Paper Citation Record · LEDGER

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

As of 20 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 16 inbound Pith citation observations for arXiv:2501.11873.

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

pith.paper-citation-record.v1
2501.11873 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:52:40.058197Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:52:49.484653Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:29:38.231668Z

Reference resolution

16 of 16 outbound references displayed

verified exact1
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bf2f323-164a-497e-9800-7b593a2331db · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Training Verifiers to Solve Math Word Problems

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:39.991278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ab2795-4ea1-413b-b981-c340372fe0e7 · outbound

This paper cites Mixtral of Experts.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Mixtral of Experts

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.009414Z

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Observation eaa8bfc5-f426-4971-ba7e-2167ff294735 · outbound

This paper cites MiniMax-01: Scaling Foundation Models with Lightning Attention.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models MiniMax-01: Scaling Foundation Models with Lightning Attention

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.014024Z

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Observation 1c32c31c-3782-4a90-a4ef-5015b9945b92 · outbound

This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.019023Z

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Observation 8c2629c3-2d37-482a-86b1-05d609fd0e2a · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.033283Z

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Observation 59a592fc-3e1a-4abb-ad23-b8907b8352fa · outbound

This paper cites Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.037472Z

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Observation 18ced0b9-e159-4c30-bb57-738b72c1a44e · outbound

This paper cites GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:52:40.143268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17d70408-3f98-41f6-9a8b-20090de6b482 · outbound

This paper cites OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.045727Z

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Observation ed9380e0-389a-4e8d-abcd-2e3b55c1fd38 · outbound

This paper cites Qwen2.5 Technical Report.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Qwen2.5 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.049755Z

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Observation ee0167fc-5425-4966-a27d-f180b7c770e9 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.053393Z

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Observation 2f7b7390-f2bc-45ac-ad92-acc719afa316 · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.058197Z

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Observation 3357eeef-6a9c-432c-95d6-ee2732a49980 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.028539Z

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Observation bf8ebc0e-3107-416e-b1d5-dd880f4ad923 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:39.995962Z

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Observation 095965d9-f4fb-4162-acfa-aa2f23bc5cf5 · outbound

This paper cites Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.024022Z

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Observation 6ebe77be-79c4-4605-8b17-34f279130158 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Measuring Massive Multitask Language Understanding

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.004698Z

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Observation d594c9a4-4720-4ecb-bede-1cd6ac1b492a · outbound

This paper cites Fewer Truncations Improve Language Modeling.

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models Fewer Truncations Improve Language Modeling

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T17:52:40.000179Z

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Pith citing papers

Observation 1fb44ae8-7558-427a-b060-8dc3ce7f5184 · inbound

Neural network task specialization via domain constraining cites this paper.

Neural network task specialization via domain constraining Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:52:49.484653Z

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Observation 654efa76-a041-45e8-8d27-b452aba0b77b · inbound

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs cites this paper.

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:31:19.556401Z

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Observation e8f5c733-853d-4c28-a2db-c92d08ce8e6e · inbound

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free cites this paper.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.915567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3fb5fb12-d4c3-4807-a719-3be2e722a6dd · inbound

Qwen3 Technical Report cites this paper.

Qwen3 Technical Report Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:35:28.577251Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a55a3517-79b9-4275-87dd-907dd808aa42 · inbound

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications cites this paper.

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T17:26:28.891630Z

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Observation 37873acc-e3d7-48bd-8a28-adbbf0a517ba · inbound

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution cites this paper.

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 246

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:58:59.050128Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0f700ef-2573-4db8-80a5-f9fbd5a97679 · inbound

ProPhy: Progressive Physical Alignment for Dynamic World Simulation cites this paper.

ProPhy: Progressive Physical Alignment for Dynamic World Simulation Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:08:47.973460Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4f170e1-5c34-4fc9-82d0-295786056337 · inbound

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:28.419367Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4d7779a-5cac-47e0-9902-a895b78ff088 · inbound

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:23:51.260199Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 099bf210-82e4-4240-8e4b-af340a8dc30a · inbound

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models cites this paper.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:08:54.313335Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3e42793d-2843-4518-a6e6-d98f4fe431bc · inbound

PithTrain: A Compact and Agent-Native MoE Training System cites this paper.

PithTrain: A Compact and Agent-Native MoE Training System Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:12:46.524678Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 58a1a656-1b11-4128-b2d9-0c3fb1c34225 · inbound

DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts cites this paper.

DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:16:14.421840Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8e376c96-e784-4433-b402-e7650c3cbfc6 · inbound

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning cites this paper.

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.934730Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0c5e3505-ab35-4928-9905-18b88a6de820 · inbound

Sakana Fugu Technical Report cites this paper.

Sakana Fugu Technical Report Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 207

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T06:29:38.233271Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 834c0362-1b70-4fbe-9cf8-2149a4c4dd79 · inbound

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning cites this paper.

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:04:39.217496Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a6bb9d1a-5b2b-484e-952f-d80ec9951353 · inbound

Relax Within, Balance Across: Geometry-Guided Load Balancing for Vision-Language Mixture-of-Experts cites this paper.

Relax Within, Balance Across: Geometry-Guided Load Balancing for Vision-Language Mixture-of-Experts Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T00:38:33.233904Z

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Unavailable: canonical work link unavailable.