Paper Citation Record · LEDGER

Combating Adversarial Attacks with Multi-Agent Debate

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2401.05998.

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

pith.paper-citation-record.v1
2401.05998 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:02:42.213315Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:09:33.501947Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bbf55050-5340-4f57-80c0-2e432413842d · inbound

Large Language Model Agent: A Survey on Methodology, Applications and Challenges cites this paper.

Large Language Model Agent: A Survey on Methodology, Applications and Challenges Combating Adversarial Attacks with Multi-Agent Debate

Reference 183

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:52:10.683143Z

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.

Observation 24d4a73d-fccf-4b25-9b77-7150adbbbef6 · inbound

Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness cites this paper.

Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness Combating Adversarial Attacks with Multi-Agent Debate

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:02:42.213315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3362316-18bd-46ef-8ac5-9d47d725bebf · inbound

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation cites this paper.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Combating Adversarial Attacks with Multi-Agent Debate

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:46:09.949867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdcc4c0a-21e5-4217-9436-0c79dbc4fdab · inbound

MARS$^2$: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation cites this paper.

MARS$^2$: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation Combating Adversarial Attacks with Multi-Agent Debate

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:30:18.841674Z

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.

Observation 03651095-5d44-4052-aac2-70ed9a716824 · inbound

Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience cites this paper.

Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience Combating Adversarial Attacks with Multi-Agent Debate

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T04:09:33.504373Z

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.

Observation 9c4fea56-56f1-4573-9edc-c014b46dd919 · inbound

Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems cites this paper.

Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems Combating Adversarial Attacks with Multi-Agent Debate

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T00:51:18.191920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.