MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors

Jiale Hou; Lubin Bai; Mengyu Cao; Sixue Wang; Xiang Li; Xiuyuan Zhang; Yue Pan; Zhongwei Wan

arxiv: 2606.17453 · v2 · pith:P7MFM6K6new · submitted 2026-06-16 · 💻 cs.AI

MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors

Lubin Bai , Mengyu Cao , Sixue Wang , Zhongwei Wan , Yue Pan , Jiale Hou , Xiang Li , Xiuyuan Zhang This is my paper

Pith reviewed 2026-06-27 01:29 UTC · model grok-4.3

classification 💻 cs.AI

keywords map agentsimplicit decision factorsuser satisfactionbenchmarkLLM agentsspatial decision makingbehavior data

0 comments

The pith

Map agents handle explicit task completion but fall short on implicit decision factors that drive user satisfaction.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds MapSatisfyBench to measure how map agents recover unspoken needs that shape whether users accept a response. It applies a restore-identify-filter process to large-scale real user behavior records, extracting only those factors that affect acceptance and can be known before the agent replies. Experiments find that current agents finish clear tasks reliably yet rarely gather the evidence required for satisfaction-aware choices. The work therefore reframes evaluation away from single correct answers toward full-chain assessment of everyday spatial decisions.

Core claim

The restore-identify-filter framework reconstructs complete user needs from behavior-chain evidence, isolates implicit decision factors that affect acceptance, and retains only those recoverable from pre-query information, enabling MapSatisfyBench to convert these factors into objective evaluation targets across five dimensions and revealing that agents succeed on explicit completion but remain limited on implicit factors and proactive evidence acquisition.

What carries the argument

The restore-identify-filter framework that reconstructs complete user needs from behavior-chain evidence, identifies implicit decision factors, and retains only those supported by pre-query evidence.

If this is right

Agents must proactively acquire evidence for implicit factors to raise user acceptance in map services.
Benchmarks should replace single-reference answers with multi-dimension targets grounded in behavior data.
Evaluation of map agents should track full-chain satisfaction-aware decisions rather than task completion alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same reconstruction approach could be adapted to test other everyday assistants that receive underspecified queries.
Patterns in the retained factors might indicate which map use cases most require preemptive evidence gathering.

Load-bearing premise

The restore-identify-filter framework correctly reconstructs complete user needs from behavior-chain evidence and retains only implicit decision factors that both affect user acceptance and are recoverable from information available to the agent before it responds.

What would settle it

A controlled test showing that the extracted implicit decision factors do not predict measured user acceptance rates or that agents achieve equivalent satisfaction performance without using the framework's reconstruction step.

Figures

Figures reproduced from arXiv: 2606.17453 by Jiale Hou, Lubin Bai, Mengyu Cao, Sixue Wang, Xiang Li, Xiuyuan Zhang, Yue Pan, Zhongwei Wan.

**Figure 1.** Figure 1: Motivation of MapSatisfyBench. Map-service queries often define multiple feasible responses, and satisfaction de [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Overview of MapSatisfyBench. Benchmark construction follows the restore-identify-filter principle, the deterministic [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Domain coverage statistics. 4.3 Benchmark Statistics MapSatisfyBench contains 500 behavior-grounded mapservice instances. In constructing the benchmark, we explicitly consider coverage across domains, temporal contexts, spatial settings, and the source of implicit factor. As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Tool call frequency 0.6749 and 0.6606, respectively. SES is even more restrictive because it jointly reflects satisfactory decision quality and efficiency, with the best non-thinking score reaching only 0.2755 from GPT-5.3. These results indicate that current LLMs often complete the surface task but still fail to satisfy implicit decision factors that affect whether the user would accept the response. As… view at source ↗

**Figure 5.** Figure 5: Average IISR, AR, and SES by map-service domain. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

read the original abstract

Large language model agents are increasingly integrated into map services. Since map services are embedded in everyday-life scenarios rather than professional task settings, users often express their needs informally, resulting in underspecified queries with many unspoken needs, namely, implicit decision factors that are critical for user satisfaction. Although clarification is an effective way to mitigate this issue, it increases user burden in daily interaction, and a capable agent should first proactively recover such factors from available information sources. However, evaluating this ability is challenging. The first challenge is to determine which implicit decision factors are suitable for evaluation. A factor is evaluable only if it affects user acceptance and can be recovered from information available to the agent before it responds. Second, user satisfaction cannot be reliably represented by a single reference answer, requiring a benchmark that converts satisfaction-relevant factors into objective and quantifiable evaluation targets. To address these challenges, we propose a restore-identify-filter framework that reconstructs complete user needs from behavior-chain evidence, identifies implicit decision factors, and retains only those supported by pre-query evidence. Building on this methodology, we construct MapSatisfyBench from large-scale, real-world anonymized user data and annotate ground truth from five dimensions and enables full-chain evaluation of satisfaction-aware map agents. Experiments show that current agents generally perform well on explicit task completion, but remain limited in satisfying implicit decision factors and proactively acquiring the evidence needed for satisfaction-aware decisions. These findings establish MapSatisfyBench as a benchmark for shifting map-agent evaluation from task completion toward satisfaction-aware spatial decision making.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper builds MapSatisfyBench with a restore-identify-filter method to test map agents on implicit satisfaction factors from real behavior data, but the selection of those factors lacks independent checks.

