The way I understand this works is that the researchers found a clever architectural hack to stop AI from hoarding memory when reading long documents.
Normally, when an AI transcribes a 100 page PDF, it tries to remember every single word it has already ingested. This short-term memory (the KV cache) grows linearly O(N) until the model runs out of VRAM and crashes (or caps it) To avoid this, developers are forced to build janky code that chops PDFs into individual pages, processes them one by one, and glues the text back together.
Unlimited OCR uses Reference Sliding Window Attention (R-SWA) to split the AI's focus into two paths:
Global Reference: The AI keeps full, uncompromised sight of the original document image so it never loses context.
Local Generation: The AI restricts its memory of its own typed text to a tight, moving window (like the last 128 words) and safely forgets the rest.
Will be very interesting for local AI and can’t wait to see what the community builds and extends with it!
People who are applying to jobs and are tested with LeetCode problems to assess their skill level, despite the two not really being correlated or relevant for the position
I recently bought a tablet for sheet music, mostly to replace a stack of jazz "Real Books" at jam sessions. And the phone camera scans I made are okay, but fixed in size and have a lot of artifacts. And it would be great to transpose on the fly for e.g. Bb or Eb instruments, but being a scan this is obviously not possible.
I got digging into the state of optical music recognition and came away concluding that music is basically a greenfield for AI wherever you look. Optical music recognition is pretty terrible. AI understanding of music theory is terrible (actually looking at music that is; LLMs do okay at text descriptions of theory concepts where you can imagine some online texts making it in).
I think the issue is that we still don't have great digital formats that encode the dots on paper that musicians read. Music notation is pretty rich. Midi doesn't capture all of what's needed for symbolic understanding, because it was mostly made for capturing aspects relevant for playback or performance. MusicXML seems to be the closest for a digital format that encodes the information a musician would want, but there aren't great corpora of training data that would connect a MusicXML representation to sheet music images or to audio. I think that's because MusicXML falls short of encoding enough information to engrave music. Tools like MuseScore need to track a bunch of layout information that isn't encodable in MusicXML. Lilypond format is less verbose that MusicXML and contains a bit more information that is useful to the score creators, but most people don't create sheet music in lilypond. (As an aside, Lilypond bums me out with the state of jazz fonts. I hate looking at "legit" scores in jazz context)
I realize this is mildly off topic, but every time I see people making incremental gains on OCR, which to my mind is pretty good, I am reminded of how abysmal OMR is.
I'd imagine that rendered audio that just used midi voices (even high quality "Real Instruments" midi voices) would be pretty brittle for e.g. stem separation or automatic transcription. In a best case, I think you'd start with a clean digital representation, render sheet music imagery, and then have lots of recordings by a bunch of real instrumentalists playing the same music.
On the topic of stem separation, I've wondered about creating a quasi-synthetic dataset by taking chunks of recordings by real musicians playing them back in a real space in various combinations and recording the resulting analog-blended cacophony. Could repeat in various environments like cathedrals, basement bars, etc for realism :-)
I forgot to mention ABC. I have seen a few LLMs look at that. There was a model / paper published a couple years back called ChatMusician that built around it.
With the caveat that I'm not terribly fluent in ABC, it seems to me that simple things are simple, but hard things seem to be nearly pathological. And (again, maybe a lapse in my understanding) it seems like there may be a fair number of concepts that are impossible to convey in ABC?
Lastly, if I understand correctly, ABC got its start and is mostly popular as a simplified format for church songbooks. I'd imagine that would, uh, influence the training corpora towards sounding a bit... church songbooky.
EDIT: I may have been overly dismissive of ABC on first glance. It does seem like people have extended it quite a bit, and that it's at least, in theory, capable of encoding most of what I'd expect. And it's human readable, which is a benefit. Though, readability does take a stiff penalty the more richness you add (e.g. dynamics, articulations, stacked notes, etc)
I observe that space regularly and the only really good solution is soundslice. You scan and review some edge cases and get really good results. Paid service by a small company, very worthy to be supported!
It's the opposite of shade, unless GP is being sarcastic. "Class act" is normally a compliment, and in the context here it sounds to me like they're congratulating Baidu/the researchers in being transparent about where their ideas came from.
If I would want to achieve 100% recognition results I would combine this method with an image model recreating the original document from the subscribed text and matching the layout. One can do that with using all but the page or abstract from the document you want to recreate (to avoid recreating the exact passage under test from the image artifact directly). After reconstructing you can do an optical comparison that specifically matches misaligned characters and find the errors. Rinse and repeat. Expensive but it would guarantee 100% recognition.
Pretty decent might be quiet the stretch. I'd term it almost acceptable, but only if you're using commercial solutions like amazon's textract, doing it with open source tools is at best, extremely painful and vaguely accurate.
Exactly my experience. If you try to OCR hand-filled forms with a fixed structure, traditional OCR models are great. Vision-llms can improve a bit on character recognition, but at the cost of harder to detect failure modes.
But if you are trying to ingest diverse documents with headings, multi-column layouts, headers and footers, ad space in the middle of your text, etc, vision-llms are a giant step forward. But you need the context of the previous page to make good decisions about the current page, which is where things quickly get janky (or slow, if you choose the naive approach)
Vision-llms also seem to deal much better with variance in scripts. Cursive, random Japanese in the middle of the text, weird math symbols, handwriting from three centuries ago, all "just works" without you even having to remember that this can happen
Exactly. They're both very expensive and prone to surprising you. Sometimes in a good way, sometimes in a bad way. I'd rate them 85%, but you have to run a test because they both fail in different ways on the 15%.
I haven't done much long-run OCR, so unsure of the current state, but it would seem they overcome this (from their paper):
"A widely held view is that employing a large language model (LLM) as the decoder allows the model to leverage the prior distribution of language, leading to improved OCR performance. However, the downside is equally evident: as the output sequence lengthens, the accumulated KV cache drives up memory consumption and progressively slows down generation."
