Rank Team run_ID Baseline ID errors Avg.MAP(en,es) MAP(en-en) MRR(en-en) NDCG(en-en) P@5(en-en) P@10(en-en) P@100(en-en) MAP(es-es) MRR(es-es) NDCG(es-es) P@5(es-es) P@10(es-es) P@100(es-es) MAP(en-es) MRR(en-es) NDCG(en-es) P@5(en-es) P@10(en-es) P@100(en-es) MAP_male(es-es) MAP_female(es-es) MAP_neutral(es-es) MAP_gender(es-es) RBO(es-es) MAP_male(en-es) MAP_female(en-es) MAP_neutral(en-es) MAP_gender(en-es) RBO(en-es) RBO(en-en) Avg.RBO Representation type Methodology System details LLM usage Bias mitigation External data Models Pipeline Representation LLM strategy Fine-tuned Uses external data Bias handling 1 classum 715941 no no 0.7139 0.7122 0.8752 0.8814 0.805 0.785 0.2505 0.7157 0.8678 0.8814 0.815 0.7875 0.251 0.7038 0.8597 0.874 0.82 0.7925 0.2505 0.7191 0.7242 0.7217 0.9699 0.7108 0.7099 0.7104 0.9684 0.9718 0.9700 Encoder; Decoder Retrieval; Reranking; Rank fusion Prompt engineering; LLM entity extraction; Listwise reranking No JobBERT-v3; KaLM-12B; Octen-8B; Nemotron-8B; ZEmbed1-4B; Harrier-27B; gemini-embedding-001; GPT-5.5; Claude Opus 4.6; Gemini 3 Flash; Cohere-rerank-4; zerank-2 Retrieval+Reranking Dense+Generative Reranking + augmentation No No No 2 classum 707960 no no 0.7117 0.7003 0.848 0.8712 0.805 0.775 0.25 0.7231 0.8694 0.8859 0.825 0.785 0.2507 0.7044 0.8698 0.8762 0.825 0.78 0.2487 0.7282 0.7311 0.7297 0.989 0.711 0.7101 0.7106 0.9892 0.9931 0.9904 Encoder; Decoder Retrieval; Reranking; Rank fusion Prompt engineering; LLM entity extraction; Listwise reranking No JobBERT-v3; KaLM-12B; Octen-8B; Nemotron-8B; ZEmbed1-4B; Harrier-27B; gemini-embedding-001; GPT-5.5; Claude Opus 4.6; Gemini 3 Flash; Cohere-rerank-4; zerank-2 Retrieval+Reranking Dense+Generative Reranking + augmentation No No No 3 classum 715062 no no 0.7097 0.6991 0.844 0.8683 0.805 0.765 0.25 0.7203 0.8564 0.8836 0.815 0.785 0.25 0.7037 0.8729 0.8765 0.83 0.775 0.2487 0.7243 0.7258 0.7251 0.9862 0.7131 0.7091 0.7111 0.9874 0.9929 0.9888 Encoder; Decoder Retrieval; Reranking; Rank fusion Prompt engineering; LLM entity extraction; Listwise reranking No JobBERT-v3; KaLM-12B; Octen-8B; Nemotron-8B; ZEmbed1-4B; Harrier-27B; gemini-embedding-001; GPT-5.5; Claude Opus 4.6; Gemini 3 Flash; Cohere-rerank-4; zerank-2 Retrieval+Reranking Dense+Generative Reranking + augmentation No No No 4 QTPride 696980 no no 0.6632 0.6622 0.8926 0.8651 0.805 0.755 0.2447 0.6641 0.8827 0.8657 0.815 0.76 0.245 0.6355 0.8526 0.8473 0.77 0.715 0.242 0.6763 0.6683 0.6723 0.8979 0.6534 0.6381 0.6458 0.8575 0.8971 0.8842 Encoder Retrieval RRF; Relational Graph Convolutional Network; KG-enhanced retrieval LLM entity extraction No