read the letter

The main thing here is a benchmark that moves map-agent evaluation from explicit task completion toward unspoken needs that shape user satisfaction. The restore-identify-filter framework pulls complete needs from behavior-chain evidence, flags implicit decision factors, and keeps only those recoverable from pre-query information. They build the dataset from large-scale anonymized real-world logs and annotate across five dimensions to create objective targets.

This approach is practical. Everyday map queries are often underspecified, so grounding the benchmark in actual user traces rather than made-up examples gives it more relevance than pure task benchmarks. The full-chain evaluation setup also lets them check evidence acquisition, which is a useful addition.

The reported result—that agents handle explicit parts fine but struggle with implicit factors and proactive evidence gathering—fits what most current systems are trained for. The construction itself looks like a clear step beyond single-reference answers.

The soft spot is validation of the filtered factors. The framework decides which implicit items count by looking at the same behavior data used for evaluation, and the description gives no separate user study, A/B test, or correlation with actual post-interaction satisfaction to confirm these factors drive acceptance. Without that external anchor, the performance gap could reflect the reconstruction choices more than real agent limits. The abstract also skips numbers, error analysis, or annotation reliability stats, so the strength of the central claim is hard to judge from the given material.

This is for people working on consumer-facing spatial agents who want metrics that reflect daily use rather than just correctness. It deserves peer review because the problem is real and the data source is concrete, even if the methods section will need close scrutiny on the filtering step.

Referee Report

2 major / 2 minor

Summary. The paper introduces MapSatisfyBench, a benchmark for satisfaction-aware map agents built using a restore-identify-filter framework applied to large-scale real-world user behavior data. The framework reconstructs complete user needs, identifies implicit decision factors, and filters those supported by pre-query evidence. The benchmark annotates ground truth from five dimensions for full-chain evaluation. Experiments demonstrate that current agents perform well on explicit task completion but are limited in handling implicit decision factors and acquiring necessary evidence.

Significance. If the results hold, this benchmark could meaningfully advance the field by shifting evaluation of map agents from explicit task completion to satisfaction-aware spatial decision making, highlighting important limitations in current LLM agents for everyday map services. The construction from anonymized real-world data and the emphasis on proactive evidence acquisition are notable strengths that could influence future agent design.

major comments (2)

[Methodology (restore-identify-filter framework)] The restore-identify-filter framework (described in the methodology section) selects implicit decision factors on the basis that they affect user acceptance and are recoverable from pre-response information, yet the manuscript supplies no independent validation—such as correlation with post-interaction satisfaction scores, user studies, or A/B tests—confirming that the retained factors drive acceptance rather than arising as artifacts of the reconstruction. This selection step is load-bearing for the central claim that agents remain limited on implicit factors.
[Experiments] The experiments section reports the performance gap between explicit task completion and implicit factors but provides no quantitative metrics, error analysis, inter-annotator agreement for the five annotation dimensions, or details on how ground-truth labels were validated against actual user acceptance. Without these, it is not possible to determine whether the data supports the reported limitations.

minor comments (2)

[Abstract] The abstract states that 'Experiments show...' without including any numerical results or key statistics; adding one or two headline metrics would improve informativeness.
[Annotation procedure] Clarify the precise mapping between the five annotation dimensions and the implicit decision factors retained by the filter step.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the detailed and constructive comments. We respond point by point to the major comments below, indicating where revisions will be made. The benchmark is constructed from anonymized real-world behavior data, which imposes some constraints on the types of validation possible.

read point-by-point responses

Referee: [Methodology (restore-identify-filter framework)] The restore-identify-filter framework (described in the methodology section) selects implicit decision factors on the basis that they affect user acceptance and are recoverable from pre-response information, yet the manuscript supplies no independent validation—such as correlation with post-interaction satisfaction scores, user studies, or A/B tests—confirming that the retained factors drive acceptance rather than arising as artifacts of the reconstruction. This selection step is load-bearing for the central claim that agents remain limited on implicit factors.

