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  3. Track-SQL: Enhancing Generative Language Models with Dual-Ex
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Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL

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Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL

PDF: https://arxiv.org/pdf/2603.05996v1

Source count: 0

Coverage: 17%

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

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Paper Mode

Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL

Overall score: 8/10
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

Missingness
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  • Paper mode pins trust state to the canonical paper kernel.
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Dimensions overall score 8.0

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Keep exploring

Builds On This
SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation
Score 3.0down
Builds On This
AILS-NTUA at SemEval-2026 Task 8: Evaluating Multi-Turn RAG Conversations
Score 3.0down
Builds On This
TurnWise: The Gap between Single- and Multi-turn Language Model Capabilities
Score 6.0down
Builds On This
Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL
Score 6.0down
Prior Work
An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data
Score 8.0stable
Competing Approach
TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas
Score 7.0down
Competing Approach
Schema on the Inside: A Two-Phase Fine-Tuning Method for High-Efficiency Text-to-SQL at Scale
Score 8.0stable
Competing Approach
EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolution
Score 7.0down

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