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Papers (117)
| Title | Author | Year | Description |
|---|---|---|---|
| Can LLMs Predict Their Own Failures? Self-Awareness via Internal Circuits | Amirhosein Ghasemabadi, Di Niu | 2026 | Large language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typically rely on external judges, multi-sample consistency, or text-based self-critique, which incur additional compute or correlate weakly with… |
| From Lagging to Leading: Validating Hard Braking Events as High-Density Indicators of Segment Crash Risk | Yechen Li, Shantanu Shahane, Shoshana Vasserman et al. | 2026 | Identifying high crash risk road segments and accurately predicting crash incidence is fundamental to implementing effective safety countermeasures. While collision data inherently reflects risk, the infrequency and inconsistent reporting of crashes present a major challenge to robust risk… |
| Shaping capabilities with token-level data filtering | Neil Rathi, Alec Radford | 2026 | Current approaches to reducing undesired capabilities in language models are largely post hoc, and can thus be easily bypassed by adversaries. A natural alternative is to shape capabilities during pretraining itself. On the proxy task of removing medical capabilities, we show that the simple… |
| Reinforcement Learning via Self-Distillation | Jonas Hübotter, Frederike Lübeck, Lejs Behric et al. | 2026 | Large language models are increasingly post-trained with reinforcement learning in verifiable domains such as code and math. Yet, current methods for reinforcement learning with verifiable rewards (RLVR) learn only from a scalar outcome reward per attempt, creating a severe credit-assignment… |
| Learning a Generative Meta-Model of LLM Activations | Grace Luo, Jiahai Feng, Trevor Darrell et al. | 2026 | Existing approaches for analyzing neural network activations, such as PCA and sparse autoencoders, rely on strong structural assumptions. Generative models offer an alternative: they can uncover structure without such assumptions and act as priors that improve intervention fidelity. We explore this… |
| LLaDA2.1: Speeding Up Text Diffusion via Token Editing | Tiwei Bie, Maosong Cao, Xiang Cao et al. | 2026 | While LLaDA2.0 showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generation quality has remained an elusive frontier. Today, we unveil LLaDA2.1, a paradigm shift designed to transcend this… |
| Agents of Chaos | Natalie Shapira, Chris Wendler, Avery Yen et al. | 2026 | We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under… |
| Discovering Multiagent Learning Algorithms with Large Language Models | Zun Li, John Schultz, Daniel Hennes et al. | 2026 | Much of the advancement of Multi-Agent Reinforcement Learning (MARL) in imperfect-information games has historically depended on manual iterative refinement of baselines. While foundational families like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO) rest on solid… |
| Attention Residuals | Kimi Team, Guangyu Chen, Yu Zhang et al. | 2026 | Residual connections with PreNorm are standard in modern LLMs, yet they accumulate all layer outputs with fixed unit weights. This uniform aggregation causes uncontrolled hidden-state growth with depth, progressively diluting each layer's contribution. We propose Attention Residuals (AttnRes)… |
| Composer 2 Technical Report | Cursor Research, :, Aaron Chan et al. | 2026 | Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability to efficiently solve problems for interactive use. The model is trained in two phases: first, continued pretraining to… |
| MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens | Yu Chen, Runkai Chen, Sheng Yi et al. | 2026 | Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in the field. Due to the constraints of full-attention architectures, the effective context length of large language models (LLMs) is typically limited to 1M… |
| Agents of Chaos | Natalie Shapira, Chris Wendler, Avery Yen et al. | 2026 | We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under… |
| Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems | Jiacheng Liu, Xiaohan Zhao, Xinyi Shang et al. | 2026 | Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes its comprehensive architecture by analyzing the publicly available TypeScript source code and further comparing it with OpenClaw, an independent… |
| LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels | Lucas Maes, Quentin Le Lidec, Damien Scieur et al. | 2026 | Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid… |
| Incompressible Knowledge Probes: Estimating Black-Box LLM Parameter Counts via Factual Capacity | Bojie Li | 2026 | Closed-source frontier labs do not disclose parameter counts, and the standard alternative -- inference economics -- carries $2\times$+ uncertainty from hardware, batching, and serving-stack assumptions external to the model. We exploit a tighter intrinsic bound: storing $F$ facts requires at least… |
| MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents | Dongming Jiang, Yi Li, Guanpeng Li et al. | 2026 | Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. This design limits interpretability… |
| EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning | Chuanrui Hu, Xingze Gao, Zuyi Zhou et al. | 2026 | Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems often store isolated records and retrieve fragments, limiting their ability to… |
| From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition | Yiqing Zhou, Yu Lei, Shuzheng Si et al. | 2026 | Managing extensive context remains a critical bottleneck for Large Language Models (LLMs), particularly in applications like long-document question answering and autonomous agents where lengthy inputs incur high computational costs and introduce noise. Existing compression techniques often disrupt… |
| WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning | Woongyeong Yeo, Kangsan Kim, Jaehong Yoon et al. | 2026 | Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highly challenging due to limited context capacity and the loss of critical visual details during abstraction. Existing… |
| LightMem: Lightweight and Efficient Memory-Augmented Generation | Jizhan Fang, Xinle Deng, Haoming Xu et al. | 2026 | Despite their remarkable capabilities, Large Language Models (LLMs) struggle to effectively leverage historical interaction information in dynamic and complex environments. Memory systems enable LLMs to move beyond stateless interactions by introducing persistent information storage, retrieval, and… |
| RGMem: Renormalization Group-inspired Memory Evolution for Language Agents | Ao Tian, Yunfeng Lu, Xinxin Fan et al. | 2026 | Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states. Existing approaches, including retrieval-augmented generation and explicit memory systems… |
| What Deserves Memory: Adaptive Memory Distillation for LLM Agents | Wenquan Ma, Jiayan Nan, Wenlong Wu et al. | 2026 | Memory systems for LLM agents struggle to determine what information deserves retention. Existing approaches rely on predefined heuristics such as importance scores, emotional tags, or factual templates, encoding designer intuition rather than learning from the data itself. Inspired by cognitive… |
| Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution | Zouying Cao, Jiaji Deng, Li Yu et al. | 2026 | Procedural memory enables large language model (LLM) agents to internalize "how-to" knowledge, theoretically reducing redundant trial-and-error. However, existing frameworks predominantly suffer from a "passive accumulation" paradigm, treating memory as a static append-only archive. To bridge the… |
| Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models | Qizheng Zhang, Changran Hu, Shubhangi Upasani et al. | 2026 | Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evidence, rather than weight updates. Prior approaches improve usability but often suffer from brevity bias, which drops… |
| Memory in the Age of AI Agents | Yuyang Hu, Shichun Liu, Yanwei Yue et al. | 2026 | Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attention, the field has also become increasingly fragmented. Existing works that fall under the umbrella of agent memory often… |
| Agent Harness for Large Language Model Agents: A Survey | Qianyu Meng, Yanan Wang, Liyi Chen et al. | 2026 | Nowadays, the reliability of large language model (LLM) agents in production environments is increasingly determined not by the underlying model but by the agent harness that encapsulates it. As tasks grow longer and more complex, recent studies demonstrate order-of-magnitude reliability gains… |
| Skillful joint probabilistic weather forecasting from marginals | Ferran Alet, Ilan Price, Andrew El-Kadi et al. | 2025 | Machine learning (ML)-based weather models have rapidly risen to prominence due to their greater accuracy and speed than traditional forecasts based on numerical weather prediction (NWP), recently outperforming traditional ensembles in global probabilistic weather forecasting. This paper presents… |
| AI and Generative AI Transforming Disaster Management: A Survey of Damage Assessment and Response Techniques | Aman Raj, Ankit Shetgaonkar, Lakshit Arora et al. | 2025 | Natural disasters, including earthquakes, wildfires and cyclones, bear a huge risk on human lives as well as infrastructure assets. An effective response to disaster depends on the ability to rapidly and efficiently assess the intensity of damage. Artificial Intelligence (AI) and Generative… |
| Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases | Sherman Wong, Zhenting Qi, Zhaodong Wang et al. | 2025 | Real-world software engineering tasks require coding agents that can operate over massive repositories, sustain long-horizon sessions, and reliably coordinate complex toolchains at test time. Existing research-grade coding agents offer transparency but struggle when scaled to heavier… |
