Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale | Paper Citation Record

Observation 68c73c02-9494-42cc-a276-883048081eac · outbound

This paper cites Dense passage retrieval for open-domain question answering.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Dense passage retrieval for open-domain question answering

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.848418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 23788e2a-b817-4338-a00a-af7d59ed2885 · outbound

This paper cites Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.207309Z

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Observation 6d355b2e-a4ed-4f6e-ab68-f7fb952ec525 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.822137Z

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Observation 6c2059f6-6f2f-49d3-a6b6-ef089da2b630 · outbound

This paper cites On the theoretical limitations of embedding-based retrieval.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale On the theoretical limitations of embedding-based retrieval

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.227053Z

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Observation 60febb44-6949-4893-b35e-ae8532a969b4 · outbound

This paper cites Scalable in-context ranking with generative models.arXiv preprint arXiv:2510.05396, 2025.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Scalable in-context ranking with generative models.arXiv preprint arXiv:2510.05396, 2025

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.258214Z

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Observation ea2c8939-979d-49f0-aa5f-1a65b2e6a5bb · outbound

This paper cites Eliciting in-context retrieval and reasoning for long-context large language models.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Eliciting in-context retrieval and reasoning for long-context large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.842813Z

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Observation 76a349bd-fbc6-404a-883b-c136f883a3fb · outbound

This paper cites Longbench v2: Towards deeper understanding and reason- ing on realistic long-context multitasks.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Longbench v2: Towards deeper understanding and reason- ing on realistic long-context multitasks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.840700Z

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Observation 52511b66-2390-419e-87f8-e456ca86d84b · outbound

This paper cites Context rot: How increasing input tokens impacts llm performance.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Context rot: How increasing input tokens impacts llm performance

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.838461Z

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Observation 91be52e2-032d-4042-a936-f8b56196e403 · outbound

This paper cites Scalable-Softmax Is Superior for Attention.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Scalable-Softmax Is Superior for Attention

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:48:55.216961Z

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Observation 607d663c-079a-4984-9f4f-3a56fbf6c03e · outbound

This paper cites Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.203807Z

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Observation 4d63fa7f-ee91-461e-b9ec-c193161b0a59 · outbound

This paper cites Softmax is not Enough (for Sharp Size Generalisation).

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Softmax is not Enough (for Sharp Size Generalisation)

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.234140Z

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Observation 84dccfb3-b13f-4d52-b33f-85164afb2f18 · outbound

This paper cites Transformers need glasses! informa- tion over-squashing in language tasks.Advances in Neural Information Processing Systems, 37:98111–98142, 2024.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Transformers need glasses! informa- tion over-squashing in language tasks.Advances in Neural Information Processing Systems, 37:98111–98142, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.831651Z

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Observation 395d91a2-c8ee-4ddf-a23b-221b63a16755 · outbound

This paper cites Lucid: Attention with preconditioned representations.arXiv preprint arXiv:2602.10410, 2026.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Lucid: Attention with preconditioned representations.arXiv preprint arXiv:2602.10410, 2026

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.223905Z

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Observation f0d533e4-affd-46cc-805b-fbec043a26c7 · outbound

This paper cites MoBA: Mixture of Block Attention for Long-Context LLMs.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale MoBA: Mixture of Block Attention for Long-Context LLMs

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.260804Z

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Observation 65cfaa88-2201-4ac9-84db-40ef445cdb2d · outbound

This paper cites Optimizing mixture of block attention.arXiv preprint arXiv:2511.11571, 2025.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Optimizing mixture of block attention.arXiv preprint arXiv:2511.11571, 2025

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.259068Z

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Observation ed91674a-29aa-4925-84fa-f53ea8681999 · outbound

This paper cites MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.268677Z

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Observation b469c9a2-3ede-4e49-a997-80efb6b94f51 · outbound

This paper cites Block sparse flash attention.arXiv preprint arXiv:2512.07011, 2025.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Block sparse flash attention.arXiv preprint arXiv:2512.07011, 2025

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.198105Z

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Observation 28213f5e-35d7-4b15-8edb-a88900fa5e18 · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.249214Z

Source-reported events for the cited work

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Observation cc93beff-9abb-41ed-aae1-96ca1adfe050 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.250454Z

Source-reported events for the cited work

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Observation 344970fa-652a-4cc9-b8bd-302e95c81d78 · outbound

This paper cites Transformer memory as a differentiable search index.Advances in neural information processing systems, 35:21831–21843.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Transformer memory as a differentiable search index.Advances in neural information processing systems, 35:21831–21843

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.826467Z

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Observation 23b62dfc-3736-42a1-803e-6cab659dd6ef · outbound

This paper cites A neural corpus indexer for document retrieval.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale A neural corpus indexer for document retrieval

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.816034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 7386e4e8-eabc-415d-9dce-6e649d2e988e · outbound

This paper cites Recommender systems with generative retrieval.Advances in Neural Information Processing Systems, 36:10299–10315.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Recommender systems with generative retrieval.Advances in Neural Information Processing Systems, 36:10299–10315

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.828872Z

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Observation 41e9c338-b96b-43e9-95f8-1ae9f37db0e0 · outbound

This paper cites Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.220516Z

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Observation 049560e4-c743-419d-90d9-9947375e9d5e · outbound

