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Zixin Chen

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#artificial intelligence Preprint Sep 2026

When Users Change Their Minds: Measuring and Repairing Intent Drift in LLM Agents

Intent drift is established as a measurable multi-turn failure mode and explicit state maintenance as a partial mitigation and IntentFlux is introduced, an executable benchmark that converts verifiable tasks into dialogues with controlled intent changes while preserving their original graders.

Yan-Jie Zhang, Bo-Wen Cao, Zi-Xin Chen et al. · 1 citation
Jul 2026

Informal Learning Emerges in Everyday Human-LLM Interaction

Everyday human-LLM interaction is not only answer delivery or cognitive offloading; it also contains measurable, selective and conditionally organized behavioural signatures of informal learning, which shift AI evaluation toward the preservation of cognitive opportunities for users to reason, test ideas and construct u...

Zi-Xin Chen, Haotian Li, Ziang Xiao et al. · 1 citation
Preprint Aug 2026

TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring

This work presents TACT (Taxonomy-Aligned Conversational Tutor), a human-grounded framework for post-training and evaluating pedagogically adaptive ESL tutors and develops two complementary taxonomies: the Tutor-Strategy Taxonomy with 13 tutor response strategies and the Student-Move Taxonomy characterizing learner beh...

Dongjie Yang, Siya Lin, Leixian Shen et al. · 0 citations
Preprint Jul 2026

WildTrace: Benchmarking Natural Evidence Trails in Long-Context Reasoning

WILDTRACE is introduced, a benchmark of 481 tasks over 214 naturally occurring long-form sources such as technical incident reports and lesser-known literary narratives, where all evidence trails arise from the document's own causal, temporal, and narrative logic.

Zixin Chen, Peng Liu, Haobo Li et al. · 0 citations

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