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Paper Citation Record · LEDGER

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection

As of 17 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2509.09291.

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

pith.paper-citation-record.v1
2509.09291 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:25:52.686093Z

measured 43 of 43 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

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation c4120182-0710-492d-96ac-8291d574b565 · outbound

This paper cites Automatic fingerprinting of vulnerable BLE IoT devices with static UUIDs from mobile apps,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Automatic fingerprinting of vulnerable BLE IoT devices with static UUIDs from mobile apps,

Reference 1

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source=pdf_text observed=2026-08-04T19:25:52.457257Z digest=sha256:8c41ac734a312598aee01f409a6ab4212c1d8525bc2a76fe6ecb15a9ebf71275

Observation 8318d800-5600-49be-8fff-1fad848fce8c · outbound

This paper cites BLESS: A BLE application security scanning framework,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection BLESS: A BLE application security scanning framework,

Reference 2

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source=pdf_text observed=2026-08-04T19:25:52.463720Z digest=sha256:83ad496f7f67c15a055aea74d0184f1afcfefc232e8a6cb0acb70fc8e05defb7

Observation 96a18cbd-157a-414a-976e-57ba857003d9 · outbound

This paper cites BLESA: Spoofing attacks against reconnections in bluetooth low energy,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection BLESA: Spoofing attacks against reconnections in bluetooth low energy,

Reference 3

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source=pdf_text observed=2026-08-04T19:25:52.468704Z digest=sha256:a5312b9df3cd6c740368a8877ed5d7ec33dfcd318cb98a430ebd3d82289209ab

Observation bb3ae60c-cd3e-4508-a0bb-1f22afc2351b · outbound

This paper cites Breaking secure pairing of bluetooth low energy using downgrade attacks,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Breaking secure pairing of bluetooth low energy using downgrade attacks,

Reference 4

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source=pdf_text observed=2026-08-04T19:25:52.474234Z digest=sha256:8cd749a557a8a0992db28f97f60a02c28f4ff6ec5371315fe78230247bed2971

Observation 886f8697-fde8-4483-b6a6-dabbddc0bd06 · outbound

This paper cites FirmXRay: Detecting bluetooth link layer vulnerabilities from bare-metal firmware,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection FirmXRay: Detecting bluetooth link layer vulnerabilities from bare-metal firmware,

Reference 5

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source=pdf_text observed=2026-08-04T19:25:52.480466Z digest=sha256:1f8be153af4db2938d1d6ab8a71add856808345e11da12741ee1c332bb72ed9d

Observation 9b24f59e-71e0-4580-860b-6653b626c7ff · outbound

This paper cites Bluetooth core specification v5. 1,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Bluetooth core specification v5. 1,

Reference 6

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source=pdf_text observed=2026-08-04T19:25:52.485273Z digest=sha256:a2592e074b329ba7ec73ec7b7871448b3372186816b4d9fdb0b0dc92d0d3588c

Observation 5eb33751-1dbb-4d36-9985-7c3893bb7d20 · outbound

This paper cites Attention is all you need,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Attention is all you need,

Reference 7

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source=pdf_text observed=2026-08-04T19:25:52.490903Z digest=sha256:998666fb19896722fcac0f4d2e0c92c8467e293e3f070eabd193258aa15bdda5

Observation 4b3d87e6-b035-4d1f-8a57-8b116ab4cfb5 · outbound

This paper cites If LLMs Would Just Look: Simple Line-by-line Checking Improves Vulnerability Localization.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection If LLMs Would Just Look: Simple Line-by-line Checking Improves Vulnerability Localization

Reference 8

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source=pdf_text observed=2026-08-04T19:25:52.495927Z digest=sha256:7e7c63ce08783510e4b4da3ca75eacc3d417ef25f19900c5b06f15d0d55041a3

Observation f23e1685-8a67-433f-abd9-e19afb17ba1c · outbound

This paper cites A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly,

Reference 9

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source=pdf_text observed=2026-08-04T19:25:52.502176Z digest=sha256:57e5668e43eeff91d63ff1c539fa1fef3c3e58d8b4188945cbacad1db5efb355