This paper cites Tokenselect: Efficient long-context inference and length extrapolation for llms via dynamic token-level kv cache selection.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Tokenselect: Efficient long-context inference and length extrapolation for llms via dynamic token-level kv cache selection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.817032Z

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No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 0002a371-13b7-434d-9288-c69f070497ae · outbound

This paper cites Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.256002Z

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Observation f2b03464-9351-4849-b76f-a5afd8913ad9 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Efficient Streaming Language Models with Attention Sinks

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.210283Z

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Observation 7c3814db-a664-4f25-ab22-a1d3895e8a01 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale gpt-oss-120b & gpt-oss-20b Model Card

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.246575Z

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Observation a0dd18ea-267d-4249-9439-82107d315181 · outbound

This paper cites Drowning in Documents: Consequences of Scaling Reranker Inference.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Drowning in Documents: Consequences of Scaling Reranker Inference

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.243539Z

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Observation 55d6ea9c-2a44-4dd1-b29e-181564055fdc · outbound

This paper cites Block-Attention for Efficient Prefilling.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Block-Attention for Efficient Prefilling

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.271639Z

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Observation cb3c56ed-bfc8-4d77-9284-f4f65de82539 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.850302Z

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Observation f537aebd-2b6d-4af8-8209-5c5aeb4ec7e4 · outbound

This paper cites Flex Attention: A Programming Model for Generating Optimized Attention Kernels.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Flex Attention: A Programming Model for Generating Optimized Attention Kernels

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.263246Z

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Observation db81bd62-9f91-4a7d-95d4-e130aa4004ee · outbound

This paper cites Hard negatives, hard lessons: Revisiting training data quality for robust information retrieval with llms.arXiv preprint arXiv:2505.16967, 2025.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Hard negatives, hard lessons: Revisiting training data quality for robust information retrieval with llms.arXiv preprint arXiv:2505.16967, 2025

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.193122Z

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Observation 78589c1d-c803-4b50-9fff-fa08e456ddd9 · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.265446Z

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Observation ff89b889-8384-4633-9b1a-b53fc30c81db · outbound

This paper cites Qwen3 Technical Report.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Qwen3 Technical Report

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.255322Z

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Observation 1770494f-9e40-480b-906c-49c362cea525 · outbound

This paper cites Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.836171Z

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Observation 0adc3dad-902b-4711-9c73-d6d4bd9fb9a4 · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.247802Z

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Observation e425e28a-d725-494d-9ceb-c5aca6ad867b · outbound

This paper cites Transformer feed-forward layers are key-value memories.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Transformer feed-forward layers are key-value memories

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.807968Z

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Observation 446e6d75-afb4-49aa-88fc-63509c72d373 · outbound

This paper cites Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.824294Z

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Observation c856f9b6-6af5-4d12-8cd2-3862f2d2bd50 · outbound

This paper cites In-context Learning and Induction Heads.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale In-context Learning and Induction Heads

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.240807Z

Source-reported events for the cited work

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Observation 1b32c996-6318-4062-9b9c-0358ed8abc64 · outbound

This paper cites Retrieval Head Mechanistically Explains Long-Context Factuality.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Retrieval Head Mechanistically Explains Long-Context Factuality

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.237547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation aa71f320-6f95-4995-ac0f-cbb25176b055 · outbound

This paper cites Query-focused retrieval heads improve long-context reasoning and re-ranking.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Query-focused retrieval heads improve long-context reasoning and re-ranking

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.833985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 5b838d96-7e7c-4390-9e0f-ca36f114a9f6 · outbound

This paper cites Colbert: Efficient and effective passage search via contextual- ized late interaction over bert.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Colbert: Efficient and effective passage search via contextual- ized late interaction over bert

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.844747Z

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No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 745b4324-f5d5-4f73-9840-b27737584834 · outbound

This paper cites interpreting GPT: the logit lens.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale interpreting GPT: the logit lens

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.819516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 33e4ce4b-d05a-4104-aa27-7bfc8256408c · outbound

This paper cites On Vanishing Variance in Transformer Length Generalization.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale On Vanishing Variance in Transformer Length Generalization

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:48:55.266009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation c8354509-9f14-49f2-9d9d-6f93d053cae7 · outbound

This paper cites Vasylenko, M.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Vasylenko, M

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:48:55.230383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation b13f6ccf-f3af-44de-b7d6-c571da714505 · outbound

This paper cites OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-03T20:48:55.253077Z

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Observation 02c15e89-fb24-4132-afd5-9931c5de76f0 · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale A reduction of imitation learning and structured prediction to no-regret online learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.846578Z

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No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation b0ff8bc1-e687-4930-8571-d4822e6882ec · outbound

This paper cites an unresolved cited work.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Unresolved cited work

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.812043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation 2577b33a-0fc0-4283-b9e6-588e72ccdd45 · outbound

This paper cites max score(pos)≤ max score(neg).

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale max score(pos)≤ max score(neg)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T01:50:35.814058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Observation f0fa78d6-a531-4951-a4ac-7875ccabfb8e · outbound

This paper cites Given a web search query, retrieve relevant passages that answer the query.

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale Given a web search query, retrieve relevant passages that answer the query

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-07-05T01:50:35.810147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers