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Paper Citation Record · LEDGER

Kinetics: Rethinking Test-Time Scaling Laws

As of 7 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 2 inbound Pith citation observations for arXiv:2506.05333.

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

pith.paper-citation-record.v1
2506.05333 v3

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:30:34.483002Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:02:10.742834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:07:26.497548Z

Reference resolution

71 of 71 outbound references displayed

  • verified exact1
  • verified fuzzy8
  • unresolved61
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c3e1c9aa-981e-44c0-8247-7aadfae2d0be · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Kinetics: Rethinking Test-Time Scaling Laws Generating Long Sequences with Sparse Transformers

Reference 6

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source=pdf_text observed=2026-08-07T10:30:34.136063Z digest=sha256:6986cfa177d4b901ecbb02c831e04acbe53367a901c0248b3696ec99c0549e4f

Observation 7aa74093-ead3-4172-96c2-0b2c6f2007ea · outbound

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

Kinetics: Rethinking Test-Time Scaling Laws DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 8

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source=pdf_text observed=2026-08-07T10:30:34.146751Z digest=sha256:138f69444fb063baaf3ed0a83d930ff72bfa5a39baca19651f1e93ca011cacbd

Observation fc427bd5-eeff-4989-8953-b7c0243effef · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Kinetics: Rethinking Test-Time Scaling Laws FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 9

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source=pdf_text observed=2026-08-07T10:30:34.153354Z digest=sha256:90e810e80ac23e347c31e1f83bfcb94841dc19f0267c9e5feb51646b109f452d

Observation cf4f7989-60bf-438d-b22d-0a3edc53b3a2 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Kinetics: Rethinking Test-Time Scaling Laws FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 10

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source=pdf_text observed=2026-08-07T10:30:34.160223Z digest=sha256:86af1beb28e6b638fe95c538725c6d86bb6a529d0c0a97e3ad56e93ccdd20815

Observation f07d6d81-b35a-4556-88e7-8332586eead3 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Kinetics: Rethinking Test-Time Scaling Laws PaLM-E: An Embodied Multimodal Language Model

Reference 12

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source=pdf_text observed=2026-08-07T10:30:34.170568Z digest=sha256:090b2efb12e2441199c87bf1629abff966b41ef9896b909edb34540d0149d4a6

Observation d1bb6067-0114-418b-88a9-1221a291043c · outbound

This paper cites GLaM: Efficient Scaling of Language Models with Mixture-of-Experts.

Kinetics: Rethinking Test-Time Scaling Laws GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Reference 13

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source=pdf_text observed=2026-08-07T10:30:34.175386Z digest=sha256:e25af8b44e4de4d488abc88b21ae8e84c3e88cde5e4e265fa6c84c541a3f77d7

Observation 650cc220-635d-40a7-87eb-790fc13501dd · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

Kinetics: Rethinking Test-Time Scaling Laws Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 14

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source=pdf_text observed=2026-08-07T10:30:34.180416Z digest=sha256:4af3a44ad62d99b640cd27dbb202c164a8a50cef1cade54ee94e5d51422a07f5

Observation e8808f87-8187-46b3-9431-efdea686e8d3 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Kinetics: Rethinking Test-Time Scaling Laws GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 15

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source=pdf_text observed=2026-08-07T10:30:34.185621Z digest=sha256:a633de94cb6290b27ded12e8628d5fc9355d66d12f9bec45331a247f76f77494

Observation cd607189-bf74-479a-b376-433772d9fc8f · outbound

This paper cites Efficiently Scaling LLM Reasoning with Certaindex.

Kinetics: Rethinking Test-Time Scaling Laws Efficiently Scaling LLM Reasoning with Certaindex

Reference 16

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source=pdf_text observed=2026-08-07T10:30:34.191878Z digest=sha256:497f10a707d3e2fd7d4cc4feaf8b7a9e7fb39c1157ab8710e5dcd56e3c359f0e

Observation 4d7580f4-6f6a-4601-8cf5-dec88e8761c1 · outbound

This paper cites The Llama 3 Herd of Models.

