Pith. sign in

Paper Citation Record · LEDGER

Model evaluation for extreme risks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 47 inbound Pith citation observations for arXiv:2305.15324.

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

pith.paper-citation-record.v1
2305.15324 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 47 of 47 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:20:17.538978Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

58
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 604417e3-3264-4da5-b336-4c161674a669 · inbound

AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction cites this paper.

AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction Model evaluation for extreme risks

Reference 91

Resolution
verified exact
arxiv_id, observed 2026-05-24T08:49:13.910243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-24T08:47:23.231930Z digest=sha256:b4a8457341c0e07e76fc970ac9dbc90ac33bb83d797c8ed7e754963806043d0c

Observation 322984e0-ed5c-4f59-bc4a-fdd194faa95f · inbound

Gemini: A Family of Highly Capable Multimodal Models cites this paper.

Gemini: A Family of Highly Capable Multimodal Models Model evaluation for extreme risks

Reference 95

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:03:55.499819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-24T05:00:28.453838Z digest=sha256:bd065e3e0cce96abc988a40f2e0c41b9d5695c4e9326e7365fa87f3ecc2234b1

Observation e567a0de-1275-4d07-9569-096c39ef0453 · inbound

TrustLLM: Trustworthiness in Large Language Models cites this paper.

TrustLLM: Trustworthiness in Large Language Models Model evaluation for extreme risks

Reference 178

Resolution
verified exact
arxiv_id, observed 2026-05-18T11:17:08.559994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T11:17:08.108565Z digest=sha256:5ddc2bf8f8b17b98ea34708355298a636cc75633c3bc07b69233a4075d11c0db

Observation 5c16d358-cca0-46ce-8ba2-7bc8d01dac6a · inbound

Gemma 2: Improving Open Language Models at a Practical Size cites this paper.

Gemma 2: Improving Open Language Models at a Practical Size Model evaluation for extreme risks

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-10T12:11:16.458916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T12:11:16.326752Z digest=sha256:00badc373a172024064ea05f82ef4db72ea22531a87ee2ecaf608824ebb4748f

Observation 609483f9-1026-42f3-8c03-700914fe8f04 · inbound

Enabling External Scrutiny of AI Systems with Privacy-Enhancing Technologies cites this paper.

Enabling External Scrutiny of AI Systems with Privacy-Enhancing Technologies Model evaluation for extreme risks

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-09T05:20:17.538978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T05:20:17.538978Z digest=sha256:7bd065d25862ad6500534c57abec66bf5703bb103205860c658c6e3963f2a31a

Observation 203605c6-35df-4a78-a504-9babfc3fd8af · inbound

Towards an AI co-scientist cites this paper.

Towards an AI co-scientist Model evaluation for extreme risks

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T13:02:44.555404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-11T13:02:43.571234Z digest=sha256:b5a870c8ef007c8cd321657f9c12d46bddfec722b8d410c6191055720c1e2889

Observation 068c3fb0-56a3-459c-88dc-cfb232f39dda · inbound

Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks cites this paper.

Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks Model evaluation for extreme risks

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T14:09:51.655116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:51.655116Z digest=sha256:bacf9d636ee2de77c5c94f4cb70614d78caa24ee7e465fb4071a307a942b0684

Observation 24a86f83-83d2-4712-9042-e6e893aa06df · inbound

Evaluating LLM Agent Adherence to Hierarchical Safety Principles: A Lightweight Benchmark for Probing Foundational Controllability Components cites this paper.

Evaluating LLM Agent Adherence to Hierarchical Safety Principles: A Lightweight Benchmark for Probing Foundational Controllability Components Model evaluation for extreme risks

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T11:28:21.649808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:28:21.649808Z digest=sha256:e4d760667ddbbf661c52daf4ea77083eb5df6da5ce2165be63b5c13cd139e702

Observation a5823278-1eef-4d29-9d9f-d89cf8709360 · inbound

Benchmarking Misuse Mitigation Against Covert Adversaries cites this paper.

Benchmarking Misuse Mitigation Against Covert Adversaries Model evaluation for extreme risks

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:32:14.669979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T10:29:05.104520Z digest=sha256:a7068364c7f96592371db148aeebab7b9970ab59ba1aeeceb17c40c208bf0bbe

Observation ae0ef045-e116-48ed-84e6-14f9f36aac9a · inbound

MalGEN: A Testbed for Modeling and Evaluating Malware Behaviors cites this paper.

