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Paper Citation Record · LEDGER

Adversarial Prompts for Acceptance Collapse in Speculative Decoding

As of 8 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2607.21804.

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

pith.paper-citation-record.v1
2607.21804 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:43:38.629810Z

measured 56 of 56 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

56 of 56 outbound references displayed

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  • verified fuzzy0
  • unresolved56
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 36cc452a-de89-4f7e-bbc9-487ccf0b6c8d · outbound

This paper cites GPT-4 Technical Report.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-01T06:43:30.211857Z digest=sha256:f310035d8d3aa48ed2d532bd820d3e691e78dcffa974730236fe0382e4021e6f

Observation 2e2771bf-0f99-4f3d-a97c-eb2c2d182798 · outbound

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

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 2

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source=pdf_text observed=2026-08-01T06:43:30.362906Z digest=sha256:d1f4f6e7209211ad280ab724adbea2f818aab299a6152e7547ab6997285a19cf

Observation 434af7a5-67e7-4177-8249-aa30c138303a · outbound

This paper cites Qwen Technical Report.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Qwen Technical Report

Reference 3

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source=pdf_text observed=2026-08-01T06:43:30.526812Z digest=sha256:1016d2737ce54bc5edf76370931d9f704e63ae20bbe1db0cd177ddfe9a77274d

Observation 6326d741-9ee9-4805-b3d1-b82fe338a715 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding A Simple and Effective Pruning Approach for Large Language Models

Reference 4

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source=pdf_text observed=2026-08-01T06:43:30.783914Z digest=sha256:9294622005de1bd1514eb62c6a4c9f9171831d24ce83f1823783f5c44acad5a3

Observation 3b6d71b3-da42-4a24-b47e-3d07d06dad4d · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 5

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source=pdf_text observed=2026-08-01T06:43:30.950850Z digest=sha256:c26bc87782c4d7d9dade7cbd4702c00da2f9689a9ab97f4104dea15dd6eace29

Observation aebb7d6a-9501-445c-bd96-3f2ee082ecbf · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 6

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source=pdf_text observed=2026-08-01T06:43:31.116990Z digest=sha256:494b6b90ed7af8a6b3b953c93b5b0c96f565dd61878c71bc3e0d81df517e20cc

Observation 1efb1a35-165c-4471-b933-5d2416ecfb9d · outbound

This paper cites Q-bert: Hessian based ultra low precision quantization of bert.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Q-bert: Hessian based ultra low precision quantization of bert

Reference 7

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source=pdf_text observed=2026-08-01T06:43:31.290972Z digest=sha256:8de04e13af3a654b171123d9c705ba3fbd25f19ff50844c4f7a08f2d0bd360e5

Observation 42571301-9eed-410b-b020-70df8476847f · outbound

This paper cites Speculative decoding: Exploiting speculative execution for accelerating seq2seq generation.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Speculative decoding: Exploiting speculative execution for accelerating seq2seq generation

Reference 8

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source=pdf_text observed=2026-08-01T06:43:31.517813Z digest=sha256:a5880b40ad88bc12357323c63cc2ab9d187b8d3ab1c8314f9fd2d32a24e42302

Observation 66aad189-0dc5-4b22-8b5a-0d5b7c968646 · outbound

This paper cites Fast inference from transformers via speculative decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Fast inference from transformers via speculative decoding

Reference 9

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source=pdf_text observed=2026-08-01T06:43:31.687500Z digest=sha256:f0708bb18ed25da61859449c63d5c47f6dab6debe25a8a9ac0d41c8619d69699

Observation 6bd85a32-09fa-42eb-b292-c2c0fa36bf5e · outbound

This paper cites Specinfer: Accelerating generative large language model serving with tree-based speculative inference and verification.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Specinfer: Accelerating generative large language model serving with tree-based speculative inference and verification

Reference 10

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source=pdf_text observed=2026-08-01T06:43:31.853850Z digest=sha256:3ed9357168a5e5584413b4fb7f37f5c8648b868bdf25e5a6569ceedcd83be9a9

Observation 59a69af9-e966-4265-9552-f04253ddd09a · outbound

This paper cites Lee, Deming Chen, and Tri Dao.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Lee, Deming Chen, and Tri Dao

Reference 11

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source=pdf_text observed=2026-08-01T06:43:32.055991Z digest=sha256:32bc0dc85d4be3d2f9d7b0faf85718460fe2a40617e49fbdf623f028edd993eb

Observation 85e8470a-94dd-4269-890f-dd631d2c6e53 · outbound

This paper cites Adaspec: Adap- tive speculative decoding for fast, SLO-aware large language model serving.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Adaspec: Adap- tive speculative decoding for fast, SLO-aware large language model serving

Reference 12

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source=pdf_text observed=2026-08-01T06:43:32.200371Z digest=sha256:c3615144aeec17257d3b9f7b3590e3737237a270f703b111acd1ca13ff230020

Observation 319cc9b7-9dbd-4128-9cb5-b0dfb73ac4b3 · outbound

This paper cites Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding

Reference 13

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source=pdf_text observed=2026-08-01T06:43:32.358745Z digest=sha256:a13d15d5092bf03d6efd74782748fff406b1cfa65525f5682c16621e6d6795ad

Observation 7ee31093-68c5-43bf-aba9-3ead543b1258 · outbound

This paper cites Overcoming joint intractability with lossless hierarchical speculative decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Overcoming joint intractability with lossless hierarchical speculative decoding

Reference 14

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source=pdf_text observed=2026-08-01T06:43:32.611994Z digest=sha256:39bca884beff56a724a78024a87f3bbf79493cc841f7c70878b60bfa01d48b10

Observation 5018f9fa-88d0-4c05-ad5d-1e788eb0544d · outbound

This paper cites Block Verification Accelerates Speculative Decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Block Verification Accelerates Speculative Decoding

Reference 15

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source=pdf_text observed=2026-08-01T06:43:32.749283Z digest=sha256:628d01780ff7ff7f14dfeb56cee95491d0339f84bb26f270d2e1a9258f10f72d

Observation 03ec5bc3-724e-4581-9992-ce475360babb · outbound

This paper cites Optimized multi-token joint decoding with auxiliary model for llm inference.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Optimized multi-token joint decoding with auxiliary model for llm inference

Reference 16

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source=pdf_text observed=2026-08-01T06:43:32.953608Z digest=sha256:34c9135cde25ef9dd5993993a81f65697ba47df2ff7c7303c664099089fbed27

Observation be9f0022-779b-42c1-86e7-bc9e43858442 · outbound

This paper cites Eagle: speculative sampling requires rethinking feature uncertainty.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Eagle: speculative sampling requires rethinking feature uncertainty

Reference 17

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source=pdf_text observed=2026-08-01T06:43:33.101838Z digest=sha256:6cec032ac86aa7a10356975ff4bda3ce3db1a78c65df68ace2b88ef64d55e759

Observation 68aa7663-5557-4ac3-8213-da5832f8b13d · outbound

This paper cites Eagle-2: Faster inference of language models with dynamic draft trees.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Eagle-2: Faster inference of language models with dynamic draft trees

Reference 18

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source=pdf_text observed=2026-08-01T06:43:33.273619Z digest=sha256:d5d099ecc6eb18149fb62147e416cbcd2aa323dd52c0b266e48eec37cc4aa557

Observation 9807f0df-2268-4dda-abc0-b15169d5b8c3 · outbound

This paper cites EAGLE-3: Scaling up inference acceleration of large language models via training-time test.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding EAGLE-3: Scaling up inference acceleration of large language models via training-time test

Reference 19

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source=pdf_text observed=2026-08-01T06:43:33.424436Z digest=sha256:24ae8fbe90968104fdafae5b96ca21c9a704666271e82f78d504680e0b56582f

Observation 128692cd-87b5-42b1-8a3a-318255054b62 · outbound

This paper cites Litevlm: A low-latency vision-language model inference pipeline for resource-constrained environments.arXiv preprint arXiv:2506.07416, 2025.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Litevlm: A low-latency vision-language model inference pipeline for resource-constrained environments.arXiv preprint arXiv:2506.07416, 2025

Reference 20

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source=pdf_text observed=2026-08-01T06:43:33.519184Z digest=sha256:c78abc77742f9417d36d78a464bece3f8592298056f3fff29141d2e156a493a4

Observation 617c93f7-0a1a-4646-8270-3f80f861e267 · outbound

This paper cites Accelerating decode-heavy LLM inference with speculative decoding on AWS trainium and vLLM | artificial intelligence, 2026.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Accelerating decode-heavy LLM inference with speculative decoding on AWS trainium and vLLM | artificial intelligence, 2026

Reference 21

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source=pdf_text observed=2026-08-01T06:43:33.572843Z digest=sha256:fc35665b5b415977b81565479257e76e8d70230db14ebdb8565488171f76f40c

Observation 2b845c7a-f319-498f-8fe9-0208037326f7 · outbound

This paper cites Speculative speculative decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Speculative speculative decoding

Reference 22

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source=pdf_text observed=2026-08-01T06:43:33.728849Z digest=sha256:98d83f08a2a5963ed92da6e96810eed9208244f1d7856d27d8ca13726c67e776