Observation 6e0182fa-3224-4db7-8b28-aa73467a5b05 · outbound

This paper cites Llm- based test-driven interactive code generation: User study and empirical evaluation,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Llm- based test-driven interactive code generation: User study and empirical evaluation,

Reference 10

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source=pdf_text observed=2026-08-04T19:25:52.506998Z digest=sha256:218dee81a2cd496b9462ce0c1e09ccf109367d81d3ef6a1903bb7068d71111f4

Observation 54f24f52-14be-414f-b28f-0c711e939c19 · outbound

This paper cites An efficient cryptographic protocol verifier based on prolog rules,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection An efficient cryptographic protocol verifier based on prolog rules,

Reference 11

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source=pdf_text observed=2026-08-04T19:25:52.511931Z digest=sha256:a40e4cbcaf5bff103af0463c0ea7d7534b6da0fa0dc6f538460e655e01ab3806

Observation 68dd644b-8600-4835-9354-a47ff4d4599b · outbound

This paper cites Multitask-based evaluation of open- source LLM on software vulnerability,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Multitask-based evaluation of open- source LLM on software vulnerability,

Reference 12

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source=pdf_text observed=2026-08-04T19:25:52.516424Z digest=sha256:cf60d468944b5ff19e2d6ab1883715411c5f72f79826807466c25bad3d5c0c7d

Observation 84f05787-da1b-4643-b415-e4d5a805d04e · outbound

This paper cites To Err is Machine: Vulnerability Detection Challenges LLM Reasoning.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection To Err is Machine: Vulnerability Detection Challenges LLM Reasoning

Reference 13

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source=pdf_text observed=2026-08-04T19:25:52.524061Z digest=sha256:fb54d16480b0da611e6966893deeb1c2998cfad62d82df369b4e185ee8957dc9

Observation 6df943cb-c42f-48f4-9c14-975a3c5973de · outbound

This paper cites Vulnerability detection with code language models: How far are we?.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Vulnerability detection with code language models: How far are we?

Reference 14

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source=pdf_text observed=2026-08-04T19:25:52.531069Z digest=sha256:0565a3281ef454916127841d68216e1de5f7acc539d1d5a29c49074828ae9d64

Observation 5bcef70e-3e69-430f-b53e-daf1799f5289 · outbound

This paper cites Androzoo: Collecting millions of android apps for the research community,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Androzoo: Collecting millions of android apps for the research community,

Reference 15

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source=pdf_text observed=2026-08-04T19:25:52.537040Z digest=sha256:870d335b0613846de01210a43c49ed7d5149bf6d04595d3560b7e8aa15b5ef9a

Observation 290450dd-c4f1-4ffb-b550-3ce0a6addba5 · outbound

This paper cites A study of the feasibility of co- located app attacks against BLE and a Large-Scale analysis of the current Application-Layer security landscape,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection A study of the feasibility of co- located app attacks against BLE and a Large-Scale analysis of the current Application-Layer security landscape,

Reference 16

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source=pdf_text observed=2026-08-04T19:25:52.541808Z digest=sha256:515e9055f2cc07a3607bd80d7c178b8d7ffd7efbfaf55b7803ff310a86b159c6

Observation 117b0f94-742f-411d-bf1c-9f2f5e92bdfb · outbound

This paper cites SweynTooth: unleashing mayhem over bluetooth low energy,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection SweynTooth: unleashing mayhem over bluetooth low energy,

Reference 17

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source=pdf_text observed=2026-08-04T19:25:52.547083Z digest=sha256:a9f0e11ccc7bef46b5f52327e44889bb702764e03613b4f000f68599fa23075a

Observation 199c6d85-16ae-4f70-83f9-7e7c10bf03a0 · outbound

This paper cites Finding traceability attacks in the bluetooth low energy specification and its im- plementations,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Finding traceability attacks in the bluetooth low energy specification and its im- plementations,