multilingual-e5-large-instruct Retrieval Dense Augmentation No No No 5 QTPride 716138 no no 0.6573 0.649 0.8777 0.8555 0.795 0.715 0.2445 0.6656 0.9181 0.8687 0.81 0.77 0.2477 0.5999 0.8533 0.8305 0.74 0.6725 0.236 0.6774 0.6734 0.6754 0.9023 0.6287 0.5939 0.6113 0.7888 0.8741 0.8551 Encoder Retrieval RRF; Relational Graph Convolutional Network; KG-enhanced retrieval LLM entity extraction No multilingual-e5-large-instruct Retrieval Dense Augmentation No No No 6 bipboopbipboop 715693 no no 0.6532 0.6552 0.9186 0.8686 0.835 0.715 0.239 0.6512 0.9116 0.8675 0.8 0.7325 0.2435 0.6438 0.8986 0.8631 0.84 0.7225 0.238 0.6618 0.657 0.6594 0.8837 0.6531 0.6491 0.6511 0.8631 0.8714 0.8727 Encoder; Decoder Retrieval; Reranking Geometric-mean fusion; Cosine similarity Loss; LoRA Prompt engineering; LLM-as-a-judge; Pointwise reranking; Tournament listwise reranker No Qwen3-Reranker-8B; Qwen3-235B; BGE-LoRA; MiniLM Retrieval+Reranking Dense+Generative Reranking + augmentation Yes No No 7 Skillberg 709705 no no 0.6496 0.6608 0.8703 0.8567 0.795 0.7425 0.2415 0.6384 0.8475 0.8469 0.765 0.73 0.2428 0.6221 0.8222 0.8345 0.745 0.7025 0.2353 0.6446 0.648 0.6463 0.9336 0.6311 0.6303 0.6307 0.9309 0.9369 0.9338 Encoder; Lexical Retrieval; Reranking Hybrid retrieval; Curriculum Learning; GISTEmbedLoss; MatryoshkaLoss; Proprietary job-skill knowledge graph; Cross-Encoder reranking Prompt engineering; Data Augmentation No multilingual-e5-large-instruct; BM25; Mistral; Jina cross-encoder v2 Retrieval+Reranking Dense+Lexical Augmentation Yes No No 8 bipboopbipboop 715145 no no 0.6496 0.6552 0.9186 0.8686 0.835 0.715 0.239 0.6439 0.9228 0.865 0.81 0.735 0.2415 0.6239 0.8412 0.8486 0.825 0.7025 0.2335 0.6452 0.6583 0.6518 0.8795 0.6445 0.6239 0.6342 0.8539 0.8714 0.8683 Encoder; Decoder Retrieval; Reranking Geometric-mean fusion; Cosine similarity Loss; LoRA Prompt engineering; LLM-as-a-judge; Pointwise reranking; Tournament listwise reranker No Qwen3-Reranker-8B; Qwen3-235B; BGE-LoRA; MiniLM Retrieval+Reranking Dense+Generative Reranking + augmentation Yes No No 9 Skillberg 690318 no no 0.6481 0.6562 0.8981 0.8571 0.815 0.7425 0.2383 0.64 0.8548 0.8443 0.775 0.73 0.2357 0.6373 0.8786 0.8472 0.79 0.75 0.2313 0.6485 0.645 0.6468 0.9353 0.6458 0.6421 0.6439 0.9351 0.9285 0.9330 Encoder; Lexical Retrieval; Reranking Hybrid retrieval; Curriculum Learning; GISTEmbedLoss; MatryoshkaLoss; Proprietary job-skill knowledge graph; Cross-Encoder reranking Prompt engineering; Data Augmentation No multilingual-e5-large-instruct; BM25; Mistral; Jina cross-encoder v2 Retrieval+Reranking Dense+Lexical Augmentation Yes No No 10 bipboopbipboop 714706 no no 0.6351 0.6263 0.8968 0.854 0.81 0.7125 0.2355 0.6439 0.9228 0.865 0.81 0.735 0.2415 0.6239 0.8412 0.8486 0.825 0.7025 0.2335 0.6452 0.6583 0.6518 0.8795 0.6445 0.6239 0.6342 0.8539 0.8673 0.8669 Encoder; Decoder Retrieval; Reranking Geometric-mean fusion; Cosine similarity Loss; LoRA Prompt engineering; LLM-as-a-judge; Pointwise reranking; Tournament listwise reranker No Qwen3-Reranker-8B; Qwen3-235B; BGE-LoRA; MiniLM Retrieval+Reranking Dense+Generative Reranking + augmentation Yes No No 11 bipboopbipboop 709829 no no 0.6348 0.6263 0.8968 0.854 0.81 0.7125 0.2355 0.6434 0.8734 0.8603 0.835 0.7475 0.2415 0.6325 0.9201 0.8617 0.835 0.7325 0.2335 0.6421 0.6629 0.6525 0.8802 0.6442 0.6391 0.6417 0.8533 0.8673 0.8669 Encoder; Decoder Retrieval; Reranking Geometric-mean fusion; Cosine similarity Loss; LoRA Prompt engineering; LLM-as-a-judge; Pointwise reranking; Tournament listwise reranker No Qwen3-Reranker-8B; Qwen3-235B; BGE-LoRA; MiniLM Retrieval+Reranking Dense+Generative Reranking + augmentation Yes No No 12 ui-nlp 715491 no no 0.6264 0.6327 0.9043 0.8527 0.775 0.72 0.2385 0.6202 0.8739 0.8414 0.775 0.7125 0.238 0.5995 0.847 0.8295 0.765 0.685 0.2303 0.6301 0.6352 0.6327 0.8291 0.6196 0.6014 0.6105 0.7789 0.8251 0.8110 Encoder; Lexical Retrieval; Reranking Rank Fusion No multilingual-e5-large-instruct; bge-large-en-v1.5; BM25; ms-marco-MiniLM-L12-v2 cross-encoder Retrieval+Reranking Dense+Lexical None No No No 13 QTPride 696943 no no 0.6255 0.6236 0.8675 0.8466 0.795 0.72 0.2345 0.6274 0.8303 0.8422 0.77 0.72 0.235 0.5894 0.8448 0.8232 0.735 0.6775 0.231 0.6372 0.6331 0.6351 0.8662 0.6131 0.5898 0.6014 0.7701 0.8273 0.8212 Encoder Retrieval RRF; Relational Graph Convolutional Network; KG-enhanced retrieval LLM entity extraction No multilingual-e5-large-instruct Retrieval Dense Augmentation No No No 14 CYUT 702458 no no 0.6093 0.5974 0.817 0.8238 0.7 0.6575 0.24 0.6213 0.8591 0.8435 0.785 0.7225 0.2377 0.629 0.6305 0.6298 0.8795 0.8528 Encoder; Decoder; Lexical Retrieval; Reranking Weighted RRF Hypothetical Document Embeddings Query Expansion; Prompt engineering; Listwise reranking Yes bge-m3; Gemma-3-27B; BM25 Retrieval+Reranking Dense+Lexical+Generative Reranking + augmentation No Yes No 15 CYUT 702388 no no 0.6093 0.5974 0.817 0.8238 0.7 0.6575 0.24 0.6213 0.8591 0.8435 0.785 0.7225 0.2377 0.5851 0.8182 0.8211 0.68 0.6575 0.2395 0.629 0.6305 0.6298 0.8795 0.5938 0.6067 0.6003 0.8421 0.8528 0.8581 Encoder; Decoder; Lexical Retrieval; Reranking Weighted RRF Hypothetical Document Embeddings Query Expansion; Prompt engineering; Listwise reranking Yes bge-m3; Gemma-3-27B; BM25 