MalGEN: A Testbed for Modeling and Evaluating Malware Behaviors Model evaluation for extreme risks

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T11:07:15.347544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T11:04:23.938028Z digest=sha256:3baf57ca63289139f81c6cc4fb5f3db17397c710da09b106307a37c56bb0e111

Observation 1c6d6086-2acf-4e2d-bf5f-454524113818 · inbound

UCD: Unlearning in LLMs via Contrastive Decoding cites this paper.

UCD: Unlearning in LLMs via Contrastive Decoding Model evaluation for extreme risks

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T04:22:59.887715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:22:59.887715Z digest=sha256:7de885369150b9b37c60aae666e295d68b0fbe93d022b7ff0d2bbbdeb1594eef

Observation f7fa0bd3-765b-4d2d-828e-b39219f73bbf · inbound

A Conceptual Framework for AI Capability Evaluations cites this paper.

A Conceptual Framework for AI Capability Evaluations Model evaluation for extreme risks

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-06T23:25:51.618603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:25:51.618603Z digest=sha256:4b55e59a8470f2615331ec2fc506a251f9614e1c1ac70c191a34013b48cbbea0

Observation 7bb39678-e27a-493a-87d7-193ec5fc5c53 · inbound

On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals" cites this paper.

On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals" Model evaluation for extreme risks

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T21:57:43.091137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:57:43.091137Z digest=sha256:7b726b1355cdcf33ef031abd18b7c24c01097618b5e14b0b0b584a2d971c8317

Observation cd4dda47-dcb5-4268-8aac-5b76de9ed102 · inbound

From Turing to Tomorrow: The UK's Approach to AI Regulation cites this paper.

From Turing to Tomorrow: The UK's Approach to AI Regulation Model evaluation for extreme risks

Reference 158

Resolution
unresolved
no resolver link, observed 2026-08-06T20:32:41.866495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:32:41.866495Z digest=sha256:7fd2ad2d39ea8a0172c164c4ee146705923994d0e8ac5ae24d568fb6787d3276

Observation 3b823d75-1aec-4bdb-afaa-6f87ed338aaa · inbound

Domestic frontier AI regulation, an IAEA for AI, an NPT for AI, and a US-led Allied Public-Private Partnership for AI: Four institutions for governing and developing frontier AI cites this paper.

Domestic frontier AI regulation, an IAEA for AI, an NPT for AI, and a US-led Allied Public-Private Partnership for AI: Four institutions for governing and developing frontier AI Model evaluation for extreme risks

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T19:11:07.257632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:11:07.257632Z digest=sha256:0f657f8033e3c885493ca70e96bdb4da1d20ec3b9ffe5ef813cce6f2adae5445

Observation a2704126-6a95-405e-b8d2-e321a4781f2c · inbound

Technical Requirements for Halting Dangerous AI Activities cites this paper.

Technical Requirements for Halting Dangerous AI Activities Model evaluation for extreme risks

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T17:50:30.606349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:50:30.606349Z digest=sha256:6745ebdfab9e01e27f51aad6ad3568143c705049bb7357f983e858cbea30f85f

Observation 30fbed42-5157-40ac-b14a-eeaa3bcee6a0 · inbound

ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors cites this paper.

ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors Model evaluation for extreme risks

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T23:01:41.829098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T23:01:41.829098Z digest=sha256:702a608f7d42353d00154bcb194cb918b055fb7c1558cf42bbecf7dc017e2108

Observation 3c1b739c-c238-469b-a268-36ebc6ea9d53 · inbound

Designing Incident Reporting Systems for Harms from General-Purpose AI cites this paper.

Designing Incident Reporting Systems for Harms from General-Purpose AI Model evaluation for extreme risks

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T00:20:32.268321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T00:16:17.173186Z digest=sha256:f73a389baa4f453b929c328d063eca32057d65b9206b7da077f7ac9a04756a8e

Observation e229ee5b-ed62-464e-b88a-83c79af78af3 · inbound

Internal Deployment in the AI Act cites this paper.

Internal Deployment in the AI Act Model evaluation for extreme risks

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T17:54:18.544842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-21T17:51:47.841707Z digest=sha256:84f3d38122debb5f3946dfb0d04064d7f044f884bb25e84451a634830f414fca

Observation ae434422-e6ff-4489-91f0-5e33c4893222 · inbound

LLM-Guided Prompt Evolution for Password Guessing cites this paper.

LLM-Guided Prompt Evolution for Password Guessing Model evaluation for extreme risks

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:31:01.210679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T14:50:46.308625Z digest=sha256:c7166e93ce6452c6fe4b9f7c2a7ed5eebfcdf43360714830bf6b8f59cad6a088

Observation 1f6a3b20-f614-4c80-a058-4dff33e7f0d8 · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning Model evaluation for extreme risks

Reference 39

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T06:06:19.409992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T06:01:46.885030Z digest=sha256:a7a167b295ac4f204724b7298572b5a9b74fa762f78ad829ca055c512bfb9458

Observation ef47b39b-b3c0-486c-99b4-91d58f4a3192 · inbound

Who Defines "Best"? Towards Interactive, User-Defined Evaluation of LLM Leaderboards cites this paper.

Who Defines "Best"? Towards Interactive, User-Defined Evaluation of LLM Leaderboards Model evaluation for extreme risks

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:21:07.365784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T21:58:05.584559Z digest=sha256:8e4464be0badf6cd9c88249e16449f4db2422d9f7868446e3e5f78359df2f3af

Observation e57dc426-ca56-4c82-b65b-78808d36c75f · inbound

Risk Reporting for Developers' Internal AI Model Use cites this paper.

Risk Reporting for Developers' Internal AI Model Use Model evaluation for extreme risks

Reference 41

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T23:16:16.432362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-07T17:47:21.321820Z digest=sha256:5ecdf300c29f3215404b7bcc05d1cc9f8376dc74b8981caf672b2e538cd4fc8f

Observation fa8dc43b-6271-499b-9812-a4d0c020d8c0 · inbound

Evaluation without Generation: Non-Generative Assessment of Harmful Model Specialization with Applications to CSAM cites this paper.

Evaluation without Generation: Non-Generative Assessment of Harmful Model Specialization with Applications to CSAM Model evaluation for extreme risks

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:31:14.196298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-07T16:55:19.775120Z digest=sha256:c1b280da0d692a64ef390f2cc210367d80e47b2a24641835b084caf290f24730

Observation 70fa7b83-c04f-41ad-b983-ff44680cfb62 · inbound

Artificial Jagged Intelligence as Uneven Optimization Energy Allocation Capability Concentration, Redistribution, and Optimization Governance cites this paper.

Artificial Jagged Intelligence as Uneven Optimization Energy Allocation Capability Concentration, Redistribution, and Optimization Governance Model evaluation for extreme risks

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:01:09.016460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T14:13:59.908810Z digest=sha256:8c3e534776c424c13f7cd462b8ddbd1941f9e652d7296ca2e05b30ebb58d855c

Observation bcc58897-32c0-4dcf-93e3-8d1564eb52cd · inbound

A Validated Prompt Bank for Malicious Code Generation: Separating Executable Weapons from Security Knowledge in 1,554 Consensus-Labeled Prompts cites this paper.

A Validated Prompt Bank for Malicious Code Generation: Separating Executable Weapons from Security Knowledge in 1,554 Consensus-Labeled Prompts Model evaluation for extreme risks

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:40:43.534363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T18:11:29.066362Z digest=sha256:7a5edf188621104c764f3671e815991402869535e9d533d73b7f8ee8f79709bd

Observation 612496ef-2aad-468a-b2f1-e8b1fdc66928 · inbound

When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels cites this paper.

When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels Model evaluation for extreme risks

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-08T21:39:24.651511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T12:07:02.778631Z digest=sha256:6f4a2b0fa840e52b389dcc9199ea27a1d3cf38b56ed550d4ff730e1fe8e8c811

Observation 4abdc819-bec3-4252-9092-d4bf6a8d8648 · inbound

Overtrained, Not Misaligned cites this paper.