| Recursive Language Models | Alex L. Zhang, Tim Kraska, Omar Khattab | 2025 | We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically… |
| Aligning machine and human visual representations across abstraction levels | Lukas Muttenthaler, Klaus Greff, Frieda Born et al. | 2025 | Abstract Deep neural networks have achieved success across a wide range of applications, including as models of human behaviour and neural representations in vision tasks 1,2 . However, neural network training and human learning differ in fundamental ways, and neural networks often fail to… |
| PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations | Ruining He, Lukasz Heldt, Lichan Hong et al. | 2025 | Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit significantly from the sequence modeling capabilities and world knowledge inherent in these large models. In this paper, we… |
| PaperBench: Evaluating AI's Ability to Replicate AI Research | Giulio Starace, Oliver Jaffe, Dane Sherburn et al. | 2025 | We introduce PaperBench, a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research. Agents must replicate 20 ICML 2024 Spotlight and Oral papers from scratch, including understanding paper contributions, developing a codebase, and successfully executing experiments… |
| Chasing the Tail: Effective Rubric-based Reward Modeling for Large Language Model Post-Training | Junkai Zhang, Zihao Wang, Lin Gui et al. | 2025 | Reinforcement fine-tuning (RFT) often suffers from \emph{reward over-optimization}, where a policy model hacks the reward signals to achieve high scores while producing low-quality outputs. Our theoretical analysis shows that the key lies in reward misspecification at the high-reward tail: the… |
| Text-to-LoRA: Instant Transformer Adaption | Rujikorn Charakorn, Edoardo Cetin, Yujin Tang et al. | 2025 | While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation of datasets and repeated fine-tuning of the underlying model. Fine-tuning techniques enable practitioners to adapt… |
| From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users | Sadia Sultana Chowa, Riasad Alvi, Subhey Sadi Rahman et al. | 2025 | The pursuit of human-level artificial intelligence (AI) has significantly advanced the development of autonomous agents and Large Language Models (LLMs). LLMs are now widely utilized as decision-making agents for their ability to interpret instructions, manage sequential tasks, and adapt through… |
| MemVerse: Multimodal Memory for Lifelong Learning Agents | Junming Liu, Yifei Sun, Weihua Cheng et al. | 2025 | Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember. Without reliable memory, agents catastrophically forget past experiences, struggle with long-horizon reasoning, and fail to operate coherently in multimodal… |
| MMAG: Mixed Memory-Augmented Generation for Large Language Models Applications | Stefano Zeppieri | 2025 | Large Language Models (LLMs) excel at generating coherent text within a single prompt but fall short in sustaining relevance, personalization, and continuity across extended interactions. Human communication, however, relies on multiple forms of memory, from recalling past conversations to adapting… |
| Sophia: A Persistent Agent Framework of Artificial Life | Mingyang Sun, Feng Hong, Weinan Zhang | 2025 | The development of LLMs has elevated AI agents from task-specific tools to long-lived, decision-making entities. Yet, most architectures remain static and reactive, tethered to manually defined, narrow scenarios. These systems excel at perception (System 1) and deliberation (System 2) but lack a… |
| Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI | Samarth Sarin, Lovepreet Singh, Bhaskarjit Sarmah et al. | 2025 | Agentic memory is emerging as a key enabler for large language models (LLM) to maintain continuity, personalization, and long-term context in extended user interactions, critical capabilities for deploying LLMs as truly interactive and adaptive agents. Agentic memory refers to the memory that… |
| A Simple Yet Strong Baseline for Long-Term Conversational Memory of LLM Agents | Sizhe Zhou, Jiawei Han | 2025 | LLM-based conversational agents still struggle to maintain coherent, personalized interaction over many sessions: fixed context windows limit how much history can be kept in view, and most external memory approaches trade off between coarse retrieval over large chunks and fine-grained but… |
| General Agentic Memory Via Deep Research | B. Y. Yan, Chaofan Li, Hongjin Qian et al. | 2025 | Memory is critical for AI agents, yet the widely-adopted static memory, aiming to create readily available memory in advance, is inevitably subject to severe information loss. To address this limitation, we propose a novel framework called \textbf{general agentic memory (GAM)}. GAM follows the… |
| O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents | Piaohong Wang, Motong Tian, Jiaxian Li et al. | 2025 | Recent advancements in LLM-powered agents have demonstrated significant potential in generating human-like responses; however, they continue to face challenges in maintaining long-term interactions within complex environments, primarily due to limitations in contextual consistency and dynamic… |
| RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory | Jun Liu, Zhenglun Kong, Changdi Yang et al. | 2025 | Multi-agent large language model (LLM) systems have shown strong potential in complex reasoning and collaborative decision-making tasks. However, most existing coordination schemes rely on static or full-context routing strategies, which lead to excessive token consumption, redundant memory… |
| Enabling Personalized Long-term Interactions in LLM-based Agents through Persistent Memory and User Profiles | Rebecca Westhäußer, Wolfgang Minker, Sebatian Zepf | 2025 | Large language models (LLMs) increasingly serve as the central control unit of AI agents, yet current approaches remain limited in their ability to deliver personalized interactions. While Retrieval Augmented Generation enhances LLM capabilities by improving context-awareness, it lacks mechanisms… |
| Livia: An Emotion-Aware AR Companion Powered by Modular AI Agents and Progressive Memory Compression | Rui Xi, Xianghan Wang | 2025 | Loneliness and social isolation pose significant emotional and health challenges, prompting the development of technology-based solutions for companionship and emotional support. This paper introduces Livia, an emotion-aware augmented reality (AR) companion app designed to provide personalized… |
| D-SMART: Enhancing LLM Dialogue Consistency via Dynamic Structured Memory And Reasoning Tree | Xiang Lei, Qin Li, Min Zhang et al. | 2025 | Large Language Models (LLMs) often exhibit factual inconsistencies and logical decay in extended, multi-turn dialogues, a challenge stemming from their reliance on static, pre-trained knowledge and an inability to reason adaptively over the dialogue history. Prevailing mitigation strategies, such… |
| WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research | Zijian Li, Xin Guan, Bo Zhang et al. | 2025 | This paper tackles \textbf{open-ended deep research (OEDR)}, a complex challenge where AI agents must synthesize vast web-scale information into insightful reports. Current approaches are plagued by dual-fold limitations: static research pipelines that decouple planning from evidence acquisition… |
| CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension | Rui Li, Zeyu Zhang, Xiaohe Bo et al. | 2025 | Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents. Despite the emergence of some heuristic… |
| Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue | Sangyeop Kim, Yohan Lee, Sanghwa Kim et al. | 2025 | Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for… |
| Mem-α: Learning Memory Construction via Reinforcement Learning | Yu Wang, Ryuichi Takanobu, Zhiqi Liang et al. | 2025 | Large language model (LLM) agents are constrained by limited context windows, necessitating external memory systems for long-term information understanding. Current memory-augmented agents typically depend on pre-defined instructions and tools for memory updates. However, language models may lack… |
| SGMem: Sentence Graph Memory for Long-Term Conversational Agents | Yaxiong Wu, Yongyue Zhang, Sheng Liang et al. | 2025 | Long-term conversational agents require effective memory management to handle dialogue histories that exceed the context window of large language models (LLMs). Existing methods based on fact extraction or summarization reduce redundancy but struggle to organize and retrieve relevant information… |
| In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents | Zhen Tan, Jun Yan, I-Hung Hsu et al. | 2025 | Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limits their effectiveness in applications requiring sustained personalization. External memory mechanisms have been… |
| Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations | Xu Huang, Jianxun Lian, Yuxuan Lei et al. | 2025 | Recommender models capture ever-changing user preferences by training with in-domain user behavior data. These models are typically lightweight, facilitating real-time and large-scale online services. However, these models often falter when tasked with providing more sophisticated functionalities… |
| ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA | Jiaang Li, Quan Wang, Zhongnan Wang et al. | 2025 | Large language models (LLMs) require model editing to efficiently update specific knowledge within them and avoid factual errors. Most model editing methods are solely designed for single-time use and result in a significant forgetting effect in lifelong editing scenarios, where sequential edits… |
| Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects | Chris Latimer, Nicoló Boschi, Andrew Neeser et al. | 2025 | Agent memory has been touted as a dimension of growth for LLM-based applications, enabling agents that can accumulate experience, adapt across sessions, and move beyond single-shot question answering. The current generation of agent memory systems treats memory as an external layer that extracts… |
| SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience | Zeyi Sun, Ziyu Liu, Yuhang Zang et al. | 2025 | Repurposing large vision-language models (LVLMs) as computer use agents (CUAs) has led to substantial breakthroughs, primarily driven by human-labeled data. However, these models often struggle with novel and specialized software, particularly in scenarios lacking human annotations. To address this… |
| Agent Workflow Memory | Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried et al. | 2025 | Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action trajectories. In contrast, humans can flexibly solve complex tasks by learning reusable task workflows from past… |
| JARVIS-1: Open-World Multi-Task Agents With Memory-Augmented Multimodal Language Models | Zihao Wang, Shaofei Cai, Anji Liu et al. | 2025 | Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could… |
| MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation | Hongjin Qian, Zheng Liu, Peitian Zhang et al. | 2025 | |
| SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs | Yige Xu, Xu Guo, Zhiwei Zeng et al. | 2025 | Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks by generating intermediate reasoning steps. However, most existing approaches focus on hard token decoding, which constrains reasoning within the discrete vocabulary space and may not always be… |
| SeCom: On Memory Construction and Retrieval for Personalized Conversational Agents | Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang et al. | 2024 | To deliver coherent and personalized experiences in long-term conversations, existing approaches typically perform retrieval augmented response generation by constructing memory banks from conversation history at either the turn-level, session-level, or through summarization techniques. In this… |
| Memolet: Reifying the Reuse of User-AI Conversational Memories | Ryan Yen, Jian Zhao | 2024 | |
| Human-inspired Episodic Memory for Infinite Context LLMs | Zafeirios Fountas, Martin Benfeghoul, Adnan Oomerjee et al. | 2024 | Large language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences. In contrast, the human brain excels at organising and retrieving episodic experiences across vast… |
| Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory Augmentation | Wazeer Deen Zulfikar, Samantha Chan, Pattie Maes | 2024 | |
| RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models | Noah Wang, Z.y. Peng, Haoran Que et al. | 2024 | The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characters. However, the closed-source nature of state-of-the-art LLMs and their general-purpose training limit role-playing… |
| MovieChat: From Dense Token to Sparse Memory for Long Video Understanding | Enxin Song, Wenhao Chai, Guanhong Wang et al. | 2024 | Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory… |
| Self-Updatable Large Language Models by Integrating Context into Model Parameters | Yu Wang, Xinshuang Liu, Xiusi Chen et al. | 2024 | Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with sur- rounding objects, remains a substantial challenge. Two critical factors in assimilating these experiences are (1) **Efficacy**: the ability… |
| WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models | Peng Wang, Zexi Li, Ningyu Zhang et al. | 2024 | |
| Online Adaptation of Language Models with a Memory of Amortized Contexts | Jihoon Tack, Jaehyung Kim, Eric Mitchell et al. | 2024 | |
| Neighboring Perturbations of Knowledge Editing on Large Language Models | Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang et al. | 2024 | Despite their exceptional capabilities, large language models (LLMs) are prone to generating unintended text due to false or outdated knowledge. Given the resource-intensive nature of retraining LLMs, there has been a notable increase in the development of knowledge editing. However, current… |
| CharacterGLM: Customizing Social Characters with Large Language Models | Jinfeng Zhou, Zhuang Chen, Dazhen Wan et al. | 2024 | Character-based dialogue (CharacterDial) has become essential in the industry (e.g., Character.AI), enabling users to freely customize social characters for social interactions. However, the generalizability and adaptability across various conversational scenarios inherent in customizing social… |
| SAGE: Self-evolving Agents with Reflective and Memory-augmented Abilities | 2024 | ||
| Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models | Ling Yang, Zhaochen Yu, Tianjun Zhang et al. | 2024 | |
| RecMind: Large Language Model Powered Agent For Recommendation | Yancheng Wang, Ziyan Jiang, Zheng Chen et al. | 2024 | While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale… |
| ExpeL: LLM Agents Are Experiential Learners | Andrew Zhao, Daniel Huang, Quentin Xu et al. | 2024 | The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks, finetuning them for specific tasks is… |
| RazorAttention: Efficient KV Cache Compression Through Retrieval Heads | Hanlin Tang, Yang Lin, Jing Lin et al. | 2024 | The memory and computational demands of Key-Value (KV) cache present significant challenges for deploying long-context language models. Previous approaches attempt to mitigate this issue by selectively dropping tokens, which irreversibly erases critical information that might be needed for future… |
| SnapKV: LLM Knows What You are Looking for Before Generation | Yuhong Li, Yingbing Huang, Bowen Yang et al. | 2024 | |
| Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens | Weiyao Luo, Suncong Zheng, Heming Xia et al. | 2024 | Large language models (LLMs) have shown promising efficacy across various tasks, becoming powerful tools in numerous aspects of human life. However, Transformer-based LLMs suffer a performance degradation when modeling long-term contexts due to they discard some information to reduce computational… |
| Were RNNs All We Needed? | Leo Feng, Frederick Tung, Mohamed Osama Ahmed et al. | 2024 | The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers have since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence… |
| Attention Is All You Need | Ashish Vaswani, Noam Shazeer, Niki Parmar et al. | 2023 | The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the… |
| CALYPSO: LLMs as Dungeon Master's Assistants | Andrew Zhu, Lara Martin, Andrew Head et al. | 2023 | The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters, synthesize scenes to present to other players, and respond to the players' interactions with the scene. Doing all of… |
| Prompted LLMs as Chatbot Modules for Long Open-domain Conversation | Gibbeum Lee, Volker Hartmann, Jongho Park et al. | 2023 | In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning. Our method utilizes pre-trained large language models (LLMs) as individual modules for long-term consistency and flexibility, by using… |
| MemoryBank: Enhancing Large Language Models with Long-Term Memory | Wanjun Zhong, Lianghong Guo, Qiqi Gao et al. | 2023 | Revolutionary advancements in Large Language Models have drastically reshaped our interactions with artificial intelligence systems. Despite this, a notable hindrance remains-the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in… |
| Character-LLM: A Trainable Agent for Role-Playing | Yunfan Shao, Linyang Li, Junqi Dai et al. | 2023 | Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human… |
| Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning | Hyungho Na, Yunkyeong Seo, Il-chul Moon | 2023 | In cooperative multi-agent reinforcement learning (MARL), agents aim to achieve a common goal, such as defeating enemies or scoring a goal. Existing MARL algorithms are effective but still require significant learning time and often get trapped in local optima by complex tasks, subsequently failing… |
| CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models | Cheng Qian, Chi Han, Yi Fung et al. | 2023 | Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning, particularly when both planning and execution are involved. To overcome these limitations, we propose CREATOR, a novel… |
| Reflexion: Language Agents with Verbal Reinforcement Learning | Noah Shinn, Federico Cassano, Edward Berman et al. | 2023 | Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning… |
| A Machine with Short-Term, Episodic, and Semantic Memory Systems | Taewoon Kim, Michael Cochez, Vincent Francois-Lavet et al. | 2023 | Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released… |
| Efficient Streaming Language Models with Attention Sinks | Guangxuan Xiao, Yuandong Tian, Beidi Chen et al. | 2023 | Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses two major challenges. Firstly, during the decoding stage, caching previous tokens' Key and Value states (KV) consumes extensive memory… |
| Augmenting Language Models with Long-Term Memory | Weizhi Wang, Li Dong, Hao Cheng et al. | 2023 | |
| Adapting Language Models to Compress Contexts | Alexis Chevalier, Alexander Wettig, Anirudh Ajith et al. | 2023 | |
| Learning to Compress Prompts with Gist Tokens | Jesse Mu, Xiang Li, Noah Goodman | 2023 | |
| Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time | Zichang Liu, Aditya Desai, Fangshuo Liao et al. | 2023 | |