Reference 18

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source=pdf_text observed=2026-08-04T19:25:52.552263Z digest=sha256:b43ea48a839a18aa4a18b04395ebcfb14c10d44a79384c2e17f622f8f9905f2c

Observation 8abf8edc-b9be-4fe1-b94d-a0d739183666 · outbound

This paper cites Extrapolating formal analysis to uncover attacks in bluetooth passkey entry pairing.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Extrapolating formal analysis to uncover attacks in bluetooth passkey entry pairing

Reference 19

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source=pdf_text observed=2026-08-04T19:25:52.559592Z digest=sha256:da406618768ea9da40c6a4ff5139590a00c653c774941ccab6900566c783b894

Observation 2ccafd6b-8624-4e74-a638-c05f751ea754 · outbound

This paper cites BlueSW AT: A lightweight state-aware security framework for bluetooth low energy,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection BlueSW AT: A lightweight state-aware security framework for bluetooth low energy,

Reference 20

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source=pdf_text observed=2026-08-04T19:25:52.565391Z digest=sha256:c52902e2a2e0cf2f3b1237ee8199a0b6dffd1375269d5ff6d1a6fff4a97adc82

Observation bc57a07a-3f0f-42f9-bedc-56cddeabea8b · outbound

This paper cites Eddystone- eid: Secure and private infrastructural protocol for ble beacons,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Eddystone- eid: Secure and private infrastructural protocol for ble beacons,

Reference 21

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source=pdf_text observed=2026-08-04T19:25:52.571159Z digest=sha256:6eed82b60ae2c9687555c3472f0146d1da5a1c4acb984b2af17f2fe84f2e4e7b

Observation fb080a56-5e9b-4593-8b56-f1dae7bf53de · outbound

This paper cites MiniBLE: Exploring insecure BLE API usages in mini-programs,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection MiniBLE: Exploring insecure BLE API usages in mini-programs,

Reference 22

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source=pdf_text observed=2026-08-04T19:25:52.577161Z digest=sha256:853dd136bfd324b7eaf030c6dd6560e840a95399f96105ad520843abaf8b94bb

Observation 05d45fb7-d9d9-4857-a671-4106d7d3bb6e · outbound

This paper cites Vul-RAG: Enhancing LLM-based vulnerability detection via knowledge-level RAG,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Vul-RAG: Enhancing LLM-based vulnerability detection via knowledge-level RAG,

Reference 23

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source=pdf_text observed=2026-08-04T19:25:52.582400Z digest=sha256:bb5accb2238cd7907509b57357426e6ad053b353a2ca8573372780bb3e4c813f

Observation a95a7975-1992-4db7-8413-5318869550b5 · outbound

This paper cites On hardware security bug code fixes by prompting large language models,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection On hardware security bug code fixes by prompting large language models,

Reference 24

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source=pdf_text observed=2026-08-04T19:25:52.589003Z digest=sha256:23f7e195a6901810da5a3a00a5606cfee297037414dec992798df0bfec026b71

Observation 98820469-28cc-416b-ba2f-8eb104ef2059 · outbound

This paper cites Transformer-based language models for software vulnera- bility detection,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Transformer-based language models for software vulnera- bility detection,

Reference 25

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source=pdf_text observed=2026-08-04T19:25:52.594296Z digest=sha256:6c689ba17f5966d28ea6f576de980cf3caabf4851edc0a93bfa92b6cd3719371

Observation 2aa75e9c-02a4-4557-bdb9-ff6780cfa5c4 · outbound

This paper cites Software vul- nerability detection using large language models,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Software vul- nerability detection using large language models,

Reference 26

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source=pdf_text observed=2026-08-04T19:25:52.600031Z digest=sha256:7c601b0bc7f292827e6b38bb7aba999d5e93a212ce959bd4972fd476a9ed78e4

