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ARXIV:2605.03308 · LLM REASONING · SUBMITTED 06 MAY · 20:26 UTC · FRESHNESS STALE
ARXIV:2605.03308LLM REASONINGSUBMITTED 06 MAY · 20:26 UTCFRESHNESS STALEBo-Wen Zhang · Jin Ye · Peng-Yu Hua · Jia-Wei Cao · Jie-Jing Shao · Yu-Feng Li · +1 at arXiv
A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits.
Opportunity summary
Pain A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability…
Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our results highlight a clear contrast in performance: while LLMs are proficient in extracting explicit constraints, they struggle to infer implicit, open-world requirements. Code…
LLM Reasoning moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
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A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits.
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10.48550/arXiv.2605.03308A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits.
Abstract
Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it difficult to analyze the root causes of failures. To bridge this gap, we decompose travel planning into five constituent atomic sub-capabilities, including \emph{Constraint Extraction}, \emph{Tool Use}, \emph{Plan Generation}, \emph{Error Identification}, and \emph{Error Correction}. We implement a decoupled evaluation protocol leveraging oracle intermediate contexts to rigorously isolate these components, thereby measuring the atomic performance boundary without the noise of cascading errors. Our results highlight a clear contrast in performance: while LLMs are proficient in extracting explicit constraints, they struggle to infer implicit, open-world requirements. Furthermore, they exhibit structural biases in plan generation and suffer from ineffective self-correction, characterized by excessive sensitivity and erroneous persistence. These findings offer precise directions for improving LLM reasoning and planning abilities.
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PROBLEM
A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it di...
METHOD
Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it difficult to analyze the root...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our results highlight a clear contrast in performance: while LLMs are proficient in extracting explicit constraints, they struggle to infer implicit, open-world requirements. Code availability is flagged...
WHY NOW
LLM Reasoning moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it difficult to analyze the root causes of failures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it difficult to analyze the root causes of failures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our results highlight a clear contrast in performance: while LLMs are proficient in extracting explicit constraints, they struggle to infer implicit, open-world requirements. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Reasoning moved forward this cycle; last verified May 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A decoupled evaluation framework for LLM travel planning that isolates atomic capabilities to pinpoint reasoning and planning deficits.
Segment
LLM Reasoning
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Commercial read
5.0/10 public viability
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