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Paper Citation Record · LEDGER

Graph Coloring for Multi-Task Learning

As of 17 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2509.16959.

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

pith.paper-citation-record.v1
2509.16959 v5

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:55:32.890575Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved8
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3eccb46-bc71-4861-ab24-8446894fa94c · outbound

This paper cites an unresolved cited work.

Graph Coloring for Multi-Task Learning Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-15T15:55:33.163807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.857945Z digest=sha256:8d08d53f2b231a95777979cee53752e453ee43903323bb3ec2acdeecb76e781b

Observation 693c02af-a86d-4c0e-8e21-5f4d5f1f61d0 · outbound

This paper cites This maintains consistent training dynamics across tasks.

Graph Coloring for Multi-Task Learning This maintains consistent training dynamics across tasks

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:55:33.153131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.861679Z digest=sha256:cb8ec9eb5e1b878ad9a54a3f869db36999ffd9a15165a22560b32b1633e94be4

Observation 3596105c-603d-4846-995b-99bb440cdfc0 · outbound

This paper cites The Natural Language Decathlon: Multitask Learning as Question Answering.

Graph Coloring for Multi-Task Learning The Natural Language Decathlon: Multitask Learning as Question Answering

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T15:55:32.844403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:55:32.844403Z digest=sha256:a18492bb2de2b83ec6ce9e2f7cae54e200a83636f54ad2a66d9ebf8d37e29243

Observation d0cc8ba6-e08f-49c5-95c0-dec5ec3a4048 · outbound

This paper cites It uses an online, per-step rule (no pairwise gradient ops), adding negligible overhead while remaining robust to loss-scale differences.

Graph Coloring for Multi-Task Learning It uses an online, per-step rule (no pairwise gradient ops), adding negligible overhead while remaining robust to loss-scale differences

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:55:33.066412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.883247Z digest=sha256:da80941d43eb7fceec9c5845adf8e987064fca95dfdf1c204328f9ba023a7bfc

Observation 94df40a9-1632-4cda-ada0-e717d32bd07c · outbound

This paper cites The parameterα controls the trade-off between average performance and fairness.

Graph Coloring for Multi-Task Learning The parameterα controls the trade-off between average performance and fairness

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:55:33.053895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.886497Z digest=sha256:1fa4b4987ada485a59a415eec0f3fcdea20e3465ae09fdafc2a8f97532c38e78

Observation bfec0c02-fa40-4a16-909d-4bd483f0f8fe · outbound

This paper cites Weights are obtained by solving a small inner problem (e.g., via CCP) using the gradient Gram matrix.

Graph Coloring for Multi-Task Learning Weights are obtained by solving a small inner problem (e.g., via CCP) using the gradient Gram matrix

Reference 6

Resolution
malformed identifier
raw_fallback, observed 2026-08-15T15:55:32.992085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.890575Z digest=sha256:284e48e70b48199b75efd8c4722abd2400ddbb237dcf6ea7d39671449cd0c87d

Observation 3a3a2045-0283-406e-b97c-2f9f0632368f · outbound

This paper cites an unresolved cited work.

Graph Coloring for Multi-Task Learning Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-15T15:55:33.141674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.865593Z digest=sha256:64dd0307d6526b9bec5a6098aa0399068b52d2b93d6dae80dfd1a2b208b5fb20

Observation 551b163a-1184-4a2e-908b-c2dac6101070 · outbound

This paper cites an unresolved cited work.

Graph Coloring for Multi-Task Learning Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-15T15:55:33.118390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.873024Z digest=sha256:a8d7fbebcbe629b92eea618992733ca5b5def8d9984fbe9d4f0542e006419d95

Observation 9eab2c51-d3bc-48df-b1ca-b1ab3485d02a · outbound

This paper cites This proves more nuanced modifications to gradients than binary projection.

Graph Coloring for Multi-Task Learning This proves more nuanced modifications to gradients than binary projection

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:55:33.102730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.876366Z digest=sha256:ccc8c9cdeb835fb6889c41fa24ed11e2b781a33fb06300c832772479ef49fb7f

Observation e1138b99-6401-47eb-8167-1adb520b39a3 · outbound

This paper cites an unresolved cited work.

Graph Coloring for Multi-Task Learning Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-15T15:55:33.079459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.879783Z digest=sha256:38444bb82d769f1101838d96bb94cca61d38d831cfdeea6107fb80866ef4f18d

Observation fc0fe487-bb54-4fc2-97e6-e5fd61e6da4a · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Graph Coloring for Multi-Task Learning An Overview of Multi-Task Learning in Deep Neural Networks

Reference 2004

Resolution
unresolved
no resolver link, observed 2026-08-15T15:55:32.848324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:55:32.848324Z digest=sha256:b9fd10b185e723844e56b10d7fd15f029b7521ab19c28865c1c7d05b8f0a402c

Observation d26eb3cc-fff2-4a69-bba6-eccc97bab4c0 · outbound

This paper cites 36 K.2 State-of-the-art models.

Graph Coloring for Multi-Task Learning 36 K.2 State-of-the-art models

Reference 2014

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:55:33.130321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.869469Z digest=sha256:6d1fee8814f4182495ca2039ab972f78d23fb22e0196a87dd7ed8c16665f4cb0

Observation b6d1012c-49fd-413b-9537-f69e85095cf0 · outbound

This paper cites J.2 CIF AR-10 The CIFAR-10 (Krizhevsky et al., 2009) dataset contains 60,00032× 32color images across 10 generic classes.

Graph Coloring for Multi-Task Learning J.2 CIF AR-10 The CIFAR-10 (Krizhevsky et al., 2009) dataset contains 60,00032× 32color images across 10 generic classes

Reference 2015

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T15:55:33.173904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:55:32.853494Z digest=sha256:39bb650d289db43777f4364851e4b0f4e5f2fd1332a9ab8a9bf270c04878d4e9

Observation 1c751349-b699-4d22-9be0-e4d8d755fed0 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Graph Coloring for Multi-Task Learning Deep Residual Learning for Image Recognition

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-15T15:55:32.840193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:55:32.840193Z digest=sha256:0839d67f77812cd4422c6d488b11aa160e08d682159f2f75e8c65ec51835aee9

Observation c0ec671d-e25d-4f90-b41c-912806629ddc · outbound

This paper cites Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction.

Graph Coloring for Multi-Task Learning Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-15T15:55:32.835363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:55:32.835363Z digest=sha256:440e4351c8c1b2af3a58df8a6263b110e0d888cd461d959c22b908ac99fa8a75

Pith citing papers

No inbound Pith citation observations are available.