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Paper Citation Record · LEDGER

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies

As of 17 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.17420.

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

pith.paper-citation-record.v1
2505.17420 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:51:53.154620Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e01a6648-6985-4ca2-8f98-16d4141d8053 · outbound

This paper cites GPT-4 Technical Report.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies GPT-4 Technical Report

Reference 1

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no resolver link, observed 2026-08-07T14:51:51.340639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.340639Z digest=sha256:c4553d373f06db34e18f2e8167eb1952f1545ac3c902dd15412395d777755360

Observation c38aa4c6-73ca-4688-8c6c-c156e5555b90 · outbound

This paper cites an unresolved cited work.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Unresolved cited work

Reference 2

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no resolver link, observed 2026-08-07T14:51:51.362700Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T14:51:51.362700Z digest=sha256:e3bcaa423f0a9b9d26b7b4eb199f4e39931c85f6492501c973fd39a13474381f

Observation 8d4a2d47-2c56-452f-9eb4-b5de198700b0 · outbound

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

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 3

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no resolver link, observed 2026-08-07T14:51:51.422845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.422845Z digest=sha256:846f45296502ef7b8e80fff83fb35ae6c59dd441ee70978e131ad7ba65820259

Observation 76a8c7e2-caa3-4204-8c28-77bb60f2d59f · outbound

This paper cites SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.501090Z digest=sha256:8a7c7a5f0978821a623f529741c96871caabf697c7e412b5fed9fff5e440737f

Observation 73ed9899-7c75-4117-a10b-8539a60bc3e4 · outbound

This paper cites LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.597620Z digest=sha256:deb26fa8f2daef00515451cb1bf38298607467eb2ce504414c5cf7b1a6d1ca07

Observation 65a5f1c8-cb1f-41ac-8afe-021d6ac540f2 · outbound

This paper cites Not All Layers of LLMs Are Necessary During Inference.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Not All Layers of LLMs Are Necessary During Inference

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.721100Z digest=sha256:0ea76ba0373375230631f5efce5d1dbd4043659ea14fe4c47ce7ab4fd74ea6f9

Observation d0d265e9-9b28-40c8-9e1e-20c73c06c549 · outbound

This paper cites The Llama 3 Herd of Models.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies The Llama 3 Herd of Models

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.855677Z digest=sha256:7272b89b5898549c7f3cb130d1e3b52db752b7d5f9d21de772a91b1a4ef5e2b0

Observation 73d78bdd-b6f6-4b71-863d-41687c3c861e · outbound

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

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.962063Z digest=sha256:24b78051e0fdb4619c23cbe1dedd1c08f5616b94746f6243840482d99e80d61b

Observation fd1e5ce3-886b-4861-9a6d-d54f080ccb42 · outbound

This paper cites an unresolved cited work.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Unresolved cited work

Reference 9

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raw_fallback, observed 2026-08-07T14:51:54.034180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.086524Z digest=sha256:4b42580d42c75077006103a425d98fd249a7f3f715e8d64e9ab8a72faa33803f

Observation 18ed080e-0432-4a98-8c6b-f03e02882084 · outbound

This paper cites AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference

Reference 10

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.225072Z digest=sha256:db5ba287085865033d9b744d350613ae0b3b448f02f6f1115b5db689a8ea8d33

Observation 5647efe6-cb62-4864-9568-14b5cf30957d · outbound

This paper cites Measuring Massive Multitask Language Understanding.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Measuring Massive Multitask Language Understanding

Reference 11

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.345584Z digest=sha256:77758b98b40eff4de42868f4e1c8afe86dc303965fa878aee6c38c61cd26d483

Observation df9dcbfb-2cff-4c29-b1ba-04daa3e9a950 · outbound

This paper cites FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping

Reference 12

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no resolver link, observed 2026-08-07T14:51:52.466772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.466772Z digest=sha256:a25e9410eba900b42efddf8cdf2dcd0cbab1d0c7a71397bb6b1370b66c154e98

Observation 4c017860-a3d9-485e-91f7-1e1be7523979 · outbound

This paper cites Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.490908Z digest=sha256:57d8915e66935318c559b00717b5ebb3ddfae06391d8fa40453f6b19bdf1f0d3

Observation 16070eb8-038d-4783-9b7f-478af00109b5 · outbound

This paper cites an unresolved cited work.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Unresolved cited work

Reference 14

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raw_fallback, observed 2026-08-07T14:51:53.858909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.498739Z digest=sha256:1c66acbd8b9b72f6fc8c88e72135b9880cfbce1626b9178a641a8f8493f6a689

Observation eb5cbf14-7e40-4a43-b3cd-e3f72118b92b · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 15

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no resolver link, observed 2026-08-07T14:51:52.519822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.519822Z digest=sha256:6a7d3f657370dc43566e3a2c4ebee30bb6cd7f67937342e032044e0a04fdd563

Observation 03767f2f-ddb6-4a35-ad71-b4fc76ee627c · outbound

This paper cites Pointer Sentinel Mixture Models.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Pointer Sentinel Mixture Models

Reference 16

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no resolver link, observed 2026-08-07T14:51:52.592199Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.592199Z digest=sha256:87ec46c5272fac5ad730b2ea8489272708eb475d853ff97d2f0ded3ee2fd073f

Observation 39859a22-f103-42b3-83f8-b417879e57a2 · outbound

This paper cites Get To The Point: Summarization with Pointer-Generator Networks.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Get To The Point: Summarization with Pointer-Generator Networks

Reference 17

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no resolver link, observed 2026-08-07T14:51:52.681122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.681122Z digest=sha256:a6a2903a52d936161f02731a4547900279e9ebe943412589a4c1ea2de912ff70

Observation 1334dc08-0bfb-416d-9eae-d010615e7abb · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

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no resolver link, observed 2026-08-07T14:51:52.760222Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.760222Z digest=sha256:e6a4f9a2bd1882900e90077025b3c6661fb238ce95b906d68ac58b871b555a98

Observation aa018563-c17f-483f-9b5a-025aa500af79 · outbound

This paper cites an unresolved cited work.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Unresolved cited work

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.857897Z digest=sha256:03c718b702b65f3c3c0a176a6d99b6c183ef7da20ab802f4c4416d3bcfa97232

Observation 39088ef9-e16d-4a31-8e9d-4d56750a9c15 · outbound

This paper cites an unresolved cited work.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Unresolved cited work

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.927099Z digest=sha256:daac7e9e084d1d9dd3322d4cfbef463f1ed616c66097e85766257ca2d77ac34a

Observation 4e3742c7-26a1-4820-beaa-8c220cde2144 · outbound

This paper cites Qwen3 Technical Report.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies Qwen3 Technical Report

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.969311Z digest=sha256:f43cfb56c8e3017d51dfc9f901cd8a1d672ca9a36e0422a6dd4741d9b6e91e0e

Observation 1189219a-5dd6-4dd8-a199-69fe0222df11 · outbound

This paper cites online" 'onlinestring :=.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies online" 'onlinestring :=

Reference 22

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:53.071698Z digest=sha256:2b4bfa672fdde8ff94d835ebca04dc070d3df801dbbd8582ab88523172923495

Observation 7412d5f8-21d9-4ceb-a8d5-560e5a6d7dca · outbound

This paper cites write newline.

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies write newline

Reference 23

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:53.154620Z digest=sha256:a6648a731ab3a2ec1ef19c4a269a5db90dae4628d6a47dbae9db6755f2e31b51

Pith citing papers

No inbound Pith citation observations are available.