Observation 4d98dbeb-7e96-4337-8b47-eff40b6ce4d1 · outbound

This paper cites DrAttack: Prompt decomposition and reconstruction makes powerful LLMs jailbreakers,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection DrAttack: Prompt decomposition and reconstruction makes powerful LLMs jailbreakers,

Reference 27

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source=pdf_text observed=2026-08-04T19:25:52.604730Z digest=sha256:6fbf5c407833c1e26620ec76d50769cbc299b5ae949358f6e05c469ed8344ef1

Observation f32d4043-e7f0-4355-8943-f07a0a2df8fb · outbound

This paper cites COLD-attack: Jailbreaking LLMs with stealthiness and controllability,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection COLD-attack: Jailbreaking LLMs with stealthiness and controllability,

Reference 28

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source=pdf_text observed=2026-08-04T19:25:52.609245Z digest=sha256:b47595115953e7663c43c254651689479070f1577f20334f9e036ed0ecac242d

Observation 57a90d98-905b-422e-af2e-3e29d6647873 · outbound

This paper cites DeepInception: Hypnotize large language model to be jailbreaker,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection DeepInception: Hypnotize large language model to be jailbreaker,

Reference 29

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source=pdf_text observed=2026-08-04T19:25:52.615025Z digest=sha256:52ca3235626dd942c9ea8b6f94923c8770392a0b6434fcd6574373a28475e1f1

Observation e250a2a3-b243-4b35-8981-9abf74f066f6 · outbound

This paper cites Can large language models provide security & privacy advice? measuring the ability of llms to refute misconceptions,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Can large language models provide security & privacy advice? measuring the ability of llms to refute misconceptions,

Reference 30

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source=pdf_text observed=2026-08-04T19:25:52.619804Z digest=sha256:5d32c8de5113a76647d670fcd968fb7786f9340effbb691f0c28d30823ba50c6

Observation bd46c3cc-7f5b-43a4-bff9-ae916fb05e43 · outbound

This paper cites Examining zero-shot vulnerability repair with large language models,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Examining zero-shot vulnerability repair with large language models,

Reference 31

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source=pdf_text observed=2026-08-04T19:25:52.624672Z digest=sha256:6361f9cd1b453b63507df33553a59885536fb2e122d512a69bd89cc95b1fd18d

Observation 373a6e38-f342-43d2-9606-195e5ae26a56 · outbound

This paper cites On protecting the data privacy of large language models (LLMs) and LLM agents: A literature review,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection On protecting the data privacy of large language models (LLMs) and LLM agents: A literature review,

Reference 32

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source=pdf_text observed=2026-08-04T19:25:52.629253Z digest=sha256:2ce8ec204e12bb00076d133833b5f80314525228592e68ae429863e9f2b7e77b

Observation 086910a0-0916-4bb2-abd6-74e85e514e33 · outbound

This paper cites LLM-guided formal verification coupled with mutation testing,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection LLM-guided formal verification coupled with mutation testing,

Reference 33

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source=pdf_text observed=2026-08-04T19:25:52.635109Z digest=sha256:ed9efb0c8eaca09593b8cbe36dc82fc5302d114dbc1175326a45c894166b9608

Observation 6b199154-fcfd-4294-854d-3e01943c00f5 · outbound

This paper cites SecureFalcon: Are we there yet in automated software vulnerability detection with LLMs?.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection SecureFalcon: Are we there yet in automated software vulnerability detection with LLMs?

Reference 34

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source=pdf_text observed=2026-08-04T19:25:52.639824Z digest=sha256:8d71e92615e93175b87803e57594990e20a1ba27163497a8aa09c69b2b986c74

Observation 8c0045ce-d124-4136-b3d3-c50d7abf8c98 · outbound

This paper cites Effectiveness of large language models to generate formally verified C code,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Effectiveness of large language models to generate formally verified C code,

Reference 35

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source=pdf_text observed=2026-08-04T19:25:52.649000Z digest=sha256:bbbf4767aeec7d3a16781db9b3b9f5e4343ba05b117393021d8e7178c019e8f9

