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Paper Citation Record · LEDGER

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

As of 8 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 2026-08-08T06:32:00.761636+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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.340639Z digest=sha256:72ab10bdfb70381682eb7754684f6a0da38fbfffb5f8117abea2f279f40f4feb

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.362700Z digest=sha256:5b3c04879ccb44b226f1c111a2cba4d0aec025e02d04941f223078ea60113d1e

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:f7480ee9d7cc8afae7f4a02027a0e13e16cec662e27883912df63a680eb3f2eb

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:fbcd3ffbe52057a819cb6dfab4074333dd5f3bf8261e542950ac7061c666b51f

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:c6d3a07f7045f7c218ef7b46d8cb481df3e49d471b40e0c40817a72f23e711ac

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=sha256:58591b38cde20d1e6d3d387a624c8c93b55c748ff68ce94e9660551e4bc94195

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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no resolver link, observed 2026-08-07T14:51:51.855677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.855677Z digest=sha256:ae48f89c79336c97b80adfb25a6ce3c035e3a5552d2e0b5d70179550503d57a8

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:51.962063Z digest=sha256:fbf3ca46ed8ef043fe833a3b4111aa66db29709a93aabba5aea71594fcf576ca

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.086524Z digest=sha256:c57e3866a801ce8eeecd4c99cd4b0fbc46b1acf76144960fb7af1b650f49b829

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.225072Z digest=sha256:d34c9458e309118d2643ff5f0f22ae6e61c282fd8d0d28ec6de53f9cc57c4b28

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.345584Z digest=sha256:97e98bffd9140cf9773f119bc03bc54c31818356eab113cd8f52a82690d81eda

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=sha256:75cfcea7bee4b2ea9cb3a516d081d34253ca68207bd4f0a8eb78f8100b025484

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.490908Z digest=sha256:9eabbe396169bd8ecad90a099f4bc589771f65867966b65c8ff89578559d610a

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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unresolved
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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.498739Z digest=sha256:8d86e2e88c00dddd322ee29720f886d7b941fdbd882c9ae7ef70bd2a2e091a16

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.519822Z digest=sha256:ad104f3e2279379ee58528324618ba434ee6f162878ba2482a9292ede742f823

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.592199Z digest=sha256:eed8df0003e79a612e1e0b96b44c509079293ed741bca6068de80829a458863c

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:a96d5ab7164180fb0e6ab14187ecd2d6dfa0440449ac850bff37b2a0c3a63a9f

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:52.760222Z digest=sha256:703a8e57ddc73d1827dd2b1a9afc6f5e730dbb394df10615680b7a753523b454

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.857897Z digest=sha256:f90fb4b6fc60f3c58479e2de264acd64c37894f1a16e88ffb0854754c6c417a9

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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raw_fallback, observed 2026-08-07T14:51:53.478609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T14:51:52.927099Z digest=sha256:db28cd110b0ff0814a3c751f83a9a8717e8ea389b14d36df095a55203ae64673

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=sha256:61819ff458fcc6a6b88605c3ad52061c6f42b8eecc05ab9325352afd4c1a13ea

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=sha256:84d75b04d1e41178f7a95acf3d07c02f7d278dbe5bfc3176d8dc94fa70fdfe33

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:51:53.154620Z digest=sha256:d4131e9201bbbb40dbc5f840c74ebe81921cd3fb23752c3de38d1b9cf24e9580

Pith citing papers

No inbound Pith citation observations are available.