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Paper Citation Record · LEDGER

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 6 inbound Pith citation observations for arXiv:2507.07341.

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

pith.paper-citation-record.v1
2507.07341 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:58:12.233861Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:19:34.923841Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:57:25.939777Z

Reference resolution

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy16
  • unresolved7
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe69d402-7470-4b7d-9933-a0db35ac2131 · outbound

This paper cites an unresolved cited work.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:58:12.423612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.169746Z digest=sha256:b142666362260e7a35138d2310b45f695f236f4ebe09aff286d332b385d2ca21

Observation 8a43ddb7-cfd5-456e-94af-d96a72e563cd · outbound

This paper cites , n− 1}, and computes s ← re mod n, where e = 2t is a tower of repeated squarings (i.e., t squarings of r).

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment , n− 1}, and computes s ← re mod n, where e = 2t is a tower of repeated squarings (i.e., t squarings of r)

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.415545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.172303Z digest=sha256:724483c84d6456af5785b60a2a3ec514a5714158d9a8f437c0740a86cc9d4230

Observation 5baf7d0c-44c7-4ca3-962c-9eeeb3c24b6e · outbound

This paper cites Returns (Z, s).

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Returns (Z, s)

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.408136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.174834Z digest=sha256:f0b7d2cd099b99a07f0f12f0a0e6d0e84924f12cab2dcb9f8edfa21e1a0e52d2

Observation acd774a3-3239-41e0-b6f7-52350fcf5806 · outbound

This paper cites Note that Sol computes s using t repeated squarings, each of which is inexpensive, but the full process requires Θ(t) sequential steps.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Note that Sol computes s using t repeated squarings, each of which is inexpensive, but the full process requires Θ(t) sequential steps

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.400530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.178052Z digest=sha256:99eefc9f03c16c6d0c8e6ffb791718b9d16cbcc5ba356cb492cb458d4e1efadf

Observation 8b291c00-dcc9-4a85-b140-1c913a702369 · outbound

This paper cites an unresolved cited work.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Unresolved cited work

Reference 6

Resolution
parse uncertain
raw_fallback, observed 2026-08-06T18:58:12.392727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.180750Z digest=sha256:eba998f7d5a53826afb357e4efe915af2d8e52f4f77558aec806dde1ce99d308

Observation d46fd4b8-f49b-4033-bc75-73fee52d2e27 · outbound

This paper cites , zi−1, before the if statement (step 8 of Algorithm 1) is executed, the distribution of q is uniform in [k].

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment , zi−1, before the if statement (step 8 of Algorithm 1) is executed, the distribution of q is uniform in [k]

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.385979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.183128Z digest=sha256:39b871dcc06679321aa3483d0d1e5648857062db0b3f47a22e8e05195414aa5e

Observation fad5a9c2-f919-47d0-9e85-9c97058d7ce3 · outbound

This paper cites an unresolved cited work.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:58:12.378541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.185511Z digest=sha256:48e36a5fce20532c6adb40c04d7177468ae364fa5a11a262d94f7306632678ef

Observation ecaaba91-1c55-41ff-a4ef-154395ac7631 · outbound

This paper cites an unresolved cited work.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:58:12.371479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.188145Z digest=sha256:a3d7fd3a7df1061db11931f04e8b6dd1b492db0640e7915af0bb74b4ab1360a5

Observation dfa16466-05f2-49f2-be26-90984e80612c · outbound

This paper cites threshold randomness.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment threshold randomness

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.363827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.190469Z digest=sha256:9e8746033cd278565654c6bea613b49edc7b267bc3c176e0c27a243fb1369695

Observation 691dccf3-bebd-4577-a6dc-eb29da9853b2 · outbound

This paper cites With probability 1 − λ−Ω(1) over z ← G the distributions M ′(z) and M (z) are equal.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment With probability 1 − λ−Ω(1) over z ← G the distributions M ′(z) and M (z) are equal

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.350294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.194932Z digest=sha256:83d06e75aafdd9aeaf347a05c0b1bbe5cd8b5de2a509e7e0d83c9b5ab0492eb5