Observation 75017214-bdaa-483b-a705-8edcbe10eae6 · outbound

This paper cites Domain- adapted LLMs for VLSI design and verification: A case study on formal verification,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Domain- adapted LLMs for VLSI design and verification: A case study on formal verification,

Reference 36

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source=pdf_text observed=2026-08-04T19:25:52.654073Z digest=sha256:5e33362405fa06864a2bade6b165d01eded7401d33fbc9f54d8da89252a9c98a

Observation a4e95339-6073-46d2-affe-ada1c8bb6a71 · outbound

This paper cites Generative AI augmented induction-based formal verification,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Generative AI augmented induction-based formal verification,

Reference 37

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no resolver link, observed 2026-08-04T19:25:52.659063Z

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source=pdf_text observed=2026-08-04T19:25:52.659063Z digest=sha256:0a1af35f01981586dab85c1a4f71f1002c0e557c352356830f7fed06cb1986ff

Observation ea23dd1c-61af-492e-9786-b58650e20ec3 · outbound

This paper cites (Security) assertions by large language models,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection (Security) assertions by large language models,

Reference 38

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source=pdf_text observed=2026-08-04T19:25:52.663774Z digest=sha256:edfd8e6a0b1057e35fc42e8e53d716d2a39787a78c5196b1fe47df3a4fa79c3d

Observation fe53e082-26a4-48a4-b19b-3ec2c3f13591 · outbound

This paper cites Don’t trust: Verify-grounding LLM quantitative reasoning with autoformalization,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Don’t trust: Verify-grounding LLM quantitative reasoning with autoformalization,

Reference 39

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source=pdf_text observed=2026-08-04T19:25:52.668613Z digest=sha256:5d04309037fd0603a0a0eca798fd01a43d1287c9b13d99ab4def1aa963b3088e

Observation f1a8193c-5e3b-40b3-83db-17b528187155 · outbound

This paper cites VeriPlan: Integrating formal verification and LLMs into end-user planning,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection VeriPlan: Integrating formal verification and LLMs into end-user planning,

Reference 40

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no resolver link, observed 2026-08-04T19:25:52.672884Z

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source=pdf_text observed=2026-08-04T19:25:52.672884Z digest=sha256:bd70ef620e2cd2d46643746210e6ee2a0ee538ca1259a2a52cccd5fb7b356e60

Observation f0382076-1f89-4f7a-b192-df0362bd8c00 · outbound

This paper cites Formal-LLM: Integrating formal language and natural language for controllable LLM- based agents,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection Formal-LLM: Integrating formal language and natural language for controllable LLM- based agents,

Reference 41

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source=pdf_text observed=2026-08-04T19:25:52.677591Z digest=sha256:74531f3b31a351b67e642572e00b7fa877233a3dfc79700fb7384f898b59f7e7

Observation 7be0b5d8-f23b-4ce1-aef8-536a8999c021 · outbound

This paper cites FVEL: Interactive formal verification environment with large language models via theorem proving,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection FVEL: Interactive formal verification environment with large language models via theorem proving,

Reference 42

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no resolver link, observed 2026-08-04T19:25:52.681797Z

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source=pdf_text observed=2026-08-04T19:25:52.681797Z digest=sha256:7c9c94f0463b63fbb441df09b208d972919673f2b3da095026f252a9e9d37157

Observation 28842885-112d-4e50-9b49-29dac31d6427 · outbound

This paper cites CryptoFormalEval: Integrating large language models and formal verification for automated crypto- graphic protocol vulnerability detection,.

What You Code Is What We Prove: Translating BLE App Logic into Formal Models with LLMs for Vulnerability Detection CryptoFormalEval: Integrating large language models and formal verification for automated crypto- graphic protocol vulnerability detection,

Reference 43

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source=pdf_text observed=2026-08-04T19:25:52.686093Z digest=sha256:242cf25b42ad7e3906728b13d816799f80bfe5f1ab86ed56262d18238631b5d3

Pith citing papers

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