Authors: The restore-identify-filter framework reconstructs complete user needs from observable behavior-chain evidence in the anonymized dataset and retains only factors recoverable from pre-query information. We agree that additional independent validation, such as correlation with post-interaction satisfaction scores, would strengthen the claims. However, the anonymized historical data does not contain post-interaction satisfaction metrics, precluding such analyses without new data collection. The primary grounding remains the behavior evidence itself. We will add an explicit limitations discussion on this point in the revised manuscript. revision: partial
Referee: [Experiments] The experiments section reports the performance gap between explicit task completion and implicit factors but provides no quantitative metrics, error analysis, inter-annotator agreement for the five annotation dimensions, or details on how ground-truth labels were validated against actual user acceptance. Without these, it is not possible to determine whether the data supports the reported limitations.

Authors: We will revise the experiments section to include quantitative metrics on the performance gaps, a detailed error analysis, inter-annotator agreement scores for the five annotation dimensions, and expanded details on how ground-truth labels were derived from the behavior evidence and related to user acceptance. These additions will be incorporated in the next version of the manuscript. revision: yes

standing simulated objections not resolved

Independent validation of retained implicit decision factors through correlation with post-interaction satisfaction scores, user studies, or A/B tests cannot be performed with the existing anonymized historical dataset without conducting new experiments outside the current scope.

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The restore-identify-filter framework is a methodological proposal for constructing the benchmark from real-world anonymized user behavior data by reconstructing needs, identifying factors, and retaining those with pre-query support. The central experimental claim (strong explicit-task performance but limitations on implicit factors) is obtained via downstream evaluation and annotation on the resulting benchmark rather than reducing by definition or construction to the framework inputs. No equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text that would force equivalence between outputs and inputs. The derivation remains self-contained against the external data source.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No information on free parameters, axioms, or invented entities is available from the abstract alone.

pith-pipeline@v0.9.1-grok · 5832 in / 1072 out tokens · 58065 ms · 2026-06-27T01:29:56.505459+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

58 extracted references · 2 linked inside Pith

[1]

Transportation Science, 51(2): 566–591

Customizable Route Planning in Road Networks. Transportation Science, 51(2): 566–591. Dubois, Y .; Li, C. X.; Taori, R.; Zhang, T.; Gulrajani, I.; Ba, J.; Guestrin, C.; Liang, P.; and Hashimoto, T. B. 2023. Al- pacaFarm: A Simulation Framework for Methods that Learn from Human Feedback. InAdvances in Neural Information Processing Systems, volume 36. Feng,...

Pith/arXiv arXiv 2023
[2]

NeurIPS 2025 Workshop MTI-LLM

AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents. NeurIPS 2025 Workshop MTI-LLM. LBS-IntentBench Contributors. 2026. LBS-IntentBench: A Real-World Benchmark for Implicit Intent Inference and Spatio-Temporal Reasoning. https://github.com/lbs- researcher/LBS-IntentBench. GitHub repository; accessed 2026-06-03. Li, T...

Pith/arXiv arXiv 2025
[3]

Villegas, N

The Orienteering Problem: A Survey.European Jour- nal of Operational Research, 209(1): 1–10. Villegas, N. M.; S´anchez, C.; D´ıaz-Cely, J.; and Tamura, G
[4]

Walker, M

Characterizing Context-Aware Recommender Sys- tems: A Systematic Literature Review.Knowledge-Based Systems, 140: 173–200. Walker, M. A.; Litman, D. J.; Kamm, C. A.; and Abella, A
[5]

InProceedings of the 35th Annual Meeting of the Association for Computational Linguistics

PARADISE: A Framework for Evaluating Spoken Di- alogue Agents. InProceedings of the 35th Annual Meeting of the Association for Computational Linguistics. Wang, J.; Mo, F.; Ma, W.; Sun, P.; Zhang, M.; and Nie, J.-Y . 2024. A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models. InProceedings of the 2024 Conference on Empirical Methods i...

arXiv 2024
[6]

Based on ‘full_intent‘, simulate a real user who knows their complete need

Answer on demand. Based on ‘full_intent‘, simulate a real user who knows their complete need. Only when the Agent explicitly requires a user response, provide a minimally sufficient answer to the Agent’s question. You are not helping the Agent complete the task, and you must not proactively fill in all unasked information for the Agent
[7]

When the termination conditions are met and the current turn does not contain any new request, new question, or new information request, immediately output ‘[Finish Conversation ]‘

Strict termination. When the termination conditions are met and the current turn does not contain any new request, new question, or new information request, immediately output ‘[Finish Conversation ]‘. Highest-priority override rules:
[8]

It is unrelated to whether an earlier turn asked a question, whether the user’s previous turn already answered it, or whether the current task still fails to satisfy ‘full_intent‘

Whether the user is allowed to output any new task information is determined only by whether the Agent’s latest message explicitly requests a user response. It is unrelated to whether an earlier turn asked a question, whether the user’s previous turn already answered it, or whether the current task still fails to satisfy ‘full_intent‘
[9]

If the Agent’s latest turn does not proactively ask a question, request supplementary information, or ask for confirmation or selection, the user must not proactively output any new information from ‘full_intent‘. This holds even when the Agent’s response is wrong, partial, incomplete, or deviates from ‘full_intent‘; the user still must not proactively co...
[10]

