Selected work

Representative Papers

Diffusion Image Editing via Asynchronous Token Decoding

Aug 10, 2026

This work addresses the challenge of global semantic drift in text-guided diffusion-based image editing, which often compromises the identity and background of unedited regions. To mitigate this issue, the authors propose ATDEdit, a novel framework that treats each sampling step during inference as a globally coupled token matrix, enabling simultaneous local editing and background preservation. ATDEdit introduces an asynchronous token decoding mechanism that dynamically identifies editable positions based on conditional surprise, applies conditional corrections to target tokens, and reuses source key/value memories while back-projecting latent representations in preserved regions—eliminating the need for spatial masks or model fine-tuning. Experiments demonstrate that ATDEdit achieves state-of-the-art fidelity on PIE-Bench (PSNR 27.44 dB, LPIPS 0.055) while maintaining strong semantic alignment.

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Intent Signal Theory: A Computational Framework for Intent-State Control in Human-AI Interaction

May 24, 2026

This work addresses a critical limitation in current AI interaction paradigms, which treat prompts as the primary unit of exchange while overlooking users’ underlying source intentions. The paper introduces Intent Signal Theory (IST), the first formal framework to articulate the multi-layered structure of user intent, distinguishing between source intent, intent proxies, encoded carriers, and model outputs, and establishes the Irreversible Intent Loss Theorem. By reframing prompt engineering as intent protocol design, IST reveals a missing computational layer in contemporary systems. Empirical validation across four studies, six large language models, three languages, and three task domains confirms core theoretical predictions, including structure–fidelity decoupling, metric disentanglement, and weight tolerance.

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Dimension-Level Intent Fidelity Evaluation for Large Language Models: Evidence from Structured Prompt Ablation

May 14, 2026

Current holistic evaluation approaches struggle to distinguish between structural replication and fidelity to user intent in large language model outputs. This work proposes a dimension-level intent fidelity assessment framework that, through structured prompt ablation, human evaluation, and weight perturbation, reveals for the first time a systematic divergence between structural fidelity and intent fidelity. Experiments show that among high-scoring outputs in both Chinese and English, 25.7% and 58.6%, respectively, exhibit deficiencies in dimensional intent alignment. Moreover, dimension-level scores demonstrate significantly higher agreement with human judgments than holistic scores, offering a more precise reflection of output quality flaws.

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Structured Intent as a Protocol-Like Communication Layer: Cross-Model Robustness, Framework Comparison, and the Weak-Model Compensation Effect

Mar 31, 2026

This study addresses the challenge of reliably maintaining user goal consistency across multiple models, languages, and prompting frameworks. The authors propose a protocol-like communication layer grounded in structured intent representations—specifically the 5W3H schema—and systematically evaluate its cross-lingual and cross-domain alignment efficacy on Claude, GPT-4o, and Gemini 2.5 Pro. Leveraging both automated evaluation with DeepSeek-V3 and a user study involving 50 participants, the results demonstrate that structured prompting substantially reduces cross-lingual goal drift, lowering the standard deviation of alignment scores from 0.470 to 0.020. The findings further reveal a weak-model compensation effect and the critical role of dimensional decomposition. Notably, Gemini exhibits a +1.006 improvement in goal alignment, accompanied by a 60% reduction in interaction turns and an increase in user satisfaction from 3.16 to 4.04.

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Does Structured Intent Representation Generalize? A Cross-Language, Cross-Model Empirical Study of 5W3H Prompting

Mar 26, 2026

This study investigates whether a structured intent representation based on the 5W3H framework can effectively generalize across multiple languages (Chinese, English, and Japanese) and diverse large language models to enhance intent alignment and accessibility. Leveraging the PPS framework, the authors conduct 2,160 cross-lingual and cross-model controlled experiments under four prompting conditions. They demonstrate for the first time that AI-generated 5W3H prompts perform comparably to human-crafted ones, significantly reducing user input burden. The work also uncovers a “dual inflation bias” in unstructured prompts, whose deceptively low output variance misrepresents model behavior. Results show that structured prompting not only improves target alignment but also reshapes and more accurately reflects cross-model output variance, offering a novel paradigm for prompt engineering.