Kinetics: Rethinking Test-Time Scaling Laws The Llama 3 Herd of Models

Reference 17

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source=pdf_text observed=2026-08-07T10:30:34.197508Z digest=sha256:fa0175551b32a1410bb1854f052b9618b7d11f8859dbab83584aa2163470190d

Observation 2de62fc4-092b-4c85-b8e4-cf4f2fee8dcb · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Kinetics: Rethinking Test-Time Scaling Laws Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 18

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source=pdf_text observed=2026-08-07T10:30:34.202494Z digest=sha256:a60a15e120653577dd5c31b962fdf15256bdd96a74d9a07b0903c0d9aa1584e3

Observation 319cf6c5-7717-482b-82be-f44cf6209a58 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Kinetics: Rethinking Test-Time Scaling Laws Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 19

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source=pdf_text observed=2026-08-07T10:30:34.208033Z digest=sha256:088e9b8fc6b82364a30b0c75b7fcee0e56619304b1ea13d87958f4551d4075bc

Observation 3213fc27-0748-4506-95da-a7dcec5555fc · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Kinetics: Rethinking Test-Time Scaling Laws DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 20

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source=pdf_text observed=2026-08-07T10:30:34.212638Z digest=sha256:5f89166638187fc8a70f874050c638abf80b4bf24b15aaeddf44c445db14a0ef

Observation 4e81297c-22a1-4a77-a4fa-b7a48f9c5cc0 · outbound

This paper cites Training Large Language Models to Reason in a Continuous Latent Space.

Kinetics: Rethinking Test-Time Scaling Laws Training Large Language Models to Reason in a Continuous Latent Space

Reference 21

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source=pdf_text observed=2026-08-07T10:30:34.218780Z digest=sha256:054219e46daa6f6d8036b28e7d349d254f54411c43046dfd1a13983a6f0a4974

Observation 25305e44-e40b-428b-8584-0e4fcba07bfe · outbound

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

Kinetics: Rethinking Test-Time Scaling Laws FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines

Reference 22

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source=pdf_text observed=2026-08-07T10:30:34.224262Z digest=sha256:966973dce92a2695c3bcd11d178dfc1128b7e24524926f93f7a85dd23403cbc5

Observation aebe028f-b09b-4c04-a860-f3b569b824fc · outbound

This paper cites Training Compute-Optimal Large Language Models.

Kinetics: Rethinking Test-Time Scaling Laws Training Compute-Optimal Large Language Models

Reference 23

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source=pdf_text observed=2026-08-07T10:30:34.229702Z digest=sha256:40f11d87055b7bd7d98e56403c0f1c37c1f6de4d45058932409b5c897bb7596b

Observation 3ff9ac0f-3a18-4191-aadb-294c016b1eaa · outbound

This paper cites RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning.

Kinetics: Rethinking Test-Time Scaling Laws RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning

Reference 24

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source=pdf_text observed=2026-08-07T10:30:34.235097Z digest=sha256:9dce4264f6a52bfd33f1a5d4418279b77b3ac15dc51faf7238a79acc9f5eaf2f

Observation e4684d1b-ae3d-4eea-adfa-6af9104d73fb · outbound

This paper cites Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents.

Kinetics: Rethinking Test-Time Scaling Laws Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Reference 25

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source=pdf_text observed=2026-08-07T10:30:34.240829Z digest=sha256:356abaf4f9252217bc75eef50e9ae84b7fb1cb0095e4c445e9bc68007db82ca6

Observation 9de9fd75-cbd9-49e7-a418-2b2aa24193d3 · outbound

This paper cites OpenAI o1 System Card.

