Reasoning-aware Text Extraction (RTE)

• Reasoning-aware text extraction is a method that constructs intermediate structured representations from raw inputs to enable advanced logical and relational reasoning.
• It utilizes modular architectures such as headless modules and multi-step neural pipelines to improve extraction accuracy and measurable performance metrics.
• The approach enhances explanation quality and cross-modal alignment, facilitating tasks like temporal reasoning, relational extraction, and multimodal integration.

Reasoning-aware Text Extraction (RTE) is a paradigm in information extraction and multimodal understanding that emphasizes the generation of intermediate representations which capture structured, compositional, and relational knowledge from data modalities such as text or images. Unlike traditional extraction pipelines which focus on superficial or shallow cues, RTE modules are designed to translate raw input—be it paragraphs, relational statements, or scenes—into a form suitable for downstream structured reasoning, logical inference, and explanation-aware decision making.

1. Foundational Principles of Reasoning-aware Text Extraction

Reasoning-aware text extraction entails the explicit construction of intermediate representations, typically as structured text, symbolic forms, or embeddings. These representations are not mere encodings but enumerate object attributes, relations, events, or logical dependencies necessary for high-level reasoning. In the CORTEX framework, for example, the RTE module converts raw images into variable-length sets of natural-language sentences, each detailing color, shape, size, and pairwise spatial relationships among objects. These structured descriptions make implicit scene relationships explicit and accessible for subsequent modules focused on relational reasoning (Park et al., 28 Nov 2025).

In logic-based textual entailment systems, RTE converts texts into first-order logical forms, which are inter-operable with ontological knowledge bases, enabling deductive inference (Wotzlaw et al., 2013).

In one-shot relation extraction, the reasoning mechanism decomposes input sentences into relational propositions and anchors them with key reasoning keywords, facilitating integrative multi-step reasoning optimized by reinforcement learning (Guo et al., 7 Oct 2025).

2. Architectural Instantiations and Workflows

Reasoning-aware extraction architectures differ across domains but share a modular structure that separates content extraction from reasoning:

• Headless RTE Modules: As in CORTEX, RTE is constructed as a "headless" component—entirely reliant on pre-trained vision-LLMs (VLMs) to transform images into structured sentences and frozen text encoders for embedding, with no trainable parameters or fine-tuned projections inside the module. The prompt engineering and the decision to enumerate each object and relationship are central design choices (Park et al., 28 Nov 2025).
• Multi-step Neural Pipelines: In complex text reasoning (e.g., over paragraphs), multi-step reasoning networks chain retrieval, compositional, and scoring modules. These steps include soft sentence retrieval, logical composition via attention modules, and candidate prediction, with joint training for all differentiable modules (Liu et al., 2020).
• Logic-based Systems: The extraction pipeline commences with shallow (token, POS, NE) and deep (HPSG, RMRS/MRS) syntactic/semantic parsing, with subsequent translation to first-order logic for model-theoretic or resolution-based reasoning (Wotzlaw et al., 2013).
• Temporal Reasoning Systems: Extraction identifies intervals/events, temporal expressions, and durations from text, which are then mapped to constraint networks (Allen’s Interval Algebra, point algebra). Symbolic reasoning modules enforce global consistency through ILP or MLN frameworks and transitive closures (Leeuwenberg et al., 2020).

3. Mathematical Formalizations and Representations

RTE is frequently formalized as a sequence of transformations:

• Image to Reasoning Text (CORTEX):

$$T_{s} = \mathrm{VLM}(I_{s}, \text{Prompt}), \quad t_s = [E_{\mathrm{text}}(T_{s}^1), \dots, E_{\mathrm{text}}(T_{s}^{K_s})]^T$$

$$f_{\mathrm{rte}} = [t_{bef}; t_{aft}] \in \mathbb{R}^{(N+M)\times c}$$

Each sentence encoder $E_{\mathrm{text}}$ (e.g., BERT) produces a fixed-dimensional embedding.

• Logic-based Textual Entailment:

Raw input $T$ and hypothesis $H$ are translated to first-order logic with equality (FOLE). The inference test is

$T \wedge K \models H$

with $K$ as a conjunction of background knowledge axioms (Wotzlaw et al., 2013).

