Data collection and refinement

[Qlalq]

Designed to help users quickly grasp the Agent's execution logic and to curate trajectory data for post-training

To achieve this, the following two tasks must be accomplished:

1. Comprehensive Data Collection
   Data should be aggregated from two primary sources: APIs (capturing single-turn Q&A interactions) and Agents (recording tool/skill invocations). Additionally, capturing LLMs' Chain-of-Thought (CoT) traces is essential—specifically by recovering encrypted or hidden reasoning steps. For reference on CoT extraction, please see: https://arxiv.org/pdf/2608.09867.

2. Curated Summary Documentation
   The output format is flexible (e.g., Markdown or JSON key-value pairs are both acceptable). The current schema includes the following fields, with room for future expansion:

   • Model Name
   • Timestamp
   • Token Consumption
   • User Input
   • Response : Given that Agent responses can be excessively long, storing them in full is often impractical. Instead, responses should be segmented by logical flow, with each segment accompanied by a concise summary.

For example :

  "model_name": "Qwen3.8-Max",
  "timestamp": "2026-09-04 11:53:00",
  "token_consumption": {
    "prompt_tokens": 1250,
    "completion_tokens": 3840,
    "total_tokens": 5090
  },
  "user_input": "Analyze the user churn reasons for our SaaS product over the last three months and provide improvement suggestions.",
  "response": [
    {
      "segment_id": 1,
      "logical_section": "Current Status Overview",
      "summary": "Overall churn rate increased by 12% MoM in the past quarter, primarily among SMB customers, with 68% of losses occurring within 30 days post-conversion."
    },
    {
      "segment_id": 2,
      "logical_section": "Root Cause Analysis",
      "summary": "Data analysis tool invocation revealed three key drivers: (1) Onboarding completion rate at only 41%; (2) Competitor's low-price plan triggered migration of price-sensitive users; (3) Average support ticket response time exceeded 4 hours, with negative feedback concentrated in after-sales service."
    },
    {
      "segment_id": 3,
      "logical_section": "Recommendations & Prioritization",
      "summary": "Proposed P0-P2 prioritized actions: P0 - Optimize onboarding flow (estimated +8% retention); P1 - Launch exclusive renewal discounts for existing customers; P2 - Reduce support SLA from 4h to 1h and deploy AI pre-replies."
    }
  ]
}```