Kinetics: Rethinking Test-Time Scaling Laws OpenAI o1 System Card

Reference 26

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source=pdf_text observed=2026-08-07T10:30:34.245848Z digest=sha256:ed0fe254a64c4870de7af8b6738dedd5ab5daab3434e0cecd9cb04effb46157d

Observation 84c4ef5b-3e5d-487b-9efe-7f809c9c3acc · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Kinetics: Rethinking Test-Time Scaling Laws LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 27

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source=pdf_text observed=2026-08-07T10:30:34.250861Z digest=sha256:78d25729dada38d5fef33a8ce0bae95293c0d4b53fa356104159f309db56a4a3

Observation d77db131-9fdd-4270-b3d4-b60bceb9474a · outbound

This paper cites Mixtral of Experts.

Kinetics: Rethinking Test-Time Scaling Laws Mixtral of Experts

Reference 28

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source=pdf_text observed=2026-08-07T10:30:34.256194Z digest=sha256:695048c5385fbddb121c6857687757867cd26ebe6b1510ed9dda248a96b5d0b7

Observation 4b210226-1ba2-46f0-94b8-7912b1eae50a · outbound

This paper cites Hydragen: High-Throughput LLM Inference with Shared Prefixes.

Kinetics: Rethinking Test-Time Scaling Laws Hydragen: High-Throughput LLM Inference with Shared Prefixes

Reference 29

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source=pdf_text observed=2026-08-07T10:30:34.260984Z digest=sha256:9e3e88dbb6689e3a5f32255228262bca62e501c1eed7a6426dc09de4d2ba9e20

Observation 5bc68a94-3af1-46cf-a565-c8e3bcc9ba3e · outbound

This paper cites Scaling Laws for Neural Language Models.

Kinetics: Rethinking Test-Time Scaling Laws Scaling Laws for Neural Language Models

Reference 30

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source=pdf_text observed=2026-08-07T10:30:34.265868Z digest=sha256:20ae7d870b51eaec98458c68cb1c85c39a6b547a1c9fa53533764fc6cd4f8e21

Observation 76025da5-ef94-4ebe-8eeb-f32aeb2484fd · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Kinetics: Rethinking Test-Time Scaling Laws Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 31

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source=pdf_text observed=2026-08-07T10:30:34.271141Z digest=sha256:a9d164dc21ee2e2fd84256d97dd7682553c3cf4b5c4fb010bb1993dbb8cb1d17

Observation 1d234a47-80c2-4656-af6e-d0da2da7c236 · outbound

This paper cites Scaling Laws for Precision.

Kinetics: Rethinking Test-Time Scaling Laws Scaling Laws for Precision

Reference 32

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source=pdf_text observed=2026-08-07T10:30:34.276380Z digest=sha256:d0e3d7a33d90f8bfd61bfe364081b9cc7fa61c594e33350494008e3f3ebeaf31

Observation dd93f266-aebd-4860-898d-fa8039fe0722 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

Kinetics: Rethinking Test-Time Scaling Laws Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 33

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source=pdf_text observed=2026-08-07T10:30:34.281722Z digest=sha256:f81b420e5e75af9efe39d07293a51d0096474a4eb0a8a2e2e0f574ff1d026003

Observation b2454521-1f96-4c0d-9db6-19094b230dda · outbound

This paper cites SnapKV: LLM Knows What You are Looking for Before Generation.

Kinetics: Rethinking Test-Time Scaling Laws SnapKV: LLM Knows What You are Looking for Before Generation

Reference 34

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source=pdf_text observed=2026-08-07T10:30:34.286618Z digest=sha256:3bdc256bfdd09dec6b07db49cb46051d3589481d8a6b7c0489bc63e6c9ae8572

Observation 3f9b9dfd-afad-454e-860b-10b93b92b3a4 · outbound

This paper cites QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving.

Kinetics: Rethinking Test-Time Scaling Laws QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 35

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source=pdf_text observed=2026-08-07T10:30:34.291765Z digest=sha256:4ce3ccddbcaa117545370d56e5510ba1a96adaa92dadb4efaca8c5d0b8e9d29f

Observation ae6aa1cd-1746-42ff-8082-e452897bb7ce · outbound

This paper cites KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache.

