Early Long-context Document Visual Question Answering (DocVQA) methods struggle with preserving visual semantics or handling finite context windows.Conversely, recent RAG-based approaches suffer from "semantic gaps" and "structural disconnections" due to passive retrieval mechanisms that ignore logical dependencies.To address these challenges, we introduce TRACE (Traversal Retrieval-Augmented Chain of Evidence).By navigating a Bi-Layered Graph that encodes both physical adjacency and semantic relevance, TRACE transforms retrieval from static matching into adaptive evidence chain construction.Furthermore, we propose M5BookVQA, a benchmark designed to assess deep, multihop reasoning in books, addressing the limitations of existing datasets.Extensive experiments show that TRACE achieves an average accuracy improvement of 14.07% on M5BookVQA and exhibits robust generalization with a 13.38% gain across four established benchmarks.

Word segmentation stands as a cornerstone of Natural Language Processing (NLP).Based on the concept of "comprehend first, segment later", we propose a new framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs) and evaluate the semantic understanding capabilities of LLMs based on word segmentation.We employ current mainstream LLMs to perform word segmentation across multiple languages to assess LLMs' "comprehension".Our findings reveal that LLMs are capable of following simple prompts to segment raw text into words.There is a trend suggesting that models with more parameters tend to perform better on multiple languages.Additionally, we introduce a novel unsupervised method, termed LLACA (Large Language Model-Inspired Aho-Corasick Automaton).Leveraging the advanced pattern recognition capabilities of Aho-Corasick automata, LLACA innovatively combines these with the deep insights of well-pretrained LLMs.This approach not only enables the construction of a dynamic n-gram model that adjusts based on contextual information but also integrates the nuanced understanding of LLMs, offering significant improvements over traditional methods.

Chain-of-thought (CoT) reasoning has exhibited impressive performance in language models for solving complex tasks and answering questions. However, many real-world questions require multi-modal information, such as text and images. Previous research on multi-modal CoT has primarily focused on extracting fixed image features from off-the-shelf vision models and then fusing them with text using attention mechanisms. This approach has limitations because these vision models were not designed for complex reasoning tasks and do not align well with language thoughts. To overcome this limitation, we introduce a novel approach for multi-modal CoT reasoning that utilizes latent space learning via diffusion processes to generate effective image features that align with language thoughts. Our method fuses image features and text representations at a deep level and improves the complex reasoning ability of multi-modal CoT. We demonstrate the efficacy of our proposed method on multi-modal ScienceQA and machine translation benchmarks, achieving state-of-the-art performance on ScienceQA. Overall, our approach offers a more robust and effective solution for multi-modal reasoning in language models, enhancing their ability to tackle complex real-world problems.