Paper Citation Record · LEDGER

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System

As of 15 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 4 inbound Pith citation observations for arXiv:2502.07254.

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

pith.paper-citation-record.v1
2502.07254 v2

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:22:39.640549Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:41:03.966651Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T04:30:53.163728Z

Reference resolution

8 of 8 outbound references displayed

verified exact1
verified fuzzy7
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3657497e-2b22-4ea3-8b2e-6e3b73568a4d · outbound

This paper cites This is a critical issue because the biases emerge as a result of the dynamic nature of the system, which is not present in single-agent environments (Eccles et al., 20019).

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System This is a critical issue because the biases emerge as a result of the dynamic nature of the system, which is not present in single-agent environments (Eccles et al., 20019)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:22:39.928928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8941b48f-dd53-4686-a4f6-bab41d77282d · outbound

This paper cites Resource allocation in these settings becomes inherently complex when fairness is considered, as agents’ objectives may conflict.

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System Resource allocation in these settings becomes inherently complex when fairness is considered, as agents’ objectives may conflict

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:22:39.914121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d5bfb5c2-53f7-4517-96e1-b233eae7c478 · outbound

This paper cites Efficiency Balancing Competing Objectives: A critical challenge is balancing fairness with system efficiency.

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System Efficiency Balancing Competing Objectives: A critical challenge is balancing fairness with system efficiency

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:22:39.898289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 58550c30-bb71-4777-84cd-bb5b157430d7 · outbound

This paper cites For instance, an agent might manipulate the fairness criteria to make itself appear disadvantaged and thus gain more resources than it truly needs (Zuo et al., 2023; Yuan et.

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System For instance, an agent might manipulate the fairness criteria to make itself appear disadvantaged and thus gain more resources than it truly needs (Zuo et al., 2023; Yuan et

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:22:39.881970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation befcd765-b191-461e-9031-f569e881970c · outbound

This paper cites The framework incorporates the key elements required for fairness to emerge dynamically as a result of the interactions between agents.

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System The framework incorporates the key elements required for fairness to emerge dynamically as a result of the interactions between agents

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:22:39.866838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5853af08-7efb-4aed-8377-43612aa96970 · outbound

This paper cites These constraints are designed to guide the interactions between agents in a way that ensures equitable outcomes.

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System These constraints are designed to guide the interactions between agents in a way that ensures equitable outcomes

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:22:39.850431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5526f1ac-0a91-4bd8-9fd7-a0b5ba1b48a9 · outbound

This paper cites Our framework introduces a bias detection and mitigation mechanism that continuously monitors agent interactions for potential biases.

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System Our framework introduces a bias detection and mitigation mechanism that continuously monitors agent interactions for potential biases

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:22:39.834346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ecbfb1d8-a77f-4ef0-9397-c06aa2516593 · outbound

This paper cites Biases for Emergent Communication in Multi-agent Reinforcement Learning.

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System Biases for Emergent Communication in Multi-agent Reinforcement Learning

Reference 8

Resolution: verified exact; raw_fallback, observed 2026-08-08T13:22:39.818190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 0722eee9-eabb-47b0-a263-1deb3e9108e1 · inbound

Safety Degradation in AI Agents cites this paper.

Safety Degradation in AI Agents Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:03.966651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25b04b3a-1c54-4207-a0a6-8d24e68d8665 · inbound

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines cites this paper.

FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:54.040575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9e7ad7f-7628-4bf1-a348-d76185cc55d0 · inbound

A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks cites this paper.

A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:30:53.167239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fb9d668b-7a16-4188-b37c-f1e4712b328e · inbound

LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk cites this paper.

LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:04:03.429088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.