Kinetics: Rethinking Test-Time Scaling Laws KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

Reference 36

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source=pdf_text observed=2026-08-07T10:30:34.297272Z digest=sha256:df633af190311ee83928c0b7fb877e8831c4d8eae078ca09416af723ffaa1e81

Observation 2d9c9fdf-5334-4227-925f-8f8302f45092 · outbound

This paper cites an unresolved cited work.

Kinetics: Rethinking Test-Time Scaling Laws Unresolved cited work

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.302439Z digest=sha256:80ee736e364c8bcde32fd6294617f1ce885880075531e9cb97278d035676df6f

Observation 327fddec-e36e-4899-b590-8eb377646723 · outbound

This paper cites Inference-time sparse attention with asymmetric indexing.

Kinetics: Rethinking Test-Time Scaling Laws Inference-time sparse attention with asymmetric indexing

Reference 38

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source=pdf_text observed=2026-08-07T10:30:34.307068Z digest=sha256:649dd398ce93c5be6f10df69e46e296a28de6c424c8063e095a400cf757fc7b0

Observation 6d6b4959-a0ee-4c86-bf6a-b733d232845e · outbound

This paper cites SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification.

Kinetics: Rethinking Test-Time Scaling Laws SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification

Reference 39

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source=pdf_text observed=2026-08-07T10:30:34.312390Z digest=sha256:4b50c3e6f23dbfe6be6732265a3f536754755333b440a2d22f6664e256572bae

Observation a848a160-e230-4f4b-b078-7d83ce424bda · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

Kinetics: Rethinking Test-Time Scaling Laws Accelerating Sparse Deep Neural Networks

Reference 40

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source=pdf_text observed=2026-08-07T10:30:34.317338Z digest=sha256:1ab621276a922bd03f0ac106a0902ab27074ae162468a93c9de6fe4dc46c7ded

Observation 743c9c38-d04b-4836-b21a-06d09e09ade4 · outbound

This paper cites CoTFormer: A Chain-of-Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference.

Kinetics: Rethinking Test-Time Scaling Laws CoTFormer: A Chain-of-Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference

Reference 41

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source=pdf_text observed=2026-08-07T10:30:34.322488Z digest=sha256:fa4f61a60ae6693027f53554c2b32394f711be452c9af24ac8486bcc588752f3

Observation a713a549-6d60-4aff-ba70-eaf95ecab93c · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Kinetics: Rethinking Test-Time Scaling Laws WebGPT: Browser-assisted question-answering with human feedback

Reference 43

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source=pdf_text observed=2026-08-07T10:30:34.332718Z digest=sha256:0d308b22219a71aa2beab66791113aadb88805c618b293cb9861b90f822da123

Observation c18310f8-bbd4-46bd-84b3-15266c4354f5 · outbound

This paper cites The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs.

Kinetics: Rethinking Test-Time Scaling Laws The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs

Reference 44

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source=pdf_text observed=2026-08-07T10:30:34.338653Z digest=sha256:2b750ce8bb4aa0a05744c4d88138225fcf1009057e15c92a9cd9c8a393c41230

Observation f6c34e59-d711-4541-a1ac-66f583637435 · outbound

This paper cites Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation.

Kinetics: Rethinking Test-Time Scaling Laws Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

Reference 45

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source=pdf_text observed=2026-08-07T10:30:34.344195Z digest=sha256:eb0a14a87846fab9c57d3f15fa1d3f6b3028b920505bcd1f1e0256ca93a5fe4a

Observation 9e2c1442-353e-4789-9106-62e5ca832e3e · outbound

This paper cites Daniele Paliotta, Junxiong Wang, Matteo Pagliardini, Kevin Y.

Kinetics: Rethinking Test-Time Scaling Laws Daniele Paliotta, Junxiong Wang, Matteo Pagliardini, Kevin Y

Reference 46

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.350075Z digest=sha256:6e8e71ba260f7242ad8fc52372b83b6416fb51ccec2a5a0a8e3190bd521d1aa1

Observation 5a899b0a-ddf2-4b4d-925a-6af2c1f3fde8 · outbound

This paper cites Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners.