Observation 07cc4dbf-ae00-4362-ad47-ab7bb782af4b · outbound

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

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Accelerating Large Language Model Decoding with Speculative Sampling

Reference 23

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source=pdf_text observed=2026-08-01T06:43:33.867061Z digest=sha256:3be9ef81a9a0639d50893dd4e5b3e32b70df867b37a1078220d497f8a58f3c78

Observation 08ff9b0d-39ea-4092-935e-a913e35fa248 · outbound

This paper cites Snakes and ladders: Accelerating ssm inference with speculative decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Snakes and ladders: Accelerating ssm inference with speculative decoding

Reference 24

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source=pdf_text observed=2026-08-01T06:43:33.999149Z digest=sha256:6838ec4bb01469ec6df748f4243e8a7f87f33ea2dc4f36be3d0761492ebff9ff

Observation 9e8962f1-d70b-425f-84d7-74dc816c5883 · outbound

This paper cites SDSAT: Accelerating LLM Inference through Speculative Decoding with Semantic Adaptive Tokens.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding SDSAT: Accelerating LLM Inference through Speculative Decoding with Semantic Adaptive Tokens

Reference 25

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source=pdf_text observed=2026-08-01T06:43:34.152595Z digest=sha256:0ffb2ea25d7855a2a454ac627af0b5e1d4efeba1c4bfcc7af2d853e3df419e4d

Observation dfb9eacd-c7cd-45fb-8e4c-efb1814d344a · outbound

This paper cites Online Speculative Decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Online Speculative Decoding

Reference 26

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source=pdf_text observed=2026-08-01T06:43:34.296087Z digest=sha256:23b82cfd9a9223fa1e6c2df07de60c2f21710cd81f8f175896e0cfd36ef9e810

Observation e188b815-cd0c-40e2-9bb2-ac489dd188d6 · outbound

This paper cites Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs

Reference 27

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source=pdf_text observed=2026-08-01T06:43:34.444889Z digest=sha256:164f2aaf699194a8f18635d2ddb5dd0d6f88b86f332534234657170b11d88e47

Observation 9a0e2a23-cb98-4eaf-9238-1e9cef3171b5 · outbound

This paper cites AdaEDL: Early Draft Stopping for Speculative Decoding of Large Language Models via an Entropy-based Lower Bound on Token Acceptance Probability.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding AdaEDL: Early Draft Stopping for Speculative Decoding of Large Language Models via an Entropy-based Lower Bound on Token Acceptance Probability

Reference 28

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source=pdf_text observed=2026-08-01T06:43:34.549130Z digest=sha256:ccd8cc35c276e9950b3cba74603c2cdd7241db205ce6c45a71e3a6c8812e7ce1

Observation 1cf89e8e-1fce-429d-825a-2b827c7bd4e8 · outbound

This paper cites Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment

Reference 29

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source=pdf_text observed=2026-08-01T06:43:34.620346Z digest=sha256:ee3ad785ccdedfdbad3da20de51bf42a4bba88cbb80917109abdf3f27015c739

Observation da45cb12-dc80-48ef-b988-b9305bd51b25 · outbound

This paper cites Alignment-augmented speculative decoding with alignment sampling and conditional verification.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Alignment-augmented speculative decoding with alignment sampling and conditional verification

Reference 30

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source=pdf_text observed=2026-08-01T06:43:34.724988Z digest=sha256:8a7957a87d4ede6c7430617bbc409f26fdfd9094afc3bff48a591d0dc88c0194

Observation d662796e-96ad-460d-ba29-52b562f477fd · outbound

This paper cites Decoding speculative decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Decoding speculative decoding

Reference 31

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source=pdf_text observed=2026-08-01T06:43:34.934990Z digest=sha256:dc6d1c2f82425ae4757ff53171477cf03d53225022b6915cf440f282003f1bbd

Observation 9b10f90e-a9d3-48c4-8726-cad81e0150dc · outbound

This paper cites When speculation spills secrets: Side channels via speculative decoding in LLMs.arXiv preprint arXiv:2411.01076, 2024.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding When speculation spills secrets: Side channels via speculative decoding in LLMs.arXiv preprint arXiv:2411.01076, 2024

Reference 32

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source=pdf_text observed=2026-08-01T06:43:35.101906Z digest=sha256:ac4ba8d48d8b0b0a93c6254dd93f8be59aa5f4862072014240bb7ee4b0d70b06

Observation f64570c1-5536-4dd3-a183-3249d52b3042 · outbound

This paper cites Speculative safety-aware decoding.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Speculative safety-aware decoding