I don't think that's a universal statement that aplies to every kind of documents and languages. Mistral OCR is able to do things no "traditional" OCR was ever able to.
> I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
Well... the idea seems to be (as far as I understand it, at least) that optical errors and artifacts can now be compensated as the OCR engine is now context-aware.
Say, for example, some random long ass name chemical. It's not going to be in a word correction database, but a context-aware engine (ideally, one that has been supplemented with chemistry data) can now correct "bad" reads of the chemical's name.
Of course, there remains the issue of how to prevent the infamous Xerox bug [1]...
The way I understand this works is that the researchers found a clever architectural hack to stop AI from hoarding memory when reading long documents.
Normally, when an AI transcribes a 100 page PDF, it tries to remember every single word it has already ingested. This short-term memory (the KV cache) grows linearly O(N) until the model runs out of VRAM and crashes (or caps it) To avoid this, developers are forced to build janky code that chops PDFs into individual pages, processes them one by one, and glues the text back together.
Unlimited OCR uses Reference Sliding Window Attention (R-SWA) to split the AI's focus into two paths:
Global Reference: The AI keeps full, uncompromised sight of the original document image so it never loses context.
Local Generation: The AI restricts its memory of its own typed text to a tight, moving window (like the last 128 words) and safely forgets the rest.
Will be very interesting for local AI and can’t wait to see what the community builds and extends with it!
I got digging into the state of optical music recognition and came away concluding that music is basically a greenfield for AI wherever you look. Optical music recognition is pretty terrible. AI understanding of music theory is terrible (actually looking at music that is; LLMs do okay at text descriptions of theory concepts where you can imagine some online texts making it in).
I think the issue is that we still don't have great digital formats that encode the dots on paper that musicians read. Music notation is pretty rich. Midi doesn't capture all of what's needed for symbolic understanding, because it was mostly made for capturing aspects relevant for playback or performance. MusicXML seems to be the closest for a digital format that encodes the information a musician would want, but there aren't great corpora of training data that would connect a MusicXML representation to sheet music images or to audio. I think that's because MusicXML falls short of encoding enough information to engrave music. Tools like MuseScore need to track a bunch of layout information that isn't encodable in MusicXML. Lilypond format is less verbose that MusicXML and contains a bit more information that is useful to the score creators, but most people don't create sheet music in lilypond. (As an aside, Lilypond bums me out with the state of jazz fonts. I hate looking at "legit" scores in jazz context)
I realize this is mildly off topic, but every time I see people making incremental gains on OCR, which to my mind is pretty good, I am reminded of how abysmal OMR is.
It may not be necessary…a lot of the training pairs/data for this could probably be procedurally created via code.
Would be pretty fun to work on and see it come to life.
On the topic of stem separation, I've wondered about creating a quasi-synthetic dataset by taking chunks of recordings by real musicians playing them back in a real space in various combinations and recording the resulting analog-blended cacophony. Could repeat in various environments like cathedrals, basement bars, etc for realism :-)
With the caveat that I'm not terribly fluent in ABC, it seems to me that simple things are simple, but hard things seem to be nearly pathological. And (again, maybe a lapse in my understanding) it seems like there may be a fair number of concepts that are impossible to convey in ABC?
Lastly, if I understand correctly, ABC got its start and is mostly popular as a simplified format for church songbooks. I'd imagine that would, uh, influence the training corpora towards sounding a bit... church songbooky.
EDIT: I may have been overly dismissive of ABC on first glance. It does seem like people have extended it quite a bit, and that it's at least, in theory, capable of encoding most of what I'd expect. And it's human readable, which is a benefit. Though, readability does take a stiff penalty the more richness you add (e.g. dynamics, articulations, stacked notes, etc)
Class Act.
A simple example is words that are supposed to be in other languages being automatically translated to English, which ruins the effect
I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
OCR still sucks in 2026. Hopefully this might improve the situation but I haven't tested it yet.
But if you are trying to ingest diverse documents with headings, multi-column layouts, headers and footers, ad space in the middle of your text, etc, vision-llms are a giant step forward. But you need the context of the previous page to make good decisions about the current page, which is where things quickly get janky (or slow, if you choose the naive approach)
Vision-llms also seem to deal much better with variance in scripts. Cursive, random Japanese in the middle of the text, weird math symbols, handwriting from three centuries ago, all "just works" without you even having to remember that this can happen
- marker (with --force-ocr) gives me the best results
- Mistral OCR (seems really great, but I never managed to get it work)
- Mathpix (tried a long time ago)
- docling (gives me garbage, I must use it wrong)
- Unlimited OCR (will try it)
- ???
- AWS Textract
We use it on 200 page IEEE standards that are notoriously complex, filled with tables and diagram. Highly reccomend.
"A widely held view is that employing a large language model (LLM) as the decoder allows the model to leverage the prior distribution of language, leading to improved OCR performance. However, the downside is equally evident: as the output sequence lengthens, the accumulated KV cache drives up memory consumption and progressively slows down generation."
CJK have lots of character and high confusion rate.
Arabic scripts are complex and have lots of morphs.
Vietnamese have easily confused diacritics.
Thai have lots of non-standard fonts.
Well... the idea seems to be (as far as I understand it, at least) that optical errors and artifacts can now be compensated as the OCR engine is now context-aware.
Say, for example, some random long ass name chemical. It's not going to be in a word correction database, but a context-aware engine (ideally, one that has been supplemented with chemistry data) can now correct "bad" reads of the chemical's name.
Of course, there remains the issue of how to prevent the infamous Xerox bug [1]...
[1] https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...
In your opinion, what is SOTA here?