Kinetics: Rethinking Test-Time Scaling Laws Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners

Reference 47

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source=pdf_text observed=2026-08-07T10:30:34.355793Z digest=sha256:df39bff9d1722d7733d15224e080cb125cd887e0e1f9bd89331265e38097bca7

Observation cad4fde1-361c-49c6-b4aa-03316ac2335f · outbound

This paper cites MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding.

Kinetics: Rethinking Test-Time Scaling Laws MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding

Reference 48

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source=pdf_text observed=2026-08-07T10:30:34.361014Z digest=sha256:28c20e91b85f12933cc77c17dd44868e19dafd3d3059ee6964ec162f2d7d901e

Observation b8e7e5aa-9c34-4451-bf7a-0d520a1403d1 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Kinetics: Rethinking Test-Time Scaling Laws Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 49

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source=pdf_text observed=2026-08-07T10:30:34.366945Z digest=sha256:bd2e563349445052b94433e5d5c6a47f6b1850acf4c87c40c6759d584cb105a7

Observation 644ba042-e62d-43e8-b36d-cda667cc8f7d · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Kinetics: Rethinking Test-Time Scaling Laws Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 50

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source=pdf_text observed=2026-08-07T10:30:34.371804Z digest=sha256:f66aad90e26042bcbeb2730329b3f03f4dc7925eb6bda6c2322b265566d6b7ee

Observation 2122038e-f8b7-4b8f-8ae8-c0b6172ef580 · outbound

This paper cites ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference.

Kinetics: Rethinking Test-Time Scaling Laws ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference

Reference 51

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source=pdf_text observed=2026-08-07T10:30:34.376950Z digest=sha256:66331427231fc09c54c365b08cde3584c6d240e5ae8309c57bad03f40097d8f5

Observation 1f9d6edd-9263-434b-b776-3b3437af8323 · outbound

This paper cites LLM Pretraining with Continuous Concepts.

Kinetics: Rethinking Test-Time Scaling Laws LLM Pretraining with Continuous Concepts

Reference 52

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source=pdf_text observed=2026-08-07T10:30:34.382006Z digest=sha256:686894311debff59d535afeb012c17d429139fc4455b77ec63d6dece7acdfaac

Observation 8b93b88b-f13c-4dd1-a139-1f5daea7b325 · outbound

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

Kinetics: Rethinking Test-Time Scaling Laws Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference

Reference 53

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source=pdf_text observed=2026-08-07T10:30:34.386755Z digest=sha256:983fc52b0eb048bcf46c85624234312c60fdb9ac311fe17c9720b07f2683f942

Observation d6006ae1-8995-429c-99c8-0c3945142982 · outbound

This paper cites M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models.

Kinetics: Rethinking Test-Time Scaling Laws M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models

Reference 55

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source=pdf_text observed=2026-08-07T10:30:34.398504Z digest=sha256:daf6b2292dc85982bc62a4c6421e7537583ad1fab291ee7b955b7bf83d3ddf6c

Observation 7ba3c48d-c8c0-46ff-823f-a30d79e9b952 · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

Kinetics: Rethinking Test-Time Scaling Laws Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 56

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source=pdf_text observed=2026-08-07T10:30:34.403798Z digest=sha256:e6dbd5fa423a460cecba88b00791c57f9b1104313697060189e89ae7752c1f61

Observation e73ac66e-3bca-4c1e-aa00-dc59004ac2fe · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Kinetics: Rethinking Test-Time Scaling Laws Efficient Streaming Language Models with Attention Sinks

Reference 57

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source=pdf_text observed=2026-08-07T10:30:34.409258Z digest=sha256:478ee72185f3882179282a4ac52d872eb70667fa54b77a1ce8d5ada735d60f69

Observation b07c3763-8c89-4860-8531-e0bfb19271a4 · outbound

This paper cites Qwen2 Technical Report.

