Paper Citation Record · LEDGER

Simple Optimizers for Convex Aligned Multi-Objective Optimization

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2509.05811.

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

pith.paper-citation-record.v1
2509.05811 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:05:47.806428Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

48 of 48 outbound references displayed

verified exact3
verified fuzzy18
unresolved27
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External citation measurements

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Outbound references

Observation 5d4c4c69-5123-4c8c-a4b2-04f32051bf2c · outbound

This paper cites @esa (Ref.

Simple Optimizers for Convex Aligned Multi-Objective Optimization @esa (Ref

Reference 1

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Observation 7a0ba77d-bdc3-4d61-b31b-c9b1d3871567 · outbound

This paper cites an unresolved cited work.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Unresolved cited work

Reference 2

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Observation 482f5d90-8968-4453-a2ef-09a933aeb8e2 · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Unresolved cited work

Reference 3

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Observation 779f8dd3-6e84-4617-95d3-e2860c6eeeb2 · outbound

This paper cites Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning

Reference 4

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Observation 1fd540ea-a664-46cb-9210-9abc56b912fc · outbound

This paper cites Predicting with proxies: Transfer learning in high dimension.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Predicting with proxies: Transfer learning in high dimension

Reference 5

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Observation a632d600-137c-45f1-8971-adec2c6275aa · outbound

This paper cites Robust optimization--methodology and applications.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Robust optimization--methodology and applications

Reference 6

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Observation ec058cce-3e5b-4e0b-a56e-d10c3b43ada8 · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Robust optimization

Reference 7

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Source-reported events for the cited work

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Observation 002810de-9a0f-426e-a9be-4752d92f805d · outbound

This paper cites Convex optimization.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Convex optimization

Reference 8

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Observation df290a41-f198-4caf-9fbe-e30c33809f96 · outbound

This paper cites Multitask learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Multitask learning

Reference 9

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Observation 5d0f6e5d-f0ec-44fd-af1f-630111095662 · outbound

This paper cites Prediction, learning, and games.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Prediction, learning, and games

Reference 10

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Observation 759cce91-1f53-4612-a430-c9b8c171c07f · outbound

This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Entropy-sgd: Biasing gradient descent into wide valleys

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.226883Z

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Observation a9b415cf-b50f-4669-9293-ebc866b54adb · outbound

This paper cites Robust optimization for non-convex objectives.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Robust optimization for non-convex objectives

Reference 12

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Source-reported events for the cited work

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Observation c268fde1-a0dd-4a54-8ba8-bd81e54be34d · outbound

This paper cites Reinforcement learning can be more efficient with multiple rewards.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Reinforcement learning can be more efficient with multiple rewards

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.210947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation efb6e9eb-2f88-441f-a3f6-1ff5a5f3a702 · outbound

This paper cites The road less scheduled.

Simple Optimizers for Convex Aligned Multi-Objective Optimization The road less scheduled

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.202991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8ba952df-173c-40fe-b674-9c1bd1cd6a21 · outbound

This paper cites Sharing Knowledge in Multi-Task Deep Reinforcement Learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Sharing Knowledge in Multi-Task Deep Reinforcement Learning

Reference 15

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Observation 8cc2f367-92ca-420d-9c8b-8610dd83718a · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Adaptive subgradient methods for online learning and stochastic optimization

Reference 16

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Observation 5f1cbe14-6a9c-4fba-8cf1-a4f97fd5a8b8 · outbound

This paper cites Aligned Multi Objective Optimization.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Aligned Multi Objective Optimization

Reference 17

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Observation d91a4b53-d59c-4438-a424-6ac9cff72136 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 18

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Observation bbb9af5a-2fcd-4497-889a-f59b8b55a04c · outbound

This paper cites A review of multi-objective optimization: Methods and its applications.

Simple Optimizers for Convex Aligned Multi-Objective Optimization A review of multi-objective optimization: Methods and its applications

Reference 19

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Source-reported events for the cited work

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Observation 42169af6-ea40-4b29-930b-6a562da0e558 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 20

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Observation e607a1ba-7829-44b6-bd5f-047daea1bc44 · outbound

This paper cites Revisiting the Polyak step size.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Revisiting the Polyak step size

Reference 21

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Observation 7c74445a-5d5d-4212-a26f-83cc539a41a6 · outbound

This paper cites Introduction to online convex optimization.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Introduction to online convex optimization

Reference 22

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Observation 0e4a40a9-46b1-4c06-afdc-1a5238452b05 · outbound

This paper cites Reinforcement Learning with Unsupervised Auxiliary Tasks.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Reinforcement Learning with Unsupervised Auxiliary Tasks