Retrieval+Reranking Dense+Lexical+Generative Reranking + augmentation No Yes No 16 CYUT 715707 no no 0.6057 0.5902 0.7626 0.8133 0.68 0.6575 0.24 0.6213 0.8591 0.8435 0.785 0.7225 0.2377 0.5356 0.7351 0.787 0.65 0.6125 0.2293 0.629 0.6305 0.6298 0.8795 0.5473 0.5538 0.5506 0.8231 0.8520 0.8515 Encoder; Decoder; Lexical Retrieval; Reranking Weighted RRF Hypothetical Document Embeddings Query Expansion; Prompt engineering; Listwise reranking Yes bge-m3; Gemma-3-27B; BM25 Retrieval+Reranking Dense+Lexical+Generative Reranking + augmentation No Yes No 17 DevoMatcher 999999 no no 0.5989 0.5966 0.8062 0.8251 0.73 0.69 0.2313 0.6012 0.9129 0.8456 0.78 0.7275 0.2245 0.5293 0.763 0.7858 0.69 0.6475 0.2135 0.6071 0.6101 0.6086 0.9114 0.5395 0.5433 0.5414 0.8667 0.9492 0.9091 Encoder; Decoder; Lexical Retrieval; Reranking ESCO graph calibration score; Weighted RRF Listwise reranking Demographic Masking No BM25; BGE-M3; SkillMatch MPNet; OpenAI GPT-5.2 Retrieval+Reranking Dense+Lexical+Generative Reranking No No Demographic Masking 18 DevoMatcher 708398 no no 0.5762 0.5877 0.8101 0.8217 0.725 0.6975 0.2313 0.5647 0.8932 0.8274 0.755 0.6975 0.2245 0.4864 0.765 0.767 0.64 0.5725 0.2135 0.564 0.5834 0.5737 0.9082 0.4953 0.5021 0.4987 0.8629 0.9488 0.9066 Encoder; Decoder; Lexical Retrieval; Reranking ESCO graph calibration score; Weighted RRF Listwise reranking Demographic Masking No BM25; BGE-M3; SkillMatch MPNet; OpenAI GPT-5.2 Retrieval+Reranking Dense+Lexical+Generative Reranking No No Demographic Masking 19 CYUT 715170 no no 0.5693 0.5915 0.7461 0.8128 0.68 0.66 0.2407 0.5471 0.5739 0.7761 0.58 0.61 0.2385 0.5617 0.7894 0.8067 0.69 0.6525 0.2293 0.5576 0.5731 0.5653 0.8569 0.572 0.5725 0.5722 0.8233 0.8408 0.8403 Encoder; Decoder; Lexical Retrieval; Reranking Weighted RRF Hypothetical Document Embeddings Query Expansion; Prompt engineering; Listwise reranking Yes bge-m3; Gemma-3-27B; BM25 Retrieval+Reranking Dense+Lexical+Generative Reranking + augmentation No Yes No 20 HR_NLP 715595 no no 0.5605 0.554 0.7417 0.7875 0.625 0.585 0.236 0.5669 0.815 0.8031 0.66 0.6325 0.237 0.5518 0.7315 0.7816 0.615 0.5925 0.2337 0.5727 0.5775 0.5751 0.9538 0.5637 0.5586 0.5611 0.9466 0.9558 0.9521 Encoder Retrieval Triplet margin loss; Teacher-student Knowledge distillation; Student Contrastive Loss Yes JobBERT; JobBERT-v3; RoBERTa Retrieval Dense None Yes Yes No 21 HR_NLP 714275 no no 0.5605 0.554 0.7417 0.7875 0.625 0.585 0.236 0.5669 0.815 0.8031 0.66 0.6325 0.237 0.5727 0.5775 0.5751 0.9538 0.9558 Encoder Retrieval Triplet margin loss; Teacher-student Knowledge distillation; Student Contrastive Loss Yes