ZipRerank delivers state-of-the-art multimodal listwise reranking accuracy for long documents at up to 10x lower latency via early interaction and single-pass scoring.
Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , pages=
8 Pith papers cite this work. Polarity classification is still indexing.
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A text-supervised global layout embedding augments local patch representations in late-interaction VDR, yielding +2.4 nDCG@5 and +2.3 MAP@5 gains over ColPali/ColQwen baselines on ViDoRe-v2.
MasterSet is a new large-scale benchmark for must-cite citation recommendation in AI/ML, using LLM-annotated tiers on 150k papers and Recall@K evaluation.
M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
Verbal-R3 uses a verbal reranker to generate analytic narratives that guide retrieval and reasoning in LLMs, achieving SOTA results on complex QA benchmarks.
Changing the internal reasoning structure of large reasoning models through simple supervised fine-tuning on 1K examples produces strong safety alignment that generalizes across tasks and languages.
User-specific behavioral signals, especially prior search queries, outperform population-level demand patterns and static profiles for inferring gender, age, category, and size from underspecified e-commerce queries.
TGS-RAG adds graph-to-text re-ranking with global voting and text-to-graph orphan path bridging to improve precision and efficiency in multi-hop RAG over prior baselines.
citing papers explorer
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Very Efficient Listwise Multimodal Reranking for Long Documents
ZipRerank delivers state-of-the-art multimodal listwise reranking accuracy for long documents at up to 10x lower latency via early interaction and single-pass scoring.
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Beyond Bag-of-Patches: Learning Global Layout via Textual Supervision for Late-Interaction Visual Document Retrieval
A text-supervised global layout embedding augments local patch representations in late-interaction VDR, yielding +2.4 nDCG@5 and +2.3 MAP@5 gains over ColPali/ColQwen baselines on ViDoRe-v2.
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MasterSet: A Large-Scale Benchmark for Must-Cite Citation Recommendation in the AI/ML Literature
MasterSet is a new large-scale benchmark for must-cite citation recommendation in AI/ML, using LLM-annotated tiers on 150k papers and Recall@K evaluation.
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M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
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Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
Verbal-R3 uses a verbal reranker to generate analytic narratives that guide retrieval and reasoning in LLMs, achieving SOTA results on complex QA benchmarks.
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Reasoning Structure Matters for Safety Alignment of Reasoning Models
Changing the internal reasoning structure of large reasoning models through simple supervised fine-tuning on 1K examples produces strong safety alignment that generalizes across tasks and languages.
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IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search
User-specific behavioral signals, especially prior search queries, outperform population-level demand patterns and static profiles for inferring gender, age, category, and size from underspecified e-commerce queries.
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Text-Graph Synergy: A Bidirectional Verification and Completion Framework for RAG
TGS-RAG adds graph-to-text re-ranking with global voting and text-to-graph orphan path bridging to improve precision and efficiency in multi-hop RAG over prior baselines.