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-0.6Bthrough 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.