Iโve fixed the Space and brought it back to life: - โ Working again after being broken for a while - โ Updated to Gradio 6 - โ Compatible with ZeroGPU - โ Output videos now preserve original resolution and FPS
I also added advanced controls so you can experiment more (tracking, seed, motion, sketch).
Today weโre publicly releasing Kanon 2 Enricher, and with it, an entirely new class of AI model that weโre calling a hierarchical graphitization model. This is fundamentally different from both universal extraction models and generative models.
As a hierarchical graphitization model, Kanon 2 Enricher natively outputs a ๐ธ๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ ๐ด๐ฟ๐ฎ๐ฝ๐ต rather than tokens, which makes it architecturally incapable of hallucinating or inventing text that wasnโt present in the input.
What that enables in practice is unlike any other model or ML architecture on the market:
โข ๐ก๐ผ ๐ต๐ฎ๐น๐น๐๐ฐ๐ถ๐ป๐ฎ๐๐ถ๐ผ๐ป๐ ๐ค It cannot hallucinate. All references and links are stored as spans, meaning exact character offsets anchored to the original text.
โข ๐๐ถ๐ฒ๐ฟ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐ฐ๐ฎ๐น ๐๐ฒ๐ด๐บ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป, ๐ป๐ผ๐ ๐ท๐๐๐ ๐ฒ๐ ๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป ๐ It deconstructs a documentโs full nested hierarchy, down to chapters, sections, clauses, schedules, signatures, and even singular sentences, and classifies each span with dozens of contextual features.
โข ๐๐ป๐๐ถ๐๐ ๐ฒ๐ ๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป, ๐ฑ๐ถ๐๐ฎ๐บ๐ฏ๐ถ๐ด๐๐ฎ๐๐ถ๐ผ๐ป, ๐ฎ๐ป๐ฑ ๐น๐ถ๐ป๐ธ๐ถ๐ป๐ด ๐ It resolves what references actually point to, then links entities, citations, and cross-references into a single coherent graph.
โข ๐๐ฟ๐ฎ๐ฝ๐ต-๐ณ๐ถ๐ฟ๐๐ ๐ฒ๐ณ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ ๐โโก๏ธ Small enough to run locally on a consumer PC with sub-second latency, and it stays reliable on long documents where front
Have you tried the xLLM web API? It allows you to fine-tune and debug an agentic multi-LLM in real time. The input data is part of the anonymized corporate corpus of a Fortune 100 company, dealing with AI policies, documentation, integration, best practices, references, onboarding, and so on. It features one sub-LLM. The full corpus is broken down into 15 sub-LLMs.
One of the goals is to return concise but exhaustive results, using acronyms (a specific table for each sub-LLM) to map multi-tokens found in prompts but not in the corpus, with multi-tokens in the corpus. Exhaustivity is the most overlooked metric when evaluating LLMs designed for search / retrieval. Using xLLM in combination with another LLMs is one of the best approaches, and both can be used to evaluate each other. Yet, thanks to fast in-memory processing, no weight, and no training, the xLLM web API is one of its kind, with capabilities not found in any competing product, free or not.
