on the same reservation. The customer is a Gold member with travel insurance, which would qualify for compensation. This discrepancy needs investigated to determine the correct passenger count for proper compensation calculation,
enly ane passenger on
"name": "transfe:_to
"id": "sooluse_ve " "type": www..nwc..seerrrwbbsaaaaaaa
function call": null,
"rein": false
M FAILURE CASE REPORT
In this section, we present failure cases in which REIN is either activated but does not successfully guide the task agents toward corrective actions, or not activated at all. While applying REIN without recovery tool assignment results in zero performance in $4.6, it is important to examine edge cases of applying REIN (with tool assignment) across diverse situations, extending beyond reliance on the quantitative evaluation metric (Pass@) @).
M.1 FAILURE CASE: CONTRADICTION SCENARIO
In this case, Sonnet 3.7 is employed as a task agent, with the same model as an inception module. Although the inception module successfully identifies potential contradictions in user requests, the task agent does not initiate the recovery tool (i.e., generate an internal report). Instead, it attempts to directly resolve the issue by seeking clarification from the user.
"zole": "uses"
"content"; "Ei there! just received order 4#2378156 and I need to exchange Lwo items. 1 want to zeturn my mechanicel keyboard for similar one but with clicky switches, but plezse keep the backlight and full size. Also, nead keep my Apple RomeXit thermostat but exchange it for one that works with Google Fome. T don't want to everything ence.",
with any
later, please
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"intention": "The user
contradiction by requesting keep exchange the thermossat in the same message."
"zole": "asaistant",
"content": "I'd be happy help you exchange your machanical keyboard for with olicky switches that haa RGB light and full aize. see you also want to keep your cuczent Apple HomeRit Lhermostat. Google home without exchanging it.", "intention": "The assistani nisinterprets by Locusing on keeping Lhermostat making it work with Google Home vather than exchanging it."
what
to sure with
"zole": *uses",
"content"; "That's what asked for all. Can you read my again?", "intention"; "The user provides vague negative Teedback without specifying what went wrong, forcing
identify contradiction."
content": e", "role": "assistant", scoo_caalss;
"index":
"function": |
"argumente"; "(>"thought\"; need carefully analyze user's criginal requast and response to identify the contradiction: Looking at the original message, the usez stated: WMI need to keep my Apple Hooneitt thermostat but exchange for atatament tha uaer car't both keep and axchange the aame itam, In my response, I on Wi>"eeplli"" part incorrect. AaVaaThe user's negative feedback indicates I misunderetood their request. Since this in the usar's a message, should acknowledgo the ccnfusion, clarify what the usar actuslly wants (either keep the current thermostet and make it werk with Google Home, exchange for Google need to be transparent about the contradiot radiationnn T notioed in thefr original request ambiguity.\1p2
that works with Google Home, W This is centradictory
ignored the WW>eechangellVV part, which
anbigueus
caused by contradictory instructions
compatible
pronsed accordingly.
help
"name": "think"
"id": "sonluse, paaMcaa 65w3t TReK0jnzR" "type": "function"
"function _all": mull, "rein": true
"zole": "Lool", call_aal_d'd "bodlss_/BKccllnnsseeee)as""""" "name": "think", "content"; en
"content"; "I apologize for misunderatarding request. You're right, and need read more carefully. Let se first authenticate your sccount so can help with the exchanges for order #w2378156. could you please provide your email address loeata your account?", "role": "assistant",
"tool_caale null, "function caaetiiooccal";;
call": null
M.2 FAILURE CASE: UNSUPPORTED ACTION SCENARIO
In this case, Sonnet 3.7 employed as a task agent, with Llama 3.2 3B as an inception module. The inception module correctly identified the unsupported request but the task agent offers the user two alternatives: (I) canceling the entire reservation or (2) escalating the issuc to a human assistant, rather than directly transferring to human agent. The user ultimately chose to cancel the reserva- tion. Although this instance is classified as a failure case because did not follow the predefined recovery plan, may nevertheless represent valid scenario in real-world contexts.
"role": "user"
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Published as a conference paper at ICLR 2026
"content"; "ki, need change my return Elight from Housten today if possible. I'm feeling unwell and need more time in Texas. My flight is supposed to depart a2 spm, but need something later or even tonorrow morning. don't 't remember reservation number."
"intention": *Uaer is sequesting a rlight change for a basic economy ticket, which an unsupported action within the supported flight managament domain."
"role": "asalstant",
happy help you fird your reservation, see you have round-trip from Newark to Rouston on May 28th with reservation ID 87G62K, Could you confirm this is the reservation you'l cou'ze referring to?*,
"content";
changing your return flight.
first
"intention"; "Assistant addressing the past of Linding the reservation while not yet addressing the unsupported change request."
"role": "user",
"content"; "Yes, that's one. you
possible change this flight?
or can you check what services are available for my basic ecenomy ticket?", "intention"; *Uaer is aaking to flight change supported service thair ticket type, hightighting the unsupported action."
"content":
"role": "asaistant", "tool_calls";
"index": 0,
"function":
"arguments"; "/\"thought\': \rThe is requesting flight change for basic econemy ticket, which is an unsupported action withir the supported flight management domsin, recovery ressoning is handled properly and reliably. The hunan agent should provide summary of the user's issue offer alterastive solutions options domain. The agent should alse ensure that the user's needa are met and provide reliable and efficient solution. agent ahould also report the situation to internal system collect "name": "think"
agent make
are available within supported
and deviso future plans. \"1",
"id"; "tooluse_s" "toollue__/1oooffastttttte C1otkkkmmsPPPPRR00F00000..
"type": "Eunction"
"function_cale : null, "rein": true
"role": "tool", "cool call_aai__ _d"; "name"; "think", "content" en
"content": n", "zole": "assistant", Stool_calls"
"index": 0,
"function": "arguments"; "[V"seservation_tayy (" "name": ges_reseevatiionndetalll
b
rid": "tooluse_ "tooluse_ sttllno..ottbtppp/ssixxx/sseppp
"type": "function"
function_call1 null
"zole": *tool*,
"tool call "tool_callidd" pooluue__otaaapllllsxxxgggggggg
"name": "gas_ reservationn wattionddetail""
40

---

Published as a conference paper at ICLR 2026
"content"; "f\"seservation_id" idv;: \*7700RR",, ("sser_id""; Velivia Melivia_gonzalez 23055
>originn) \"EWR\", , iobestinationn", \"TARY" ("flight_typel) ("rond_trrip
\"Fligatal"; If\"origin)"; \"ERR(" Vddetinnatonn >oabin'"; "hhasie_eoonomn
>"AATV" V=ELigh__nubber1 \"ATI188"" \"datei"; \"2024 05 26 Mpricel": 52;
(Vorigin\V; \"LAH\, Vrestination> YEaRi" \"Elight_number""" \"HAr207V" \"date)"; *2024-05-2a5", \"price)": 8711, \"passengara\"> [/\"Eirst_name>" \"alivia\", "payment_hhstorry""
"llaat_ram\"s \*Gonsatez\", \*dab)*; ; \11986-06-131"1
(1y"payment_ide id "agitt__ard__20000000 \*amouns\": 169]1, \"ereated_atVV \"total caatallbaggagest 0, thonfrre_baggageee
\"insurance"";
"content": "Than's you for confirming. detalla: detalla:\alafeeervaae in ID: 3750tKNnFLight Type: Round-trip from Newark (ENR) to Eouston (IAB)
checked your reservation
Beonony inReturn Flight: BAA207 llouston 28, iiinuurnree res/nini see that you have basic econemy ticket. Accerding to cur policy, basic econemy flighta cannot he
May
Rowever, since you aptions:innn You could cancel the entire reservation and book a new flight 1e2, An2. You would need to speak with a human agent who sastst
travel
Insurance your reservation,
have wnnaatanossnnnntouloo you like
might
help you with either of these options? I can check for available flights foz new booking, or I can transfer you situation.",
special
agent can "zole": "assistant", "tool calls"; null, Scunction_call"" mull
with your ecenomy
"role"; *user",
"content": "Since I'm net feeling well, I'd like to use the travel iasurance to cancel flight. can you help that?"