Reference 33

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source=pdf_text observed=2026-08-01T06:43:35.226454Z digest=sha256:b556422ef3e94266d781a7f161f268664a3705dfda21eecc50724a15306626ef

Observation 2e88d3c8-3a31-4485-a60f-d643f897ca28 · outbound

This paper cites Sponge examples: Energy-latency attacks on neural networks.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Sponge examples: Energy-latency attacks on neural networks

Reference 34

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source=pdf_text observed=2026-08-01T06:43:35.318331Z digest=sha256:252572876c573eb04eb5dd34712d6e755670bb9dd0c190bd2da4e8c922d95367

Observation 8c91eae2-fdca-455c-ba8d-79691f935cb9 · outbound

This paper cites LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks

Reference 35

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source=pdf_text observed=2026-08-01T06:43:35.459929Z digest=sha256:7286ef65b055d7f70b398204a252b9fcbf04ff12654ba303b8b5d519154305cc

Observation 46d7f7b7-2b13-4e4e-a703-cfae0a29ed61 · outbound

This paper cites An engorgio prompt makes large language model babble on.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding An engorgio prompt makes large language model babble on

Reference 36

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source=pdf_text observed=2026-08-01T06:43:35.638466Z digest=sha256:e8f3f57a320999ca2a57e0820cd72a83bfb6dd087593f93cdb524de678143b32

Observation 313be6c6-f4b9-440f-8fb3-54e0cdeee70e · outbound

This paper cites Rethinking latency denial-of-service: Attacking the LLM serving framework, not the model.arXiv preprint arXiv:2602.07878, 2026.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Rethinking latency denial-of-service: Attacking the LLM serving framework, not the model.arXiv preprint arXiv:2602.07878, 2026

Reference 37

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source=pdf_text observed=2026-08-01T06:43:35.765826Z digest=sha256:8692e11c3553b6496b49d383b524c299614010893c3c2fa3d97c745f1fdacdf4

Observation bba15d69-9548-43c4-9040-0a68fd086097 · outbound

This paper cites Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference

Reference 38

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source=pdf_text observed=2026-08-01T06:43:35.900404Z digest=sha256:b02f64e80cb059ed02676a1bb1d75a21cc1e8cf357ff5f8e3e9afb800c0fb15b

Observation 727fd2bb-7d5a-4e2f-93e6-f165e9326e1e · outbound

This paper cites Speculative decoding: Performance or illusion?arXiv preprint arXiv:2601.11580, 2026.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Speculative decoding: Performance or illusion?arXiv preprint arXiv:2601.11580, 2026

Reference 39

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source=pdf_text observed=2026-08-01T06:43:36.034195Z digest=sha256:03a9640294f0f9ba2dc72f8b5f6429ff8e0fc0d6b70f57b099cbd22634722d3e

Observation a3860f65-e856-4ea1-bde1-91d7c5bbe848 · outbound

This paper cites An empirical study of speculative decoding for small language models.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding An empirical study of speculative decoding for small language models

Reference 40

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source=pdf_text observed=2026-08-01T06:43:36.154005Z digest=sha256:f2142e6d79776550a7a7476d60a60f30b582544fc5fbe16b043536bebf02ea62

Observation d989a324-c5a8-4197-bca5-19dfaaa028bb · outbound

This paper cites Efficient memory management for large language model serving with PagedAttention.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Efficient memory management for large language model serving with PagedAttention

Reference 41

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source=pdf_text observed=2026-08-01T06:43:36.226837Z digest=sha256:f2f8b33d1b0d4918eaff85d4bcaffc9326062fe008fcd056002e6c8a0d15302b

Observation 80287d12-fdfa-488e-88d7-dfc44950d6cf · outbound

This paper cites Text generation inference (TGI).

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Text generation inference (TGI)

Reference 42

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source=pdf_text observed=2026-08-01T06:43:36.361065Z digest=sha256:62b89445cbb20934903460e3ff4a17e72c0a13a240a543b06dc0061afa1c0ca9

Observation a98711de-7c55-4f48-a6c5-984f10c9c2a2 · outbound

This paper cites Continuous batching from first principles.https://huggingface.co/blog/continuous_batching, 2025.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Continuous batching from first principles.https://huggingface.co/blog/continuous_batching, 2025

Reference 43

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source=pdf_text observed=2026-08-01T06:43:36.505120Z digest=sha256:d62b2b5745ace4903b163df305ce7d8f2493742daa9bf84f9694524ef8896a4e

Observation 91608996-c3a5-411b-a781-254d10ea9c7b · outbound

This paper cites Openai api pricing.https://openai.com/api/pricing/, 2026.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Openai api pricing.https://openai.com/api/pricing/, 2026

Reference 44

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no resolver link, observed 2026-08-01T06:43:36.644623Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:43:36.644623Z digest=sha256:5a3ae2c48540d3644ce1b9368cd46c3ef08da158790662ea8a1f930f4754a4a8

Observation 7558b853-f37f-4a13-b87b-2611ddd99c2f · outbound

This paper cites Rate limits.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Rate limits

Reference 45

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source=pdf_text observed=2026-08-01T06:43:36.761972Z digest=sha256:6a043e393a3f2571a7dffec58e391ae56011ac56b48c15e566fda34100303c72

Observation f3a9e20a-55ab-4fbe-8a64-98006e6512d5 · outbound

This paper cites Performance and latency.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Performance and latency

Reference 46

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:43:36.896910Z digest=sha256:b8e133cc3e3fdfa2fc954d6e300369690fa9a75175ee50e3b1cc092373649f41

Observation 13c94e2c-5011-43ba-8fce-deaacbaf7391 · outbound

This paper cites Use provisioned throughput.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Use provisioned throughput

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:43:37.033865Z digest=sha256:3a83258a00e0ee96d1b7c53a3b478cbbb84031e5238656e5cf3d588ad021d231

Observation ceaf2eae-8ce3-45a0-aa1d-d7ae0549b7e7 · outbound

This paper cites Training verifiers to solve math word problems, 2021.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Training verifiers to solve math word problems, 2021

Reference 48

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no resolver link, observed 2026-08-01T06:43:37.236466Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:43:37.236466Z digest=sha256:56f13773e1455d487e5ac1f46ac958795507ca6d99fbfad4716512a39faece1e

Observation 8f97cb47-8ae9-43ea-8f3f-8de0458f6748 · outbound

This paper cites an unresolved cited work.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Unresolved cited work

Reference 49

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source=pdf_text observed=2026-08-01T06:43:37.316899Z digest=sha256:f6571061efaaa857d48c041618b9181744d8074a84ef6431338199c3de75b0bf

Observation d36121a6-9a77-4751-b9ba-46173ff13414 · outbound

This paper cites Liu, and Christopher D.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Liu, and Christopher D

Reference 50

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no resolver link, observed 2026-08-01T06:43:37.526744Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:43:37.526744Z digest=sha256:2180a4f6f37aa50185ea809da0d615d20796ecd180cccfcbe0bfa6d603bee145

Observation bc42a5de-a1f8-4c09-ad63-51a88dac7e07 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 51

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source=pdf_text observed=2026-08-01T06:43:37.685720Z digest=sha256:0852ddc18a8145e321925b3e97e6afc58a1c0fd11e097b22ffe7fab04b9a77a4

Observation dbb54776-e048-416e-a0b6-f48d5b6a01ea · outbound

This paper cites Detecting Language Model Attacks with Perplexity.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Detecting Language Model Attacks with Perplexity

Reference 52

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source=pdf_text observed=2026-08-01T06:43:37.895141Z digest=sha256:1169b0cdc589b81be65338d7bfdf0b08d9468ccee8e67b6e885011eb7057278d

Observation 3337a22f-2507-40e5-94ae-1c5790780f32 · outbound

This paper cites Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 53

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source=pdf_text observed=2026-08-01T06:43:38.107533Z digest=sha256:3120dc2d8c5407bdf190abd6f98c0f262403a2d2b094c2768ca92be20dd83b62

Observation f0c396ab-fd2e-4dee-b66c-3512be1894f2 · outbound

This paper cites LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked

Reference 54

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source=pdf_text observed=2026-08-01T06:43:38.283156Z digest=sha256:7775929b6b41c4eaa35ddf14a9f4517137181b3d1e5179b73a66d6857d5f860f

Observation 09d2e296-6221-4395-85e2-89740a9e6204 · outbound

This paper cites The art of defending: A systematic evaluation and analysis of LLM defense strategies on safety and over-defensiveness.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding The art of defending: A systematic evaluation and analysis of LLM defense strategies on safety and over-defensiveness

Reference 55

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:43:38.449198Z digest=sha256:a5498ee8ea83d79425cce07cff9c604bbfc864981b21f76d51395954794501e4

Observation 28c3ba7d-adf4-48f9-992c-80267ff1645b · outbound

This paper cites @drawable/box.

Adversarial Prompts for Acceptance Collapse in Speculative Decoding @drawable/box

Reference 56

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T06:43:38.629810Z digest=sha256:a143e7e3cf9804afca913e5517a848db3b7c258c7b7aeea5ec14ae5075c39de6

Pith citing papers

No inbound Pith citation observations are available.