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cs.CL2026
DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models
Yuxuan Yang, Feiyang Li, Yile Wang
The Abstraction and Reasoning Corpus (ARC) contains tasks that require summarizing patterns from limited grid samples and predicting output grids. Recently, many large language mod…
cs.CL2026
Evaluating Memory Capability in Continuous Lifelog Scenario
Jianjie Zheng, Zhichen Liu, Zhanyu Shen +6
Nowadays, wearable devices can continuously lifelog ambient conversations, creating substantial opportunities for memory systems. However, existing benchmarks primarily focus on on…
cs.CL2026
DeCoVec: Building Decoding Space based Task Vector for Large Language Models via In-Context Learning
Feiyang Li, Yile Wang
Task vectors, representing directions in model or activation spaces that encode task-specific behaviors, have emerged as a promising tool for steering large language models (LLMs).…