| Focused Transformer: Contrastive Training for Context Scaling | Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek et al. | 2023 | |
| H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models | Zhenyu Zhang, Ying Sheng, Tianyi Zhou et al. | 2023 | |
| Fast Model Editing at Scale | Eric Mitchell, Charles Lin, Antoine Bosselut et al. | 2021 | While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers… |
| K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters | Ruize Wang, Duyu Tang, Nan Duan et al. | 2021 | |
| Detecting Local Insights from Global Labels: Supervised and Zero-Shot Sequence Labeling via a Convolutional Decomposition | Allen Schmaltz | 2021 | Abstract We propose a new, more actionable view of neural network interpretability and data analysis by leveraging the remarkable matching effectiveness of representations derived from deep networks, guided by an approach for class-conditional feature detection. The decomposition of the… |
| Memorizing Transformers | Yuhuai Wu, Markus Norman Rabe, DeLesley Hutchins et al. | 2021 | Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights. We instead envision language models that can simply read and memorize new data at inference time, thus acquiring new knowledge immediately. In this work, we extend… |
| GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism | Yanping Huang, Youlong Cheng, Ankur Bapna et al. | 2019 | Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or… |
| Pointer Networks | Oriol Vinyals, Meire Fortunato, Navdeep Jaitly | 2017 | We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence and Neural Turing… |
| Neural Message Passing for Quantum Chemistry | Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley et al. | 2017 | Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a… |
| A simple neural network module for relational reasoning | Adam Santoro, David Raposo, David G. T. Barrett et al. | 2017 | Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We… |
| AdaGAN: Boosting Generative Models | Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet et al. | 2017 | Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images. However, they are notoriously hard to train and can suffer from the problem of missing modes where the model is not able to produce examples… |
| Order Matters: Sequence to sequence for sets | Oriol Vinyals, Samy Bengio, Manjunath Kudlur | 2016 | Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule… |
| Multi-Scale Context Aggregation by Dilated Convolutions | Fisher Yu, Vladlen Koltun | 2016 | State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module… |
| Neural Machine Translation by Jointly Learning to Align and Translate | Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio | 2016 | Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed… |
| Identity Mappings in Deep Residual Networks | Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | 2016 | Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly… |
| Recurrent Neural Network Regularization | Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals | 2015 | We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to… |
| Deep Residual Learning for Image Recognition | Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | 2015 | Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of… |
| ELLA: An Efficient Lifelong Learning Algorithm | Paul Ruvolo, Eric Eaton | 2013 | The problem of learning multiple consecutive tasks, known as lifelong learning, is of great importance to the creation of intelligent, general-purpose, and flexible machines. In this paper, we develop a method for online multi-task learning in the lifelong learning setting. The proposed Efficient… |
| ImageNet Classification with Deep Convolutional Neural Networks | Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton | 2012 | |
| Nested Learning: The Illusion of Deep Learning Architecture | Ali Behrouz, Meisam Razaviyayn, Peilin Zhong et al. | Over the last decades, developing more powerful neural architectures and simultaneously designing optimization algorithms to effectively train them have been the core of research efforts to enhance the capability of machine learning models. Despite the recent progresses, particularly in developing… | |
| Learning Domain-Driven Design | Vlad Khononov | ||
| Authors Maxim Massenkoff and Peter McCrory | Ruth Appel, Tim Belonax, Keir Bradwell et al. | ||
| On Layer Normalization in the Transformer Architecture | Ruibin Xiong, Yunchang Yang, Di He et al. | The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyperparameter tunings. In… |
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Books (4)
| Title | Author | Year | Description |
|---|---|---|---|
| AI engineering: building applications with foundation models | Chip Huyen | 2025 | |
| The status game | Will Storr | 2021 | |
| Kolmogorov Complexity and Algorithmic Randomness | A. Shen, V. Uspensky, N. Vereshchagin | 2017 | |
| Computer systems: a programmer's perspective | Randal E. Bryant, David Richard O'Hallaron | 2011 | "Computer Systems: A Programmer's Perspective, Second Edition, introduces the important and enduring concepts that underlie computer systems by showing how these ideas affect the correctness, performance, and utility of application programs. Other systems books, written from a builder's… |
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Articles (1)
| Title | Author | Year | Description |
|---|---|---|---|
| Rubric-Based Rewards for RL | Deep (Learning) Focus | 2026 | Extending the benefits of large-scale RL training to non-verifiable domains... |
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Others (128)
| Title | Author | Year | Description |
|---|---|---|---|
| MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory | Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou et al. | 2026 | The hallmark of human intelligence is the self-evolving ability to master new skills by learning from past experiences. However, current AI agents struggle to emulate this self-evolution: fine-tuning is computationally expensive and prone to catastrophic forgetting, while existing memory-based… |
| Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents | Yi Yu, Liuyi Yao, Yuexiang Xie et al. | 2026 | Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components, relying on heuristics or… |
| MOOM: Maintenance, Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues | Weishu Chen, Jinyi Tang, Zhouhui Hou et al. | 2025 | Memory extraction is crucial for maintaining coherent ultra-long dialogues in human-robot role-playing scenarios. However, existing methods often exhibit uncontrolled memory growth. To address this, we propose MOOM, the first dual-branch memory plugin that leverages literary theory by modeling plot… |
| A Multi-Memory Segment System for Generating High-Quality Long-Term Memory Content in Agents | Gaoke Zhang, Bo Wang, Yunlong Ma et al. | 2025 | In the current field of agent memory, extensive explorations have been conducted in the area of memory retrieval, yet few studies have focused on exploring the memory content. Most research simply stores summarized versions of historical dialogues, as exemplified by methods like A-MEM and… |
| Semantic Anchoring in Agentic Memory: Leveraging Linguistic Structures for Persistent Conversational Context | Maitreyi Chatterjee, Devansh Agarwal | 2025 | Large Language Models (LLMs) have demonstrated impressive fluency and task competence in conversational settings. However, their effectiveness in multi-session and long-term interactions is hindered by limited memory persistence. Typical retrieval-augmented generation (RAG) systems store dialogue… |
| ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning | Juyuan Wang, Rongchen Zhao, Wei Wei et al. | 2025 | Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given the LLM's diminished reasoning over extended context and its high computational cost, retrieval-based… |
| Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark | Yuxuan Cai, Yipeng Hao, Jie Zhou et al. | 2025 | As AI advances toward general intelligence, the focus is shifting from systems optimized for static tasks to creating open-ended agents that learn continuously. In this paper, we introduce Experience-driven Lifelong Learning (ELL), a framework for building self-evolving agents capable of continuous… |
| Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory | Lin Long, Yichen He, Wentao Ye et al. | 2025 | We introduce M3-Agent, a novel multimodal agent framework equipped with long-term memory. Like humans, M3-Agent can process real-time visual and auditory inputs to build and update episodic and semantic memories, gradually accumulating world knowledge. Its memory is organized in an entity-centric… |
| Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning | Sikuan Yan, Xiufeng Yang, Zuchao Huang et al. | 2025 | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external… |
| Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory | Sizhe Yuen, Francisco Gomez Medina, Ting Su et al. | 2025 | Multi-agent systems built on Large Language Models (LLMs) show exceptional promise for complex collaborative problem-solving, yet they face fundamental challenges stemming from context window limitations that impair memory consistency, role adherence, and procedural integrity. This paper introduces… |
| MIRIX: Multi-Agent Memory System for LLM-Based Agents | Yu Wang, Xi Chen | 2025 | Although memory capabilities of AI agents are gaining increasing attention, existing solutions remain fundamentally limited. Most rely on flat, narrowly scoped memory components, constraining their ability to personalize, abstract, and reliably recall user-specific information over time. To this… |
| Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents | Haoran Sun, Shaoning Zeng | 2025 | Long-term memory is one of the key factors influencing the reasoning capabilities of Large Language Model Agents (LLM Agents). Incorporating a memory mechanism that effectively integrates past interactions can significantly enhance decision-making and contextual coherence of LLM Agents. While… |
| G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems | Guibin Zhang, Muxin Fu, Guancheng Wan et al. | 2025 | Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to… |
| Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization | Taeyoon Kwon, Dongwook Choi, Hyojun Kim et al. | 2025 | LLM-powered embodied agents have shown success on conventional object-rearrangement tasks, but providing personalized assistance that leverages user-specific knowledge from past interactions presents new challenges. We investigate these challenges through the lens of agents' memory utilization… |
| MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents | Yiming Du, Bingbing Wang, Yang He et al. | 2025 | Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities for long-term memory utilization. However, these methods are primarily based on semantic similarity, overlooking task… |
| Pre-training Limited Memory Language Models with Internal and External Knowledge | Linxi Zhao, Sofian Zalouk, Christian K. Belardi et al. | 2025 | Neural language models are black-boxes--both linguistic patterns and factual knowledge are distributed across billions of opaque parameters. This entangled encoding makes it difficult to reliably inspect, verify, or update specific facts. We introduce Limited Memory Language Models (LMLM), a new… |
| Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory | Prateek Chhikara, Dev Khant, Saket Aryan et al. | 2025 | Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues. We introduce Mem0, a scalable memory-centric architecture… |
| Zep: A Temporal Knowledge Graph Architecture for Agent Memory | Preston Rasmussen, Pavlo Paliychuk, Travis Beauvais et al. | 2025 | We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark. Additionally, Zep excels in more comprehensive and challenging evaluations than DMR that better reflect real-world enterprise use… |
| R$^3$Mem: Bridging Memory Retention and Retrieval via Reversible Compression | Xiaoqiang Wang, Suyuchen Wang, Yun Zhu et al. | 2025 | Memory plays a key role in enhancing LLMs' performance when deployed to real-world applications. Existing solutions face trade-offs: explicit memory designs based on external storage require complex management and incur storage overhead, while implicit memory designs that store information via… |
| A-MEM: Agentic Memory for LLM Agents | Wujiang Xu, Zujie Liang, Kai Mei et al. | 2025 | While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current memory systems enable basic storage and retrieval but lack sophisticated memory organization, despite recent attempts to… |
| Unveiling Privacy Risks in LLM Agent Memory | Bo Wang, Weiyi He, Shenglai Zeng et al. | 2025 | Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations, introducing new privacy risks for LLM agents. In this work, we systematically… |
| Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation | Lingfeng Zhang, Yuecheng Liu, Zhanguang Zhang et al. | 2025 | Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, enabling agents to leverage commonsense and spatial reasoning for efficient exploration in unfamiliar environments. Existing LLM-based approaches convert… |
| MemLoRA: Distilling Expert Adapters for On-Device Memory Systems | Massimo Bini, Ondrej Bohdal, Umberto Michieli et al. | 2025 | Memory-augmented Large Language Models (LLMs) have demonstrated remarkable consistency during prolonged dialogues by storing relevant memories and incorporating them as context. Such memory-based personalization is also key in on-device settings that allow users to keep their conversations and data… |
| Pretraining with hierarchical memories: separating long-tail and common knowledge | Hadi Pouransari, David Grangier, C. Thomas et al. | 2025 | The impressive performance gains of modern language models currently rely on scaling parameters: larger models store more world knowledge and reason better. Yet compressing all world knowledge into parameters is unnecessary, as only a fraction is used per prompt, and impractical for edge devices… |
| Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models | Jiaqi Cao, Jiarui Wang, Rubin Wei et al. | 2025 | Large Language Models (LLMs) have shown strong abilities in general language tasks, yet adapting them to specific domains remains a challenge. Current method like Domain Adaptive Pretraining (DAPT) requires costly full-parameter training and suffers from catastrophic forgetting. Meanwhile… |
| MLP Memory: A Retriever-Pretrained Memory for Large Language Models | Rubin Wei, Jiaqi Cao, Jiarui Wang et al. | 2025 | Modern approaches to enhancing Large Language Models' factual accuracy and knowledge utilization face a fundamental trade-off: non-parametric retrieval-augmented generation (RAG) provides flexible access to external knowledge but suffers from high inference latency and shallow integration, while… |
| Similarity-Distance-Magnitude Activations | Allen Schmaltz | 2025 | We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax activation function, adding Similarity (i.e., correctly predicted depth-matches into training) awareness and Distance-to-training-distribution awareness to… |
| Towards General Continuous Memory for Vision-Language Models | Wenyi Wu, Zixuan Song, Kun Zhou et al. | 2025 | Language models (LMs) and their extension, vision-language models (VLMs), have achieved remarkable performance across various tasks. However, they still struggle with complex reasoning tasks that require multimodal or multilingual real-world knowledge. To support such capabilities, an external… |
| M+: Extending MemoryLLM with Scalable Long-Term Memory | Yu Wang, Dmitry Krotov, Yuanzhe Hu et al. | 2025 | Equipping large language models (LLMs) with latent-space memory has attracted increasing attention as they can extend the context window of existing language models. However, retaining information from the distant past remains a challenge. For example, MemoryLLM (Wang et al., 2024a), as a… |
| MemEvolve: Meta-Evolution of Agent Memory Systems | Guibin Zhang, Haotian Ren, Chong Zhan et al. | 2025 | Self-evolving memory systems are unprecedentedly reshaping the evolutionary paradigm of large language model (LLM)-based agents. Prior work has predominantly relied on manually engineered memory architectures to store trajectories, distill experience, and synthesize reusable tools, enabling agents… |
| FLEX: Continuous Agent Evolution via Forward Learning from Experience | Zhicheng Cai, Xinyuan Guo, Yu Pei et al. | 2025 | Autonomous agents driven by Large Language Models (LLMs) have revolutionized reasoning and problem-solving but remain static after training, unable to grow with experience as intelligent beings do during deployment. We introduce Forward Learning with EXperience (FLEX), a gradient-free learning… |
| Scaling Agent Learning via Experience Synthesis | Zhaorun Chen, Zhuokai Zhao, Kai Zhang et al. | 2025 | While reinforcement learning (RL) can empower autonomous agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts, limited task diversity, unreliable reward signals, and infrastructure complexity, all of which obstruct the collection… |
| UFO2: The Desktop AgentOS | Chaoyun Zhang, He Huang, Chiming Ni et al. | 2025 | Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural language. However, most existing CUAs remain conceptual prototypes, hindered by shallow OS integration, fragile… |
| PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents | Namyoung Kim, Kai Tzu-iunn Ong, Yeonjun Hwang et al. | 2025 | Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning. However, existing approaches to strategy planning for proactive dialogue face several limitations: limited strategy coverage, preference bias in… |
| Training-Free Group Relative Policy Optimization | Yuzheng Cai, Siqi Cai, Yuchen Shi et al. | 2025 | Recent advances in Large Language Model (LLM) agents have demonstrated their promising general capabilities. However, their performance in specialized real-world domains often degrades due to challenges in effectively integrating external tools and specific prompting strategies. While methods like… |
| ToolMem: Enhancing Multimodal Agents with Learnable Tool Capability Memory | Yunzhong Xiao, Yangmin Li, Hewei Wang et al. | 2025 | Agents utilizing tools powered by large language models (LLMs) or vision-language models (VLMs) have demonstrated remarkable progress in diverse tasks across text and visual modalities. Unlike traditional tools such as calculators, which give deterministic outputs, neural tools perform uncertainly… |
| H$^2$R: Hierarchical Hindsight Reflection for Multi-Task LLM Agents | Shicheng Ye, Chao Yu, Kaiqiang Ke et al. | 2025 | Large language model (LLM)-based agents have shown strong potential in multi-task scenarios, owing to their ability to transfer knowledge across diverse tasks. However, existing approaches often treat prior experiences and knowledge as monolithic units, leading to inefficient and coarse-grained… |
| BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions | Tao Yu, Zhengbo Zhang, Zhiheng Lyu et al. | 2025 | Efficiently solving real-world problems with LLMs increasingly hinges on their ability to interact with dynamic web environments and autonomously acquire external information. While recent research like Search-R1 and WebDancer demonstrates strong performance in solving web tasks, they heavily rely… |
| LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation | Dongge Han, Camille Couturier, Daniel Madrigal Diaz et al. | 2025 | We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories into reusable memory units and flexibly allocates them across orchestrators and task agents to support planning and… |
| Alita-G: Self-Evolving Generative Agent for Agent Generation | Jiahao Qiu, Xuan Qi, Hongru Wang et al. | 2025 | Large language models (LLMs) have been shown to perform better when scaffolded into agents with memory, tools, and feedback. Beyond this, self-evolving agents have emerged, but current work largely limits adaptation to prompt rewriting or failure retries. Therefore, we present ALITA-G, a… |
| ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory | Siru Ouyang, Jun Yan, I.-Hung Hsu et al. | 2025 | With the growing adoption of large language model agents in persistent real-world roles, they naturally encounter continuous streams of tasks. A key limitation, however, is their failure to learn from the accumulated interaction history, forcing them to discard valuable insights and repeat past… |
| Memento: Fine-tuning LLM Agents without Fine-tuning LLMs | Huichi Zhou, Yihang Chen, Siyuan Guo et al. | 2025 | In this paper, we introduce a novel learning paradigm for Adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approaches are often either rigid, relying on static, handcrafted reflection workflows, or computationally intensive, requiring… |
| Memp: Exploring Agent Procedural Memory | Runnan Fang, Yuan Liang, Xiaobin Wang et al. | 2025 | Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters. In this work, we investigate strategies to endow agents with a learnable, updatable, and lifelong procedural memory. We… |
| Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving | Xiangru Tang, Tianrui Qin, Tianhao Peng et al. | 2025 | AI agent frameworks operate in isolation, forcing agents to rediscover solutions and repeat mistakes across different systems. Despite valuable problem-solving experiences accumulated by frameworks like smolagents, OpenHands, and OWL, this knowledge remains trapped within individual systems… |
| MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations | Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah et al. | 2025 | Large Language Model (LLM) agents have shown significant autonomous capabilities in dynamically searching and incorporating relevant tools or Model Context Protocol (MCP) servers for individual queries. However, fixed context windows limit effectiveness in multi-turn interactions requiring… |
| Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents | Jenny Zhang, Shengran Hu, Cong Lu et al. | 2025 | Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely, that would accelerate AI development and allow us to reap its benefits much sooner. Meta-learning can automate the… |
| Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution | Jiahao Qiu, Xuan Qi, Tongcheng Zhang et al. | 2025 | Recent advances in large language models (LLMs) have enabled agents to autonomously perform complex, open-ended tasks. However, many existing frameworks depend heavily on manually predefined tools and workflows, which hinder their adaptability, scalability, and generalization across domains. In… |
| SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills | Boyuan Zheng, Michael Y. Fatemi, Xiaolong Jin et al. | 2025 | To survive and thrive in complex environments, humans have evolved sophisticated self-improvement mechanisms through environment exploration, hierarchical abstraction of experiences into reuseable skills, and collaborative construction of an ever-growing skill repertoire. Despite recent… |
| LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark | Guangyi Liu, Pengxiang Zhao, Liang Liu et al. | 2025 | Mobile GUI agents show promise in automating tasks but face generalization challenges in diverse real-world scenarios. Traditional approaches using pre-training or fine-tuning with massive datasets struggle with the diversity of mobile applications and user-specific tasks. We propose enhancing… |
| Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models | Zhengliang Shi, Yuhan Wang, Lingyong Yan et al. | 2025 | Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool-using LLMs, adopting information retrieval (IR) models to select useful tools from large toolsets is a critical… |
| Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory | Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi et al. | 2025 | Despite their impressive performance on complex tasks, current language models (LMs) typically operate in a vacuum: Each input query is processed separately, without retaining insights from previous attempts. Here, we present Dynamic Cheatsheet (DC), a lightweight framework that endows a black-box… |
| Inducing Programmatic Skills for Agentic Tasks | Zora Zhiruo Wang, Apurva Gandhi, Graham Neubig et al. | 2025 | To succeed in common digital tasks such as web navigation, agents must carry out a variety of specialized tasks such as searching for products or planning a travel route. To tackle these tasks, agents can bootstrap themselves by learning task-specific skills online through interaction with the web… |
| COLA: A Scalable Multi-Agent Framework For Windows UI Task Automation | Di Zhao, Longhui Ma, Siwei Wang et al. | 2025 | With the rapid advancements in Large Language Models (LLMs), an increasing number of studies have leveraged LLMs as the cognitive core of agents to address complex task decision-making challenges. Specially, recent research has demonstrated the potential of LLM-based agents on automating Windows… |
| Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning | Mufan Xu, Gewen Liang, Kehai Chen et al. | 2025 | Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving… |
| From RAG to Memory: Non-Parametric Continual Learning for Large Language Models | Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi et al. | 2025 | Our ability to continuously acquire, organize, and leverage knowledge is a key feature of human intelligence that AI systems must approximate to unlock their full potential. Given the challenges in continual learning with large language models (LLMs), retrieval-augmented generation (RAG) has become… |
| AgentEvolver: Towards Efficient Self-Evolving Agent System | Yunpeng Zhai, Shuchang Tao, Cheng Chen et al. | 2025 | Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in diverse environments. However, current approaches to developing such agents remain costly and inefficient, as they… |
| Agent Learning via Early Experience | Kai Zhang, Xiangchao Chen, Bo Liu et al. | 2025 | A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents from experience data with reinforcement learning remains difficult in many environments, which either lack verifiable… |
| Scaling Agents via Continual Pre-training | Liangcai Su, Zhen Zhang, Guangyu Li et al. | 2025 | Large language models (LLMs) have evolved into agentic systems capable of autonomous tool use and multi-step reasoning for complex problem-solving. However, post-training approaches building upon general-purpose foundation models consistently underperform in agentic tasks, particularly in… |
| Auto-scaling Continuous Memory for GUI Agent | Wenyi Wu, Kun Zhou, Ruoxin Yuan et al. | 2025 | We study how to endow GUI agents with scalable memory that help generalize across unfamiliar interfaces and long-horizon tasks. Prior GUI agents compress past trajectories into text tokens, which balloons context length and misses decisive visual cues (e.g., exact widget size and position). We… |
| Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks | Yuxiang Zhang, Jiangming Shu, Ye Ma et al. | 2025 | Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existing approaches rely on external mechanisms that lack awareness of the agent's reasoning state, leading to suboptimal… |
| IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling | Guoxin Chen, Zile Qiao, Xuanzhong Chen et al. | 2025 | Recent advances in deep-research agents have shown promise for autonomous knowledge construction through dynamic reasoning over external sources. However, existing approaches rely on a mono-contextual paradigm that accumulates all information in a single, expanding context window, leading to… |
| MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning | Qianhao Yuan, Jie Lou, Zichao Li et al. | 2025 | Typical search agents concatenate the entire interaction history into the LLM context, preserving information integrity but producing long, noisy contexts, resulting in high computation and memory costs. In contrast, using only the current turn avoids this overhead but discards essential… |
| AgentFold: Long-Horizon Web Agents with Proactive Context Management | Rui Ye, Zhongwang Zhang, Kuan Li et al. | 2025 | LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that… |
| PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning | Hieu Tran, Zonghai Yao, Nguyen Luong Tran et al. | 2025 | Inspired by the dual-process theory of human cognition from \textit{Thinking, Fast and Slow}, we introduce \textbf{PRIME} (Planning and Retrieval-Integrated Memory for Enhanced Reasoning), a multi-agent reasoning framework that dynamically integrates \textbf{System 1} (fast, intuitive thinking) and… |
| Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval | Jiwen Yu, Jianhong Bai, Yiran Qin et al. | 2025 | Recent advances in interactive video generation have shown promising results, yet existing approaches struggle with scene-consistent memory capabilities in long video generation due to limited use of historical context. In this work, we propose Context-as-Memory, which utilizes historical context… |
