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Paper Citation Record · LEDGER

Smoothed Embeddings for Robust Language Models

As of 20 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 4 inbound Pith citation observations for arXiv:2501.16497.

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

pith.paper-citation-record.v1
2501.16497 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T12:52:40.970136Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:21:58.795021Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:26:48.381752Z

Reference resolution

21 of 21 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cddc8ebe-d7a3-4923-a5e4-b3493db63cd9 · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

Smoothed Embeddings for Robust Language Models Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 3

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no resolver link, observed 2026-08-10T12:52:40.901600Z

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source=pdf_text observed=2026-08-10T12:52:40.901600Z digest=sha256:2a60c6721677969eea8f407fdb06dd244a68a1eee33ed0e3e83d67ab5fd24160

Observation 55507cc1-c7c8-4152-a8d3-8dbb6791a3f4 · outbound

This paper cites JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models.

Smoothed Embeddings for Robust Language Models JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 4

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no resolver link, observed 2026-08-10T12:52:40.906279Z

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source=pdf_text observed=2026-08-10T12:52:40.906279Z digest=sha256:06c3770514f7c4e15177fe9ecdfd27a82d4d63dcc55f49f8ae0ef07160d11ba9

Observation 771fb426-63a2-494e-9124-f89507a13361 · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

Smoothed Embeddings for Robust Language Models Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 7

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no resolver link, observed 2026-08-10T12:52:40.918088Z

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source=pdf_text observed=2026-08-10T12:52:40.918088Z digest=sha256:adeca13f5ade4ae58574dd89a9dc2ecc153f4de453a13f034632536a6e540793

Observation 41b4c813-94a4-414c-88ef-f7b0c896e911 · outbound

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

Smoothed Embeddings for Robust Language Models Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 8

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no resolver link, observed 2026-08-10T12:52:40.921697Z

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source=pdf_text observed=2026-08-10T12:52:40.921697Z digest=sha256:d8ae93d014fa45bc9b90daea01178b0638d07a3ac88a5dd6b2f3f48704a3f883

Observation 5b723e18-9619-4d71-9548-821801dbacb8 · outbound

This paper cites Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing.

Smoothed Embeddings for Robust Language Models Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing

Reference 9

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no resolver link, observed 2026-08-10T12:52:40.925285Z

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source=pdf_text observed=2026-08-10T12:52:40.925285Z digest=sha256:d043f5532ed865bc3998af4b9a8b6c63fb6ceef970a2b1821276674da22acfa4

Observation edc496c7-288c-4817-adef-d181b7edf0c2 · outbound

This paper cites Certifying LLM Safety against Adversarial Prompting.

Smoothed Embeddings for Robust Language Models Certifying LLM Safety against Adversarial Prompting

Reference 10

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no resolver link, observed 2026-08-10T12:52:40.928821Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T12:52:40.928821Z digest=sha256:61fbc8c0207ace481d938d2de0883a4c84a612ad4ede69061035a2a489c65be9

Observation a7bf8c9c-f92d-4333-aa04-34ba76ba72c5 · outbound

This paper cites Certified robustness to adversarial examples with differential privacy.

Smoothed Embeddings for Robust Language Models Certified robustness to adversarial examples with differential privacy

Reference 11

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no resolver link, observed 2026-08-10T12:52:40.932369Z

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source=pdf_text observed=2026-08-10T12:52:40.932369Z digest=sha256:065e0653e70e069107ca99d33986a28470fa2d5e127307929118d2c231d0855b

Observation c674cead-cb03-4c3a-af86-32cd35e037b1 · outbound

This paper cites The Llama 3 Herd of Models.

Smoothed Embeddings for Robust Language Models The Llama 3 Herd of Models

Reference 12

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no resolver link, observed 2026-08-10T12:52:40.936140Z

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source=pdf_text observed=2026-08-10T12:52:40.936140Z digest=sha256:189debd373aa19707b79179283240c0ec6d77a8bfa833b890d54dc14e3075ccb

Observation 7b0efcbd-0fed-4e74-a645-366594bc9cba · outbound

This paper cites HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.

