Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search

Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search | Route /signal-canvas/probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search",
    "query_text": "Summarize Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search",
  "normalized_query": "2603.15262",
  "route": "/signal-canvas/probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search",
  "paper_ref": "probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search

PDF: https://arxiv.org/pdf/2603.15262v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Signal Canvas receipt window

Watch and verify: Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search

/buildability/probe-then-plan-environment-aware-planning-for-industrial-e-commerce-search

Watchwatch

Subject: Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
To resolve this conflict, we propose Environment-Aware Search Planning (EASP), reformulating search planning as a dynamic reasoning process grounded in environmental reality.
Implicationpartial
Directly stated in abstract as the core contribution of the paper
Verificationpartial
partial
Evidencepartial
Extensive offline evaluations and online A/B testing on JD.com demonstrate that EASP significantly improves relevant recall and achieves substantial lifts in UCVR and GMV.
Implicationpartial
Explicitly stated in abstract with reference to extensive evaluations and A/B testing
Verificationpartial
partial
Evidencepartial
The Planner is initialized via Supervised Fine-Tuning (SFT) to internalize diagnostic capabilities, then aligned with business outcomes (conversion rate) via Reinforcement Learning (RL).
Implicationpartial
Directly described in abstract methodology section
Verificationpartial
partial
Evidencepartial
EASP has been successfully deployed in JD.com's AI-Search system.
Implicationpartial
Explicitly stated in abstract as a deployment outcome
Verificationpartial
partial
Evidencepartial
existing LLM-based paradigms face a fundamental blindness-latency dilemma: query rewriting is agnostic to retrieval capabilities and real-time inventory, yielding invalid plans
Implicationpartial
Directly stated in abstract as problem motivation
Verificationpartial
partial
Evidencepartial
deep search agents rely on iterative tool calls and reflection, incurring seconds of latency incompatible with industrial sub-second budgets
Implicationpartial
Directly stated in abstract as a specific limitation of existing approaches
Verificationpartial
partial
Evidencepartial
Adaptive Online Serving: A complexity-aware routing mechanism selectively activates planning for complex queries, ensuring optimal resource allocation.
Implicationpartial
Directly described in abstract methodology section
Verificationpartial
partial
Evidencepartial
Offline Data Synthesis: A Teacher Agent synthesizes diverse, execution-validated plans by diagnosing the probed environment.
Implicationpartial
Directly described in abstract methodology section
Verificationpartial
partial

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Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search

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