| DeepAgent: A General Reasoning Agent with Scalable Toolsets | Xiaoxi Li, Wenxiang Jiao, Jiarui Jin et al. | 2025 | Large reasoning models have demonstrated strong problem-solving abilities, yet real-world tasks often require external tools and long-horizon interactions. Existing agent frameworks typically follow predefined workflows, which limit autonomous and global task completion. In this paper, we introduce… |
| ACON: Optimizing Context Compression for Long-horizon LLM Agents | Minki Kang, Wei-Ning Chen, Dongge Han et al. | 2025 | Large language models (LLMs) are increasingly deployed as agents in dynamic, real-world environments, where success requires both reasoning and effective tool use. A central challenge for agentic tasks is the growing context length, as agents must accumulate long histories of actions and… |
| ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization | Xixi Wu, Kuan Li, Yida Zhao et al. | 2025 | Large Language Model (LLM)-based web agents excel at knowledge-intensive tasks but face a fundamental conflict between the need for extensive exploration and the constraints of limited context windows. Current solutions typically rely on architectural modifications, e.g., internal memory tokens… |
| Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management | Mo Li, L. H. Xu, Qitai Tan et al. | 2025 | Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier parts of the context disrupts reasoning and memory recall. While most research focuses on external memory systems to… |
| MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent | Hongli Yu, Tinghong Chen, Jiangtao Feng et al. | 2025 | Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing. We directly optimize for long-text tasks in an… |
| MemGen: Weaving Generative Latent Memory for Self-Evolving Agents | Guibin Zhang, Muxin Fu, Shuicheng Yan | 2025 | Agent memory shapes how Large Language Model (LLM)-powered agents, akin to the human brain, progressively refine themselves through environment interactions. Existing paradigms remain constrained: parametric memory forcibly adjusts model parameters, and retrieval-based memory externalizes… |
| Conflict-Aware Soft Prompting for Retrieval-Augmented Generation | Eunseong Choi, June Park, Hyeri Lee et al. | 2025 | Retrieval-augmented generation (RAG) enhances the capabilities of large language models (LLMs) by incorporating external knowledge into their input prompts. However, when the retrieved context contradicts the LLM's parametric knowledge, it often fails to resolve the conflict between incorrect… |
| MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation | Hao Shi, Bin Xie, Yingfei Liu et al. | 2025 | Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it and struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived… |
| MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents | Zijian Zhou, Ao Qu, Zhaoxuan Wu et al. | 2025 | Modern language agents must operate over long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This… |
| LM2: Large Memory Models | Jikun Kang, Wenqi Wu, Filippos Christianos et al. | 2025 | This paper introduces the Large Memory Model (LM2), a decoder-only Transformer architecture enhanced with an auxiliary memory module that aims to address the limitations of standard Transformers in multi-step reasoning, relational argumentation, and synthesizing information distributed over long… |
| Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting | Siru Zhong, Weilin Ruan, Ming Jin et al. | 2025 | Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy. While text provides contextual understanding, it often lacks fine-grained temporal details. Conversely, vision captures intricate temporal patterns but lacks semantic… |
| Embodied VideoAgent: Persistent Memory from Egocentric Videos and Embodied Sensors Enables Dynamic Scene Understanding | Yue Fan, Xiaojian Ma, Rongpeng Su et al. | 2024 | This paper investigates the problem of understanding dynamic 3D scenes from egocentric observations, a key challenge in robotics and embodied AI. Unlike prior studies that explored this as long-form video understanding and utilized egocentric video only, we instead propose an LLM-based agent… |
| AI PERSONA: Towards Life-long Personalization of LLMs | Tiannan Wang, Meiling Tao, Ruoyu Fang et al. | 2024 | In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community mainly focus on scaling data and compute for improved capabilities of LLMs, we argue that it is also very important to enable LLM systems, or language… |
| OASIS: Open Agent Social Interaction Simulations with One Million Agents | Ziyi Yang, Zaibin Zhang, Zirui Zheng et al. | 2024 | There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been… |
| Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension | Yongdong Luo, Xiawu Zheng, Guilin Li et al. | 2024 | Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employing GPT-based agents have emerged as promising solutions. However, fine-tuning LVLMs would require extensive… |
| From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs | Alireza Rezazadeh, Zichao Li, Wei Wei et al. | 2024 | Recent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree, an algorithm that leverages a dynamic, tree-structured memory representation to optimize the organization, retrieval… |
| Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism | Yimin Tang, Yurong Xu, Ning Yan et al. | 2024 | Transformers have a quadratic scaling of computational complexity with input size, which limits the input context window size of large language models (LLMs) in both training and inference. Meanwhile, retrieval-augmented generation (RAG) besed models can better handle longer contexts by using a… |
| Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs | Zheng Wang, Zhongyang Li, Zeren Jiang et al. | 2024 | In the age of mobile internet, user data, often referred to as memories, is continuously generated on personal devices. Effectively managing and utilizing this data to deliver services to users is a compelling research topic. In this paper, we introduce a novel task of crafting personalized agents… |
| AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents | Petr Anokhin, Nikita Semenov, Artyom Sorokin et al. | 2024 | Advancements in the capabilities of Large Language Models (LLMs) have created a promising foundation for developing autonomous agents. With the right tools, these agents could learn to solve tasks in new environments by accumulating and updating their knowledge. Current LLM-based agents process… |
| Toward Conversational Agents with Context and Time Sensitive Long-term Memory | Nick Alonso, Tomás Figliolia, Anthony Ndirango et al. | 2024 | There has recently been growing interest in conversational agents with long-term memory which has led to the rapid development of language models that use retrieval-augmented generation (RAG). Until recently, most work on RAG has focused on information retrieval from large databases of texts, like… |
| Enhancing Long-Term Memory using Hierarchical Aggregate Tree for Retrieval Augmented Generation | Aadharsh Aadhithya A, Sachin Kumar S, Soman K. P | 2024 | Large language models have limited context capacity, hindering reasoning over long conversations. We propose the Hierarchical Aggregate Tree memory structure to recursively aggregate relevant dialogue context through conditional tree traversals. HAT encapsulates information from children nodes… |
| Towards Lifelong Dialogue Agents via Timeline-based Memory Management | Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak et al. | 2024 | To achieve lifelong human-agent interaction, dialogue agents need to constantly memorize perceived information and properly retrieve it for response generation (RG). While prior studies focus on getting rid of outdated memories to improve retrieval quality, we argue that such memories provide rich… |
| HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models | Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu et al. | 2024 | In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting. Despite the impressive accomplishments, large language models (LLMs)… |
| INMS: Memory Sharing for Large Language Model based Agents | Hang Gao, Yongfeng Zhang | 2024 | While Large Language Model (LLM) based agents excel at complex tasks, their performance in open-ended scenarios is often constrained by isolated operation and reliance on static databases, missing the dynamic knowledge exchange of human dialogue. To bridge this gap, we propose the INteractive… |
| Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback | Jingwei Sun, Zhixu Du, Yiran Chen | 2024 | Large language models (LLMs) have demonstrated remarkable proficiency in a range of natural language processing tasks. Once deployed, LLMs encounter users with personalized factual knowledge, and such personalized knowledge is consistently reflected through users' interactions with the LLMs. To… |
| From Local to Global: A Graph RAG Approach to Query-Focused Summarization | Darren Edge, Ha Trinh, Newman Cheng et al. | 2024 | The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections. However, RAG fails on global questions directed at an entire text… |
| AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models | Junfeng Fang, Houcheng Jiang, Kun Wang et al. | 2024 | Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates. To achieve this, a prevailing paradigm is the locating-then-editing approach, which first locates influential parameters… |