Reference 23

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Observation dd6b618f-8d61-4e36-b66c-48d593e9f63c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Adam: A Method for Stochastic Optimization

Reference 24

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Observation 08b365fb-2bee-41a9-a0a6-d488ce7a6612 · outbound

This paper cites Visualizing the loss landscape of neural nets.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Visualizing the loss landscape of neural nets

Reference 25

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Observation 019ccfb8-d411-47bb-9ca5-a92e2816ccf0 · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Let's Verify Step by Step

Reference 26

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Observation ee725920-ff1c-4638-9fc0-16a5d081868c · outbound

This paper cites Conflict-averse gradient descent for multi-task learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Conflict-averse gradient descent for multi-task learning

Reference 27

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Simple Optimizers for Convex Aligned Multi-Objective Optimization End-to-end multi-task learning with attention

Reference 28

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Observation 0bd565d1-b27f-49aa-bf7f-c56b1d80d968 · outbound

This paper cites Multi-Task Learning as a Bargaining Game.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Multi-Task Learning as a Bargaining Game

Reference 29

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Observation 82268159-1f08-45cd-8023-f129e81bc20a · outbound

This paper cites Online Learning: A Modern Introduction Using Convex Optimization.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Online Learning: A Modern Introduction Using Convex Optimization

Reference 30

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Observation d4a125e3-0293-46d5-b7ac-270bd44a275d · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Relative flatness and generalization

Reference 31

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Observation ced2c016-8ed1-4b18-a548-31e6686e89af · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Introduction to optimization

Reference 32

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Observation f22c1377-fd84-423c-8cb1-70d6ff92b1a3 · outbound

This paper cites DeepOBS: A Deep Learning Optimizer Benchmark Suite.

Simple Optimizers for Convex Aligned Multi-Objective Optimization DeepOBS: A Deep Learning Optimizer Benchmark Suite

Reference 33

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Observation 77990a53-bad0-420f-a25e-91e192c9979f · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Multi-task learning as multi-objective optimization

Reference 34

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Observation 7ad4716c-7216-467c-b649-ef9bd066a287 · outbound

This paper cites Proxybo: Accelerating neural architecture search via bayesian optimization with zero-cost proxies.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Proxybo: Accelerating neural architecture search via bayesian optimization with zero-cost proxies

Reference 35

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Observation 7720a5da-35fa-42ec-a85d-c08ed7d75ba1 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 36

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Observation 45453a89-a1c4-4414-956a-4dd080680efc · outbound

This paper cites Distral: Robust multitask reinforcement learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Distral: Robust multitask reinforcement learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.127965Z

Source-reported events for the cited work

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Observation be10c388-6559-4303-941e-423a4d55ee44 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Solving math word problems with process- and outcome-based feedback

Reference 38

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Observation 9b7b5684-a286-4793-8079-07b185aa20da · outbound

This paper cites Discovery of useful questions as auxiliary tasks.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Discovery of useful questions as auxiliary tasks

Reference 39

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Source-reported events for the cited work

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Observation 7ea01283-52c7-40bd-90b1-7bb4b817d335 · outbound

This paper cites Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning

Reference 40

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Observation 44cdc482-e30e-4333-9737-dbb930216f06 · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Robust markov decision processes

Reference 41

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This paper cites Two losses are better than one: Faster optimization using a cheaper proxy.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Two losses are better than one: Faster optimization using a cheaper proxy

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.103835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a7b4a45-a4fd-419f-927f-ee26a590ef4d · outbound

This paper cites MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models.

Simple Optimizers for Convex Aligned Multi-Objective Optimization MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:05:47.837973Z

Source-reported events for the cited work

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Observation 216a11bc-c564-4a44-a80d-d0caa81a382f · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Gradient surgery for multi-task learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.094320Z

Source-reported events for the cited work

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Observation 47165959-8999-4b30-9b94-f629881a964a · outbound

This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.085110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 61a90f3c-4a01-44c5-894f-45765ddd24fe · outbound

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Simple Optimizers for Convex Aligned Multi-Objective Optimization Visual classification with multitask joint sparse representation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.076120Z

Source-reported events for the cited work

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Observation a7336221-1dc2-49a0-9ec8-b6219b6e60b5 · outbound

This paper cites Facial landmark detection by deep multi-task learning.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Facial landmark detection by deep multi-task learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:05:48.067041Z

Source-reported events for the cited work

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Observation 2416c1ff-6400-4ea0-9916-b2d5d01c7fb3 · outbound

This paper cites Online convex programming and generalized infinitesimal gradient ascent.

Simple Optimizers for Convex Aligned Multi-Objective Optimization Online convex programming and generalized infinitesimal gradient ascent

Reference 48

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Pith citing papers

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