Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T13:22:39.640549Z
Paper Citation Record · LEDGER
Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System
As of 10 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:41:03.966651Z
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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.