The digital preservation of ancient Chinese literature requires robust capabilities spanning layout analysis and text recognition. This paper presents a comprehensive framework addressing two fundamental challenges: (1) Layout Element Analysis (Task B) for detecting page elements (text, image, book_edge, seal) amidst degradation, nested structures, and extreme class imbalance; and (2) Text Recognition (Tasks A & C) for end-to-end transcription of printed and handwritten classical documents. For layout analysis, we propose a dual-modality solution. The Closed Modality formulates this as a sequence-to-sequence problem using Vision-Language Models (VLMs), introducing spatial discretization tokenization and a Frequency-Aware Sequential Curriculum Learning framework with dynamic memory replay. The Open Modality presents HistLayout-DETR, a set prediction architecture integrating an Augmented Morphological Encoder and a Polygon Boundary Refinement head. For text recognition, we formulate OCR as a domain-constrained visual language generation task using Qwen2.5-VL with LoRA fine-tuning. We employ structured prompts encoding reading order and Traditional Chinese character preservation across domains. Extensive experiments on the EvaHan 2026 dataset validate our framework’s superiority. In layout analysis, our curriculum-guided paradigm achieves a Macro F1 of 0.7992 and mAP@[.5:.95] of 0.5438. In text recognition, we achieve CERs of 0.0271 on printed and 0.0433 on handwritten texts.

More than a dozen spoken dialogue systems have recently claimed to be "full-duplex," yet the term has been used to describe substantially different capabilities. Existing surveys collapse them onto a single axis (cascaded/end-to-end, or engineered/learned) and miss the distinctions that matter most for builders. We argue that much of this ambiguity is taxonomical: current terminology does not specify where duplex decisions are made, which interaction types are supported, or how a system behaves moment by moment. This paper introduces three complementary frameworks: (i) an L,0-L,3 Architectural Hierarchy that locates where duplex decisions are made; (ii) a T x Ix R Interaction Ontology that specifies the temporal relation, user intent, and required system response for each interaction; and (i) a Decision State Machine(LDLE/LISTEN/SPEAK/WAIT/DUAL) that describes how systems move between states.Across published systems and benchmarks, our audit reveals a realization gap: although many architectures can in principle operate in full-duplex states, their observed behavior remains constrained by the interaction patterns represented in training and evaluation.We identify the open-source-versus-industrial data gap, as well as the still-unrealized goal of L3 representation-level modeling, as key frontiers for future research on full-duplex dialogue. The related material is available at https://github.com/MM-Speech/DuplexSurvey.

While large language models (LLMs) have shown excellent capabilities in language understanding, text generation and many other tasks, they still struggle in complex multi-step reasoning problems such as mathematical reasoning. In this paper, through a newly proposed arithmetical puzzle problem, we show that the model can perform well on multi-step reasoning tasks via fine tuning on high-quality synthetic data. Experiments with the open-llama-3B model on three different test datasets show that not only the model can reach a zero-shot pass@1 at 0.44 on the in-domain dataset, it also demonstrates certain generalization capabilities on the out-of-domain datasets. Specifically, this paper has designed two out-of-domain datasets in the form of extending the numerical range and the composing components of the arithmetical puzzle problem separately. The fine-tuned model have shown encouraging performance on these two far more difficult tasks with the zero-shot pass@1 at 0.33 and 0.35 correspondingly.