| $\text{Memory}^3$: Language Modeling with Explicit Memory | Hongkang Yang, Zehao Lin, Wenjin Wang et al. | 2024 | The training and inference of large language models (LLMs) are together a costly process that transports knowledge from raw data to meaningful computation. Inspired by the memory hierarchy of the human brain, we reduce this cost by equipping LLMs with explicit memory, a memory format cheaper than… |
| From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions | Changle Qu, Sunhao Dai, Xiaochi Wei et al. | 2024 | Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent in their pre-training data. In this process, tool documentation plays a crucial role by providing usage instructions for… |
| Planning from Imagination: Episodic Simulation and Episodic Memory for Vision-and-Language Navigation | Yiyuan Pan, Yunzhe Xu, Zhe Liu et al. | 2024 | Humans navigate unfamiliar environments using episodic simulation and episodic memory, which facilitate a deeper understanding of the complex relationships between environments and objects. Developing an imaginative memory system inspired by human mechanisms can enhance the navigation performance… |
| RepairAgent: An Autonomous, LLM-Based Agent for Program Repair | Islem Bouzenia, Premkumar Devanbu, Michael Pradel | 2024 | Automated program repair has emerged as a powerful technique to mitigate the impact of software bugs on system reliability and user experience. This paper introduces RepairAgent, the first work to address the program repair challenge through an autonomous agent based on a large language model… |
| FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making | Yangyang Yu, Zhiyuan Yao, Haohang Li et al. | 2024 | Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks require multiple interactions with a… |
| Towards Completeness-Oriented Tool Retrieval for Large Language Models | Changle Qu, Sunhao Dai, Xiaochi Wei et al. | 2024 | Recently, integrating external tools with Large Language Models (LLMs) has gained significant attention as an effective strategy to mitigate the limitations inherent in their pre-training data. However, real-world systems often incorporate a wide array of tools, making it impractical to input all… |
| ToolGen: Unified Tool Retrieval and Calling via Generation | Renxi Wang, Xudong Han, Lei Ji et al. | 2024 | As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional methods rely on inputting tool descriptions as context, which is constrained by context length and requires separate, often… |
| Agent S: An Open Agentic Framework that Uses Computers Like a Human | Saaket Agashe, Jiuzhou Han, Shuyu Gan et al. | 2024 | We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks. Agent S aims to address three key challenges in automating computer… |
| Titans: Learning to Memorize at Test Time | Ali Behrouz, Peilin Zhong, Vahab Mirrokni | 2024 | Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending to the entire context window, capturing the… |
| ChatHaruhi: Reviving Anime Character in Reality via Large Language Model | Cheng Li, Ziang Leng, Chenxi Yan et al. | 2023 | Role-playing chatbots built on large language models have drawn interest, but better techniques are needed to enable mimicking specific fictional characters. We propose an algorithm that controls language models via an improved prompt and memories of the character extracted from scripts. We… |
| MemGPT: Towards LLMs as Operating Systems | Charles Packer, Sarah Wooders, Kevin Lin et al. | 2023 | Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing… |
| GameGPT: Multi-agent Collaborative Framework for Game Development | Dake Chen, Haoyang Zhang, Hanbin Wang et al. | 2023 | The large language model (LLM) based agents have demonstrated their capacity to automate and expedite software development processes. In this paper, we focus on game development and propose a multi-agent collaborative framework, dubbed GameGPT, to automate game development. While many studies have… |
| Lyfe Agents: Generative agents for low-cost real-time social interactions | Zhao Kaiya, Michelangelo Naim, Jovana Kondic et al. | 2023 | Highly autonomous generative agents powered by large language models promise to simulate intricate social behaviors in virtual societies. However, achieving real-time interactions with humans at a low computational cost remains challenging. Here, we introduce Lyfe Agents. They combine low-cost with… |
| MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework | Sirui Hong, Mingchen Zhuge, Jiaqi Chen et al. | 2023 | Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies… |
| MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation | Junru Lu, Siyu An, Mingbao Lin et al. | 2023 | We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations. We demonstrate a long-range open-domain conversation through iterative… |
| Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models | Qingyue Wang, Yanhe Fu, Yanan Cao et al. | 2023 | Recently, large language models (LLMs), such as GPT-4, stand out remarkable conversational abilities, enabling them to engage in dynamic and contextually relevant dialogues across a wide range of topics. However, given a long conversation, these chatbots fail to recall past information and tend to… |
| S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents | Chen Gao, Xiaochong Lan, Zhihong Lu et al. | 2023 | Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited… |
| RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text | Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui et al. | 2023 | The fixed-size context of Transformer makes GPT models incapable of generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a language-based simulacrum of the recurrence mechanism in RNNs. RecurrentGPT is built upon a large language model (LLM) such as ChatGPT and uses natural… |
| RET-LLM: Towards a General Read-Write Memory for Large Language Models | Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz et al. | 2023 | Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) through their extensive parameters and comprehensive data utilization. However, existing LLMs lack a dedicated memory unit, limiting their ability to explicitly store and retrieve knowledge for… |
| Generative Agents: Interactive Simulacra of Human Behavior | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai et al. | 2023 | Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human… |
| HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge | Haochun Wang, Chi Liu, Nuwa Xi et al. | 2023 | Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks. Nevertheless, LLMs have not yet performed optimally in biomedical domain tasks due to the need for medical expertise in the responses. In… |
| SCM: Enhancing Large Language Model with Self-Controlled Memory Framework | Bing Wang, Xinnian Liang, Jian Yang et al. | 2023 | Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information. To address this limitation, in this paper, we propose the Self-Controlled Memory (SCM) framework to enhance the ability of LLMs to maintain long-term… |
| Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture | Sangjun Park, JinYeong Bak | 2023 | Making neural networks remember over the long term has been a longstanding issue. Although several external memory techniques have been introduced, most focus on retaining recent information in the short term. Regardless of its importance, information tends to be fatefully forgotten over time. We… |
| ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs | Yujia Qin, Shihao Liang, Yining Ye et al. | 2023 | Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning largely focuses on basic language tasks but… |
| Toolformer: Language Models Can Teach Themselves to Use Tools | Timo Schick, Jane Dwivedi-Yu, Roberto Dessì et al. | 2023 | Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel. In this paper… |
| Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization | Weiran Yao, Shelby Heinecke, Juan Carlos Niebles et al. | 2023 | Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objective oriented multi-step tasks on their own, rather than merely responding to queries from human users. Most existing… |
| In-context Autoencoder for Context Compression in a Large Language Model | Tao Ge, Jing Hu, Lei Wang et al. | 2023 | We propose the In-context Autoencoder (ICAE), leveraging the power of a large language model (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes. ICAE is first pretrained using both autoencoding and language modeling… |
| XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model | Ho Kei Cheng, Alexander G. Schwing | 2022 | We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video object segmentation typically only uses one type of feature memory. For videos longer than a minute, a single feature… |
| Editing Factual Knowledge in Language Models | Nicola De Cao, Wilker Aziz, Ivan Titov | 2021 | The factual knowledge acquired during pre-training and stored in the parameters of Language Models (LMs) can be useful in downstream tasks (e.g., question answering or textual inference). However, some facts can be incorrectly induced or become obsolete over time. We present KnowledgeEditor, a… |
| The Unreasonable Effectiveness of Recurrent Neural Networks | |||
| Aman's AI Journal • Primers • Hyperparameter Tuning | |||
| Attention, You Had One Job!! | Vipul Sehgal | DeepSeek shrank it, Moonshot hacked it, and now the whole architecture playbook is up for grabs | |
| OpenAI Model Spec | The Model Spec specifies desired behavior for the models underlying OpenAI's products (including our APIs). | ||
| The Persona Selection Model: Why AI Assistants might Behave like Humans | |||
| Reading List | Vipul Sehgal | Papers, books, articles, and other things I’ve found valuable | |
| OpenReview | Promoting openness in scientific communication and the peer-review process |
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