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Paper Citation Record · LEDGER

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods

As of 12 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.12092.

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

pith.paper-citation-record.v1
2412.12092 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:21:47.763351Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

29 of 29 outbound references displayed

  • verified exact6
  • verified fuzzy0
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ecfb091e-d5a7-43f3-a22a-fca252569460 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods , " * write output.state after.block = add.period write newline

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7592737a-5392-49a7-85ff-1016f398aba1 · outbound

This paper cites write newline.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods write newline

Reference 2

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Observation e373b4e9-aa81-46d9-9247-bb6c75882f92 · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 3

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation a2793c01-5141-4ce0-82f6-7a7dd9bdd997 · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 4

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation a2c10b64-29f9-4601-835c-b5f1c0937c75 · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 5

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.596775Z digest=sha256:9da1e11d92484c617cc36fa9031a82c1b268a3054b23391f5fc4f18f723ac049

Observation c7bd4f68-d892-4114-bc35-46b00d1086e8 · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 6

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raw_fallback, observed 2026-08-11T14:21:48.365932Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 4b94ac19-bec3-45e7-ab12-b307a68ded1f · outbound

This paper cites GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

Reference 7

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.606878Z digest=sha256:744bad5a333005e4dd8c79c94c7a694607b7fd39909d45b5566b640ee04e1758

Observation 723bb327-0b2a-4e4b-abd9-44ff7d5f0c13 · outbound

This paper cites DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.618805Z digest=sha256:5cd953129d339881476c520e8c4b339705aa2c51c363e087dc0583114e74ac83

Observation bd9aeb0b-0e34-4cc5-89bb-1ef5e7f6b80b · outbound

This paper cites RotoGrad: Gradient Homogenization in Multitask Learning.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods RotoGrad: Gradient Homogenization in Multitask Learning

Reference 9

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.625133Z digest=sha256:d9c21b9b923d4a479ac4dce28d117a6ea98978cfbdd5a8bee6edcb60f70a8527

Observation c1788037-4051-4d2f-92bf-11bed3670ef0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Adam: A Method for Stochastic Optimization

Reference 10

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Unavailable: canonical work link unavailable.

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Observation 1962bcac-fb42-4820-bae1-cfad6bdbe64f · outbound

This paper cites UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory

Reference 11

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verified exact
local_arxiv, observed 2026-08-11T14:21:48.146374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.636351Z digest=sha256:5d3be1431cf4e4e53933e86bc95cb35a88499a7bcb7bb384a8a9b0d657785961

Observation 378f6cb0-1490-43c9-9a31-d8a4e126633f · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 12

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raw_fallback, observed 2026-08-11T14:21:48.348996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.641795Z digest=sha256:9f4884bb70e6f8a8a884a777a67069860815e3e42d5da6e487f4eb13018f0f20

Observation 5812636e-5472-4a5b-b7e5-35ae49846202 · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 13

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raw_fallback, observed 2026-08-11T14:21:48.330368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 84e20450-6dbd-4d51-8cda-23a73c800ebe · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 14

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raw_fallback, observed 2026-08-11T14:21:48.313170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.653434Z digest=sha256:c63b2852cc398767b6cf7dac5aef7e07bd4ce08ce3f0dfbe60cfb66675cbd8c3

Observation 35321d46-1eda-4c94-bf26-e7309d7927ed · outbound

This paper cites Multi-Label Learning to Rank through Multi-Objective Optimization.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Multi-Label Learning to Rank through Multi-Objective Optimization

Reference 15

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verified exact
local_arxiv, observed 2026-08-11T14:21:48.122514Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.658607Z digest=sha256:1a63765ddd628d5962dd1d01288f12b03628e3afdc7d24b7a143f6a8908d9c4f

Observation b53a66de-db0b-45b2-a2be-e73b8268da22 · outbound

This paper cites Cross-stitch Networks for Multi-task Learning.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Cross-stitch Networks for Multi-task Learning

Reference 16

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Unavailable: canonical work link unavailable.

