Steve0927/qwen3-embedding-0.6b-lrat-trajectory-graph-calibrated-v1

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.