Smoothed Embeddings for Robust Language Models HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 13

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no resolver link, observed 2026-08-10T12:52:40.939580Z

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source=pdf_text observed=2026-08-10T12:52:40.939580Z digest=sha256:c952e0106971fed96e51fa1ddcd580ca7f05e8d290c1a534395dfc9660daf895

Observation acfdb9bd-c0c1-4c3d-917e-4abc7ae5493f · outbound

This paper cites RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content.

Smoothed Embeddings for Robust Language Models RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content

Reference 16

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no resolver link, observed 2026-08-10T12:52:40.950778Z

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source=pdf_text observed=2026-08-10T12:52:40.950778Z digest=sha256:76336b13db4d6d0d2d7aee4eddc5e567050b5ec345972e6df24cb61fdb7f11c6

Observation a6ccf470-8148-4224-a140-da4f9f4c6297 · outbound

This paper cites Certified Robustness for Large Language Models with Self-Denoising.

Smoothed Embeddings for Robust Language Models Certified Robustness for Large Language Models with Self-Denoising

Reference 17

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no resolver link, observed 2026-08-10T12:52:40.954479Z

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source=pdf_text observed=2026-08-10T12:52:40.954479Z digest=sha256:33133dd353b3b22caebee0a8a148b4ff0fb51132bb2113feace147b27e721627

Observation 0a871956-3469-497e-9c05-198552ffdb80 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Smoothed Embeddings for Robust Language Models Instruction-Following Evaluation for Large Language Models

Reference 18

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source=pdf_text observed=2026-08-10T12:52:40.958213Z digest=sha256:dd4c0a7a5182474f59db2ac186d8c47ba25cfdbbf21eaae27c7d93528cf7e73b

Observation 1ea8bff1-ff9d-4a21-b742-509db9db4b3f · outbound

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

Smoothed Embeddings for Robust Language Models Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 19

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no resolver link, observed 2026-08-10T12:52:40.961922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T12:52:40.961922Z digest=sha256:dec36c21c3e4f3313c86d780922dd20133439ef0af7ed3bc5dd6091cb0ecb42c

Observation eaec8b96-f231-411f-94f0-81063e48212a · outbound

This paper cites [USER-CONTENT].

Smoothed Embeddings for Robust Language Models [USER-CONTENT]

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-10T12:52:41.207625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T12:52:40.965842Z digest=sha256:9cb2d833b58a9ece5cd6e93e08e64cc010191c4af2a7d6e055e3b5216d8496cb

Observation 015894f3-18ac-4def-94e9-a82a6bbf0ad9 · outbound

This paper cites The results summarized in Table 2 show that 13 Figure 10: Process to get a response with a smoothed response prefix.

Smoothed Embeddings for Robust Language Models The results summarized in Table 2 show that 13 Figure 10: Process to get a response with a smoothed response prefix

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-10T12:52:41.195061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T12:52:40.970136Z digest=sha256:cfe4817aec2b257a1120b31b62265e77697114edb10e6d61e44d499fdf06883d

Observation 28a6b165-b2ab-4ae8-bc6b-1dc2542a9e6e · outbound

This paper cites Intriguing properties of neural networks.

Smoothed Embeddings for Robust Language Models Intriguing properties of neural networks

Reference 2014

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no resolver link, observed 2026-08-10T12:52:40.946943Z

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source=pdf_text observed=2026-08-10T12:52:40.946943Z digest=sha256:16706a799df22ce4ab4f7b1a36bca34397227742b6f8786879473673c03af3d1

Observation 685459c6-a38e-46cf-93a1-fc0532d84f3c · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Smoothed Embeddings for Robust Language Models Explaining and Harnessing Adversarial Examples

Reference 2015

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no resolver link, observed 2026-08-10T12:52:40.914111Z