Observation 1e515cec-d1a8-4e04-8766-9ad6dc5e8502 · outbound

This paper cites remember.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment remember

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.342434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.197775Z digest=sha256:6b6ed243d88f00b29d044eb35e1e6ed37a952d0f669a10ed978ab60fa4dc6ee0

Observation ff162a5c-ffdc-4ecc-8778-200eb4f33e10 · outbound

This paper cites With all but negligible in λ probability over z ← G the distributions M ′(z) and M (z) are equal.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment With all but negligible in λ probability over z ← G the distributions M ′(z) and M (z) are equal

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.336253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.202926Z digest=sha256:44239cab83079c01664c8e95883c88cd5f1ecd3e9faa0bf310e7017e8940f5e8

Observation 9b583590-d2fd-4471-bcc5-214846fbcfc9 · outbound

This paper cites an unresolved cited work.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:58:12.328507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.205269Z digest=sha256:345d194e9658ec83ca9ab9efa0df2bd768157b2b141c4f6854ca31fc5c2c80d4

Observation cb9a0965-1034-4335-b85e-7193e0c32ff0 · outbound

This paper cites For every malicious prompt m ∈ SH, the following two distributions are close: {M ′(z) | z ← G′(m)} , {M (m)}.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment For every malicious prompt m ∈ SH, the following two distributions are close: {M ′(z) | z ← G′(m)} , {M (m)}

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.356904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.207466Z digest=sha256:e50465998c87c6c7b4ffcc51a7024e96c0d736afe5645365a25ec3f4675d8e9a

Observation 8030ea81-0e17-4d5d-9be6-a7a7a7e91807 · outbound

This paper cites an unresolved cited work.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:58:12.313074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.212095Z digest=sha256:9728f8309f34d227f6048b03cdf7bdbe723f6e5f414518ca07354f72e1f45f46

Observation 77dd7ad5-381e-4cbc-8a27-e993a48fb012 · outbound

This paper cites For every malicious prompt m ∈ SH, with all but negligible in λ probability over z ← M ′(m),16 it holds that H′(z) = Ω Ez←M (m)[H(z)].

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment For every malicious prompt m ∈ SH, with all but negligible in λ probability over z ← M ′(m),16 it holds that H′(z) = Ω Ez←M (m)[H(z)]

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.306843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.215474Z digest=sha256:ad7e0474f1c3bcc4f6d94aa9e6ee7997a24d7b5f50c814846216e8574de5ccf5

Observation bb1bb0bc-dc53-4c78-9f22-9f5de51de429 · outbound

This paper cites With all but negligible probability in λ over z ← M ()17 H′(z) = H(z).

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment With all but negligible probability in λ over z ← M ()17 H′(z) = H(z)

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.299835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.217875Z digest=sha256:1a4d0643050d7c4002e6d83bb200da767d490df6d95d901b63a5c70dcc2d48b8

Observation cd6aabf9-c86b-4db4-ba34-d1232c57496c · outbound

This paper cites running time.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment running time

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.292654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.220098Z digest=sha256:2ea04c8e287c51d46a63bfd9042885dedc689a9fa8f5f7216fedfe278cdbffbd

Observation bd40fa10-5628-4348-bb1b-e73ff970e608 · outbound

This paper cites For every prompt-mitigation filter F running in polynomial-time with all but negligible in λ probability over z ← G′ it holds that F (z) = ⊥ or M ′(z) is harmful.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment For every prompt-mitigation filter F running in polynomial-time with all but negligible in λ probability over z ← G′ it holds that F (z) = ⊥ or M ′(z) is harmful

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.285864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.222677Z digest=sha256:789e11a655baa470bd8f8bbcf1ebc20fc5e67e8c01d15f2c51aba3f658c3e871

Observation 01fc0440-95cf-4888-8da8-718ed9507b76 · outbound

This paper cites With all but negligible in λ probability over z ← G the distributions M ′(z) and M (z) are equal.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment With all but negligible in λ probability over z ← G the distributions M ′(z) and M (z) are equal

