WP-CS-YXM/taxflow-v8-1-evidence-node-lora

TaxFlow V8.1 Evidence Node LoRA

TaxFlow V8.1 is a competition/research LoRA adapter for Qwen3-VL-2B-Instruct.

It is trained for a narrow accounting-firm workflow task: composing conservative, reviewable tax workpaper evidence nodes from upstream OCR/PDF text-layer output, official tax-form template context, Evidence RAG retrieval, page/cell metadata, and quality gates.

This adapter is not a general OCR model, not a tax-law reasoning model, and not a manager-review replacement.

What Was Trained

The adapter was trained to make the model behave more like a TaxFlow workpaper component:

• preserve form_code, field_id, source page/cell, and visible value context;
• keep official instruction / RAG source metadata near the field;
• output reviewable Evidence Node JSON instead of free-form prose;
• mark OCR/template/RAG conflicts as conflict_requires_review;
• provide DataSnipper-style handoff fields when source anchors are available;
• avoid pushing uncertain values directly into O SWP workpapers.

Training Run

• Hardware: single RTX 4090 24GB training run
• Base model: Qwen/Qwen3-VL-2B-Instruct
• Fine-tuning: LoRA adapter
• Final checkpoint: checkpoint-342
• Final eval loss: 0.1521
• Adapter files exported at repository root

Small-Sample Fair Compare

This is a small smoke benchmark, not a statistically significant production benchmark.

Split	Model	N	Readiness	Parse	Value	Stress Reject	Conflict Status	RAG Source	DataSnipper
mixed	base	8	43.8%	87.5%	87.5%	87.5%	87.5%	0.0%	0.0%
mixed	taxflow	8	35.4%	50.0%	50.0%	50.0%	50.0%	50.0%	0.0%
stress	base	12	47.2%	100.0%	100.0%	0.0%	0.0%	0.0%	0.0%
stress	taxflow	12	61.8%	66.7%	66.7%	66.7%	66.7%	66.7%	66.7%
showcase	base	8	47.9%	100.0%	87.5%	100.0%	100.0%	0.0%	0.0%
showcase	taxflow	8	47.9%	62.5%	62.5%	62.5%	62.5%	62.5%	12.5%

Safe Claim

TaxFlow V8.1 shows a narrow but defensible advantage in targeted production-stress conflict cases: compared with the base model, it is more likely to return review-required Evidence Nodes with RAG source context and DataSnipper handoff fields.

Explicit Non-Claims

Do not claim from this run that:

• TaxFlow is generally better than the base model across all extraction tasks;
• the LoRA improves raw OCR visual acuity;
• the model is production-ready on 11th-gen CPU office laptops;
• the model can replace human reviewer judgment;
• the benchmark is statistically significant.

Mixed/showcase samples did not show a general win. The next iteration should shorten the schema, use constrained decoding / JSON repair, and run a larger benchmark.

Files

• adapter_model.safetensors: LoRA adapter weights
• adapter_config.json: PEFT adapter config
• TRAINING_SUMMARY_PUBLIC.json: sanitized training metadata
• eval/: score JSON, dashboard, and fair-compare result archive
• docs/HF_MODEL_CARD_V8_1_RESULT.md: detailed release note

Public Release Hygiene

This public-sanitized package intentionally excludes raw training logs, raw prediction archives, local machine paths, and base tokenizer files. Use the base model tokenizer/processor from Qwen/Qwen3-VL-2B-Instruct.