JobBERT; JobBERT-v3; RoBERTa Retrieval Dense None Yes Yes No 22 VerbaNex 710446 no no 0.5576 0.5614 0.8448 0.7666 0.705 0.68 0.2317 0.5538 0.8202 0.7571 0.725 0.6825 0.226 0.374 0.6398 0.632 0.5 0.48 0.211 0.5757 0.562 0.5689 0.7216 0.3962 0.3827 0.3894 0.5334 0.7318 0.6623 Encoder; Decoder; Lexical Retrieval; Reranking Cross-Encoder reranking; Score fusion Prompt engineering; Query expansion No bge-m3; BM25; mMiniLMv2 cross-encoder Retrieval+Reranking Dense+Lexical+Generative Augmentation No No No 23 VerbaNex 709530 no no 0.5571 0.5596 0.8595 0.7573 0.705 0.695 0.2237 0.5547 0.84 0.7598 0.725 0.67 0.228 0.3564 0.6206 0.6078 0.455 0.44 0.2053 0.5676 0.5604 0.564 0.7453 0.3815 0.3665 0.374 0.5848 0.7580 0.6960 Encoder; Decoder; Lexical Retrieval; Reranking Cross-Encoder reranking; Score fusion Prompt engineering; Query expansion No bge-m3; BM25; mMiniLMv2 cross-encoder Retrieval+Reranking Dense+Lexical+Generative Augmentation No No No 24 hr_gradient 716035 no no 0.534 0.5339 0.7705 0.7865 0.69 0.6175 0.1973 0.534 0.7661 0.7837 0.68 0.6175 0.1955 0.5348 0.7671 0.7837 0.665 0.6125 0.198 0.5434 0.5356 0.5395 0.9488 0.5434 0.5379 0.5407 0.9489 0.9664 0.9547 25 hr_gradient 715784 no no 0.5208 0.5237 0.7796 0.7823 0.68 0.6375 0.1978 0.5179 0.7319 0.773 0.66 0.62 0.198 0.5214 0.746 0.7773 0.67 0.6225 0.1978 0.5263 0.5222 0.5242 0.9481 0.5289 0.5271 0.528 0.9485 0.9644 0.9537 26 qwerity 699840 no no 0.5066 0.5516 0.8074 0.7331 0.735 0.6825 0.2138 0.4616 0.7744 0.6815 0.625 0.5725 0.2107 0.4696 0.469 0.4693 0.8883 0.9017 Encoder; Lexical Retrieval; Reranking Late-Interaction reranking No JobBERT-v3; SPLADE; jina-colbert-v2 Retrieval+Reranking Dense+Lexical None No No No 27 hr_gradient 716067 no no 0.5061 0.5127 0.6959 0.7672 0.645 0.6125 0.1872 0.4994 0.7057 0.7608 0.645 0.585 0.1888 0.5024 0.6786 0.7602 0.62 0.58 0.1912 0.507 0.5053 0.5061 0.9443 0.5091 0.5085 0.5088 0.9447 0.9708 0.9533 28 VerbaNex 703373 no no 0.4954 0.4927 0.7401 0.7083 0.64 0.6125 0.2235 0.4981 0.7824 0.7015 0.66 0.6275 0.219 0.4613 0.7579 0.6823 0.64 0.595 0.2165 0.5105 0.5095 0.51 0.6569 0.4856 0.47 0.4778 0.6284 0.7019 0.6624 Encoder; Decoder; Lexical Retrieval; Reranking Cross-Encoder reranking; Score fusion Prompt engineering; Query expansion No bge-m3; BM25; mMiniLMv2 cross-encoder Retrieval+Reranking Dense+Lexical+Generative Augmentation No No No 29 qwerity 699843 no no 0.4907 0.5516 0.8074 0.7331 0.735 0.6825 0.2138 0.4299 0.7565 0.6613 0.625 0.545 0.2107 0.4398 0.4377 0.4387 0.8794 0.9017 Encoder; Lexical Retrieval; Reranking Late-Interaction reranking No JobBERT-v3; SPLADE; jina-colbert-v2 Retrieval+Reranking Dense+Lexical