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Paper Citation Record · LEDGER

LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2403.06504.

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

pith.paper-citation-record.v1
2403.06504 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:32.342788Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T10:19:59.839971Z

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 9347a12c-dc38-4126-87ec-492546f5dec0 · inbound

Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage cites this paper.

Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:32.342788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:00:32.342788Z digest=sha256:095eb050d0e224793c2125291aa37e00b9fbe2f81ae5305d1e21391b8bf5045e

Observation fbf75f83-4a47-4bff-87af-6be3b3d6d34b · inbound

Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System cites this paper.

Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-05T19:30:24.199300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:30:24.199300Z digest=sha256:0c047ece4b8312def13916cd008094f1d5ff810b1d20310827c0d271ff0ad479

Observation ae8bfc6c-65c6-4abc-a016-282111a76e94 · inbound

MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall cites this paper.

MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T11:38:39.507558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:38:39.507558Z digest=sha256:bae0c4def6f0549779816f3e86d4196ae49c36d89e46c5d328a6b38b77f2e570

Observation 922ed063-0bef-483d-9fab-166ecbfb377b · inbound

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU cites this paper.

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-13T23:47:39.219131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:47:39.219131Z digest=sha256:0f0dce146560ec905bd434317f6c48c5573a4df7b739ca5f258aaa24406af986

Observation 3a09c57e-15fa-4eea-b333-08beb65365d1 · inbound

Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains cites this paper.

Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:55:49.191901Z

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.

source=pdf_text observed=2026-05-10T19:29:30.436621Z digest=sha256:d12762993737ee184b92f78a88969ed6fb4744052d1b63e890040f0e5cc4d00e

Observation 2aaca9df-63b7-4828-9d9b-82a4246ba323 · inbound

Efficient Training on Multiple Consumer GPUs with RoundPipe cites this paper.

Efficient Training on Multiple Consumer GPUs with RoundPipe LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:31:26.811674Z

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.

source=pdf_text observed=2026-05-07T10:37:22.251566Z digest=sha256:8a3d1727aa53791f3360926434e8e7507450888c41673d45161c976d6717b929

Observation ac1498c0-bcfe-4b13-aa53-39cda53d554b · inbound

Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments cites this paper.

Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-21T10:19:59.842208Z

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.

source=pdf_text observed=2026-05-21T10:17:29.611062Z digest=sha256:56c5793461bfaeef43f365282e9beb4f6988f2567da3785788870b5eec3323a1