arXiv AI July 20, 2026 By Haonan Yu, Junhao Liu, Xin Zhang

MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformation

arXiv:2502. 11068v3 Announce Type: replace-cross Abstract: Anchors is a popular local model-agnostic explanation technique whose applicability is limited by its computational inefficiency.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

arXiv AI

Jun 30

TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding

arXiv:2508. 07683v2 Announce Type: replace-cross Abstract: Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries.

By Chaohong Guo, Xun Mo, Yongwei Nie, Fei Ma, Xuemiao Xu, Chengjiang Long

llms agents reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

1d ago

Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning

arXiv:2608. 16345v1 Announce Type: new Abstract: Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation to new tasks.

By Zhiming Xu, Huiyu Yi, Zhen-Hao Xie, Baile Xu, Furao Shen, Jian Zhao, Suorong Yang

arXiv Machine Learning

Jun 9

ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning

arXiv:2601. 03093v2 Announce Type: replace Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (LLMs) toward improved reasoning and efficiency without updating model parameters.

By Tuc Nguyen, Thai Le

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 17

Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation

arXiv:2607. 14895v1 Announce Type: new Abstract: Reasoning language models (RLMs) have demonstrated impressive performance in domains such as mathematics and coding.

By Yu-Du Feng, Niels M\"undler-Sasahara, Mark Vero, Martin Vechev

llms nlp reinforcement-learning fine-tuning

arXiv AI

Jul 28

MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models

arXiv:2607. 22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments.

By Dong Li, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Xintao Wu, Zhong Chen, Chen Zhao, Haifeng Chen

llms

arXiv AI

Jul 13

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.

By Carmen Quiles-Ram\'irez, Leticia L. Rodr\'iguez, Nicol\'as Martorell, Natalia D\'iaz-Rodr\'iguez

llms multimodal benchmarks

Related stories

TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding

Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning

ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning

Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation

MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers