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arxiv: 2605.27882 · v1 · pith:3JIWLAUTnew · submitted 2026-05-27 · 💻 cs.CL · cs.AI

VibeSearchBench: Benchmarking Long-horizon Proactive Search in the Wild

Xiaohongshu Inc This is my paper

Pith reviewed 2026-06-29 13:11 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords benchmarkLLM agentsmulti-turn searchintent elicitationknowledge graphproactive agentsevaluation gapVibeSearch
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The pith

LLM agents remain inadequate for real-world vague multi-turn search, scoring at most 30.3 F1.

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

Existing search benchmarks rely on over-specified queries and single-turn interactions that fail to capture how users actually work with agents to refine vague intents across many dialogue turns. The paper defines this collaborative paradigm as VibeSearch and introduces VibeSearchBench, a set of 200 bilingual tasks across 20 domains that pair user personas with schema-free ground-truth knowledge graphs. Evaluation runs through a progressive-disclosure simulator and graph-matching scoring. When seven frontier models are tested under ReAct and OpenClaw frameworks, the strongest result reaches only 30.30 F1, pointing to shortfalls in long-context reasoning, proactive elicitation, and structured knowledge building.

Core claim

VibeSearchBench shows that no tested model supports VibeSearch at usable levels. In VibeSearch users begin with underspecified goals and agents must elicit details over extended turns while assembling accurate structured knowledge; the benchmark measures this process directly and finds a maximum F1 of 30.30.

What carries the argument

VibeSearchBench, evaluated through a progressive-disclosure simulator and graph-matching against schema-free ground-truth knowledge graphs.

If this is right

  • Agents must improve long-context reasoning to sustain coherent multi-turn refinement.
  • Proactive intent elicitation needs to become a primary design target rather than an optional behavior.
  • Structured knowledge construction from conversational input must advance before practical deployment.
  • Single-turn and over-specified benchmarks systematically overestimate readiness for actual search use.

Where Pith is reading between the lines

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

  • User satisfaction in deployed search may hinge more on handling ambiguity than on answering explicit queries.
  • The simulator-plus-graph approach could transfer to other long-horizon interactive tasks such as planning or tutoring.
  • Closing the benchmark-to-experience gap would require new training signals that reward progressive disclosure and knowledge assembly.

Load-bearing premise

The 200 manually curated tasks and the progressive-disclosure simulator plus graph-matching evaluation accurately reflect real-world user search behavior and satisfaction gaps.

What would settle it

A study in which models scoring below 35 F1 on the benchmark nevertheless produce high real-user satisfaction in uncontrolled multi-turn searches would indicate the tasks or simulator do not capture the intended gap.

Figures

Figures reproduced from arXiv: 2605.27882 by Xiaohongshu Inc.

Figure 1
Figure 1. Figure 1: Figure 1: Overview of VibeSearchBench. (Left) A user persona [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Domain distribution of VibeSearchBench. We propose an information-entailment-based eval￾uation framework that uses an LLM-as-judge to perform graph matching, accommodating seman￾tically equivalent expressions (e.g., entity aliases, relation synonyms) unlike exact matching. For recall, the judge determines whether each ground￾truth triple is “covered” by the predicted graph, considering direct matches, subs… view at source ↗
Figure 3
Figure 3. Figure 3: Resource consumption vs. F1 score. Top row: output tokens vs. F1; bottom row: total tool calls [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original abstract

LLM-based agents score well on search benchmarks, yet real users consistently find results unsatisfying, revealing a persistent evaluation-experience gap. We attribute this gap to existing benchmarks' reliance on over-specified queries, single-turn interactions, and fixed-schema evaluation, none of which reflect real search behavior where users and agents collaboratively refine vague intent through multi-turn dialogue. We term this paradigm VibeSearch and introduce VibeSearchBench, a benchmark comprising 200 manually curated bilingual (Chinese and English) tasks across 20 domains, split into VibeSearch-Pro (professional) and VibeSearch-Daily (daily-life) subsets. Each task pairs a user persona with a schema-free ground-truth knowledge graph, and is evaluated through a progressive-disclosure user simulator and a graph-matching evaluation framework. We benchmark seven frontier models under both the ReAct framework and the OpenClaw agent harness. Results show that all models remain substantially inadequate for VibeSearch (best F1: 30.30), highlighting the need for fundamental advances in long-context reasoning, proactive intent elicitation, and structured knowledge construction.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces VibeSearchBench to address an evaluation-experience gap in LLM agent search benchmarks. It defines the VibeSearch paradigm as multi-turn collaborative refinement of vague intents and presents a benchmark of 200 manually curated bilingual tasks across 20 domains (split into professional and daily-life subsets). Each task pairs a persona with a schema-free ground-truth knowledge graph and is evaluated via a progressive-disclosure user simulator plus graph-matching F1. Seven frontier models are tested under ReAct and OpenClaw, with the best reported F1 of 30.30, from which the authors conclude that current models are substantially inadequate and that fundamental advances in long-context reasoning, proactive intent elicitation, and structured knowledge construction are required.

Significance. If the simulator and graph-matching metric are shown to track real user satisfaction, the benchmark could usefully redirect research away from over-specified single-turn tasks toward realistic long-horizon proactive search. The work supplies a concrete testbed and reproducible harness that could support follow-on model development.

major comments (2)
  1. [Abstract / Evaluation] Abstract and Evaluation section: The headline claim that all models remain substantially inadequate (best F1 30.30) is load-bearing on the assertion that the progressive-disclosure simulator plus graph-matching F1 accurately reflects real-world user satisfaction gaps. No human correlation data, inter-rater reliability statistics for the 200 KG constructions, or ablation showing that simulator trajectories produce satisfaction distributions matching real multi-turn search logs are supplied; without such evidence the inadequacy conclusion does not follow from the reported numbers.
  2. [Benchmark Construction] Benchmark Construction section: The 200 tasks are described as manually curated with persona+schema-free KG pairs, yet no details on curation protocol, domain coverage criteria, or validation that the tasks instantiate the claimed vague-intent multi-turn behavior are given. This directly affects whether the performance gap generalizes beyond the specific 200 instances.
minor comments (2)
  1. [Abstract] The abstract states results for both ReAct and OpenClaw but does not indicate whether the reported best F1 aggregates across frameworks or reports the stronger of the two; clarify in the results table.
  2. [Evaluation Framework] Notation for the graph-matching F1 (precision/recall over KG nodes/edges) should be defined explicitly with an equation or pseudocode in the evaluation framework section.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback highlighting important aspects of validation and reproducibility in VibeSearchBench. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract / Evaluation] Abstract and Evaluation section: The headline claim that all models remain substantially inadequate (best F1 30.30) is load-bearing on the assertion that the progressive-disclosure simulator plus graph-matching F1 accurately reflects real-world user satisfaction gaps. No human correlation data, inter-rater reliability statistics for the 200 KG constructions, or ablation showing that simulator trajectories produce satisfaction distributions matching real multi-turn search logs are supplied; without such evidence the inadequacy conclusion does not follow from the reported numbers.

    Authors: We agree that the manuscript does not include human correlation data or ablations against real user logs, so the claim of real-world inadequacy rests on the design rationale of the simulator and metric rather than direct empirical validation. The progressive-disclosure approach and graph-matching F1 were chosen to measure collaborative refinement of vague intents in a schema-free setting, which we view as a necessary step beyond existing single-turn benchmarks. We will revise the Evaluation and Limitations sections to explicitly discuss this gap, qualify the conclusions as applying to performance on the proposed benchmark, and outline directions for future human studies. We will not add new empirical correlation results, as none were collected. revision: partial

  2. Referee: [Benchmark Construction] Benchmark Construction section: The 200 tasks are described as manually curated with persona+schema-free KG pairs, yet no details on curation protocol, domain coverage criteria, or validation that the tasks instantiate the claimed vague-intent multi-turn behavior are given. This directly affects whether the performance gap generalizes beyond the specific 200 instances.

    Authors: We acknowledge that the current description of task curation is high-level. The 200 tasks were created manually by the authors with explicit attention to ensuring initial user intents are vague and require multi-turn elicitation. We will expand the Benchmark Construction section to include the curation protocol (domain selection for balance across professional and daily-life categories, persona-KG pairing guidelines, and internal checks for multi-turn potential). This addition will improve reproducibility without altering the reported results. revision: yes

standing simulated objections not resolved
  • Human correlation data, inter-rater reliability statistics for the 200 KG constructions, or ablations matching simulator trajectories to real multi-turn search logs, as these were not part of the original study and cannot be supplied in revision.

Circularity Check

0 steps flagged

No circularity in benchmark construction or evaluation

full rationale

The paper presents an empirical benchmark (VibeSearchBench) with 200 manually curated tasks, a progressive-disclosure simulator, and graph-matching F1 evaluation. No derivation chain, first-principles prediction, or fitted parameter is claimed; results are direct model runs on the benchmark. No self-citations are load-bearing, and the central claim (model inadequacy at F1 30.30) does not reduce to any input by construction. The work is self-contained as a standard benchmark study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that the curated tasks and evaluation framework capture real user behavior; no free parameters or invented entities with independent evidence are described.

axioms (1)
  • domain assumption The 200 tasks and progressive-disclosure simulator reflect real search behavior where users refine vague intent through multi-turn dialogue.
    Invoked in the motivation and benchmark design description.
invented entities (1)
  • VibeSearch paradigm no independent evidence
    purpose: To name the collaborative multi-turn vague-intent search interaction style.
    New term introduced to frame the benchmark.

pith-pipeline@v0.9.1-grok · 5718 in / 1134 out tokens · 30393 ms · 2026-06-29T13:11:24.190687+00:00 · methodology

discussion (0)

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