Kinetics: Rethinking Test-Time Scaling Laws Qwen2 Technical Report

Reference 58

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source=pdf_text observed=2026-08-07T10:30:34.414362Z digest=sha256:107023d1af60305e342532ec8d3f3b5f5dfbd05cfe53a42d59a08f852cf20822

Observation 13e7a974-fe98-4837-832b-e201e5be6bc5 · outbound

This paper cites Qwen3 Technical Report.

Kinetics: Rethinking Test-Time Scaling Laws Qwen3 Technical Report

Reference 59

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.419331Z digest=sha256:ab8d68f3d4594ff2097083d86e15c4c285dc1f63d4cb50f05752b345a2ab7667

Observation 4c04b01e-0c77-456a-bba7-8938d89d8292 · outbound

This paper cites Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention.

Kinetics: Rethinking Test-Time Scaling Laws Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Reference 60

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.424839Z digest=sha256:0823d1ccf62153ea0a56d17acb95ef8c4f0abad9c7b6598992661ae26689f48c

Observation dcfdf893-6348-4cbd-95bc-e83b1ef50f6a · outbound

This paper cites LLM Inference Unveiled: Survey and Roofline Model Insights.

Kinetics: Rethinking Test-Time Scaling Laws LLM Inference Unveiled: Survey and Roofline Model Insights

Reference 61

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source=pdf_text observed=2026-08-07T10:30:34.430496Z digest=sha256:c804916ab5ea075410a778c8497ae3a6d1e5be7b6ffe89e2eef36b7a7d85b495

Observation 3ed5c8bc-4d41-4e67-944b-094d4c152d89 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Kinetics: Rethinking Test-Time Scaling Laws OPT: Open Pre-trained Transformer Language Models

Reference 62

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source=pdf_text observed=2026-08-07T10:30:34.435806Z digest=sha256:13c880cb17fdca857feecdc9b04141abfe013e680283f805081a40bfbcc5b990

Observation 9636216e-aac4-4212-a1ed-e54d8b11d05d · outbound

This paper cites NanoFlow: Towards Optimal Large Language Model Serving Throughput.

Kinetics: Rethinking Test-Time Scaling Laws NanoFlow: Towards Optimal Large Language Model Serving Throughput

Reference 63

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source=pdf_text observed=2026-08-07T10:30:34.441416Z digest=sha256:6061bb9b289cbb02bf72109cbbb898d2d55c2aff2b5787bc87335af0a2f92001

Observation 144903df-542b-4753-9db1-1e5b0d74243d · outbound

This paper cites For the Qwen3 series, this additional overhead is bounded by 3.57% for the 0.6B model and by 1.56% for the 32B model.

Kinetics: Rethinking Test-Time Scaling Laws For the Qwen3 series, this additional overhead is bounded by 3.57% for the 0.6B model and by 1.56% for the 32B model

Reference 64

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raw_fallback, observed 2026-08-07T10:30:35.726672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.446985Z digest=sha256:bec6cdd0a1bb1f048eda5cc43a0851f5fde64cef8366aa0a452ddbc9cc750a59

Observation 0f312401-9f31-4b72-a821-c92e42ddf92a · outbound

This paper cites LiveCodeBench features complex programming problems from recent coding contests, while AIME25 consists of challenging math problems.

Kinetics: Rethinking Test-Time Scaling Laws LiveCodeBench features complex programming problems from recent coding contests, while AIME25 consists of challenging math problems

Reference 65

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.451857Z digest=sha256:ead080b58735eb74f638a4a17b1604151d2c0c326d1810208a1dd7da088a45e0

Observation cfb97397-1dab-46e5-9431-1a22820548a2 · outbound

This paper cites In parallel, approaches like FlashAttention (Dao et al., 2022; Dao,.

