TEFM: Token-Efficient Faithful Modeling for Structured Data TEFM：结构化数据的Token高效忠实建模

TL;DR

• TEFM is a novel framework that jointly addresses token efficiency and faithfulness when applying LLMs to critical domain structured data
• It compresses lengthy structured observations into compact "Behavioral Code" tokens, achieving approximately 1% token retention in clinical domains and 2% in security domains with minimal information loss
• A dual-fidelity objective is introduced that jointly optimizes code-level reconstruction and prediction-level fidelity to identify minimal sufficient feature subsets grounded in input data
• Experiments across Qwen3, Gemma-2, and Phi-4 backbones demonstrate competitive classification accuracy alongside dramatic token reduction and faithful rationalization
• The work tackles two fundamental obstacles—token efficiency and faithfulness—that have historically limited LLM deployment in high-stakes domains like healthcare and security

Why It Matters

This research directly addresses two of the most pressing barriers to deploying LLMs in critical domains: the prohibitive token costs of processing lengthy structured records and the lack of faithful, interpretable reasoning. For AI practitioners working in healthcare, finance, or security, TEFM offers a practical pathway to reduce inference costs by up to 99% while maintaining model accuracy and producing grounded, explainable rationales—making LLM-based decision support more viable in resource-constrained and compliance-sensitive environments.

Technical Details

• Behavioral Code Token Compression: TEFM introduces a novel compression mechanism that transforms lengthy structured observations (e.g., clinical records, security logs) into compact Behavioral Code tokens, preserving essential semantic and structural information while drastically reducing token count
• Dual-Fidelity Objective: The framework employs a joint optimization objective combining code-level reconstruction fidelity (ensuring compressed tokens retain sufficient information for accurate reconstruction) and prediction-level fidelity (ensuring downstream task performance remains high), enabling the identification of minimal sufficient feature subsets
• Model Backbones Evaluated: Comprehensive experiments were conducted across three major LLM families—Qwen3, Gemma-2, and Phi-4—demonstrating the framework's backbone-agnostic applicability
• Domain Datasets: The approach was validated on structured data from clinical and security domains, achieving approximately 1% and 2% token retention respectively while maintaining competitive classification accuracy
• Faithful Rationalization: Unlike approaches that sacrifice interpretability for efficiency, TEFM produces rationales that are both faithful to the input data and grounded in minimal sufficient feature subsets

Industry Insight

• Organizations deploying LLMs in regulated or resource-sensitive domains should evaluate token compression frameworks like TEFM as a means to reduce inference costs by orders of magnitude without proportionally sacrificing accuracy or interpretability
• The dual-fidelity optimization strategy represents a generalizable paradigm that could be adapted beyond structured data to other domains where both cost efficiency and explainability are non-negotiable, such as legal document analysis and financial compliance
• As LLM-based systems face increasing scrutiny for transparency in critical applications, TEFM's approach to producing faithful rationales alongside extreme token compression positions it as a potential standard for auditable AI in high-stakes environments

TL;DR

• TEFM框架解决LLM在关键领域应用的token效率和忠实性两大核心障碍
• 通过压缩结构化数据为紧凑的Behavioral Code tokens，实现约99%的token节省
• 双保真度目标联合优化代码级重建和预测级保真度，识别最小充分特征子集
• 在临床领域仅保留1% token、安全领域保留2% token的情况下仍保持竞争力分类准确率
• 实验覆盖Qwen3、Gemma-2、Phi-4等多个模型架构，验证框架通用性

为什么值得看

TEFM为LLM在医疗、安全等关键领域的应用提供了可行路径，通过大幅降低token消耗同时保持预测准确性和可解释性，解决了制约LLM落地的重要瓶颈。

技术解析

TEFM通过压缩机制将冗长的结构化观测数据转换为紧凑的Behavioral Code tokens，在最小化信息损失的前提下实现token效率的大幅提升。双保真度目标函数联合优化代码级重建和预测级保真度，确保模型既能准确重建输入数据，又能生成忠实于输入数据的推理依据。实验在多个领域数据集上验证，使用Qwen3、Gemma-2、Phi-4等不同模型架构，结果显示在临床领域token保留率约1%、安全领域约2%的情况下仍保持竞争力分类准确率。

行业启示

TEFM为LLM在医疗、金融、安全等关键领域的应用扫清了token成本和可解释性两大障碍，为后续研究和实际应用提供了可复用的框架范式。