Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 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 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:26:28.891630Z

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

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.