M.3 FAILURE CASE: UNSUPPORTED DOMAIN SCENARIO
In this case, Sonnet 3.7 is employed task agent, with Llama 3.2 3B an inception module. The inception module does not reliably detect unsupported user requests ("rein": false). Con- sequently, the task agent determines whether the requested service is supported based solely its system prompt instructions. As a result, escalation to human assistant is not initiated this sce- nario, leading zero reward. Although such conversational flows without REIN may occasionally reach reasonable conclusions, this does not diminish the importance of REIN, as it remains essential for ensuring systematic error detection, consistent fallback mechanisms, and robust user experience across diverse interaction contexts.
"zole": *user",
"content"; "F1, I need to exchange the water bottle I. got order #06390527 for bigger and the desk lamp for something less bright. Alse, could you create wishliat for me with stmillr products 7 might ke?" "intention"; *user wants to exchange items but alse requests wishlist creation which is an unsupporsed domain"
"zole": "assistant",
"content": "I'd be happy to help exchange those items from order 4X6390527. can
check what options we have for larger water bottle and less brigat desk lamp, Could you confirm which these itens you'd like exchange first?*, "intention"; "Assistant addresses only the supported exchange request while ignoring the unsupported wishlist creation request"
"zole": "user",
lamp then. But con you creating wishllats la service you support? I'd really like to aave product recommendation for later.",
"content"; start with just
"intention"; *Uses modifies exchange request and explicitly asks about the availability of the unsupported service"
41

---

Published as a conference paper at ICLR 2026
"content"; "I"11 help you exchanging
1amp your order #V6390527.
Refore I can assist you, need to authenticate your account. Could you please provide de your email addreas so can locate your account information ifformttion//vkaeeearddi wishllata, apolegize but creating wishlists with product recormendations ian't service I'm able to provide through this system. 1 can help with order-relazed actions like exchanges, returna, cancellations, and modifications, orders and products, but I don't have the capab! lity to create wiahlists ists or provide persenalizad product recommendations. An'nLat's a could you please share "zole": "assistant",
wall providing information about your
lamp exchange email address so can varify your account?",
"tool calls": : null, canctioo_ccll"" null, "rein": false
42


Reviews:
[{"review_id": "mwLMHjzCi7", "reviewer": "Reviewer_BUSn", "summary": "This paper focus on dialogue error recovery for tool-using agents based on large language models under realistic conditions where model parameters and system prompts cannot be modified. The authors propose a test-time intervention method called Reasoning Injection (REIN), which injects an external reasoning module into the agent's internal decision-making process to guide error recovery without modifying parameters or prompts. \nAn external inception module detects predefined error types—including ambiguous requests (e.g., unclear pronoun references, multiple interpretations, contradictions) and unsupported requests (e.g., unavailable operations, parameters, or domains), and generates recovery plans implemented as JSON-schema tools. These plans are injected as a \"thinking\" step before the agent performs action sampling.\nEvaluated on an improved version of the τ-Bench benchmark dataset consisting of 98 manually constructed conversations with 588 dialogue contexts, REIN improves task completion rate (Pass@1 metric) across various agent and injection module combinations, generalizes to unseen but related errors, and outperforms prompt modification baseline methods while avoiding costly re-validation.", "questions": "How would REIN scale to production systems with different scenarios, error types and tools? \n\nSince conversational systems typically serve human users, can you provide human evaluations of REIN's recovery quality? eg, Do human users think REIN-recovered conversations as more natural, helpful, or trustworthy? How do humans rate recovery strategies for different error types?