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Paper Citation Record · LEDGER

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models

As of 11 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 2 inbound Pith citation observations for arXiv:2501.13629.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.13629 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:50:22.077513Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:32:31.119922Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-19T03:37:00.945663Z

Reference resolution

53 of 53 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved46
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c94ecbd-862a-4fe4-ab6c-55395bb102d1 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.815849Z digest=sha256:5e093bbe9ceca14c89a9620fcdf994f094cf8a5e94b07aee4e037ce0f059194b

Observation f15bdfe8-a257-4a2b-99f6-332d94dff2e6 · outbound

This paper cites an unresolved cited work.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.064477Z digest=sha256:499c83da75732ea9559c309b86584d357d28e2e66b98477220096164dd70da4a

Observation 975c55ac-f182-4b34-9ee7-74e2f95ebfab · outbound

This paper cites Stable LM 2 1.6B Technical Report.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Stable LM 2 1.6B Technical Report

Reference 5

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source=pdf_text observed=2026-08-10T15:50:21.836712Z digest=sha256:901c3567206246597cd56dcb0ddd2282c27dda68bbba789e2b61a652427f3ee9

Observation 278f5b85-2afd-4c40-b38b-280b2bcc6710 · outbound

This paper cites Longformer: The Long-Document Transformer.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Longformer: The Long-Document Transformer

Reference 6

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source=pdf_text observed=2026-08-10T15:50:21.841537Z digest=sha256:ed93a789cca4a717933f5ca8eb992c68e725c6196469b95256a1719dd5f1e778

Observation def6a48e-8f04-4e46-a548-266286d98b33 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 8

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source=pdf_text observed=2026-08-10T15:50:21.851361Z digest=sha256:5ccdd5b357e9c2e8e29b48a365655f9c587ac474b1c982dadb3a0802b0ac4eb0

Observation 5fc5cdeb-a7c1-4b39-9625-dbc7b46180be · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 9

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source=pdf_text observed=2026-08-10T15:50:21.856599Z digest=sha256:ebd63f394310baa2e36849eba0a94587234c8afbfbe143a8d19f3845b3871638

Observation 035c2ef5-0855-4056-8ba5-6fac47e83ee7 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Evaluating Large Language Models Trained on Code

Reference 10

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source=pdf_text observed=2026-08-10T15:50:21.861854Z digest=sha256:5a5697bdc4f745c25dfafda09bb6720c31ca1bb675c650598587b3f6bd2acf27

Observation 295a5ccb-bbeb-4423-8888-ce02474df9ff · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 11

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source=pdf_text observed=2026-08-10T15:50:21.866755Z digest=sha256:21bed10ef64662797fbb5273f319a3f96c9e102b0b63156426d51af5455c5fe9

Observation 062b003f-1df7-44e2-8f13-5c5db5248b25 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 14

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source=pdf_text observed=2026-08-10T15:50:21.880969Z digest=sha256:8898db285bb65b59ab0be72f95735a9246300fa898ed4ea4638c9eed88d1c587

Observation 439cc4b4-6d82-4bad-9ff3-74072317e416 · outbound

This paper cites Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster

Reference 15

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source=pdf_text observed=2026-08-10T15:50:21.885970Z digest=sha256:2ad9a60eb3cef661411fb2064a5551de3ed6a38d61d64bb025a107fb98c7a780

Observation 829f0445-1862-4a57-9094-7be4c354c2ad · outbound

This paper cites The Llama 3 Herd of Models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models The Llama 3 Herd of Models

Reference 16

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source=pdf_text observed=2026-08-10T15:50:21.891051Z digest=sha256:8291a1b63c69c6842aca325e9e5b08f50911da5ab74335cae37d165d8321c9fd

Observation 24c2ab63-4ef5-482e-a2b8-e449b945103d · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 17

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source=pdf_text observed=2026-08-10T15:50:21.895633Z digest=sha256:1d889d3bfe32bd11b5ab1ff3fcf47ab3707b8b6cf988c2e3229e726ec90c3d90

Observation 3ee3c350-8471-4513-b338-7772702138d9 · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models OLMo: Accelerating the Science of Language Models

Reference 18

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source=pdf_text observed=2026-08-10T15:50:21.900910Z digest=sha256:ef8e928c92bd43a0f1713a98f730f2e0422c56ca374e435ff11730e482f7974f

Observation 1d893888-6a44-44b9-84fd-15ad4464c20f · outbound

This paper cites Textbooks Are All You Need.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Textbooks Are All You Need

Reference 19

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source=pdf_text observed=2026-08-10T15:50:21.905868Z digest=sha256:0c072bb06af3740b4cd115fd98e25b1e4dbc98e34486c411ddbcb4fea6a05e71