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Observation 57cdfda6-3b66-462a-9b17-2f264fc4ea28 · outbound

This paper cites Constrained Reinforcement Learning Has Zero Duality Gap.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Constrained Reinforcement Learning Has Zero Duality Gap

Reference 17

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verified exact
local_arxiv, observed 2026-08-11T14:21:48.069096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.671525Z digest=sha256:ba51e860ef385f868353967ffc4b1e201eae1f7ec4caeb9ee2519d59a27ae3d0

Observation d76af85c-9153-4552-be43-d7bda7f9ff02 · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 18

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raw_fallback, observed 2026-08-11T14:21:48.295206Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.680860Z digest=sha256:30544f9c07e171f34843608565cc99599737d963d0034e499f157dbb1cf3aa1b

Observation c28c8e66-6c63-4564-b50c-c52482cb4e55 · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 19

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.690588Z digest=sha256:c5c854dc6404988d24e60f4cb6ededa9bf309b7cb213d903c38933d9a948c87e

Observation cfc42192-416b-4e1c-a9f3-42a0522e38c5 · outbound

This paper cites OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Reference 20

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Unavailable: canonical work link unavailable.

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Observation cb6ee05a-bc5f-466f-b3e0-78ed8523886d · outbound

This paper cites Responsive Safety in Reinforcement Learning by PID Lagrangian Methods.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Responsive Safety in Reinforcement Learning by PID Lagrangian Methods

Reference 21

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no resolver link, observed 2026-08-11T14:21:47.705080Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.705080Z digest=sha256:5d4fc19079b9597d3cf37e0ef487cab6d55614f6fd20c1cb32f3a7c4141c7885

Observation 0ffac2ec-35da-49fd-b715-270596f23690 · outbound

This paper cites STEM: Unleashing the Power of Embeddings for Multi-task Recommendation.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods STEM: Unleashing the Power of Embeddings for Multi-task Recommendation

Reference 22

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verified exact
local_arxiv, observed 2026-08-11T14:21:47.983105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.710482Z digest=sha256:7b76161dcaf6ecf8732467c75a335e54254da16adfff49ae19d0e59c9ab8a2bf

Observation f038ac8d-1607-4aad-aaac-2b1a6840074a · outbound

This paper cites an unresolved cited work.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Unresolved cited work

Reference 23

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raw_fallback, observed 2026-08-11T14:21:48.241006Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.720213Z digest=sha256:50041dff1a0d4abdbd3ea458109ce9cbe65a68ed5a4f40c5deeea45f27e92465

Observation 262a6870-a416-4bb5-b921-08651fdad301 · outbound

This paper cites Multi-objective Learning to Rank by Model Distillation.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Multi-objective Learning to Rank by Model Distillation

Reference 24

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verified exact
local_arxiv, observed 2026-08-11T14:21:47.944725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.726714Z digest=sha256:2b0fec67f78a1793ab0ffa2e9179fabef70978ce794d2fb36ae0ba89d1d86b44

Observation 801a9e0e-4362-4517-bb66-bbae93d772ef · outbound

This paper cites Reward Constrained Policy Optimization.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Reward Constrained Policy Optimization

Reference 25

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no resolver link, observed 2026-08-11T14:21:47.731838Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.731838Z digest=sha256:ae8aac051dfa719b6ece85a7615cf306e80a888a9ad1eb72810eb68b22e762de

Observation 9806e2c3-dd01-407a-8186-f8b9039616f2 · outbound

This paper cites Multi-Task Deep Recommender Systems: A Survey.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Multi-Task Deep Recommender Systems: A Survey

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.737295Z digest=sha256:2e77a8deb5f247ec4a2818eec81882b71810874ea0fb024057d5c6ba43d148d8

Observation 7167e032-c909-4feb-9de3-70b6712cb7f1 · outbound

This paper cites An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

Reference 27

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verified exact
local_arxiv, observed 2026-08-11T14:21:47.871611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T14:21:47.745371Z digest=sha256:bf17932203718df6117986483138c6ffeaddf3f2f24531b46fe5eeb7e8f2eef0

Observation 6c898aa1-2655-46d5-a040-ae0214fa3ffe · outbound

This paper cites Gradient Surgery for Multi-Task Learning.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Gradient Surgery for Multi-Task Learning

Reference 28

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no resolver link, observed 2026-08-11T14:21:47.756321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.756321Z digest=sha256:fd0be9e3471966c1544b4e0582e98a8815d95e416b624c44e0f799e0704a4a62

Observation f030b71e-2894-48f5-86b8-ded6d119b915 · outbound

This paper cites Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems.

No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

Reference 29

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no resolver link, observed 2026-08-11T14:21:47.763351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:21:47.763351Z digest=sha256:a064d0806505d7c89c003c861ea2640e904bd101f25608f77c21c92de1886255

Pith citing papers

No inbound Pith citation observations are available.