arXiv Machine Learning

AIBL: Augmented Instance-Based Learning with Structured Memory and Neural Embeddings

AIBL (Augmented Instance-Based Learning) extends traditional Instance-Based Learning Theory by embedding high‑dimensional sequential data into a learned vector space, enabling neural similarity matching while preserving instance storage, activation‑weighted retrieval, and utility blending. The model introduces structured memory stores—active, forgotten, and surprise—to manage novelty detection and concept drift, promoting frequently surprising instances into active memory. Experiments on five machine‑learning tasks and three simulations show that AIBL outperforms classical IBLT variants and task‑specific baselines, improving accuracy by 6 to 17 percentage points.

arXiv Computation and Language

Oct 12

Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence

The paper introduces a memory-augmented retrieval method that leverages token-level evidence instead of relying solely on single-vector embeddings. By performing sparse token matching and aggregating scores with whole-memory similarity, the approach improves retrieval performance across eight benchmarks and various model families without extra training. An optional low-rank query adapter can further boost results when supervised.

By Han Luo, Zhan Zhuang, Xu He, Jujia Zhao, Yu Li, Suqin Yuan, Dong Li, Lei Feng

rag benchmarks

arXiv Computation and Language

Oct 9

Beyond Sequences: Distilling Structured Decision Memory for LLM Recommendation

The paper introduces MARI, a Memory-Augmented Recommendation system that uses a Decision Memory Bank to store Structured Decision Memories (SDMs) capturing users’ goals, constraints, and trade-offs. Unlike prior methods that flatten heterogeneous behaviors into token sequences, MARI retrieves relevant SDMs to enhance LLM reasoning, improving performance on next-item prediction and a new Difficult Choice Prediction task while keeping latency low. The approach also provides interpretable, human-readable insights into user decision-making.

By Leikun Liang, Guoshuai Wang, Xingsheng He, Yushan Han, Yunyi Xuan, Xiaoxiao Xu, Lin Qu

llms efficiency benchmarks safety

arXiv AI

Aug 18

Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation

arXiv:2608. 11980v2 Announce Type: replace-cross Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence.

By Kangning Zhang, Haotian Fang, Xukun Luo, Hao Yin, Yang Gao, Peng Yan, Weiwen Liu, Weinan Zhang, Yong Yu

reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Sep 15

Reason What Matters: Retrieval-Grounded Reasoning for Universal Multimodal Embeddings

arXiv:2609.15296v1 Announce Type: new Abstract: Universal multimodal embedding (UME) learns unified representations across modalities, enabling a single model to support diverse retrieval tasks. Rece...

By Mingzhou Jiang, Peixi Wu, Hang Cheng, Yunhao Zhou, Biao Yang, Wei Yuan, Yun Li, Fan Yang, Wenwu Ou, Honghui He

rag efficiency multimodal benchmarks

arXiv AI

Aug 13

HCGRec: Hint-Conditioned Generative Recommendation with Semantic IDs

arXiv:2608. 11980v1 Announce Type: cross Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence.

By Kangning Zhang, Haotian Fang, Xukun Luo, Hao Yin, Yang Gao, Peng Yan, Weiwen Liu, Weinan Zhang, Yong Yu

fine-tuning benchmarks safety

arXiv AI

Aug 18

QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents

arXiv:2608. 16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence.

By Heng Wang, Yifei Li, Lingling Zhang, Pengyu Li, Xinyu Che, Xinyu Zhang, Zesheng Yang

llms agents benchmarks

arXiv AI

Jun 4

KITE: Kernelized and Information Theoretic Exemplars for In-Context Learning

arXiv:2509. 15676v2 Announce Type: replace-cross Abstract: In-context learning (ICL) has emerged as a powerful paradigm for adapting large language models (LLMs) to new and data-scarce tasks using only a few carefully selected task-specific examples presented in the prompt.

By Vaibhav Singh, Soumya Suvra Ghosal, Kapu Nirmal Joshua, Soumyabrata Pal, Sayak Ray Chowdhury

llms rag

arXiv AI

Jul 24

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

arXiv:2512. 10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data.

By Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

llms rag

arXiv AI

Jun 6

SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents

arXiv:2606. 05761v1 Announce Type: new Abstract: Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions.

By Wenxuan Wang, Haoyu Sun, Fukuan Hou, Mingyang Song, Weinan Zhang, Yu Cheng, Yang Yang

agents benchmarks

arXiv AI

Jul 16

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.

By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu

llms agents reinforcement-learning benchmarks

arXiv Computer Vision

Sep 4

Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding

The paper introduces LatentStream, a progressive latent working memory framework for streaming video understanding that replaces the traditional store‑and‑retrieve paradigm with a retrieve‑and‑internalize approach. It organizes visual history into short, mid, and long‑term levels using Jenks‑guided adaptive consolidation, then expands memory receptive fields to iteratively retrieve and internalize evidence into a compact latent memory. A confidence‑guided optimization further refines this memory, leading to state‑of‑the‑art performance on online and offline video benchmarks.

By Hongyu Qu, Guangming Yao, Ling Xing, Xiaobin Hu, Rongxing Ding, Guibin Zhang, Fan Zhang, Yi Yuan, Xiangbo Shu, Shuicheng Yan

llms multimodal benchmarks

arXiv Machine Learning

Jul 31

Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning

arXiv:2607. 27766v1 Announce Type: cross Abstract: On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference.

By Xinyu Luo, Hui Liu, Yihua Shao, Junyi Yang, Arindam Basu, Haoliang Li

llms fine-tuning multimodal benchmarks safety

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