Observation 61e915ce-4f28-43bb-8fb5-cc32721a0b38 · outbound

This paper cites Memory-efficient Transformers via Top-$k$ Attention.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Memory-efficient Transformers via Top-$k$ Attention

Reference 20

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source=pdf_text observed=2026-08-10T15:50:21.911004Z digest=sha256:e2dbb859dda5c706fa1f5840bcb2bdc5b72d240f95c61636b96363e800d69e9b

Observation 5691c93a-99ce-47cf-8d30-535656870aea · outbound

This paper cites LM-infinite: Zero-shot extreme length generalization for large language models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models LM-infinite: Zero-shot extreme length generalization for large language models

Reference 21

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raw_fallback, observed 2026-08-10T15:50:22.998026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:21.915897Z digest=sha256:25622087d558352e870e308d260a204ec39f466f4493de3f600bb2053f8059f1

Observation 274c669d-aec4-49be-8811-1b201ef457fe · outbound

This paper cites FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines

Reference 22

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source=pdf_text observed=2026-08-10T15:50:21.920956Z digest=sha256:a467b64053fa7c5f497f33bb6010a0727a142e5b216000db782abaff07196614

Observation 635abfae-34d2-4df7-bd5b-1490ff86acbe · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Measuring Massive Multitask Language Understanding

Reference 23

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source=pdf_text observed=2026-08-10T15:50:21.925987Z digest=sha256:ca6ab35394c7eea6e94f95725920c1abb5959213535fbc8f4dc5d04a936bae37

Observation b8535ed5-d8a8-4581-8a44-f55c0c1ba4f5 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 24

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source=pdf_text observed=2026-08-10T15:50:21.931406Z digest=sha256:02558a9e168c4b0598589a9ba84908d2d20b66d7824e151bbcc35a17351cc2f1

Observation 46a89846-debf-453c-b16b-631f7528a2b8 · outbound

This paper cites Scaling Laws for Neural Language Models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Scaling Laws for Neural Language Models

Reference 25

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source=pdf_text observed=2026-08-10T15:50:21.936172Z digest=sha256:b24ebe0172b357e83e2ac024d3d00e9e0586c9e2aff5290000f08104df1aa1b5

Observation 9594843b-cf1a-45de-9518-f2d03e9cac62 · outbound

This paper cites DataComp-LM: In search of the next generation of training sets for language models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models DataComp-LM: In search of the next generation of training sets for language models

Reference 26

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source=pdf_text observed=2026-08-10T15:50:21.942092Z digest=sha256:e19639104b5ecfbe087b498db96268a31c9b3da6615692f04517dcbeea5d9c92

Observation 50ebd4a3-c042-4000-9574-e7d66d5ddbf4 · outbound

This paper cites LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Reference 27

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source=pdf_text observed=2026-08-10T15:50:21.946677Z digest=sha256:db6cbdc5d927bdbdbb5d47c4a204f8ec21595e63041074d459c41950ded25228

Observation cbffae5e-3259-40a6-8775-7bd309d8af21 · outbound

This paper cites MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Reference 28

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source=pdf_text observed=2026-08-10T15:50:21.951455Z digest=sha256:b895252f2900070f437e436c4c15fc07fbe6e7e04bb65ff05a20214cd9196c69

Observation bd1c4c82-473f-4bf9-82d3-8dcd390411cf · outbound

This paper cites OpenELM: An Efficient Language Model Family with Open Training and Inference Framework.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

Reference 29

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source=pdf_text observed=2026-08-10T15:50:21.955933Z digest=sha256:357b64ea6cf5c7a07b73f3f5c78b464f131c0ede9eac578ba727beaf73de5700

Observation 935baa46-b278-4059-94c9-4216709a48d9 · outbound

This paper cites Crosslingual Generalization through Multitask Finetuning.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Crosslingual Generalization through Multitask Finetuning

Reference 30

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source=pdf_text observed=2026-08-10T15:50:21.960583Z digest=sha256:d04fc9a7f90ada2557c57f1ad8ae2bfb4ed66af17cd4f5ad71a23c714cceb095

Observation 09ae0fca-2fec-4119-865b-cfcc12c788e2 · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Fast Transformer Decoding: One Write-Head is All You Need

Reference 32

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source=pdf_text observed=2026-08-10T15:50:21.973704Z digest=sha256:8ac171115337b22dc4ddf851c4a83290fadb2a3b24adf029a28f780af5779e19

Observation 87415344-5f4d-4e26-837e-257f1bcaa594 · outbound

This paper cites You Only Cache Once: Decoder-Decoder Architectures for Language Models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models You Only Cache Once: Decoder-Decoder Architectures for Language Models

Reference 33

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source=pdf_text observed=2026-08-10T15:50:21.978594Z digest=sha256:5d9bb020ebcce12181b6808004cc747fee04ba2a912e7a01bfbb2b7dc8d80ab9