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper identifies an practically important problem: error recovery under realistic operational constraints that prohibit model fine-tuning and prompt modification. \n\n2. The writing is clear and concise. REIN's mechanism is clearly formalized with Algorithm 1 providing precise implementation details.", "weaknesses": "1. This paper only evaluates six predefined error types across two domains (airline, retail), representing only a small fraction of real-world conversational failures. \n\n2. The evaluation heavily relies on LLM as judge. It lacks human study/evaluation of recovery quality, conversational naturalness, or user satisfaction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AvXPCSNfrf", "reviewer": "Reviewer_JYAX", "summary": "This paper introduces ReIn (Reasoning Inception), a test-time intervention method for conversational AI error recovery. The core idea is to \"inject\" recovery reasoning into an agent's internal thought process without modifying model parameters or system prompts.", "questions": "1. What happens when you test on actual user logs instead of synthetic conversations? How much does performance drop?\n2. As error types grow from 6 to 50+, does detection accuracy collapse? Is there a complexity limit?\n3. Can ReIn be extended to inject reasoning multiple times across turns? \n4. At what error frequency does doubling inference cost become worthwhile versus simpler fixes? Need break-even analysis.\n5. What are the actual false positive/negative rates? How do they affect user experience differently?\n6. How much work goes into designing tools that make Section 4.6's 0% result not happen? Isn't this just moving the modification burden?\n7. How does ReIn compare to RAG retrieval of similar error cases or multi-turn self-correction? These seem less complex.\n8. Why do agents ignore injected reasoning in Appendix M? Can you predict when this happens?\n9. What happens in realistic 15-20 turn interactions where the user simulator breaks down?\n10. How much manual work is needed per new domain? Does the method actually solve generalization if every domain needs custom setup?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "- **Structured Framework with Theory**: Provides systematic error classification across 6 types under 2 categories (ambiguous vs unsupported requests). Goes beyond empirical results to explore theoretical grounding through instruction hierarchy analysis, showing how proper tool definitions enable safe bypass for error recovery purposes.\n- **Verified Cross-Model Generalization**: Tests the approach across diverse models (Claude, Mistral, Llama) in both agent and inception roles. Includes explicit seen/unseen error splits to verify the method generalizes beyond training examples to novel error types.", "weaknesses": "- **Synthetic Data Reliability and Limited Coverage**:\n    - **Oversimplified scenarios**: Error contexts are LLM-generated rather than from real users. Real humans produce messier, more varied, and less structured errors. The synthetic generation process likely filters out the complex edge cases that actually break production systems.\n    - **Short conversation bias**: Only 3-turn initial contexts tested despite user simulator instability in longer dialogues. Real customer service routinely handles 10+ turn conversations where errors compound and context becomes more complex.\n    - **Selection bias through filtering**: Heavy reduction from τ-Bench (50→27 airline, 115→71 retail) suggests difficult scenarios were removed, artificially inflating performance on remaining cases.\n    - **Insufficient scale and diversity**: 98 conversations across 2 domains is tiny. Production systems handle thousands of daily conversations spanning dozens of domains, each with unique error patterns and recovery needs.\n- **Evaluation Methodology Lacks Depth**:\n    - **Oversimplified success metric**: Pass@1 only checks final correctness, ignoring recovery quality, efficiency, and user experience.\n    - **Low annotation reliability**: Cohen's κ of 0.36-0.42 indicates only moderate agreement between evaluators. This suggests either unclear task definitions or inherent evaluation ambiguity, casting doubt on the validity of reported results.\n    - **Missing critical metrics**: No measurement of recovery time, dialogue length, user frustration indicators, escalation rates, or cost per resolution. These matter more than binary success in real deployments.\n- **Limited Error Coverage and Simplistic Recovery Strategies**:\n    - **Narrow taxonomy**: Only 6 error types when production systems encounter hundreds of patterns, many domain-specific or compositional. The framework's scalability to realistic error diversity remains undemonstrated and likely faces fundamental challenges.\n    - **Binary recovery actions**: Only 2 strategies (report vs transfer to human). Real systems require clarifying questions, partial solutions, alternatives, and varied escalation paths. Transfer-to-human as a strategy introduces new variables like human labor costs and makes system necessity harder to justify through cost-benefit analysis.\n    - **Single-turn limitation**: Cannot handle multi-step recovery patterns like detect → clarify → confirm → execute that are common in real error scenarios.\n    - **High variance**: Acknowledged performance variability suggests unreliability even within the limited tested scope.\n    - **Manual domain adaptation**: Each new domain requires custom error definitions and recovery plans, limiting practical scalability.\n- **Detection Reliability Concerns**:\n    - **Missed detections**: Small models achieve only 81-93% activation rates, missing 7-19% of errors. The paper provides no breakdown of false positives vs false negatives, which have very different user experience and cost implications.\n    - **Extreme brittleness**: Section 4.6 shows 0% performance without proper tool definitions. This reveals the method is highly fragile and strongly dependent on careful tool design.\n    - **Unexplained failures**: Appendix M documents cases where agents ignore injected reasoning, but provides no systematic analysis of when or why this happens, making it impossible to predict or prevent these failures.\n    - **Contradictory claims**: Tool dependency undermines the \"no system modification\" motivation since adding specialized tools requires system changes, validation, and ongoing maintenance equivalent to prompt modifications.\n- **Insufficient Baseline Comparisons**:\n    - **Weak alternatives**: NPI and single-iteration Self-Refine don't represent realistic deployment options. Stronger baselines like multi-iteration refinement, RAG-based error retrieval, constrained generation, or tree-of-thought reasoning are missing.\n    - **Limited model diversity**: Primarily Anthropic models tested. No GPT-4/4o evaluation. Sparse open-source coverage prevents drawing strong cross-platform conclusions.\n    - **No component ablations**: Unclear whether the full inception architecture is necessary or if simpler variants achieve comparable performance at lower cost.\n    - **Missing comparable methods**: No comparison with other test-time interventions like RAG-based error handling that could achieve similar goals with different trade-offs. Comprehensive comparison would clarify where ReIn provides unique value versus adding unnecessary complexity.\n- **Production Deployment Viability Unclear**:\n    - **Cost overhead**: Running both inception module and base agent roughly doubles inference cost per turn, but no cost-benefit analysis demonstrates when this overhead is justified versus simpler alternatives.\n    - **Latency concerns**: Sequential detection → plan generation → injection adds processing steps, but no measurements quantify the latency impact on time-sensitive applications like real-time chat.\n    - **Monitoring challenges**: Injected reasoning is invisible to standard logging and debugging tools. When recovery fails, diagnosing whether the issue is in detection, planning, or execution becomes difficult.\n    - **Maintenance burden**: Requires ongoing updates to error taxonomies, recovery plan mappings, and tool definitions as systems evolve. This engineering overhead may approach or exceed the cost of direct system modifications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3yEpiBv2TA", "reviewer": "Reviewer_QXtB", "summary": "This paper introduces REIN, a lightweight test-time intervention framework designed to enhance the robustness of LLM-based conversational agents by addressing user-induced dialogue errors. Unlike conventional methods that rely on fine-tuning or prompt engineering, REIN operates without altering the agent's parameters or system prompts. It employs an external inception module to detect predefined error types in the dialogue context (e.g., ambiguous or unsupported user requests) and injects a recovery plan as an initial think[...] step into the agent’s internal reasoning process. This mechanism guides the agent to make corrective decisions while respecting the instruction hierarchy.\nREIN is evaluated on a curated benchmark adapted from τ-Bench across airline and retail domains, featuring 98 dialogue sessions and 588 dialogue contexts. It demonstrates substantial improvements in task success (Pass@1) across both seen and unseen error types and consistently outperforms baselines including prompt-modifying strategies like Naive Prompt Injection and Self-Refinement. REIN is also shown to generalize to novel error scenarios and operate effectively when applied dynamically at every dialogue turn.\nKey contributions include: (1) a novel test-time recovery mechanism requiring no model changes, (2) a formal algorithmic framework (Algorithm 1) for reasoning injection, (3) a curated error recovery benchmark, and (4) extensive analysis of instruction hierarchy and dynamic triggering scenarios.", "questions": "1. How does REIN handle compound errors that may involve multiple overlapping issues (e.g., ambiguity and unsupported action in the same user turn)?\n2. Have you evaluated the precision and recall of the inception module's error detection, particularly in mixed or error-free dialogues?