---
language:
- en
tags:
- feature-extraction
- text-retrieval
- dense-retrieval
- agentic-search
- lrat
- xir-1170
base_model: Qwen/Qwen3-Embedding-0.6B
pipeline_tag: sentence-similarity
---

# Qwen3 Embedding 0.6B LRAT Trajectory Graph Calibrated V1

This is a `Qwen3-Embedding-0.6B` compatible dense retriever checkpoint for
XIR competition 1170.

## Method

The checkpoint starts from the compliant LRAT broad-g16-v2 retriever and is
trained on 91,324 organizer-derived rows:

- 64,606 stable trajectory rehearsal rows;
- 26,718 new query-document edges discovered and independently verified by
  three strong models over organizer-provided trajectory text.

The new edges use bounded reliability calibration based on signals fixed
before evaluation: independent-model vote fraction, minimum confidence,
minimum relevance, task-query versus trajectory-query role, and within-graph
document centrality. Calibration changes only relative loss weight within the
augmentation set; its mean weight remains 1.0.

## Data Boundary

- No external query, answer, passage, or document is used.
- No generated text is added to training.
- Strong models only relabel organizer-provided trajectories and text.
- No leaderboard query, qrel, hidden failure, or per-query result is used as
  training supervision.

## Training

- Base: `Qwen/Qwen3-Embedding-0.6B` through the compliant LRAT lineage
- Global batch: 256
- Group size: 10
- Learning rate: `2e-7`
- Epochs: 1
- Precision: BF16
- Weighted cross-device InfoNCE

## Local Evaluation

On the complete 830-query fixed local evaluation:

- Dense Recall@50: `0.313709`
- Dense Hit@50: `0.740964`
- Fixed-seed Agent recall: `50.431855%`
- Fixed-seed Agent success: `26.867470%`
- Average Search calls: `22.944578`

Local Agent totals are for paired checkpoint selection and are not claimed to
equal the organizer leaderboard score.