Observation 8fb2f21f-e0ef-42ee-8ea8-e223838bbc8b · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 34

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source=pdf_text observed=2026-08-10T15:50:21.983664Z digest=sha256:850f13021bf8df0bd7172fda7023a3a5832b3759d824b6bd444c1aced0a82fe3

Observation 7adfde4e-8019-41b0-b75e-bfa92e2aef56 · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Crowdsourcing Multiple Choice Science Questions

Reference 37

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source=pdf_text observed=2026-08-10T15:50:21.998284Z digest=sha256:b64159724a05cb19a269d87e629b8dab4efb236afde7e0e7144852b1809129a3

Observation eb0ca9fb-c7d5-43a6-98d7-ad840d762eda · outbound

This paper cites DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search

Reference 38

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source=pdf_text observed=2026-08-10T15:50:22.003332Z digest=sha256:0844cb653ed23c1be6ba58c6ce568bb6379d33d1bf066df567977a53fa7f904f

Observation b910b473-1d98-4a38-bb75-b07b9fc2a918 · outbound

This paper cites Qwen2 Technical Report.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Qwen2 Technical Report

Reference 39

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source=pdf_text observed=2026-08-10T15:50:22.008336Z digest=sha256:2243eb72da4779fc1525e7c6503c8161df0c361d31eff0ca4abbba1042efff3d

Observation ea036654-4797-48a1-85ee-43b240bba0bd · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 40

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source=pdf_text observed=2026-08-10T15:50:22.012847Z digest=sha256:4fbf859e8b237a4fb85e258afd10f38b72550265702b67000e62b3d9ee1f3cb8

Observation bb604531-9f86-4a95-a722-a19aae6b7d90 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models TinyLlama: An Open-Source Small Language Model

Reference 41

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source=pdf_text observed=2026-08-10T15:50:22.017958Z digest=sha256:a43ecae4f27c74d2567f72a15fab81b10ca2d4f26bf6a723532d5ac33ad52eb4

Observation 8e693bb6-c968-4365-b2b1-5fe037c91411 · outbound

This paper cites A Survey on Efficient Inference for Large Language Models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models A Survey on Efficient Inference for Large Language Models

Reference 42

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source=pdf_text observed=2026-08-10T15:50:22.023813Z digest=sha256:3abbbcb68098baf17e14fc3a5d4f5b0ec0140edd681033d379b53b6ae4e0e002

Observation 4ad9a14d-aa44-4051-baf3-e91343b9ea33 · outbound

This paper cites an unresolved cited work.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Unresolved cited work

Reference 43

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.029281Z digest=sha256:d086b9bf81721b9fc0fade2d14588dc660d2f389d75624a35a50283a4aa91ea2

Observation 57436a0e-d8d3-464e-9265-fec22c8ab6b7 · outbound

This paper cites an unresolved cited work.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Unresolved cited work

Reference 44

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source=pdf_text observed=2026-08-10T15:50:22.034114Z digest=sha256:4a511450fbc7751306edfefc7847baf0c255604d81fe9f52328a6e6ee352067a

Observation 11b3c6bb-3722-444c-825b-b1afd5d9021d · outbound

This paper cites MQA is a variant of the standard Multi-Head Attention (MHA) mechanism, where all query heads share a single key head and value head.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models MQA is a variant of the standard Multi-Head Attention (MHA) mechanism, where all query heads share a single key head and value head

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-10T15:50:22.947879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.039598Z digest=sha256:99f7f65e0be351f013f154667fac14ffc915e9e5cc485212364718a31ed0759f

Observation 5f356d01-08c5-4785-8ea8-fdd48ee032cb · outbound

This paper cites This operation significantly enhances inference efficiency by reducing memory usage.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models This operation significantly enhances inference efficiency by reducing memory usage

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:50:22.917344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.049757Z digest=sha256:89757689210e5b11045ed33e8de5a6232a648c04eca3eac6890ffac1113486f2

Observation c40c6226-cc4a-4d8c-b9e4-8d0d8c9105b2 · outbound

This paper cites Sel.V-top100.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Sel.V-top100

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:50:22.900479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.055569Z digest=sha256:a00842c1aa30644d5c63106b43a1edbc183f145ac4cbd23f34ef3cb890222cf3

Observation 76baeb35-e653-4ecf-b9e0-af6f355cfb82 · outbound

This paper cites We utilize the same vocabulary as Llama3 (Dubey et al., 2024), with a vocabulary size of 128k.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models We utilize the same vocabulary as Llama3 (Dubey et al., 2024), with a vocabulary size of 128k

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:50:22.883032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.059879Z digest=sha256:d566481d5127c7baae896aa307995a7b654763ed48fcc6adf3a2dd73951aced0