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source=pdf_text observed=2026-08-10T12:52:40.914111Z digest=sha256:5b9540790aaa1433402ab5d358127fefbaf7be9acb74fb6c4bf22325f26a61cb

Observation c4ad8d9a-a7a2-46bf-92f6-65d8d195a7dd · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Smoothed Embeddings for Robust Language Models Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 2019

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source=pdf_text observed=2026-08-10T12:52:40.910198Z digest=sha256:39e39a56bf9bdcc9eb6e131e518d1e49423e44dd996ce957ebd7552cec39a5d0

Observation 6b2fc66e-da60-4a7b-97b8-a89099a4423b · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

Smoothed Embeddings for Robust Language Models SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 2022

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no resolver link, observed 2026-08-10T12:52:40.943356Z

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source=pdf_text observed=2026-08-10T12:52:40.943356Z digest=sha256:d0c92961751e05f7c983d53bf6ab0063ae8ed002562fc4679a852f734b067af9

Observation 1cc8c3c0-97d1-457c-b51e-7fe5aac8f70f · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Smoothed Embeddings for Robust Language Models Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 2023

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source=pdf_text observed=2026-08-10T12:52:40.897645Z digest=sha256:e9f399991b3bdf2877c4e6d6d65683c35fd8f550f370d137bbea6238ee42b425

Observation e9f69151-b8d8-4a66-990d-fb70ec011886 · outbound

This paper cites Detecting Language Model Attacks with Perplexity.

Smoothed Embeddings for Robust Language Models Detecting Language Model Attacks with Perplexity

Reference 2024

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source=pdf_text observed=2026-08-10T12:52:40.893268Z digest=sha256:c9f64c197e38130fc88f4e2789f47325cf11872750dcff6ae843e6c3b84b4409

Pith citing papers

Observation a4037bb0-48ea-40e9-9565-1e8456f2da7a · inbound

Embedding Poisoning: Bypassing Safety Alignment via Embedding Semantic Shift cites this paper.

Embedding Poisoning: Bypassing Safety Alignment via Embedding Semantic Shift Smoothed Embeddings for Robust Language Models

Reference 14

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source=pdf_text observed=2026-08-15T16:21:58.795021Z digest=sha256:81eceb1713d1e4fad01f74d4ae5a2c44b43221943e41b6d78611eb172d2b0d3e

Observation f5aeb4fb-04a5-4fb2-a084-f490918a8417 · inbound

Towards Understanding the Robustness of Sparse Autoencoders cites this paper.

Towards Understanding the Robustness of Sparse Autoencoders Smoothed Embeddings for Robust Language Models

Reference 28

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arxiv_id, observed 2026-05-10T05:46:10.252295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T05:44:24.532548Z digest=sha256:1728c30255a742c4b8c9a664cb84878ce5f9af5fca740fc931138dc93a3a12d6

Observation 23f0ab69-a308-4108-b653-dec1e85b3c01 · inbound

Re-Triggering Safeguards within LLMs for Jailbreak Detection cites this paper.

Re-Triggering Safeguards within LLMs for Jailbreak Detection Smoothed Embeddings for Robust Language Models

Reference 5

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verified exact
arxiv_id, observed 2026-05-12T06:06:25.023194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-12T04:37:29.075413Z digest=sha256:e0450de30663989088512d59451f0db3fe21bd901c26e6f04f2b7d98acd01807

Observation 3a320c02-371d-4654-af67-062bf117574a · inbound

Auditing CoT Answer-Hijack Patches: Source-Control Certificates with Type-I Guarantees cites this paper.

Auditing CoT Answer-Hijack Patches: Source-Control Certificates with Type-I Guarantees Smoothed Embeddings for Robust Language Models

Reference 30

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arxiv_id, observed 2026-07-02T08:26:48.383245Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-28T06:00:46.938039Z digest=sha256:f8e7b9c900a6634f6af6b1b17a11813ffd40af2a410747e67f991289a0f0bc0f