Kinetics: Rethinking Test-Time Scaling Laws In parallel, approaches like FlashAttention (Dao et al., 2022; Dao,

Reference 66

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raw_fallback, observed 2026-08-07T10:30:35.691225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.457474Z digest=sha256:3f1b96203aee173c1051f203910fbc2521b89b6a3539edfaf8e8de9aaee12bff

Observation f65f81ba-b263-4109-9fa5-77478e6f9397 · outbound

This paper cites an unresolved cited work.

Kinetics: Rethinking Test-Time Scaling Laws Unresolved cited work

Reference 67

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raw_fallback, observed 2026-08-07T10:30:35.672934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.462492Z digest=sha256:38e62bb3ac3698c105724a350b72807072f224b0e2823a2b9f02c6560c91f221

Observation 63f381e5-9da3-44d9-97a1-9ea056c8ae3f · outbound

This paper cites Our analysis builds on the practical designs and implementations of these systems.

Kinetics: Rethinking Test-Time Scaling Laws Our analysis builds on the practical designs and implementations of these systems

Reference 68

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raw_fallback, observed 2026-08-07T10:30:35.657421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.468065Z digest=sha256:24cd5acb895bb738a05fc906e7a30b3d267327f101b338d5526d3f8a698ef804

Observation 26519990-d61a-47b4-893f-a381711f8071 · outbound

This paper cites Additionally, model compression and offloading (Dettmers et al., 2022; Lin et al., 29 2024a; Svirschevski et al., 2024; Sheng et al., 2023; Frantar et al.,.

Kinetics: Rethinking Test-Time Scaling Laws Additionally, model compression and offloading (Dettmers et al., 2022; Lin et al., 29 2024a; Svirschevski et al., 2024; Sheng et al., 2023; Frantar et al.,

Reference 69

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raw_fallback, observed 2026-08-07T10:30:35.638920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.472731Z digest=sha256:8c7225bf0773d455c3572d1a21e27e8203ba8760cb56c83b1cde82c07c9edc69

Observation 485c0785-0d1c-4cba-bf9b-1950eb07c03e · outbound

This paper cites an unresolved cited work.

Kinetics: Rethinking Test-Time Scaling Laws Unresolved cited work

Reference 70

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.478097Z digest=sha256:67f73ccf90177433dcd8e94f84d2aea71e68d9185a529660bc7d486a90b767ef

Observation 6afc88df-97f5-4a9d-bda0-c1a779b24991 · outbound

This paper cites Efficient reward-model-based (Wu et al., 2024; Snell et al., 2024; Sun et al., 2024c) test-time scaling algorithms are also comprehensively studied.

Kinetics: Rethinking Test-Time Scaling Laws Efficient reward-model-based (Wu et al., 2024; Snell et al., 2024; Sun et al., 2024c) test-time scaling algorithms are also comprehensively studied

Reference 71

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raw_fallback, observed 2026-08-07T10:30:35.604606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.483002Z digest=sha256:1fe0bbf07dd1adfdcb363be1ada65d635e8caa28fdfeb324c1fbe6c504e14b44

Observation 90dada2a-a842-4645-8862-d9b62d8c81e1 · outbound

This paper cites Nvidia blackwell platform: Advancing generative ai and accelerated computing.

Kinetics: Rethinking Test-Time Scaling Laws Nvidia blackwell platform: Advancing generative ai and accelerated computing

Reference 1996

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verified fuzzy
raw_fallback, observed 2026-08-07T10:30:35.744729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.392586Z digest=sha256:20abcc163b030479729476c766c5ba7468d4044998a199af673658dacb0a7767

Observation 5c49558e-3fa6-4a63-8b8f-92ce35c12372 · outbound

This paper cites s1: Simple test-time scaling.

Kinetics: Rethinking Test-Time Scaling Laws s1: Simple test-time scaling

Reference 2017

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.327205Z digest=sha256:62417aeedbabe6bfcc7c96f43416116944a089e49ab677cae133f5bc894880aa

Observation 557f2613-81ad-44b6-9090-c975746cc0df · outbound

This paper cites Rethinking Attention with Performers.