Observation df503ac4-0015-466c-965c-7ff532b32281 · outbound

This paper cites an unresolved cited work.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Unresolved cited work

Reference 51

Resolution
unresolved
raw_fallback, observed 2026-08-10T15:50:22.849794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.068540Z digest=sha256:0f1d5000784b973add97a666552fc49935ad6c28a3e2c368160b1e4a1f931ac9

Observation 06476e3c-9832-40d5-81aa-3028b31efde2 · outbound

This paper cites an unresolved cited work.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-10T15:50:22.832044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.073236Z digest=sha256:5fe53154ddfadd7cbf035135abdeaa7b0a2e010cbf74e329d50fa772d9deffab

Observation 4cbb45b9-6355-44b6-819d-8a3c5b23bdbe · outbound

This paper cites The evaluation results on various general, coding and math problem-solving benchmarks are shown in Table.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models The evaluation results on various general, coding and math problem-solving benchmarks are shown in Table

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:50:22.815410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.077513Z digest=sha256:51e442a1170948b650a2e52477f0fd1a38c0007d7a335bf84e7fc94e07f80fb2

Observation f6103bd0-c76c-49ba-a690-9e296062065f · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.964947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.964947Z digest=sha256:4b41a7a0255dc7ae958e418e6ea20729e5993918475638cee94d3f321fdc93c4

Observation 7cc7a532-dd2d-43f2-973c-29bb34010358 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.993095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.993095Z digest=sha256:0036a551a0a784c093ad4c6f6d086db1937cdb286f79eb37bdf24f2c2945ca99

Observation fc2e5ab9-fa6a-48df-824a-4ae84726c406 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Training Verifiers to Solve Math Word Problems

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.876185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.876185Z digest=sha256:6d2235a0f3b042d1b884ddd1710c94268b4831cc441c142c34a1911986e9e101

Observation 4bb69983-4524-4a16-aaa7-9f5efa17537a · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.871591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.871591Z digest=sha256:783b5523bd930b9526001b9fe86fd15558901398e1293682920ca3ee488da9fa

Observation 2a8512ca-d088-4f2a-b192-7d6577b5871b · outbound

This paper cites Reducing Transformer Key-Value Cache Size with Cross-Layer Attention.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.846487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.846487Z digest=sha256:38325a57311729c9edbf9876d43508cd77f144cd44da4d3a69f534bc6a446f3d

Observation be63b3b5-8dd8-49ae-979b-8de0f1878524 · outbound

This paper cites Llemma: An Open Language Model For Mathematics.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Llemma: An Open Language Model For Mathematics

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.831787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.831787Z digest=sha256:1ded30e080e99fabfc73ca2272c8deafa7ed4a8f6c113c5ce2ebc46ec7b6b170

Observation 9f848709-c9fe-4e2b-a873-8d53b114eaff · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.988323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.988323Z digest=sha256:0e4bfd984ed85796540e9d9f4863e23c2636a80f0dcf45c577686adb47d9f4c9

Observation 6253936d-fde1-4d71-91ac-ec2a0ebd7546 · outbound

This paper cites GQA: Training general- ized multi-query transformer models from multi-head checkpoints.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models GQA: Training general- ized multi-query transformer models from multi-head checkpoints

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T15:50:23.014540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:21.822080Z digest=sha256:4d35d131fc18a238c8c1543ddec007ef149b757d1005176297c8511a4ed11d91

Observation 4912977d-a85e-4533-baaf-f934a7d80f5a · outbound

This paper cites Program Synthesis with Large Language Models.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Program Synthesis with Large Language Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-10T15:50:21.826854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:50:21.826854Z digest=sha256:110898f51965cd5beb9cc17519eea54c6a07b93baeeb3d4526d8e07288afef67

Observation a4a29beb-0ceb-4799-893c-080363cfe96f · outbound

This paper cites an unresolved cited work.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Unresolved cited work

Reference 2048

Resolution
unresolved
raw_fallback, observed 2026-08-10T15:50:22.932979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:50:22.044486Z digest=sha256:c44e629b2395c7652d757c860d031aaf0e230a990b3a5fa5c1cbf17a8638d82a

Pith citing papers

Observation 8e4c7db0-b686-4cbb-8373-b9af37adc193 · inbound

Hardware-Efficient Attention for Fast Decoding cites this paper.

Hardware-Efficient Attention for Fast Decoding Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T13:32:31.119922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:32:31.119922Z digest=sha256:f4848c12a410ef7634b87a89b7da1587b33990aeca7d29e873bc278f476f2b31

Observation 52228e1e-b7f4-4c6d-8b9c-bc7f199c4f61 · inbound

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training cites this paper.

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-19T03:37:00.948390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-19T03:36:50.366757Z digest=sha256:5bce102b8c9168c9c5bd49d55b3d11211bf82afea8ab5427e42ad1bb57d656ba