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Paper Citation Record · LEDGER

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control

As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2607.15412.

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

pith.paper-citation-record.v1
2607.15412 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:34:18.203514Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:58:23.815109Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e96d536b-6d7b-441a-a21c-7d6e533e8485 · outbound

This paper cites The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning,

Reference 1

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Observation c64334a4-99c5-4f3f-9a04-f727d1e9cdb5 · outbound

This paper cites Three-way trade-off in multi-objective learning: Optimization, generalization and conflict- avoidance,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Three-way trade-off in multi-objective learning: Optimization, generalization and conflict- avoidance,

Reference 2

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source=pdf_text observed=2026-08-01T23:34:15.670691Z digest=sha256:710ab33e51329578364b88f3675c497ba45f972fb53512babb8912f912240d15

Observation 49f03468-1269-484a-bcf0-a1ec4dfd6721 · outbound

This paper cites Multi-task learning as multi-objective op- timization,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Multi-task learning as multi-objective op- timization,

Reference 3

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source=pdf_text observed=2026-08-01T23:34:15.820277Z digest=sha256:5eafa18f17b311ae4dc2f2fb7c8fdf3aac53b16d7e5f4328d3b556e4269daa60

Observation d4013939-1ce1-408e-b509-31d38e769e34 · outbound

This paper cites Multi-objective meta learning,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Multi-objective meta learning,

Reference 4

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source=pdf_text observed=2026-08-01T23:34:15.922600Z digest=sha256:579277cd5f5f45fb7c63fc7c7ae5d6808b901338d0a3b6dca2acab596de45102

Observation 7d9249d3-fd45-4478-b62f-bf0df25a09be · outbound

This paper cites Learning with limited samples: Meta-learning and applications to com- munication systems,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Learning with limited samples: Meta-learning and applications to com- munication systems,

Reference 5

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source=pdf_text observed=2026-08-01T23:34:16.025028Z digest=sha256:29214f4cd7bd029fb52de520a0f25c16cac2f9c2666aeab5ff899d9d98bcd79d

Observation c6d3a612-cc5b-4a19-8272-39ef16f00f2a · outbound

This paper cites Fairness constraints: Mechanisms for fair classification,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Fairness constraints: Mechanisms for fair classification,

Reference 6

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source=pdf_text observed=2026-08-01T23:34:16.120445Z digest=sha256:763b63443762aa6d7927426e9fbe1118c5017e7bca1907e71bedd5bee293a7c8

Observation aab54b34-720f-4e2c-aab8-e3abb34f07ac · outbound

This paper cites Algorithms for multicriterion optimization,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Algorithms for multicriterion optimization,

Reference 7

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source=pdf_text observed=2026-08-01T23:34:16.229470Z digest=sha256:a689b9a0201b9af7edb728f58a9bdacbdd3c25c985f43ee73dc88806cb9710dc

Observation 39fba525-e6b1-40c2-a8ce-b76be5a8aa6d · outbound

This paper cites Conflict-Averse Gradient Descent for Multi-task Learning,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Conflict-Averse Gradient Descent for Multi-task Learning,

Reference 8

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source=pdf_text observed=2026-08-01T23:34:16.350453Z digest=sha256:094351b03653d019ab59cd29d5e28f45cf9099aee405d4196a1ee4a7a74dc7f1

Observation 50b79db7-4331-4971-8cf0-8e61f7599d84 · outbound

This paper cites Steepest descent methods for multicriteria optimization,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Steepest descent methods for multicriteria optimization,

Reference 9

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source=pdf_text observed=2026-08-01T23:34:16.444499Z digest=sha256:dc84682d6944b3fa0c0a6263fc0dd49f9445fd9bae6f18aea103d9a0f42cc340

Observation 3346c30f-c92e-4777-b2c1-ebb56ee459ef · outbound

This paper cites Multiple-gradient descent algorithm (mgda) for multi- objective optimization,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Multiple-gradient descent algorithm (mgda) for multi- objective optimization,

Reference 10

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source=pdf_text observed=2026-08-01T23:34:16.546228Z digest=sha256:7197891fb3982b622e2f7867ca6822232be3fa6b23b551fbae18ea92342070e4

Observation fd08752a-9942-413e-9ec2-16f9f3140b61 · outbound

This paper cites Mitigating gradient bias in multi-objective learning: A prov- ably convergent approach,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Mitigating gradient bias in multi-objective learning: A prov- ably convergent approach,

Reference 11

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source=pdf_text observed=2026-08-01T23:34:16.667064Z digest=sha256:8a993fbcfe0dc16c767144b7c1008b557ccd6243d3d1968ea6bfa7dba1324b95

Observation f3d268a2-0df7-4400-97d6-22097e243504 · outbound

This paper cites The multiobjective steepest descent direction is not Lipschitz continuous, but is H ¨older continuous,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control The multiobjective steepest descent direction is not Lipschitz continuous, but is H ¨older continuous,