Kinetics: Rethinking Test-Time Scaling Laws Rethinking Attention with Performers

Reference 2019

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.141313Z digest=sha256:79decedb8eef12286abda090e52a00faba14f97fa23c5505d30ad0ce330d101f

Observation 5de5f4a4-4bf0-4e26-9c4d-e303b78c1df2 · outbound

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

Kinetics: Rethinking Test-Time Scaling Laws Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2020

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no resolver link, observed 2026-08-07T10:30:34.114066Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.114066Z digest=sha256:a28536693c4835202fd5f8cc2351d8396c1ce4819eff7c96ab9987939030ef32

Observation f8106308-40c9-48c7-ab8e-97516f9a1067 · outbound

This paper cites Accelerating Large Language Model Decoding with Speculative Sampling.

Kinetics: Rethinking Test-Time Scaling Laws Accelerating Large Language Model Decoding with Speculative Sampling

Reference 2021

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no resolver link, observed 2026-08-07T10:30:34.125280Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.125280Z digest=sha256:35f2905a8065a1a5b091ab43474a851c9641e95e3b7e40159aba95690c163b73

Observation 13201d32-6782-410b-ab1b-7664bb36fd8b · outbound

This paper cites SMYRF: Efficient Attention using Asymmetric Clustering.

Kinetics: Rethinking Test-Time Scaling Laws SMYRF: Efficient Attention using Asymmetric Clustering

Reference 2022

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local_arxiv, observed 2026-08-07T10:30:35.334950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:30:34.164977Z digest=sha256:95f1ab8d75639e978ae76373b7385e474382ece33734956deb76a885ddc5e686

Observation e8e64e95-ca1d-49b7-bed5-f699951ef985 · outbound

This paper cites Reinforcement Learning for Long-Horizon Interactive LLM Agents.

Kinetics: Rethinking Test-Time Scaling Laws Reinforcement Learning for Long-Horizon Interactive LLM Agents

Reference 2023

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.130495Z digest=sha256:30d6f9f6546227b728e1efa2852eee1121f004e5769b395dbaaa8497f46a4792

Observation 7a14e433-d084-41a8-b1a7-a3f68645201e · outbound

This paper cites LoCoCo: Dropping In Convolutions for Long Context Compression.

Kinetics: Rethinking Test-Time Scaling Laws LoCoCo: Dropping In Convolutions for Long Context Compression

Reference 2024

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no resolver link, observed 2026-08-07T10:30:34.119273Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:30:34.119273Z digest=sha256:419674665a385046808d6d8426cb03848aaa28c837dac6bb0cdf5067a0ffa31d

Observation bf7534e3-9452-4d0e-8355-73f3ff28b719 · outbound

This paper cites Daman Arora and Andrea Zanette.

Kinetics: Rethinking Test-Time Scaling Laws Daman Arora and Andrea Zanette

Reference 2025

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:30:34.108187Z digest=sha256:d4d726b68022ed4fe605da35afe4cc6260f93f31dd839698439ebce9f05bd6dc

Pith citing papers

Observation a9623f3c-31bf-46fb-83f7-9e303317fef2 · inbound

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models cites this paper.

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models Kinetics: Rethinking Test-Time Scaling Laws

Reference 14

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arxiv_id, observed 2026-07-02T22:07:26.499198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T19:10:53.882876Z digest=sha256:f531e11ff041caf8520bcc1646487f0242037ef75cda05a251ce15de3a7989c2

Observation 729314ea-acfa-49bb-bb0f-bc4e55f461f7 · inbound

Interpretable Adaptive Sampling for LLM Test-Time Scaling cites this paper.

Interpretable Adaptive Sampling for LLM Test-Time Scaling Kinetics: Rethinking Test-Time Scaling Laws

Reference 16

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T05:02:10.742834Z digest=sha256:956a3f971cb6a0eb7eec9cc49db6557e9df520f025410a2c3211d925b224b3f8