Proves that for bounded n, there exist MV embeddings with m vectors each whose Chamfer matrix requires single-vector dimension D = (ε² m)^Ω(1/ε) to approximate within ε, separating MV from SV expressiveness.
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On the theoretical limitations of embedding-based retrieval
24 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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representative citing papers
Proves constant-dimension 3 reachability embeddings for directed trees, O(t log n) for treewidth-t graphs, with lower bounds of Omega(n) for general DAGs and Omega(t/log(n/t)) for treewidth t, plus real-dataset results.
MaxSim similarity can exactly replicate inner products of non-negative sparse vectors of arbitrary dimension, and a proposed Signed MaxSim extension enables exact replication for arbitrary real-valued vectors.
A 0.6B LM with length-aware attention adjustments performs competitive in-context retrieval at million-token scale on MS MARCO, NQ, and LIMIT benchmarks.
TokenMinds extends Semantic ID tokenization from items to users, producing paired discrete tokens and dense embeddings via an LLM-adapted encoder-decoder for industrial recommendation.
Proves Voronoi complexity equals sign-rank for top-1 retrieval, introduces CUS diagnostic predicting retrieval failure at AUC >0.8 without labels, and AT-DW-InfoNCE objective with derived alpha^*=2.0 that improves Recall@100 on synthetic data.
Spectral Retrieval uses multi-scale sinc convolutions on token embeddings to interpolate between per-token MaxSim and mean-pooling, achieving large gains on synthetic and LIMIT-small benchmarks for localized 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.
Semantic Recall is a new evaluation metric for approximate nearest neighbor search that focuses only on semantically relevant results, with Tolerant Recall as a proxy when relevance labels are unavailable.
LLM-based dense retrievers generalize better when instruction-tuned but pay a specialization tax when optimized for reasoning; they resist typos and corpus poisoning better than encoder-only baselines yet remain vulnerable to semantic perturbations, with larger models and certain embedding geometry,
ARC-derived multi-agent LLM framework for safe, auditable process control with operator agents and deterministic orchestrator, evaluated on dairy ventilation.
EvoEmbedding generates evolvable embeddings via a latent memory updated during sequential processing, outperforming larger models on long-context retrieval and generalizing to 10x longer contexts in downstream tasks.
SMART unlocks latent multi-vector capabilities in single-vector embedding models by applying late interaction to frozen hidden states shaped by contrastive training, yielding consistent gains on MMEB-V2 and visual document retrieval.
Embeddings retrieve same-subfield papers at 45-52% but same-agenda papers at only 15-21%; citation rerank reaches 57-59% on agenda queries.
SIRA compresses multi-round exploratory retrieval into one corpus-discriminative BM25 action via LLM document enrichment, query-time term prediction, and corpus-statistic filtering, reporting top average performance on ten BEIR benchmarks and strong results on BrowseComp-Wikipedia without relevance
The authors introduce aspect-aware datasets GoldRiM and SilverRiM for math papers and AchGNN, a heterogeneous GNN that outperforms prior methods by jointly modeling textual semantics, citations, and author lineage across aspects.
Neural retrievers that double BM25 performance on QUEST collapse below 0.02 Recall@100 on the new LIMIT+ benchmark while lexical methods reach 0.96, with all methods degrading as compositional depth increases.
Triplet constraints realizable in D-dimensional Euclidean space cannot be preserved above 50% accuracy by any embedding of dimension at most cD for constant c<1, with UGC-hardness preventing better polynomial-time solutions in any dimension.
Generative retrieval beats dense retrieval and BM25 on the LIMIT dataset but degrades with hard negatives due to identifier ambiguity during decoding.
A mask-and-reconstruct fine-tuning objective lets autoregressive LLMs inject new knowledge without paraphrases and largely removes the reversal curse.
MetaEmbed trains fixed learnable Meta Tokens to produce granularity-organized multi-vector embeddings that support test-time scaling in multimodal retrieval.
A mean-field analysis of embedding-space crowding shows a phase transition and Fokker-Planck dynamics that drive retrieval-augmented agents to self-organize toward exclusive service of majority interests.
Argues for a denoising-first paradigm in LLM-oriented information retrieval, framing challenges via a four-stage progression and providing a taxonomy of signal-to-noise optimization techniques across the pipeline.
Foundation models stumble in pathology due to conceptual mismatches with biological tissue, requiring explicitly designed models rather than adaptations of natural-image methods.
citing papers explorer
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Multi-Vector Embeddings are Provably More Expressive than Single Vector Embeddings
Proves that for bounded n, there exist MV embeddings with m vectors each whose Chamfer matrix requires single-vector dimension D = (ε² m)^Ω(1/ε) to approximate within ε, separating MV from SV expressiveness.
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Compact Geometric Representations of Hierarchies
Proves constant-dimension 3 reachability embeddings for directed trees, O(t log n) for treewidth-t graphs, with lower bounds of Omega(n) for general DAGs and Omega(t/log(n/t)) for treewidth t, plus real-dataset results.
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Quantifying and Expanding the Theoretical Capacity of Late-Interaction Retrieval Models
MaxSim similarity can exactly replicate inner products of non-negative sparse vectors of arbitrary dimension, and a proposed Signed MaxSim extension enables exact replication for arbitrary real-valued vectors.
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Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale
A 0.6B LM with length-aware attention adjustments performs competitive in-context retrieval at million-token scale on MS MARCO, NQ, and LIMIT benchmarks.
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TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
TokenMinds extends Semantic ID tokenization from items to users, producing paired discrete tokens and dense embeddings via an LLM-adapted encoder-decoder for industrial recommendation.
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The Voronoi Bottleneck: Capacity-Aware Dense Retrieval for Product Search
Proves Voronoi complexity equals sign-rank for top-1 retrieval, introduces CUS diagnostic predicting retrieval failure at AUC >0.8 without labels, and AT-DW-InfoNCE objective with derived alpha^*=2.0 that improves Recall@100 on synthetic data.
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Spectral Retrieval: Multi-Scale Sinc Convolution over Token Embeddings for Localized Retrieval in LLM Multi-Agent Systems
Spectral Retrieval uses multi-scale sinc convolutions on token embeddings to interpolate between per-token MaxSim and mean-pooling, achieving large gains on synthetic and LIMIT-small benchmarks for localized retrieval.
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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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Semantic Recall for Vector Search
Semantic Recall is a new evaluation metric for approximate nearest neighbor search that focuses only on semantically relevant results, with Tolerant Recall as a proxy when relevance labels are unavailable.
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On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
LLM-based dense retrievers generalize better when instruction-tuned but pay a specialization tax when optimized for reasoning; they resist typos and corpus poisoning better than encoder-only baselines yet remain vulnerable to semantic perturbations, with larger models and certain embedding geometry,
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A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Control
ARC-derived multi-agent LLM framework for safe, auditable process control with operator agents and deterministic orchestrator, evaluated on dairy ventilation.
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EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory
EvoEmbedding generates evolvable embeddings via a latent memory updated during sequential processing, outperforming larger models on long-context retrieval and generalizing to 10x longer contexts in downstream tasks.
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Your Embedding Model is SMARTer Than You Think
SMART unlocks latent multi-vector capabilities in single-vector embedding models by applying late interaction to frozen hidden states shaped by contrastive training, yielding consistent gains on MMEB-V2 and visual document retrieval.
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Topic Is Not Agenda: A Citation-Community Audit of Text Embeddings
Embeddings retrieve same-subfield papers at 45-52% but same-agenda papers at only 15-21%; citation rerank reaches 57-59% on agenda queries.
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Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
SIRA compresses multi-round exploratory retrieval into one corpus-discriminative BM25 action via LLM document enrichment, query-time term prediction, and corpus-statistic filtering, reporting top average performance on ten BEIR benchmarks and strong results on BrowseComp-Wikipedia without relevance
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Aspect-Aware Content-Based Recommendations for Mathematical Research Papers
The authors introduce aspect-aware datasets GoldRiM and SilverRiM for math papers and AchGNN, a heterogeneous GNN that outperforms prior methods by jointly modeling textual semantics, citations, and author lineage across aspects.
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Reproducing Complex Set-Compositional Information Retrieval
Neural retrievers that double BM25 performance on QUEST collapse below 0.02 Recall@100 on the new LIMIT+ benchmark while lexical methods reach 0.96, with all methods degrading as compositional depth increases.
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Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch
Triplet constraints realizable in D-dimensional Euclidean space cannot be preserved above 50% accuracy by any embedding of dimension at most cD for constant c<1, with UGC-hardness preventing better polynomial-time solutions in any dimension.
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Generative Retrieval Overcomes Limitations of Dense Retrieval but Struggles with Identifier Ambiguity
Generative retrieval beats dense retrieval and BM25 on the LIMIT dataset but degrades with hard negatives due to identifier ambiguity during decoding.
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Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs
A mask-and-reconstruct fine-tuning objective lets autoregressive LLMs inject new knowledge without paraphrases and largely removes the reversal curse.
-
MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
MetaEmbed trains fixed learnable Meta Tokens to produce granularity-organized multi-vector embeddings that support test-time scaling in multimodal retrieval.
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The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents
A mean-field analysis of embedding-space crowding shows a phase transition and Fokker-Planck dynamics that drive retrieval-augmented agents to self-organize toward exclusive service of majority interests.
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LLM-Oriented Information Retrieval: A Denoising-First Perspective
Argues for a denoising-first paradigm in LLM-oriented information retrieval, framing challenges via a four-stage progression and providing a taxonomy of signal-to-noise optimization techniques across the pipeline.
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Beyond the Failures: Rethinking Foundation Models in Pathology
Foundation models stumble in pathology due to conceptual mismatches with biological tissue, requiring explicitly designed models rather than adaptations of natural-image methods.