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      {
        "id": "S0.F1",
        "caption": "Figure 1 : Comparison between the existing retrieval system and the proposed retrieval system. (a) The existing system combines multiple lexical and dense retrieval sources. (b) The proposed system uses model-centric retrieval as the primary semantic retrieval path and retains one syntax tree as a complementary lexical retrieval source.",
        "images": [
          "2611.00530v1/wesearch_online.png"
        ]
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        "caption": "Figure 2 : Overview of WeSearch. Clusters first define a query-specific candidate document space, over which fine-grained document-level ranking produces the final results.",
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          "2611.00530v1/wesearch_overview.png"
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        "caption": "Algorithm 1 Online inference with WeSearch 1: Query q q ; cluster collection 𝒞 \\mathcal{C} with embeddings 𝐄 𝒞 \\mathbf{E}_{\\mathcal{C}} ; OPQ-coded document representations; document-side features 2: Top- K K retrieved documents 𝒟 @ ​ K ≺ \\mathcal{D}_{@K}^{\\prec} 3: Encode query: 𝐡 q ← f θ ​ ( q ) \\mathbf{h}_{q}\\leftarrow f_{\\theta}(q) 4: // Stage 1: Cluster-level candidate-space reduction 5: Retrieve relevant clusters: 𝒞 q ← TopN c ∈ 𝒞 ⁡ s c ​ ( q , c ) \\mathcal{C}_{q}\\leftarrow\\operatorname{TopN}_{c\\in\\mathcal{C}}s_{c}(q,c) 6: Gather candidate documents: 𝒟 q ← ⋃ c ∈ 𝒞 q 𝒟 c \\mathcal{D}_{q}\\leftarrow\\bigcup_{c\\in\\mathcal{C}_{q}}\\mathcal{D}_{c} 7: // Stage 2: Document-level ranking 8: Coarse shortlist by ADC-accelerated semantic similarity: 𝒟 q P ← TopP d ∈ 𝒟 q ⁡ s coarse ​ ( q , d ) \\mathcal{D}_{q}^{P}\\leftarrow\\operatorname{TopP}_{d\\in\\mathcal{D}_{q}}s_{\\text{coarse}}(q,d) 9: Reconstruct 𝐡 ^ d \\widehat{\\mathbf{h}}_{d} from OPQ codes and gather 𝐞 f \\mathbf{e}_{f} for all d ∈ 𝒟 q P d\\in\\mathcal{D}_{q}^{P} 10: Compute fine-grained scores s d ​ ( q , d , 𝐞 f ) s_{d}(q,d,\\mathbf{e}_{f}) for all d ∈ 𝒟 q P d\\in\\mathcal{D}_{q}^{P} 11: return 𝒟 @ ​ K ≺ ← TopK d ∈ 𝒟 q P ⁡ s d ​ ( q , d , 𝐞 f ) \\mathcal{D}_{@K}^{\\prec}\\leftarrow\\operatorname{TopK}_{d\\in\\mathcal{D}_{q}^{P}}s_{d}(q,d,\\mathbf{e}_{f})",
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        "id": "S3.F3",
        "caption": "Figure 3 : The offline index construction procedure.",
        "images": [
          "2611.00530v1/offline.png"
        ]
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        "id": "S5.F4",
        "caption": "Figure 4 : Rolling mounting of fresh documents. New documents are assigned to the existing cluster space, quantized with fixed codebooks, and made visible through an atomic index update.",
        "images": [
          "2611.00530v1/rolling_moount.png"
        ]
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      {
        "id": "S6.F5",
        "caption": "Figure 5 : Experimental results on the Base corpus.",
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      },
      {
        "id": "S6.T1",
        "caption": "Clustering Doc Memory Recall@300 2 ​ k × 1 ​ k 2k\\times 1k OPQ ( 32,256 ) (32,256) 46.66% 2 ​ k × 1 ​ k 2k\\times 1k OPQ ( 32,512 ) (32,512) 47.10% 2 ​ k × 1 ​ k 2k\\times 1k OPQ ( 64,256 ) (64,256) 51.54% 10 ​ k × 200 10k\\times 200 OPQ ( 64,256 ) (64,256) 51.34% 2 ​ k × 1 ​ k 2k\\times 1k Dense( 32 32 ) 33.07% Table 1 : Ablation on clustering structure and document memory. a × b a\\times b denotes two-level RQ-KMeans with a a coarse clusters, each split into b b sub-clusters. OPQ ​ ( m , K ) \\text{OPQ}(m,K) denotes m m sub-spaces\nwith codebook size K K (Sec. 3.2.2 ); Dense( 32 32 ) stores a 32 32 -dim embedding without quantization. Cluster Recall@ 10 ​ k 10k is stable across all rows and is therefore omitted.",
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      },
      {
        "id": "S6.T2",
        "caption": "Method Relevance–1 Relevance–2 Long Dwell Effective Click Online 0.3621 0.3087 0.4407 0.4604 WeSearch w/o features & mining 0.4072 0.3277 0.4323 0.4379 WeSearch w/o mining 0.4271 0.3865 0.4961 0.4973 WeSearch (full) 0.4344 0.3794 0.5505 0.5481 Table 2 : Ablation of structured document features and training-data mining in WeSearch, measured by Recall@100. “w/o features & mining”\nuses only semantic embeddings and the basic relevance supervision;\n“w/o mining” adds structured document features; the full model\nfurther incorporates multi-source positive/negative mining.",
        "images": []
      },
      {
        "id": "S6.T3",
        "caption": "Method Satisfaction density Relevance density Online 0.1732 0.4575 WeSearch 0.1871 0.4662 WeSearch + one syntax tree 0.2088 0.4854 Table 3 : Teacher-assessed high-quality result density.",
        "images": []
      },
      {
        "id": "S6.T4",
        "caption": "Evaluation group CTR Base corpus +0.27% Expanded corpus +0.61% Expanded corpus with Fresh corpus +0.53% Long-query traffic † +1.10% Table 4 : Relative changes in CTR in the one-week online A/B test, compared with the corresponding production baselines. The first three rows report full-traffic results under each corpus configuration. † Long-query traffic is evaluated on the Expanded corpus with Fresh corpus configuration. Bold values indicate statistically\nsignificant gains ( p < 0.05 p<0.05 ).",
        "images": []
      },
      {
        "id": "S6.T5",
        "caption": "Metric Relative change CTR +0.68% Dwell time † +0.71% Article clicks ‡ +2.27% Table 5 : Relative changes in the one-month online A/B test on the Expanded corpus with Fresh corpus under full traffic, compared with the corresponding production baseline. † Average per-user dwell time on the search results page. ‡ Per-user clicks on WeChat Official Account articles from\nsearch results. Bold values indicate statistically significant gains\n( p < 0.05 p<0.05 ).",
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