A good experiment would be to also try giving it access to latency traces so it can identify issues? Wrt coding agents, giving access to observability tools often improve coding/debugging ability for me
Try https://docstrange.nanonets.com/ once, 10k docs you can use for free. Strong table performance. Do give feedback if any. Powered by bigger model compared to our open source one which is quiet popular on HF.
If with LLM's you can deanonymize at scale, on a personal level, you should also be able to figure out what posts are leading to this deanonymization and remove them or modify them.
Top 3 models on huggingface are all OCR models. Most automation projects involve documents where you need a model finetuned to understand all elements inside documents and provide grounding and confidence scores etc which is why these subset of models are gaining popularity
Would be intersting to see where funding goes to fix these issues. News would heavily impact public opinion and hence political influence and public funding.
Yes, and its not just OCR (Optical Character Recognition), it understands layouts, captures signatures, charts, watermarks etc so way beyond just characters
It boils down to information loss in compaction driven by LLM's. Either you could carefully design tools that only give compacted output with high information density so models have to auto-compact or organize information only once in a while which eventually is going to be lossy.
Or you just give loads of information without thinking much about it, assuming models will have to do frequent compaction and memory organization and hope its not super lossy.
Reason I felt like they are closely connected are because for designing tools for lets say coding agents, you have to be thoughful of context engineering.
Eg linear MCP is notorious for giving large JSONs which quickly fill up context and hard for model to understand. So tools need to be designed slightly differently for agents keeping context engineering in mind compared to how you design them for humans.
Context engineering feels like more central and first-principle approach of designing tools, agent loops.
You can always put automation for your google home to blast music at full volume at right time. And if you don't wake up from sound of music yourself, your neighbour will knock on your door for sure!
Traditional OCR's usually have detection + recognition pipeline. So they will detect every word and then try to predict the text for every word. Errors obviously can happen in both parts, eg some words not detected which will get missed from output. Or word recognized incorrectly which is also common and more comparable to hallucination. However give its trained to work only on a small patch, accuracy is often higher. Comparing this to VLM's, they are looking at entire image/context and auto-regressively generating tokens/text which can also have lot of language bias, hence hallicinations.