Reference 12

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source=pdf_text observed=2026-08-01T23:34:16.801793Z digest=sha256:b80ed5ae66fed1283a913d604bf6d3f5da465ae6b970a840fea3d2d67fe8fbdd

Observation 760060cb-6365-47a7-849d-a1cb7c1f0b22 · outbound

This paper cites Gradient Surgery for Multi-Task Learning,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Gradient Surgery for Multi-Task Learning,

Reference 13

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source=pdf_text observed=2026-08-01T23:34:16.915714Z digest=sha256:0d466b469c0a1c7c83714b66c786e76151c0bd024578a973963748020587b5f6

Observation afac0aa5-cf97-4790-90b3-57b83c01f733 · outbound

This paper cites On the convergence of stochastic multi-objective gradient manipulation and beyond,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control On the convergence of stochastic multi-objective gradient manipulation and beyond,

Reference 14

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source=pdf_text observed=2026-08-01T23:34:17.052957Z digest=sha256:3dafbfeb1dd29c5908a1bef2c12cdf4871d4453ab1b9c17b0d098bbf1e41a800

Observation a9a08b38-02dc-45ad-8928-adaa2a6adc00 · outbound

This paper cites Direction-oriented multi-objective learning: Simple and provable stochastic algorithms,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Direction-oriented multi-objective learning: Simple and provable stochastic algorithms,

Reference 15

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source=pdf_text observed=2026-08-01T23:34:17.184890Z digest=sha256:351bcb5af59e20fdfe3cc06ab9ae94ae1af7acb0875f36f6a08c8858c34a863f

Observation c6161824-b227-4391-af6d-33a5686896e3 · outbound

This paper cites FERERO: A flexible framework for preference-guided multi-objective learning,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control FERERO: A flexible framework for preference-guided multi-objective learning,

Reference 16

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Observation 262d76bf-c07e-4d51-87f3-2b952e44bfb6 · outbound

This paper cites Joint gradient balancing for data ordering in finite-sum multi-objective optimization,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Joint gradient balancing for data ordering in finite-sum multi-objective optimization,

Reference 17

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source=pdf_text observed=2026-08-01T23:34:17.485863Z digest=sha256:6ad67bc6e97ea699c709929bcfd54f21f0a30ee2623a40d556a7dbf31a3a8c9c

Observation f1588b4c-f789-4670-9398-730ddb5ac9d9 · outbound

This paper cites Variance reduction can improve trade-off in multi-objective learning,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Variance reduction can improve trade-off in multi-objective learning,

Reference 18

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source=pdf_text observed=2026-08-01T23:34:17.608367Z digest=sha256:f96bf696345b1bf479a719663c33e816c857b08c7d8116bfc6916108a84eba89

Observation e5e7f5bb-3e2b-40c3-9bc2-8ca332b84145 · outbound

This paper cites Proximal gradient methods for multiobjective optimization and their applications,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Proximal gradient methods for multiobjective optimization and their applications,

Reference 19

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source=pdf_text observed=2026-08-01T23:34:17.734318Z digest=sha256:ac93b8003f416f8d689265b68ba17bcfc77bfc7f6a68394844a2fbaf7a4493cd

Observation 0f12a12a-73fa-4642-b82a-6dc3d3867bc0 · outbound

This paper cites LibMTL: A python library for deep multi-task learning,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control LibMTL: A python library for deep multi-task learning,

Reference 20

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source=pdf_text observed=2026-08-01T23:34:17.846797Z digest=sha256:ec7db77d3c38d330fad5ddb6f083ec18ebd37d590315810c6ecef0dbd96c476e

Observation 6b1f65ec-baf6-4b39-a0c0-c62214c39d70 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation,.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Deep hashing network for unsupervised domain adaptation,

Reference 21

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source=pdf_text observed=2026-08-01T23:34:17.947311Z digest=sha256:2ef45e2fac99e4a003034140048d1112e424adb041b8b37c19d9c7aa10bd198b

Observation 0ffcedcd-7071-4f2c-8b6f-183c55fe1e40 · outbound

This paper cites an unresolved cited work.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-01T23:34:18.072643Z digest=sha256:f40ddc4aa6274a649c9e69f2789556b303e0ef3988225e45c0adaef947e20596

Observation 84a4769b-3df8-4b20-acdd-70d3a196edc5 · outbound

This paper cites an unresolved cited work.

Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-01T23:34:18.203514Z digest=sha256:1e33e236340427c81df43e2ff892c228349e600a95fd8937c125346868e9f3fd

Pith citing papers

Observation d06520bc-e856-4573-b67b-4926484401ee · inbound

Improved Convergence Rate for Stochastic Multi-Gradient Descent: A Proof Discovered with AI cites this paper.

Improved Convergence Rate for Stochastic Multi-Gradient Descent: A Proof Discovered with AI Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control

Reference 8

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source=pdf_text observed=2026-08-01T15:58:23.815109Z digest=sha256:efd4489fd2e0da6bdc527bb92b2a1d76b8ea89ee68cc073aea5b47e3bd81697e