None No No No 30 UBCS 701328 no no 0.4905 0.5144 0.8073 0.7883 0.665 0.615 0.2167 0.4666 0.7822 0.7611 0.615 0.545 0.2098 0.2845 0.4383 0.6345 0.31 0.315 0.1805 0.4759 0.4748 0.4753 0.8344 0.3126 0.292 0.3023 0.7665 0.8624 0.8211 Encoder; Encoder–Decoder; Lexical Retrieval Late fusion; Doc2Query expansion No paraphrase-multilingual-MiniLM-L12-v2; doc2query-t5-base-msmarco; BM25 Retrieval Dense+Lexical+Seq2Seq None No No No 31 qwerity 699841 no no 0.4823 0.5347 0.842 0.7343 0.755 0.64 0.2138 0.4299 0.7565 0.6613 0.625 0.545 0.2107 0.4398 0.4377 0.4387 0.8794 0.9161 Encoder; Lexical Retrieval; Reranking Late-Interaction reranking No JobBERT-v3; SPLADE; jina-colbert-v2 Retrieval+Reranking Dense+Lexical None No No No 32 VerbaNex 703277 no no 0.4531 0.4655 0.7576 0.6762 0.635 0.595 0.2107 0.4407 0.7546 0.663 0.605 0.5525 0.2107 0.4456 0.7652 0.6639 0.605 0.5525 0.2112 0.4484 0.4475 0.4479 0.664 0.4573 0.4538 0.4555 0.6685 0.7106 0.6810 Encoder; Decoder; Lexical Retrieval; Reranking Cross-Encoder reranking; Score fusion Prompt engineering; Query expansion No bge-m3; BM25; mMiniLMv2 cross-encoder Retrieval+Reranking Dense+Lexical+Generative Augmentation No No No 33 TalentCLEF baseline 690133 yes no 0.3828 0.4001 0.7436 0.7292 0.565 0.51 0.1802 0.3655 0.6878 0.7039 0.51 0.5 0.176 0.3562 0.7118 0.7024 0.475 0.4625 0.1705 0.3858 0.3731 0.3794 0.7843 0.3762 0.3659 0.3711 0.78 0.7919 0.7854 34 UBCS 701251 no no 0.3828 0.4001 0.7436 0.7292 0.565 0.51 0.1802 0.3655 0.6878 0.7039 0.51 0.5 0.176 0.3562 0.7118 0.7024 0.475 0.4625 0.1705 0.3858 0.3731 0.3794 0.7843 0.3762 0.3659 0.3711 0.78 0.7919 0.7854 Encoder; Encoder–Decoder; Lexical Retrieval Late fusion; Doc2Query expansion No paraphrase-multilingual-MiniLM-L12-v2; doc2query-t5-base-msmarco; BM25 Retrieval Dense+Lexical+Seq2Seq None No No No 35 UBCS 701316 no no 0.3828 0.4001 0.7436 0.7293 0.565 0.51 0.1802 0.3654 0.6878 0.7039 0.51 0.5 0.176 0.3561 0.7118 0.7023 0.475 0.4625 0.1705 0.3858 0.3731 0.3794 0.7843 0.3762 0.366 0.3711 0.78 0.7919 0.7854 Encoder; Encoder–Decoder; Lexical Retrieval Late fusion; Doc2Query expansion No paraphrase-multilingual-MiniLM-L12-v2; doc2query-t5-base-msmarco; BM25 Retrieval Dense+Lexical+Seq2Seq None No No No 36 UBCS 706767 no no 0.1898 0.1816 0.4255 0.5345 0.275 0.2575 0.1225 0.198 0.4372 0.5512 0.265 0.245 0.131 0.1516 0.3235 0.5146 0.16 0.1575 0.116 0.2082 0.2225 0.2154 0.7494 0.1582 0.1725 0.1653 0.6796 0.7607 0.7299 Encoder; Encoder–Decoder; Lexical Retrieval Late fusion; Doc2Query expansion No paraphrase-multilingual-MiniLM-L12-v2; doc2query-t5-base-msmarco; BM25 Retrieval Dense+Lexical+Seq2Seq None No No No