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Recent publications

Latest Papers

Diffusion Image Editing via Asynchronous Token Decoding

Aug 10, 2026

This work addresses the challenge of global semantic drift in text-guided diffusion-based image editing, which often compromises the identity and background of unedited regions. To mitigate this issue, the authors propose ATDEdit, a novel framework that treats each sampling step during inference as a globally coupled token matrix, enabling simultaneous local editing and background preservation. ATDEdit introduces an asynchronous token decoding mechanism that dynamically identifies editable positions based on conditional surprise, applies conditional corrections to target tokens, and reuses source key/value memories while back-projecting latent representations in preserved regions—eliminating the need for spatial masks or model fine-tuning. Experiments demonstrate that ATDEdit achieves state-of-the-art fidelity on PIE-Bench (PSNR 27.44 dB, LPIPS 0.055) while maintaining strong semantic alignment.

0 citationsRead paper

Intent Signal Theory: A Computational Framework for Intent-State Control in Human-AI Interaction

May 24, 2026

This work addresses a critical limitation in current AI interaction paradigms, which treat prompts as the primary unit of exchange while overlooking users’ underlying source intentions. The paper introduces Intent Signal Theory (IST), the first formal framework to articulate the multi-layered structure of user intent, distinguishing between source intent, intent proxies, encoded carriers, and model outputs, and establishes the Irreversible Intent Loss Theorem. By reframing prompt engineering as intent protocol design, IST reveals a missing computational layer in contemporary systems. Empirical validation across four studies, six large language models, three languages, and three task domains confirms core theoretical predictions, including structure–fidelity decoupling, metric disentanglement, and weight tolerance.

0 citationsRead paper

Dimension-Level Intent Fidelity Evaluation for Large Language Models: Evidence from Structured Prompt Ablation

May 14, 2026

Current holistic evaluation approaches struggle to distinguish between structural replication and fidelity to user intent in large language model outputs. This work proposes a dimension-level intent fidelity assessment framework that, through structured prompt ablation, human evaluation, and weight perturbation, reveals for the first time a systematic divergence between structural fidelity and intent fidelity. Experiments show that among high-scoring outputs in both Chinese and English, 25.7% and 58.6%, respectively, exhibit deficiencies in dimensional intent alignment. Moreover, dimension-level scores demonstrate significantly higher agreement with human judgments than holistic scores, offering a more precise reflection of output quality flaws.

0 citationsRead paper

Structured Intent as a Protocol-Like Communication Layer: Cross-Model Robustness, Framework Comparison, and the Weak-Model Compensation Effect

Mar 31, 2026

This study addresses the challenge of reliably maintaining user goal consistency across multiple models, languages, and prompting frameworks. The authors propose a protocol-like communication layer grounded in structured intent representations—specifically the 5W3H schema—and systematically evaluate its cross-lingual and cross-domain alignment efficacy on Claude, GPT-4o, and Gemini 2.5 Pro. Leveraging both automated evaluation with DeepSeek-V3 and a user study involving 50 participants, the results demonstrate that structured prompting substantially reduces cross-lingual goal drift, lowering the standard deviation of alignment scores from 0.470 to 0.020. The findings further reveal a weak-model compensation effect and the critical role of dimensional decomposition. Notably, Gemini exhibits a +1.006 improvement in goal alignment, accompanied by a 60% reduction in interaction turns and an increase in user satisfaction from 3.16 to 4.04.

0 citationsRead paper

Does Structured Intent Representation Generalize? A Cross-Language, Cross-Model Empirical Study of 5W3H Prompting

Mar 26, 2026

This study investigates whether a structured intent representation based on the 5W3H framework can effectively generalize across multiple languages (Chinese, English, and Japanese) and diverse large language models to enhance intent alignment and accessibility. Leveraging the PPS framework, the authors conduct 2,160 cross-lingual and cross-model controlled experiments under four prompting conditions. They demonstrate for the first time that AI-generated 5W3H prompts perform comparably to human-crafted ones, significantly reducing user input burden. The work also uncovers a “dual inflation bias” in unstructured prompts, whose deceptively low output variance misrepresents model behavior. Results show that structured prompting not only improves target alignment but also reshapes and more accurately reflects cross-model output variance, offering a novel paradigm for prompt engineering.

0 citationsRead paper