I cannot click links. Please directly give me the address and phone number

‘full_intent‘ is the user’s complete Table B4: Results when user profiles and historical behaviors are directly provided to the model. Model setting ECR TS IFS IISR AR Eff SES Qwen3.6-plus-th 0.9196 0.4530 0.8940 0.6960 0.6498 0.4512 0.2905 Gemini-3.1-pro-preview-th 0.9189 0.4458 0.9446 0.6710 0.6330 0.4514 0.2814 internal need and true constraints. It ha...
[11]

please",

It explicitly asks a question, ends with a question mark, or semantically requires the user to answer, such as expressions meaning "please", "help me", "tell me", "which one", "how to choose ", "whether", "do you want", "do you still need", or "can you"
[12]

It explicitly requests missing information, such as destination, time, budget, number of people, travel mode, store, entrance, or preference
[13]

It asks the user to choose or confirm, such as choosing bus or subway, whether detailed navigation is needed, or whether to continue
[14]

If the latest assistant message satisfies none of the above:

It asks the user to perform an operation that the user cannot complete, in which case the user should refuse. If the latest assistant message satisfies none of the above:
[15]

The user must not proactively supplement, correct, clarify, restate, or refine any information from ‘full_intent‘
[16]

This remains true even if the assistant’s response does not fully match ‘ full_intent‘
[17]

Even if an earlier turn asked a question or the user answered in the previous turn, the user must not continue to add new task information in the current turn unless the latest assistant message asks again
[18]

If the task is completed or should terminate, the user may only output a brief closing phrase without task information and append ‘[Finish Conversation]‘. Special note: in the following cases, the user should remain silent with respect to task information and should not proactively supplement, correct, clarify , restate, or ask follow-up questions. Only w...
[19]

Answer only the information directly involved in the assistant’s current question
[20]

Do not proactively supplement other content from ‘full_intent‘ that was not asked
[21]

Do not reveal the complete intent in advance merely to reduce turns
[22]

If the assistant asks only one slot, answer only that slot
[23]

If the assistant explicitly asks multiple points and ‘full_intent‘ contains answers to them, answer them together without omission
[24]

Any other supplements ?

When the assistant uses an open-ended question such as "Any other supplements ?" or "Any other information?", directly state the supplementary information that is already explicitly given in ‘ full_intent‘. Do not rewrite potential slots, preconditions, or decision dimensions as questions, rhetorical questions, or items waiting for confirmation. Example: ...
[25]

Only indicate your choice, confirmation, or next-step need

Avoid repeating information already provided by the Agent. Only indicate your choice, confirmation, or next-step need
[26]

Do not explain why you choose something unless required by ‘{ persona}‘

Use natural expression rather than explanation. Do not explain why you choose something unless required by ‘{ persona}‘
[27]

Minimal sufficient

"Minimal sufficient" does not mean omitting information that was asked. If the assistant explicitly asks several aspects in the current turn, cover all asked aspects without exceeding the requested scope
[28]

Do you want to take the subway or call a car?

In open-ended supplement scenarios, still follow minimal sufficiency: only add the most important known information that advances the current task, rather than restating the full ‘full_intent‘. Examples: - Full intent: Go to Wumart supermarket. The user wants public transportation, preferably a low-cost route, and wants route time and distance. - Agent: "...
[29]

Okay, that’s it. [ Finish Conversation]

The Agent’s latest turn does not proactively ask a question or continue to request information. This usually includes cases where the Agent naturally closes the conversation or gives a result without requesting a user response. Example: the user asks to go to a supermarket, says Wumart after being asked which supermarket, and the Agent then provides sever...
[30]

Any other requirements?

The Agent asks a question, but based on ‘ full_intent‘ and ‘persona‘, the user has no more useful information to provide. For example, if the Agent asks "Any other requirements?" and ‘full_intent‘ contains no additional constraints, the user may reply "No, thanks. [Finish Conversation]". ### B. Boundary and deadlock - No action capability refusal: when th...
[31]

Context information, which should be used with priority. It includes ‘adiu‘ as the unique user ID, ‘time‘ as the current time, ‘user_current_loc‘ as the user’s current longitude and latitude, ‘ user_loc_name‘ as the current location name, ‘city‘ as the current city, and ‘ history‘ as the user’s preceding same- day behaviors: {context} Available tools: The...
[38]

If the desired result still cannot be obtained, seek another way to solve the problem

Important: for a single tool, the same parameter set may be retried at most three times. If the desired result still cannot be obtained, seek another way to solve the problem. Profile version: Role: You are an intelligent map assistant. You are responsible for helping the user with the current question and the original request. Your goal is to understand ...
[39]

