DDE models class-wise positive feature Gaussians and negative label distributions to boost ID accuracy and OOD detection in zero-shot noisy TTA, reporting 3.70% harmonic mean gain and 6.20% FPR95 drop on ImageNet.
GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases
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abstract
Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product search, academic paper search, and precision-medicine inquiries. Existing hybrid retrieval systems on SKBs either use the graph only for query expansion, mix textual and structural branches under a global weighting, or rely on fine-tuned graph-traversal generators. We present GRASP, a three-stage SKB retrieval framework unifying plan-based graph retrieval, plan-conditioned fusion with a dense retriever, and a fine-tuned reranker over the fused candidates. GRASP substantially advances the state of the art on every metric across the three STaRK benchmarks, lifting average Hit@1 from 62.0 to 73.9. Ablation and sensitivity studies further confirm the effectiveness and robustness of GRASP.
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cs.CV 1years
2026 1verdicts
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Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs
DDE models class-wise positive feature Gaussians and negative label distributions to boost ID accuracy and OOD detection in zero-shot noisy TTA, reporting 3.70% harmonic mean gain and 6.20% FPR95 drop on ImageNet.