Overtrained, Not Misaligned Model evaluation for extreme risks

Reference 41

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T06:47:26.248329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T06:45:52.544674Z digest=sha256:3b13140b5e119c4eb968baee5f3f7fb8adcd96fa71d983035f7722a2c784f5c6

Observation 2a4f006a-c173-44c6-8881-bf25d2053da4 · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Model evaluation for extreme risks

Reference 94

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T04:57:17.277926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T04:55:55.013900Z digest=sha256:78dd439ee781195a6287c952f42e7575c83fb1f6767691a8784083de13f2edd0

Observation 0edff6ea-befb-4647-851b-bc7182eea175 · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Model evaluation for extreme risks

Reference 94

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T05:45:06.474151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-15T05:41:10.714594Z digest=sha256:246d369c7ee6b64f8a869e15f1adce203d236946f4053e5171f635417d629807

Observation 6aec64f0-8e08-4930-af60-720b9a55ea30 · inbound

Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands cites this paper.

Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands Model evaluation for extreme risks

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-06-30T20:55:03.991229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T20:53:04.274840Z digest=sha256:245487b161a5f6013826bdffb97664c4c703fba9420d010b02a93ac7a9d6e413

Observation ce90f471-643a-4f7f-a4ff-9105b12525d2 · inbound

Measuring Safety Alignment Effects in Autonomous Security Agents cites this paper.

Measuring Safety Alignment Effects in Autonomous Security Agents Model evaluation for extreme risks

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-20T04:28:05.922999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T04:24:46.357505Z digest=sha256:d2dae19656c125c4f6d5234df1bbf9a65c2b30be1d3dc2b1750ba4e094efb3b2

Observation d0953d61-28a0-4d0a-bf04-78c563c4c583 · inbound

Backchaining Loss of Control Mitigations from Mission-Specific Benchmarks in National Security cites this paper.

Backchaining Loss of Control Mitigations from Mission-Specific Benchmarks in National Security Model evaluation for extreme risks

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-21T02:03:54.371283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T02:01:42.033718Z digest=sha256:8ae3eb97bb14303800697e09e79b53bd931c053cc690e468229d5af47e56cfbf

Observation 501e47f3-025b-45c7-a4e5-82ee61a3cc84 · inbound

Backchaining Loss of Control Mitigations from Mission-Specific Benchmarks in National Security cites this paper.

Backchaining Loss of Control Mitigations from Mission-Specific Benchmarks in National Security Model evaluation for extreme risks

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T02:03:54.276808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T02:01:42.033718Z digest=sha256:3568f944a702fd0b57fbb4eaf7ef190308a190b1adcb6897493c8ea4e7ff68af

Observation 1463a8d7-e34d-4643-adf6-ffc0360b188b · inbound

Consistency Training while Mitigating Obfuscation via Rate Matching cites this paper.

Consistency Training while Mitigating Obfuscation via Rate Matching Model evaluation for extreme risks

Reference 138

Resolution
verified exact
arxiv_id, observed 2026-06-28T14:32:18.172629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T14:25:43.147442Z digest=sha256:009b11b4dcce500a6f21279b55d00c2c2e2b35e63857ae2902b24b8c5e36a6d0

Observation 89d9bf51-6c35-487a-ac47-5266fa246336 · inbound

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing cites this paper.

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing Model evaluation for extreme risks

Reference 114

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:16:47.473340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T06:17:01.173495Z digest=sha256:38e2289df5efa6c51fd8ad444716638b50d856ddf2c5a30bde7db048a662c816

Observation bce19943-8a43-49cf-9f23-fb169f0bc0cb · inbound

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs cites this paper.

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs Model evaluation for extreme risks

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T01:41:29.305785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T01:40:53.284131Z digest=sha256:727672ed2afa592693c9c36dbaaa2cc8639a4a30532bf57d5726cf6b8315e971

Observation 2aad5fe4-fc00-42c9-9231-205c1d2855b6 · inbound

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting cites this paper.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Model evaluation for extreme risks

Reference 97

Resolution
verified exact
arxiv_id, observed 2026-07-03T02:07:33.287703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T16:11:36.483820Z digest=sha256:1672967aeb33fc527cc8ce04763ad3ef484f77d57f40705bdd818a3003ae2bb4

Observation fae8241c-6b94-4ea4-895c-e5540ed2c83c · inbound

AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework cites this paper.

AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework Model evaluation for extreme risks

Reference 142

Resolution
verified exact
arxiv_id, observed 2026-07-03T22:08:59.936017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T23:42:20.304205Z digest=sha256:50fc1cf70e617804a57c3ae737be29e0f036bd4f0d634b8dc7c4e5233344ce17

Observation c2a1ea71-96f9-4b62-9533-dff80c7ec0c2 · inbound

Has This Checkpoint Been Abliterated? A Two-Signal Audit and Its Failure Map cites this paper.

Has This Checkpoint Been Abliterated? A Two-Signal Audit and Its Failure Map Model evaluation for extreme risks

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:58:02.494876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-03T10:54:37.039277Z digest=sha256:0dfc1e88e5543e47c9dfa63ab5997603ac0e0bcd0fc3b08bd63e1e7eeb4cc74c

Observation 8c1c4471-e678-41dd-81b0-679306396f64 · inbound

Securing Multi-Tool AI Agent Chains With Dynamic, Real-Time Compositional Policies cites this paper.

Securing Multi-Tool AI Agent Chains With Dynamic, Real-Time Compositional Policies Model evaluation for extreme risks

Reference 29

Resolution
unresolved
no resolver link, observed 2026-07-12T02:36:01.385664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T02:36:01.385664Z digest=sha256:1c9b553c109254be384dd794bbf9e62b792ca6498eaee97f0ed8344a745aefdd

Observation bcf9b8d3-76a9-4a20-8afa-d14f60399198 · inbound

Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI cites this paper.

Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI Model evaluation for extreme risks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-07-12T01:45:06.581823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T01:45:06.581823Z digest=sha256:abd0a3a58db2697617555b4ded9b9ab85be8fc9568eb636192328d6a96d83be8

Observation 215fec64-896f-44cb-9cd7-3387bec92bf5 · inbound

Open Problems in AI Incident Governance cites this paper.

Open Problems in AI Incident Governance Model evaluation for extreme risks

Reference 107

Resolution
unresolved
no resolver link, observed 2026-07-11T07:50:18.332768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T07:50:18.332768Z digest=sha256:32c469890d1b001f24f5f98061fee30a62fc72a468990470eea244d558c90f0c

Observation 49d6e1a2-c8ae-4098-810d-ef6e0cd631dc · inbound

NetInjectBench: Benchmarking Indirect Prompt Injection in Tool-Using Large Language Model Agents for Network Operations cites this paper.

NetInjectBench: Benchmarking Indirect Prompt Injection in Tool-Using Large Language Model Agents for Network Operations Model evaluation for extreme risks

Reference 48

Resolution
unresolved
no resolver link, observed 2026-07-14T11:20:52.129640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T11:20:52.129640Z digest=sha256:4cc9d61418a808ea2335f3c17b762bac55bd153d1b4d710652fac3a3108a6759

Observation ad1855e4-32f5-4754-96fd-6e96196fc2d4 · inbound

SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI cites this paper.

SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI Model evaluation for extreme risks

Reference 1997

Resolution
unresolved
no resolver link, observed 2026-08-02T16:36:34.814502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:36:34.814502Z digest=sha256:7779510021a99d32bc738057e4c4065a547ac192329d51be2ca4e3ffb51149f5

Observation 91461674-e372-43c8-b8fd-57db70a1d14f · inbound

ContainmentBench: Trace-Based Evaluation of Post-Injection Containment in Tool-Using LLM Agents cites this paper.

ContainmentBench: Trace-Based Evaluation of Post-Injection Containment in Tool-Using LLM Agents Model evaluation for extreme risks

Reference 24

Resolution
unresolved
no resolver link, observed 2026-07-31T23:24:19.561447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T23:24:19.561447Z digest=sha256:96cff71f52b028c6f8e353b204e7cb77afaa10a382de347f92b48f67613d8105

Observation fea38624-2cc2-4078-bf04-a49900f7dedd · inbound

"Allow" to Achieve, Over-Privileged Inadvertently: The Unintended Cost of Task-Completion-Driven Pop-up Decisions in Mobile GUI Agents cites this paper.

"Allow" to Achieve, Over-Privileged Inadvertently: The Unintended Cost of Task-Completion-Driven Pop-up Decisions in Mobile GUI Agents Model evaluation for extreme risks

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T17:27:27.394338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:27:27.394338Z digest=sha256:c01b020594f245033dc2fb51acf7979f737507058722ce2b45829483a39ab919