Context information, which should be used with priority. It includes ‘adiu‘ as the unique user ID, ‘time‘ as the current time, ‘user_current_loc‘ as the user’s current longitude and latitude, ‘ user_loc_name‘ as the current location name, ‘city‘ as the current city, and ‘ history‘ as the user’s preceding same- day behaviors: {context}
[40]

You may call only the tool names listed below; otherwise the call will be rejected

User profile and recent behavior: {user_profile} Available tools: The tools are bound at runtime. You may call only the tool names listed below; otherwise the call will be rejected. {tools_brief} Workflow:
[41]

Analyze the user query, extract core elements, and understand the user’s implicit intent

Intent decomposition. Analyze the user query, extract core elements, and understand the user’s implicit intent
[42]

Check whether the current context information and time information are sufficient

Resource alignment. Check whether the current context information and time information are sufficient. If not, first use tools to fill missing information, such as using POI search to identify a concrete address
[43]

Call tools as needed according to the result of the first step

Chained tool calls. Call tools as needed according to the result of the first step. If the result returned by tool A is ambiguous, adjust the parameters of tool B according to that result until a closed loop is formed
[44]

Convert raw tool results into user-friendly language and remove technical redundancy

Result delivery. Convert raw tool results into user-friendly language and remove technical redundancy. Interaction principles:
[45]

Automatically fill missing information from context when possible, such as using the user’s current location as the default starting point

Unless missing information affects safety or decision-critical executability, invalid questioning is strictly forbidden. Automatically fill missing information from context when possible, such as using the user’s current location as the default starting point
[46]

- If the user must make a choice, proactively ask a key question that is strongly related to executability

Conditions for clarification: - Missing information would make the result non-executable or highly likely to be wrong, for example when the user’s question is unclear, and it cannot be resolved through context or a reasonable default. - If the user must make a choice, proactively ask a key question that is strongly related to executability. Execution principle:
[47]

Go to a charging station near MixC

Important: for a single tool, the same parameter set may be retried at most three times. If the desired result still cannot be obtained, seek another way to solve the problem. C.3 Sample Instance This subsection gives a representative translated benchmark instance. The example illustrates how a short map query is converted into explicit factors, behavior-...

2026
[48]

White Beard HomeBar

2 hours and 33 minutes earlier, the user drove to "White Beard HomeBar", a local destination about 11.81 km away
[49]

Building 22, Sanshuiwan West District

27 minutes and 46 seconds earlier, the user drove to "Building 22, Sanshuiwan West District", a local destination about 11.79 km away
[50]

Coulomb Auto Charging Station (Gaoxin Plaza Fenghui Charging Station)

3 minutes and 16 seconds earlier, the user drove to "Coulomb Auto Charging Station (Gaoxin Plaza Fenghui Charging Station)", a local destination about 0.546 km away
[51]

Go to a charging station near MixC

2 minutes and 29 seconds earlier, the user asked: "Go to a charging station near MixC."
[52]

These charging stations are not the ones I want

2 minutes and 8 seconds earlier, the user said: "These charging stations are not the ones I want."
[53]

Ningbo MixC

1 minute and 42 seconds earlier, the user clarified: "Ningbo MixC."
[54]

Charging station. There is a parking lot and a charging station; search MixC for me

1 minute and 22 seconds earlier, the user said: "Charging station. There is a parking lot and a charging station; search MixC for me."
[55]

Go to a charging station near Ningbo MixC

58 seconds earlier, the user asked again: "Go to a charging station near Ningbo MixC." Full intent: The user wants to go to a charging station near Ningbo MixC. The target should be a charging station directly associated with a parking lot or located inside a parking lot. The user prefers selecting and planning the destination under a driving mode, and TE...
[56]

The destination scope is near MixC
[57]

The target object is a charging station
[58]

Implicit decision factors:

The user needs to go to the target place. Implicit decision factors:
[59]

These charging stations are not the ones I want

The result should prioritize charging stations located inside a parking lot or directly associated with a parking lot. - Source: preference source - Constraint type: soft - Evidence-supported weight: 1.2 - Evidence: in the current session, the user rejected previous candidates by saying "These charging stations are not the ones I want" and then added "The...
[60]

The result should provide the route under a driving mode. - Source: preference source - Constraint type: soft - Evidence-supported weight: 0.8179 - Evidence: the profile indicates that the user has a car, with driving as the top local travel-mode preference at 85.2% and the top non-local travel-mode preference at 86.4%. The current session also contains m...
[61]

find a charging station; if there is a TELD one , check that first

TELD-branded charging stations may be prioritized. - Source: preference source - Constraint type: soft - Evidence-supported weight: 0.4851 - Evidence: short-term evidence from the past three months shows 38 hits for TELD -branded charging stations, the highest option within the charging-station brand -selection dimension. The comparable total in the same ...
[62]

If the final answer provides the name, address, coordinates, administrative area, or spatial relation of Ningbo MixC or a candidate charging station, or claims that a station is inside the MixC parking lot, directly associated with a parking lot, or located near MixC, the statement must be consistent with the user input, context, actual tool returns , or ...
[63]

If the final answer provides the brand, charging-service attribute, or availability information of a candidate charging station, including whether it is a TELD-related station or whether it is a valid charging-station entity, the statement must be consistent with actual tool returns or relevant tool results
[64]

If the final answer provides driving navigation or route information, including origin, destination, travel mode, distance, estimated time, cost, or whether the route leads to the stated charging station, the statement must be consistent with the user input, context, actual tool returns, or relevant tool results

[1] [1]

Transportation Science, 51(2): 566–591

Customizable Route Planning in Road Networks. Transportation Science, 51(2): 566–591. Dubois, Y .; Li, C. X.; Taori, R.; Zhang, T.; Gulrajani, I.; Ba, J.; Guestrin, C.; Liang, P.; and Hashimoto, T. B. 2023. Al- pacaFarm: A Simulation Framework for Methods that Learn from Human Feedback. InAdvances in Neural Information Processing Systems, volume 36. Feng,...

Pith/arXiv arXiv 2023

[2] [2]

NeurIPS 2025 Workshop MTI-LLM

AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents. NeurIPS 2025 Workshop MTI-LLM. LBS-IntentBench Contributors. 2026. LBS-IntentBench: A Real-World Benchmark for Implicit Intent Inference and Spatio-Temporal Reasoning. https://github.com/lbs- researcher/LBS-IntentBench. GitHub repository; accessed 2026-06-03. Li, T...

Pith/arXiv arXiv 2025

[3] [3]

Villegas, N

The Orienteering Problem: A Survey.European Jour- nal of Operational Research, 209(1): 1–10. Villegas, N. M.; S´anchez, C.; D´ıaz-Cely, J.; and Tamura, G

[4] [4]

Walker, M

Characterizing Context-Aware Recommender Sys- tems: A Systematic Literature Review.Knowledge-Based Systems, 140: 173–200. Walker, M. A.; Litman, D. J.; Kamm, C. A.; and Abella, A

[5] [5]

InProceedings of the 35th Annual Meeting of the Association for Computational Linguistics

PARADISE: A Framework for Evaluating Spoken Di- alogue Agents. InProceedings of the 35th Annual Meeting of the Association for Computational Linguistics. Wang, J.; Mo, F.; Ma, W.; Sun, P.; Zhang, M.; and Nie, J.-Y . 2024. A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models. InProceedings of the 2024 Conference on Empirical Methods i...

arXiv 2024

[6] [6]

Based on ‘full_intent‘, simulate a real user who knows their complete need

Answer on demand. Based on ‘full_intent‘, simulate a real user who knows their complete need. Only when the Agent explicitly requires a user response, provide a minimally sufficient answer to the Agent’s question. You are not helping the Agent complete the task, and you must not proactively fill in all unasked information for the Agent

[7] [7]

When the termination conditions are met and the current turn does not contain any new request, new question, or new information request, immediately output ‘[Finish Conversation ]‘

Strict termination. When the termination conditions are met and the current turn does not contain any new request, new question, or new information request, immediately output ‘[Finish Conversation ]‘. Highest-priority override rules:

[8] [8]

It is unrelated to whether an earlier turn asked a question, whether the user’s previous turn already answered it, or whether the current task still fails to satisfy ‘full_intent‘

Whether the user is allowed to output any new task information is determined only by whether the Agent’s latest message explicitly requests a user response. It is unrelated to whether an earlier turn asked a question, whether the user’s previous turn already answered it, or whether the current task still fails to satisfy ‘full_intent‘

[9] [9]

If the Agent’s latest turn does not proactively ask a question, request supplementary information, or ask for confirmation or selection, the user must not proactively output any new information from ‘full_intent‘. This holds even when the Agent’s response is wrong, partial, incomplete, or deviates from ‘full_intent‘; the user still must not proactively co...

[10] [10]

I cannot click links. Please directly give me the address and phone number

‘full_intent‘ is the user’s complete Table B4: Results when user profiles and historical behaviors are directly provided to the model. Model setting ECR TS IFS IISR AR Eff SES Qwen3.6-plus-th 0.9196 0.4530 0.8940 0.6960 0.6498 0.4512 0.2905 Gemini-3.1-pro-preview-th 0.9189 0.4458 0.9446 0.6710 0.6330 0.4514 0.2814 internal need and true constraints. It ha...