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.321567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.226038Z digest=sha256:55137cd03a1a29855d63a29bf3913b559b9649ac28972fc1fe49caf643db435c

Observation 02281eff-8c48-4e47-bc59-68ffce24dc43 · outbound

This paper cites For every algo- rithm running in polynomial-time the advantage in distinguishing outputs of G and G′ is negligible in λ.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment For every algo- rithm running in polynomial-time the advantage in distinguishing outputs of G and G′ is negligible in λ

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.278200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.228455Z digest=sha256:3e5daa6cac5f8f6672c7a785e9c48df1f36881c794368bf774c0fdc045cd0696

Observation 53cc2ef4-869b-4e1f-bf11-585c097852bc · outbound

This paper cites an unresolved cited work.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:58:12.269202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.231336Z digest=sha256:99cad65764eec0318306ae43ea8c2f52b1c1e44b5deaa536a4fc938cdbc21ce3

Observation 4d45f48b-5366-48a4-ab81-8c896183cff9 · outbound

This paper cites E.4 Proof The proof requires a careful comparison of requirements of Theorem 11 and properties of watermark- ing schemes robust against classes of transformations.

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment E.4 Proof The proof requires a careful comparison of requirements of Theorem 11 and properties of watermark- ing schemes robust against classes of transformations

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:58:12.261759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T18:58:12.233861Z digest=sha256:37b2efa209ff910fd33e0da5607a68c5924e219a03fc573ae6579ea411934278

Observation 29fb1548-5629-450c-8d10-5f410caa377a · outbound

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

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 2000

Resolution
unresolved
no resolver link, observed 2026-08-06T18:58:12.164527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:58:12.164527Z digest=sha256:1ce48b8e1e5a95a359aa46012f52dc28f7307e1298d8f21d4036900402577af7

Pith citing papers

Observation 9c73fe2c-2ab8-4d51-ac48-520cb5bbaebb · inbound

Spore: Efficient and Training-Free Privacy Extraction Attack on LLMs via Inference-Time Hybrid Probing cites this paper.

Spore: Efficient and Training-Free Privacy Extraction Attack on LLMs via Inference-Time Hybrid Probing On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment

Reference 1

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T21:16:19.975892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-08T06:10:20.358899Z digest=sha256:d8f7c39df3e8fa5462c8d36939cdf50edbf09c51c24cd2a272d413b86cc34d6d

Observation 7889dbcd-fc97-4143-a227-790564200922 · inbound

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning cites this paper.

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment

Reference 113

Resolution
verified exact
arxiv_id, observed 2026-05-19T21:52:48.244988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-05-19T21:49:07.440832Z digest=sha256:a03e1bacf6fa68f27c6a779aecb65fc517dae0812647cc1728fbdb572e41e430

Observation 857653ea-e86a-4bc1-a68b-5f351d09e5d4 · inbound

Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense cites this paper.

Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment

Reference 1

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T19:36:08.905765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T22:17:40.164877Z digest=sha256:50f763c30f172784aff5f104c24b5dc1864040daf2fd9de7a92c64fa4927fe72

Observation 247c9dce-fb0c-4caa-a103-4b422e058c2e · inbound

Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy cites this paper.

Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment

Reference 69

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:57:25.941483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-06-27T18:36:44.265273Z digest=sha256:746b1ad992e480d9baf5d2e3ada0b380b3f9f2d746bb0b46bb5be0caed5e96d5

Observation a826c4d7-1022-49fe-b3e6-90123e186a9c · inbound

On the Limits of Support-Preserving Alignment and Bounded Filtering cites this paper.

On the Limits of Support-Preserving Alignment and Bounded Filtering On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-02T09:19:34.923841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:19:34.923841Z digest=sha256:c4faff09fce49ff11ca5c38a4a68ddedef138172ea3f91a7845b0a793af9beb4

Observation 2f80ecdc-dcd6-4e20-bd07-30b267bc2e16 · inbound

Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs cites this paper.

Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-31T22:25:22.203096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T22:25:22.203096Z digest=sha256:0278e329fef85ade6027703e21a3a1805cff711fb39153a6a681ac11fe7fa66a