• Stepwise Cognitive Reasoning (CogRE):

$$(\hat{z}, \hat{y}) \sim \mathcal{M}_\theta(\cdot | s_1, s_2)$$

$$\mathcal{R}_{\mathrm{Acc}}(\hat{y}, y) + \mathcal{R}_{\mathrm{Hit@Dict}}(\hat{z}; r_1, r_2)$$

with reinforcement learning over grouped policy ratios (Guo et al., 7 Oct 2025).

4. Integration with Structured Reasoning and Alignment

RTE output is not an end in itself; it serves structured reasoning and multimodal alignment:

• Multimodal Fusion and Alignment: In CORTEX, the RTE feature tensor is multiplicatively aligned with visual-only change detectors via cross-attention (static and dynamic). The static fusion computes

$$f_{bef}^{s(t \to i)} = \frac{1}{N}\sum_{n=1}^{N}\mathrm{Attn}(t_{bef}^n, f_{bef}, f_{bef})$$

with attention mechanisms adapted for high-dimensional alignment. Downstream losses, such as $\mathcal{L}_{sa}$ and $\mathcal{L}_{da}$, align text-augmented features to pure visual features (Park et al., 28 Nov 2025).

• Symbolic Reasoning: In temporal reasoning, extracted cues validate global consistency via ILP, MLN, or greedy closure algorithms, and the output supports timeline reconstruction or event ordering (Leeuwenberg et al., 2020).
• Neural Module Networks: Text retrieval, module chaining, and scoring compose extracted embeddings into reasoned predictions. Modules implement logical operations (AND, OR, FILTER, COMPARE) with full differentiability for end-to-end learning (Liu et al., 2020).

5. Empirical Impact and Evaluation

Ablation and benchmark studies demonstrate consistent gains for reasoning-aware extraction:

System	Setting	BLEU-4	METEOR	ROUGE-L	CIDEr	SPICE
Visual-only	Change Captioning (CORTEX ablation)	55.5	40.8	73.4	125.3	33.4
+ RTE (no ITDA)	Change Captioning	55.8	41.6	74.8	128.5	33.9

The inclusion of RTE yields measurable improvements in compositional accuracy, especially when combined with alignment modules (Park et al., 28 Nov 2025). In one-shot relation extraction, reasoning scaffolds plus RL explanation rewards boost $F_1$ absolute by +23.46% on NYT29, with human explanation quality ratings rising by 54% (Guo et al., 7 Oct 2025). Logic-based entailment systems reach 67% RTE challenge accuracy with full background knowledge integration (Wotzlaw et al., 2013). Multi-step text reasoning pipelines demonstrate up to 29% relative error reduction over baseline RoBERTa span extractors (Liu et al., 2020).

6. Open Challenges and Directions

Several fundamental questions persist:

• Scalability and Expressiveness: Rich logics and compositional models often incur intractable computation. Research seeks new tractable fragments and efficient reasoning approximations for temporal and logical constraints (Leeuwenberg et al., 2020).
• Neural-Symbolic Hybrids: End-to-end models that integrate symbolic constraints (e.g., Allen's relations) as differentiable loss terms or in multi-task architectures remain an open frontier.
• Cross-modal Generalization: Extension of RTE pipelines from document-level analysis to cross-document and multimodal inputs (images, structured data) require more sophisticated reasoning-aware extraction and alignment frameworks.
• Traceability and Explanation Quality: Maintaining stepwise traceability from extracted text to reasoning output (as in logic-based RTE) and optimizing for explanation faithfulness at scale (as in CogRE) remain vital for robust knowledge systems (Wotzlaw et al., 2013, Guo et al., 7 Oct 2025).

7. Significance of Reasoning-aware Text Extraction

Reasoning-aware text extraction reorients information systems toward richer, structured, and explainable knowledge representations. By explicitly bridging raw input and compositional reasoning processes—via modular, headless, or hybrid architectures—RTE frameworks enable fine-grained inference, transparent explanations, and improved performance in structured prediction tasks, multimodal captioning, entailment, relation extraction, and temporal analysis. The paradigm is distinguished by its commitment to intermediate structure, modular reasoning, and empirically validated improvement over naive extraction systems (Park et al., 28 Nov 2025, Wotzlaw et al., 2013, Leeuwenberg et al., 2020, Guo et al., 7 Oct 2025, Liu et al., 2020).