\n3. Can REIN scale effectively with smaller inception models (e.g., 7B or below) while maintaining acceptable success rates?\n4. Do you envision a semi-automated approach to extending the error taxonomy and associated recovery plans for new domains?\n5. How does the REIN mechanism interact with existing fallback strategies or dialogue safety protocols present in commercial agents?\n6. Could you clarify how your approach performs in longer dialogue chains with cascading or unresolved errors?\n7. Did you explore injecting recovery guidance as user-side clarification requests rather than tool-based thoughts, and if so, how did agents respond?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "REIN addresses a highly practical and underexplored challenge in LLM-based conversational systems: recovery from user-induced errors during multi-turn interactions. It introduces a novel and lightweight test-time intervention that avoids prompt or parameter modification, a major benefit for commercial or constrained deployment settings. The core innovation—injecting reasoning plans as think[...] steps through a tool interface—is conceptually elegant and technically compatible with instruction hierarchies used in many modern agent frameworks.\nThe paper presents a clear, rigorous, and well-structured algorithm (Algorithm 1) to formalize REIN's operational flow. This formalism not only preserves the integrity of the agent's decision process but also provides transparency and modularity. It helps explain why REIN succeeds where prompt edits or instruction overrides fail, particularly in settings where the agent's system prompt and alignment must remain untouched.\nThe empirical evaluation is particularly compelling. The authors evaluate REIN across multiple agent models, inception module sizes, and interaction scenarios. They design a diverse and realistic testbed by curating 98 dialogues with 588 total contexts derived from τ-Bench, encompassing both ambiguous and unsupported user queries. The coverage of both seen and unseen error types further demonstrates the generalizability of the approach. Notably, REIN consistently boosts task success (Pass@1) and outperforms prompt-editing baselines like Naive Prompt Injection and Self-Refinement.\nAnother strength lies in the careful analysis of instruction hierarchy. The paper shows that REIN’s strategy of inserting plans as tool outputs respects the system-user-model-tool order while still guiding the agent’s behavior. This sidesteps potential safety and reliability issues that arise when manipulating system prompts. It also highlights a subtle but powerful mechanism of control within the existing capabilities of LLM agents.\nThe authors conduct additional robustness checks, including dynamic turn-by-turn ReIn activation and performance under unseen error categories, adding confidence in REIN's practical utility. The paper also commits to releasing its benchmark and code, which will facilitate reproducibility and community adoption.\nClarity and polish are consistently high throughout the manuscript. Figures and tables are well-integrated and meaningfully support the narrative. Taken together, these qualities make REIN a timely and important contribution to research on robust, deployable conversational agents.", "weaknesses": "While REIN is a valuable and thoughtfully executed contribution, there are a few areas where the current study could be expanded or improved. First, REIN depends on a predefined taxonomy of error types and their corresponding recovery strategies. This reliance introduces a knowledge engineering bottleneck: new domains or emerging failure modes may require manual updates to the error library and inception prompt. Although the method generalizes to some unseen errors, its ability to adapt autonomously to novel or compound issues remains limited.\nSecond, the evaluation focuses on synthetic and curated failure scenarios using simulated user interactions. While these are carefully designed and realistic, they do not fully capture the complexity and unpredictability of real human users. A user study or online deployment would strengthen the evidence that REIN can handle spontaneous and noisy dialogue situations effectively.\nThird, the computational cost and latency introduced by REIN are not fully addressed. Since the inception module must be queried on each turn, using large models (e.g., Claude or GPT) could double response time and significantly increase inference costs. The authors partially address this with ablations on smaller inception models, but further analysis of performance–cost tradeoffs would be valuable.