[11] [11]

please",

It explicitly asks a question, ends with a question mark, or semantically requires the user to answer, such as expressions meaning "please", "help me", "tell me", "which one", "how to choose ", "whether", "do you want", "do you still need", or "can you"

[12] [12]

It explicitly requests missing information, such as destination, time, budget, number of people, travel mode, store, entrance, or preference

[13] [13]

It asks the user to choose or confirm, such as choosing bus or subway, whether detailed navigation is needed, or whether to continue

[14] [14]

If the latest assistant message satisfies none of the above:

It asks the user to perform an operation that the user cannot complete, in which case the user should refuse. If the latest assistant message satisfies none of the above:

[15] [15]

The user must not proactively supplement, correct, clarify, restate, or refine any information from ‘full_intent‘

[16] [16]

This remains true even if the assistant’s response does not fully match ‘ full_intent‘

[17] [17]

Even if an earlier turn asked a question or the user answered in the previous turn, the user must not continue to add new task information in the current turn unless the latest assistant message asks again

[18] [18]

If the task is completed or should terminate, the user may only output a brief closing phrase without task information and append ‘[Finish Conversation]‘. Special note: in the following cases, the user should remain silent with respect to task information and should not proactively supplement, correct, clarify , restate, or ask follow-up questions. Only w...

[19] [19]

Answer only the information directly involved in the assistant’s current question

[20] [20]

Do not proactively supplement other content from ‘full_intent‘ that was not asked

[21] [21]

Do not reveal the complete intent in advance merely to reduce turns

[22] [22]

If the assistant asks only one slot, answer only that slot

[23] [23]

If the assistant explicitly asks multiple points and ‘full_intent‘ contains answers to them, answer them together without omission

[24] [24]

Any other supplements ?

When the assistant uses an open-ended question such as "Any other supplements ?" or "Any other information?", directly state the supplementary information that is already explicitly given in ‘ full_intent‘. Do not rewrite potential slots, preconditions, or decision dimensions as questions, rhetorical questions, or items waiting for confirmation. Example: ...

[25] [25]

Only indicate your choice, confirmation, or next-step need

Avoid repeating information already provided by the Agent. Only indicate your choice, confirmation, or next-step need

[26] [26]

Do not explain why you choose something unless required by ‘{ persona}‘

Use natural expression rather than explanation. Do not explain why you choose something unless required by ‘{ persona}‘

[27] [27]

Minimal sufficient

"Minimal sufficient" does not mean omitting information that was asked. If the assistant explicitly asks several aspects in the current turn, cover all asked aspects without exceeding the requested scope

[28] [28]

Do you want to take the subway or call a car?

In open-ended supplement scenarios, still follow minimal sufficiency: only add the most important known information that advances the current task, rather than restating the full ‘full_intent‘. Examples: - Full intent: Go to Wumart supermarket. The user wants public transportation, preferably a low-cost route, and wants route time and distance. - Agent: "...

[29] [29]

Okay, that’s it. [ Finish Conversation]

The Agent’s latest turn does not proactively ask a question or continue to request information. This usually includes cases where the Agent naturally closes the conversation or gives a result without requesting a user response. Example: the user asks to go to a supermarket, says Wumart after being asked which supermarket, and the Agent then provides sever...

[30] [30]

Any other requirements?

The Agent asks a question, but based on ‘ full_intent‘ and ‘persona‘, the user has no more useful information to provide. For example, if the Agent asks "Any other requirements?" and ‘full_intent‘ contains no additional constraints, the user may reply "No, thanks. [Finish Conversation]". ### B. Boundary and deadlock - No action capability refusal: when th...

[31] [31]

Context information, which should be used with priority. It includes ‘adiu‘ as the unique user ID, ‘time‘ as the current time, ‘user_current_loc‘ as the user’s current longitude and latitude, ‘ user_loc_name‘ as the current location name, ‘city‘ as the current city, and ‘ history‘ as the user’s preceding same- day behaviors: {context} Available tools: The...

[32] [38]

If the desired result still cannot be obtained, seek another way to solve the problem

Important: for a single tool, the same parameter set may be retried at most three times. If the desired result still cannot be obtained, seek another way to solve the problem. Profile version: Role: You are an intelligent map assistant. You are responsible for helping the user with the current question and the original request. Your goal is to understand ...

[33] [39]

Context information, which should be used with priority. It includes ‘adiu‘ as the unique user ID, ‘time‘ as the current time, ‘user_current_loc‘ as the user’s current longitude and latitude, ‘ user_loc_name‘ as the current location name, ‘city‘ as the current city, and ‘ history‘ as the user’s preceding same- day behaviors: {context}

[34] [40]

You may call only the tool names listed below; otherwise the call will be rejected

User profile and recent behavior: {user_profile} Available tools: The tools are bound at runtime. You may call only the tool names listed below; otherwise the call will be rejected. {tools_brief} Workflow:

[35] [41]

Analyze the user query, extract core elements, and understand the user’s implicit intent

Intent decomposition. Analyze the user query, extract core elements, and understand the user’s implicit intent

[36] [42]

Check whether the current context information and time information are sufficient

Resource alignment. Check whether the current context information and time information are sufficient. If not, first use tools to fill missing information, such as using POI search to identify a concrete address

