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cs.AI2026

MemTrace: Probing What Final Accuracy Misses in Long-Term Memory

Xianxuan Long, Zhikai Chen, Shenglai Zeng +3

LLM agents increasingly maintain long-term memory of user facts across sessions. Yet such memory is usually evaluated by aggregating accuracy over question rows or episodes. Becaus…

cs.CV2026

Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehension

Shenglai Zeng, Qirui Wang, Kai Guo +3

Visual Text Comprehension (VTC) renders text into images for a vision-language model (VLM) to read, sidestepping LLM context-window limits and powering applications from long-page…

cs.AI2026

Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline

Zhikai Chen, Jialiang Gu, Junyu Yin +6

LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tuned to a single scenario (mul…

cs.CL2026

Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separation

Shouren Wang, Wang Yang, Chuang Ma +7

Hybrid-thinking language models expose explicit /think and /no_think modes, but current designs do not separate them cleanly. Even in /no_think mode, models often emit long and sel…

cs.AI2026

How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment

Hang Li, Kaiqi Yang, Xianxuan Long +9

The rapid rise of large language models (LLMs) is reshaping the landscape of automatic assessment in education. While these systems demonstrate substantial advantages in adaptabili…