Paper Citation Record · LEDGER

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization

As of 11 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2605.30452.

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

pith.paper-citation-record.v1
2605.30452 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:44:38.977180Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy0
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 16624400-aad7-4c47-8872-42b898674a67 · outbound

This paper cites Condi- tional gradient method for multiobjective optimization.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Condi- tional gradient method for multiobjective optimization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9609300-7fc4-4c25-bf1a-3c6bc49711c3 · outbound

This paper cites Fair Resource Allocation in Multi-Task Learning.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Fair Resource Allocation in Multi-Task Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 931ec840-348a-4774-abd0-6884d2461a75 · outbound

This paper cites Jacobian Descent for Multi-Objective Optimization.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Jacobian Descent for Multi-Objective Optimization

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T08:53:16.420337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 441ba2f5-ae0f-4510-b3bf-99b66afb7b81 · outbound

This paper cites Multi-Task Learning as Multi- Objective Optimization.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Multi-Task Learning as Multi- Objective Optimization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85af59dd-3684-47ab-9838-36dc28f85198 · outbound

This paper cites Convergence guarantees of linear scalarization.The aggregation rule in (23) corresponds precisely to performing gradient descent on the scalarized objective F= P i fi.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Convergence guarantees of linear scalarization.The aggregation rule in (23) corresponds precisely to performing gradient descent on the scalarized objective F= P i fi

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eb0aeab-8472-4f38-a3b1-878dd825be4b · outbound

This paper cites an unresolved cited work.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56cf3bba-61d6-4bd8-be3b-a0afae5019b4 · outbound

This paper cites an unresolved cited work.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af1e40c3-1f48-4a89-93e0-25f745416498 · outbound

This paper cites (2019) for both non-convex and convex settings.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization (2019) for both non-convex and convex settings

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8088df2-0442-40a6-918c-97cad02b05a3 · outbound

This paper cites an unresolved cited work.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a13b82bd-2fb9-4105-ba59-6678055d9b1d · outbound

This paper cites (2022) and our analysis (Theorem.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization (2022) and our analysis (Theorem

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2920bb45-3ae7-4cd8-a785-dee076fb568a · outbound

This paper cites While Navon et al.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization While Navon et al

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cca6f5e5-4276-459f-ae98-97d16b90174f · outbound

This paper cites an unresolved cited work.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 157d7c41-4311-4024-ad3d-97ef21f26ec3 · outbound

This paper cites Within our framework, Corollary 2 establishes an O(1/ √ t) convergence rate for DualProj in terms of the Pareto stationarity measure γ(wt) for the non-convex setting.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Within our framework, Corollary 2 establishes an O(1/ √ t) convergence rate for DualProj in terms of the Pareto stationarity measure γ(wt) for the non-convex setting

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cdeb452-3894-4f0c-b072-1f820e9ee717 · outbound

This paper cites an unresolved cited work.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fee9caf3-08d2-422a-a33c-be2a8de2261f · outbound

This paper cites Proof.We omit the indextin the following to simplify the notation.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Proof.We omit the indextin the following to simplify the notation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d10402a3-8a6c-4a08-855e-f356e01a929a · outbound

This paper cites For Nash-MTL (Navon et al., 2022), we adopt the official implementation’s default, which always clips the aggregated update direction to satisfy∥dt∥= 1.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization For Nash-MTL (Navon et al., 2022), we adopt the official implementation’s default, which always clips the aggregated update direction to satisfy∥dt∥= 1

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c868516-09b7-401f-98a0-a4a5ec8dd8a2 · outbound

This paper cites Specifically, we use the function libmoon.util.mtl.get_dataset("adult") to generate the train, validation, and test splits.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization Specifically, we use the function libmoon.util.mtl.get_dataset("adult") to generate the train, validation, and test splits

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69ecbd10-49d0-45d1-9cbb-6f6566e332fe · outbound

This paper cites MGDA + coefficient-clipping.

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization MGDA + coefficient-clipping

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T08:44:38.977180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.