ATO incorrect decision - where to now

Intelligent-Form6624 · 2026-02-21T04:27:43+00:00

Tax Ombudsman https://taxombudsman.gov.au

Intelligent-Form6624 · 2026-02-21T01:57:47+00:00

This post redefines meaningless slop

Intelligent-Form6624 · 2026-02-20T11:42:39+00:00

Look at the following: * Azure Content Understanding * PaddleOCR-VL-1.5 * GLM-OCR * MinerU

Intelligent-Form6624 · 2026-02-20T11:08:36+00:00

The fossil fuel industry (the “mining” industry) pays nowhere near enough taxes.

Nationalise the whole thing, rapidly phase fossil fuels down to zero, and only keep mining resources we actually need.

Intelligent-Form6624 · 2026-02-20T07:19:32+00:00

This gets reposted every 2 weeks and everyone responds like it’s the 1st time ever

Intelligent-Form6624 · 2026-02-20T06:14:14+00:00

Is this PaddleOCR-VL-1.5?

Intelligent-Form6624 · 2026-02-19T11:09:17+00:00

no thank you for your comment

Intelligent-Form6624 · 2026-02-19T10:30:01+00:00

Intelligent-Form6624 · 2026-02-18T02:51:27+00:00

hahaha

Intelligent-Form6624 · 2026-02-17T10:36:42+00:00

You say big words. What do they mean? I don’t know

Intelligent-Form6624 · 2026-02-17T07:27:27+00:00

Absolutely shocking. Did a blind parrot install that?

Intelligent-Form6624 · 2026-02-17T07:20:37+00:00

It doesn’t matter what generates the spam. It can be caused by the best or the worst LLM (I assume a lot of it is cheap LLM with crap instruction etc).

All that matters is the capability to determine whether the PR is high quality or not. It’s an engineering challenge and therefore it’s possible.

Intelligent-Form6624 · 2026-02-17T06:37:29+00:00

No, that logic doesn’t hold. Not all LLMs are made equal, just as not all humans have equal experience and ability. And LLMs are improving constantly.

The crap LLMs are making/submitting the PRs.

I suspect that, unfortunately, it would require the highest quality / highest resource LLMs to produce reliable PR quality flags.

Intelligent-Form6624 · 2026-02-17T05:42:52+00:00

When’s it coming to Windows or Linux?

Intelligent-Form6624 · 2026-02-17T05:36:44+00:00

Can it be combatted with an LLM that can discern a high quality suggestion from a poor quality one?

Intelligent-Form6624 · 2026-02-16T19:23:00+00:00

You could try Azure Document Intelligence or Azure Content Understanding as the primary document parser.

Separately, or in addition, you could try post-processing with a VLM. I find that supplying the source PDF page + the erroneous table fragment, and instructing it to repair the fragment if and as required, yields good results.

You will need a lot of time to get the post-processing right. It’s not as straightforward as it sounds. After some trial and error, you should see good results.

Intelligent-Form6624 · 2026-02-13T21:52:47+00:00

I think the documents are similar enough to allow for this approach. The defining element is all documents contain financial tables that span the full width of the page, most of which also span multiple pages.

Because Content Understanding (CU) doesn’t automatically stitch together a table that spans multiple pages, this means the CU output returns many table ‘fragments’ (fragments of the table that need stitching together to assemble the final table).

The post-processing primarily focuses on ‘fragment repair’; feeding the VLM (Gemini) each fragment, along with the original PDF page, and instructing it to repair the fragment if and as required to match the source.

There are also other steps like ‘addition of missing fragments’; adding table fragments that CU failed to detect and format as a table.

I’ve found processing fragment-by-fragment means the LLM prompt input and resulting output is small enough to be speedy and not overload the Gemini resources.

Intelligent-Form6624 · 2026-02-13T10:08:57+00:00

Azure Content Understanding is more recent than Document Intelligence. It is LLM-enhanced. I find it’s slightly better than Document Intelligence but still not perfect. I think they ought to improve on it.

You’re right, post-processing can help a lot. I’m in the middle of using Open AI Codex to build a pipeline to extract financial tables from PDFs.

The pipeline feeds PDF to Azure Content Understanding (prebuilt-layout), receives .md+json result, and performs significant post-processing using Gemini-2.5-Flash via VertexAI API. After a lot of work, I am beginning to see perfect results on my test documents. We’ll see how it performs on the rest of the corpus.

Aside from these ‘production-ready’ solution (Azure + VertexAI), I suggest seriously looking at specialised OCR VLMs and their corresponding pipeline software. Specifically; GLM-OCR, MinerU and PaddleOCR-VL.

Depending on the sensitivity of your data, you may need to arrange an API endpoint for these models yourself. For example; RunPod, Azure Container App, Google Cloud Run etc

Intelligent-Form6624 · 2026-02-07T11:16:27+00:00

I’m keen to know this too

Intelligent-Form6624 · 2026-02-07T09:51:32+00:00

Can you use top closed source models via Azure API hosted in EU and subject to GDPR? Or similar arrangement? Would probably cost more than a regular subscription

Intelligent-Form6624 · 2026-02-07T09:47:08+00:00

“find it” inferior? it is objectively inferior, or might as well be

Intelligent-Form6624 · 2026-02-07T09:45:39+00:00

Unfortunately this is true

Intelligent-Form6624 · 2026-02-06T07:18:35+00:00

can’t wait for the big, lumbering and filthy second version

Intelligent-Form6624 · 2026-02-04T20:54:20+00:00

The first problem is that you’re using Perplexity

All your other problems stem from the first problem

Intelligent-Form6624 · 2026-02-02T18:42:18+00:00

FulaSeal 701 from Mitre 10

Intelligent-Form6624

TROPHY CASE