[37] [43]

Call tools as needed according to the result of the first step

Chained tool calls. Call tools as needed according to the result of the first step. If the result returned by tool A is ambiguous, adjust the parameters of tool B according to that result until a closed loop is formed

[38] [44]

Convert raw tool results into user-friendly language and remove technical redundancy

Result delivery. Convert raw tool results into user-friendly language and remove technical redundancy. Interaction principles:

[39] [45]

Automatically fill missing information from context when possible, such as using the user’s current location as the default starting point

Unless missing information affects safety or decision-critical executability, invalid questioning is strictly forbidden. Automatically fill missing information from context when possible, such as using the user’s current location as the default starting point

[40] [46]

- If the user must make a choice, proactively ask a key question that is strongly related to executability

Conditions for clarification: - Missing information would make the result non-executable or highly likely to be wrong, for example when the user’s question is unclear, and it cannot be resolved through context or a reasonable default. - If the user must make a choice, proactively ask a key question that is strongly related to executability. Execution principle:

[41] [47]

Go to a charging station near MixC

Important: for a single tool, the same parameter set may be retried at most three times. If the desired result still cannot be obtained, seek another way to solve the problem. C.3 Sample Instance This subsection gives a representative translated benchmark instance. The example illustrates how a short map query is converted into explicit factors, behavior-...

2026

[42] [48]

White Beard HomeBar

2 hours and 33 minutes earlier, the user drove to "White Beard HomeBar", a local destination about 11.81 km away

[43] [49]

Building 22, Sanshuiwan West District

27 minutes and 46 seconds earlier, the user drove to "Building 22, Sanshuiwan West District", a local destination about 11.79 km away

[44] [50]

Coulomb Auto Charging Station (Gaoxin Plaza Fenghui Charging Station)

3 minutes and 16 seconds earlier, the user drove to "Coulomb Auto Charging Station (Gaoxin Plaza Fenghui Charging Station)", a local destination about 0.546 km away

[45] [51]

Go to a charging station near MixC

2 minutes and 29 seconds earlier, the user asked: "Go to a charging station near MixC."

[46] [52]

These charging stations are not the ones I want

2 minutes and 8 seconds earlier, the user said: "These charging stations are not the ones I want."

[47] [53]

Ningbo MixC

1 minute and 42 seconds earlier, the user clarified: "Ningbo MixC."

[48] [54]

Charging station. There is a parking lot and a charging station; search MixC for me

1 minute and 22 seconds earlier, the user said: "Charging station. There is a parking lot and a charging station; search MixC for me."

[49] [55]

Go to a charging station near Ningbo MixC

58 seconds earlier, the user asked again: "Go to a charging station near Ningbo MixC." Full intent: The user wants to go to a charging station near Ningbo MixC. The target should be a charging station directly associated with a parking lot or located inside a parking lot. The user prefers selecting and planning the destination under a driving mode, and TE...

[50] [56]

The destination scope is near MixC

[51] [57]

The target object is a charging station

[52] [58]

Implicit decision factors:

The user needs to go to the target place. Implicit decision factors:

[53] [59]

These charging stations are not the ones I want

The result should prioritize charging stations located inside a parking lot or directly associated with a parking lot. - Source: preference source - Constraint type: soft - Evidence-supported weight: 1.2 - Evidence: in the current session, the user rejected previous candidates by saying "These charging stations are not the ones I want" and then added "The...

[54] [60]

The result should provide the route under a driving mode. - Source: preference source - Constraint type: soft - Evidence-supported weight: 0.8179 - Evidence: the profile indicates that the user has a car, with driving as the top local travel-mode preference at 85.2% and the top non-local travel-mode preference at 86.4%. The current session also contains m...

[55] [61]

find a charging station; if there is a TELD one , check that first

TELD-branded charging stations may be prioritized. - Source: preference source - Constraint type: soft - Evidence-supported weight: 0.4851 - Evidence: short-term evidence from the past three months shows 38 hits for TELD -branded charging stations, the highest option within the charging-station brand -selection dimension. The comparable total in the same ...

[56] [62]

If the final answer provides the name, address, coordinates, administrative area, or spatial relation of Ningbo MixC or a candidate charging station, or claims that a station is inside the MixC parking lot, directly associated with a parking lot, or located near MixC, the statement must be consistent with the user input, context, actual tool returns , or ...

[57] [63]

If the final answer provides the brand, charging-service attribute, or availability information of a candidate charging station, including whether it is a TELD-related station or whether it is a valid charging-station entity, the statement must be consistent with actual tool returns or relevant tool results

[58] [64]

If the final answer provides driving navigation or route information, including origin, destination, travel mode, distance, estimated time, cost, or whether the route leads to the stated charging station, the statement must be consistent with the user input, context, actual tool returns, or relevant tool results