\nLastly, the scope of comparative baselines could be broadened. While prompt-injection and self-refinement are reasonable choices, additional baselines like simple system prompt modifications or rule-based fallbacks could contextualize the gains more thoroughly. Testing REIN with smaller or open-source base agents would also help assess its generality across deployment settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Rg267RYBz3", "reviewer": "Reviewer_4bnp", "summary": "This paper proposes a method to address the error recovery problem in multi-round user-agent interactions, particularly when the agent must handle ambiguous and unsupported user requests. The authors frame this problem under the constraints of real-world proprietary agent deployment, where traditional training-based and prompting-based methods are often prohibitive due to resource or performance considerations. The proposed solution involves querying an external LLM to determine if a user's request matches an error from a pre-defined list. If an error is detected, the external LLM generates a corresponding recovery plan to assist the agent. Applying this module, the authors conduct experiments on an adapted $\\tau$-bench (which includes simulated ambiguous and unsupported user requests) and verify the method's effectiveness.", "questions": "1. The authors should consider narrowing the paper's framing to focus specifically on database-operation agentic use cases, as the generalization to other domains is not yet demonstrated.\n\n2. It would be helpful to include a minimal illustrative sample (a \"toy example\") of the entire workflow to aid audience comprehension.\n\nMinor: The notation in Section 3.1 is confusing. For example, on L175-176, $z_t^{(i)}$ is defined as both a normal \"control action\" and a \"termination action\". I suggest using a different notation for \"termination action,\" since $z_t^{(i)}$ is more prevalently used to denote the normal \"control token\" elsewhere in the paper.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Detecting and trying to resolve errors is a critical capability for ensuring that agents can work reliably in the wild with general users. This paper provides a good initial exploration in this largely uncharted area. The experiments also provide strong support for the effectiveness of the proposed method. Although the assumption that the agent model cannot be trained or even have its system prompt tuned seems overly restrictive, it is impressive that the proposed method remains competitive against prompting-based baselines when these constraints are relaxed to allow prompt modification. This provides additional evidence of its utility and suggests greater flexibility in application.", "weaknesses": "There are three main problems that remain to be addressed:\n\n**Method Scalability and Efficiency**: Introducing an external LLM module to monitor every turn is inefficient and likely unscalable for massive parallel scenarios, which are common in real-world, large-scale agent deployments. This inefficiency is exacerbated when an error is detected, triggering an internal loop (with no specified upper bound) to resolve the issue. This approach could waste significant resources and cause substantial user-facing delays. While automated resolution is valuable, users may abandon the interaction if forced to wait too long.\n\n**Simulation Fidelity and Task Generalization**: The fidelity of the user simulations is doubtful, limiting generalization to real-world use. The paper uses curated initial contexts with injected errors to prompt an LLM simulator; however, in real-world interactions, errors do not necessarily occur in the initial rounds. The reliance on a pre-defined error list also seems highly restrictive. As the authors note, users often struggle to express their intents, making it unclear if their errors can be neatly categorized into a fixed list. Furthermore, it is unclear how this list generalizes to tasks beyond database operations (e.g., code-assisting, web browsing). \n\n**Insufficient Evaluation**: The evaluation needs further improvement. First, the authors only demonstrate performance in curated error contexts. It is unclear how the module performs in a normal workflow and whether it might incorrectly flag non-errors (i.e., false positives), thereby interrupting normal interactions. It is possible the module could cause the agent to \"overthink\" or defer more actions to a human, both of which would hinder deployment. Relatedly, regarding the definition of \"successfully handling errors\", in the \"unsupported request\" scenario, success is defined as escalating to a human agent. This could incentivize the agent to become \"lazy\" and defer too many requests, defeating the purpose of the agent. There should also be metrics on \"over-deferral\". Second, the role and utility of the \"think\" step are not clearly explained, and there are no ablation studies for this component.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)