We maintain a curated collection of papers exploring the path towards Foundation Agents, with a focus on formulating the core concepts and navigating the research landscape.
⌛️ Coming soon: Version 2! We're continuously compiling and updating cutting-edge insights. Feel free to suggest any related work you find valuable!
✨✨✨ Advances and Challenges in Foundation Agents (Paper)
- Core Components of Intelligent Agents
- Self-Enhancement in Intelligent Agents
- Collaborative and Evolutionary Intelligent Systems
- Building Safe and Beneficial AI
- Add SFT,RLHF,PEFT
- ReFT: Reasoning with Reinforced Fine-Tuning, arxiv 2024, [paper] [code]
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning [paper] [code]
- R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning, arxiv 2025, [paper] [code]
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Wei et al. 2022, [paper] [code]
- Voyager: An Open-Ended Embodied Agent with Large Language Models, arxiv 2023, [paper] [code]
- Reflexion: Language Agents with Verbal Reinforcement Learning, NeurIPS 2023, [paper] [code]
- ReAct meets ActRe: Autonomous Annotations of Agent Trajectories for Contrastive Self-Training, arxiv 2024, [paper] [code]
- Generative Agents: Interactive Simulacra of Human Behavior, ACM UIST 2023, [paper] [code]
- CLIP: Learning Transferable Visual Models from Natural Language Supervision, ICML 2021, [paper] [code]
- LLaVA: Visual Instruction Tuning, NeurIPS 2023, [paper] [code]
- CogVLM: Visual Expert for Pretrained Language Models, NeurIPS 2025, [paper] [code]
- Qwen2-Audio Technical Report, arxiv 2024, [paper] [code]
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning, arxiv 2025, [paper] [code]
- SKY-T1: Train Your Own o1 Preview Model Within $450, 2025, [paper] [code]
- Open Thoughts, 2025, [paper] [code]
- LIMO: Less is More for Reasoning, arxiv 2025, [paper] [code]
- STaR: Bootstrapping Reasoning with Reasoning, arxiv 2022, [paper] [code]
- ReST: Reinforced Self-Training for Language Modeling, arxiv 2023, [paper] [code]
- OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models, arxiv 2024, [paper] [code]
- LLaMA-Berry: Pairwise Optimization for o1-like Olympiad-level Mathematical Reasoning, arxiv 2024, [paper] [code]
- RAGEN: Training Agents by Reinforcing Reasoning, arxiv 2025, [paper] [code]
- Open-R1, 2024, [paper] [code]
- Inner Monologue: Embodied Reasoning through Planning with Language Models, CoRL 2023, [paper] [code]
- Self-Refine: Iterative Refinement with Self-Feedback, NeurIPS 2024, [paper] [code]
- Reflexion: Language Agents with Verbal Reinforcement Learning, NeurIPS 2023, [paper] [code]
- ExpeL: LLM Agents Are Experiential Learners, AAAI 2024, [paper] [code]
- AutoManual: Generating Instruction Manuals by LLM Agents via Interactive Environmental Learning, arxiv 2024, [paper] [code]
- ReAct meets ActRe: Autonomous Annotations of Agent Trajectories for Contrastive Self-Training, arxiv 2024, [paper] [code]
- ReAct: Synergizing Reasoning and Acting in Language Models, arxiv 2022, [paper] [code]
- Markov Chain of Thought for Efficient Mathematical Reasoning, arxiv 2024, [paper] [code]
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models, NeurIPS 2023, [paper] [code]
- Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models, ICML 2024, [paper] [code]
- Reasoning via Planning (RAP): Improving Language Models with World Models, EMNLP 2023, [paper] [code]
- Graph of Thoughts: Solving Elaborate Problems with Large Language Models, AAAI 2023, [paper] [code]
- Path of Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models, arxiv 2024, [paper] [code]
- On the Diagram of Thought, arxiv 2024, [paper] [code]
- Self-Consistency Improves Chain of Thought Reasoning in Language Models, ICLR 2023, [paper] [code]
- Self-Refine: Iterative Refinement with Self-Feedback, NeurIPS 2024, [paper] [code]
- Progressive-Hint Prompting Improves Reasoning in Large Language Models, arxiv 2023, [paper] [code]
- On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks, arxiv 2024, [paper] [code]
- Chain-of-Verification Reduces Hallucination in Large Language Models, ICLR 2024 Workshop, [paper] [code]
- MathPrompter: Mathematical Reasoning Using Large Language Models, ACL 2023, [paper] [code]
- LLMs Can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought, arxiv 2024, [paper] [code]
- Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models, COLING 2025, [paper] [code]
- Chain of Thought Prompting Elicits Reasoning in Large Language Models, NeurIPS 2022, [paper] [code]
- Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models, ICLR 2024, [paper] [code]
- Ask Me Anything: A Simple Strategy for Prompting Language Models, arxiv 2022, [paper] [code]
- Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources, arxiv 2023, [paper] [code]
- Self-Explained Keywords Empower Large Language Models for Code Generation, arxiv 2024, [paper] [code]
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, arxiv 2025, [paper] [code]
- Claude 3.7 Sonnet, 2025, [paper] [code]
- OpenAI o1 System Card, arxiv 2024, [paper] [code]
- Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking, arxiv 2024, [paper] [code]
- Chain of Continuous Thought (Coconut): Training Large Language Models to Reason in a Continuous Latent Space, arxiv 2024, [paper] [code]
- Describe, Explain, Plan and Select (DEPS): Interactive Planning with Large Language Models, arxiv 2023, [paper] [code]
- ProgPrompt: Generating Situated Robot Task Plans Using Large Language Models, ICRA 2023, [paper] [code]
- ADAPT: As-Needed Decomposition and Planning with Language Models, arxiv 2023, [paper] [code]
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models, NeurIPS 2023, [paper] [code]
- Reasoning via Planning (RAP): Improving Language Models with World Models, EMNLP 2023, [paper] [code]
- TravelPlanner: A Benchmark for Real-World Planning with Language Agents, ICML 2024, [paper] [code]
- PDDL—The Planning Domain Definition Language, 1998, [paper] [code]
- Mind2Web: Towards a Generalist Agent for the Web, NeurIPS 2023, [paper] [code]
- RecAgent: A Novel Simulation Paradigm for Recommender Systems, TOIS 2025, [paper] [code]
- CoPS: Cognitive Personalized Search: Integrating Large Language Models with an Efficient Memory Mechanism, WWW 2024, [paper]
- MemoryBank: Enhancing Large Language Models with Long‑Term Memory, AAAI 2024, [paper] [code]
- Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents, UIST 2023 Adjunct, [paper]
- VideoAgent: A Memory‑augmented Multimodal Agent for Video Understanding, ECCV 2024, [paper] [code]
- WorldGPT: Empowering LLM as Multimodal World Model, arXiv 2024, [paper] [code]
- Agent S: An Open Agentic Framework that Uses Computers Like a Human, arXiv 2024, [paper][code]
- OS‑Copilot: Towards Generalist Computer Agents with Self‑Improvement, ICLR 2024 LLMAgents Workshop, [paper] [code]
- MuLan: Multimodal‑LLM Agent for Progressive and Interactive Multi‑Object Diffusion, arXiv 2024, [paper] [code]
- MemGPT: Towards LLMs as Operating Systems, arXiv 2023, [paper] [code]
- KARMA: Augmenting Embodied AI Agents with Long‑ and Short‑Term Memory Systems, arXiv 2024, [paper] [code]
- LSFS: From Commands to Prompts: LLM‑based Semantic File System, ICLR 2025, [paper] [code]
- OSCAR: Operating System Control via State‑Aware Reasoning and Re‑Planning, ICLR 2025, [paper]
- RCI: Language Models Can Solve Computer Tasks (Recursive Criticism and Improvement), NeurIPS 2023, [paper] [code]
- Generative Agent: Interactive Simulacra of Human Behavior, UIST 2023, [paper] [code]
- RLP: Reflective Linguistic Programming (RLP): A Stepping Stone in Socially‑Aware AGI, arXiv 2023, [paper]
- CALYPSO: LLMs as Dungeon Master’s Assistants, AIIDE 2023, [paper] [code]
- HiAgent: Hierarchical Working Memory Management for Solving Long‑Horizon Agent Tasks with Large Language Model, arXiv 2024, [paper] [code]
- AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents, arXiv 2024, [paper] [code]
- RecAgent: see above
- HippoRAG: Neurobiologically Inspired Long‑Term Memory for Large Language Models, NeurIPS 2024, [paper] [code]
- MobileGPT: Augmenting LLM with Human‑like App Memory for Mobile Task Automation, ACM MobiCom 2024, [paper]
- MemoryBank: see above
- Episodic Memory Verbalization Using Hierarchical Representations of Life‑Long Robot Experience, arXiv 2024, [paper] [code]
- MrSteve: Instruction‑Following Agents in Minecraft with What‑Where‑When Memory, ICLR 2025, [paper] (project code pending)
- AAG: Analogy‑Augmented Generation for LLMs, ACL ARR 2024, [paper]
- Cradle: Empowering Foundation Agents towards General Computer Control, ICLR 2025, [paper] [code]
- JARVIS‑1: Open‑World Multi‑Task Agents with Memory‑Augmented Multimodal Language Models, NeurIPS 2023 ALOE Workshop, [paper] [code]
- LARP: Language‑Agent Role Play for Open‑World Games, arXiv 2023, [paper]
- HiAgent: Hierarchical Working Memory Management for Solving Long‑Horizon Agent Tasks with Large Language Model, ACL 2025, [paper] [code]
- LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation, arXiv 2024, [paper]
- A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts, ICML 2024, [paper] [code]
- Leveraging Metamemory Mechanisms for Enhanced Data-Free Code Generation in LLMs, arXiv 2025, [paper]
- ExpeL: LLM Agents Are Experiential Learners, AAAI 2024, [paper] [code]
- Unified Mind Model: Reimagining Autonomous Agents in the LLM Era, arXiv 2025, [paper]
- Meta‑Learning: A Survey, PAMI 2021, [paper]
- ``My agent understands me better'': Integrating Dynamic Human‑Like Memory Recall and Consolidation in LLM‑Based Agents, CHI 2024, [paper] [code]
- AgentCoord: Visually Exploring Coordination Strategy for LLM‑Based Multi‑Agent Collaboration, arXiv 2024, [paper] [code]
- Memory Sharing for Large Language Model Based Agents, arXiv 2024, [paper]
- Understanding Long Videos via LLM‑Powered Entity Relation Graphs, arXiv 2025, [paper]
- A-MEM: Agentic Memory for LLM Agents, arXiv 2025, [paper] [code]
- Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generation, Humanoids 2024, [paper]
- Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks, NeurIPS 2024, [paper] [code]
- Optimus-2: Multimodal Minecraft Agent with Goal-Observation-Action Conditioned Policy, CVPR 2025, [paper] [code]
- JARVIS-1: Multimodal Memory-Augmented Open-World Agent, NeurIPS 2023 ALOE Workshop, [paper] [code]
- Agent S: An Open Agentic Framework that Uses Computers Like a Human, ICLR 2025 Poster, [paper] [code]
- OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning, ICLR 2025, [paper]
- R2D2: Remembering, Reflecting and Dynamic Decision Making for Web Agents, ACL 2025, [paper]
- Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks, ACL ARR 2025 (submitted), [paper] [code]
- SummEdits: Edit-based Factuality-Oriented Summarization, EMNLP 2023, [paper] [code]
- SCM: Enhancing Large Language Model with Self-Controlled Memory Framework, DASFAA 2025, [paper] [code]
- Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation, arXiv 2024, [paper]
- Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models, Neurocomputing 2025, [paper]
- KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents, Findings of NAACL 2025, [paper] [code]
- AoTD: Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation, CVPR 2025, [paper]
- LDPD: Language-Driven Policy Distillation, ICLR 2024 LLM-Agents Workshop, [paper]
- Sub-goal Distillation: Bridging Large Language Models and Goal-Conditioned RL for Long-Horizon Tasks, CoLLAs 2024, [paper]
- MAGDi: Memory-Augmented Generative Debugger, ICML 2024, [paper] [code]
- Lyfe Agents: Generative Agents for Low-Cost Real-Time Social Interactions, arXiv 2023, [paper]
- TiM: Think-in-Memory Language Models, ICLR 2024 (submitted), [paper]
- MemoryBank: Enhancing Large Language Models with Long-Term Memory, AAAI 2024, [paper] [code]
- S³: Social-Network Simulation System with Large Language Model-Empowered Agents, arXiv 2023, [paper] [code]
- ``My agent understands me better'': see above
- HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models, NeurIPS 2024, [paper] [project] [code]
- TradingGPT: Multi-Agent System with Layered Memory for Simulated Stock Trading, arXiv 2023, [paper]
- LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory, ICLR 2025, [paper] [code]
- SeCom: Memory Construction and Retrieval for Long-Term Personalized Conversational Agents, ICLR 2025, [paper] [project] [blog]
- Large Memory Layers with Product Keys, NeurIPS 2019, [paper] [code]
- OSAgent: Copiloting Operating System with LLM-based Agent, IJCNN 2024, [paper]
- Neural Machine Translation by Jointly Learning to Align and Translate, ICLR 2015, [paper]
- ``My agent understands me better'': see above
- Hopfield Networks is All You Need, NeurIPS 2020, [paper] [code]
- Hopfield Networks is All You Need, ICLR 2021, [paper]
- Neural Turing Machines for the Remaining Useful Life Estimation Problem, Computers in Industry 2022, [paper] [code]
- MemoryLLM: Towards Self-Updatable Large Language Models, ICML 2024, [paper] [code]
- SELF-PARAM: Self-Parameterized Retrofitting for Large Language Models, ICLR 2025, [paper] [code]
- MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation, The Web Conference (WWW) 2025, [paper] [code]
- Learning to (Learn at Test Time): RNNs with Expressive Hidden States, ICLR 2025, [paper] [code]
- Titans: Learning to Memorize at Test Time, arXiv 2024, [paper] [unofficial code]
- R³Mem: A Third-Order Memory for Large Language Models, ICLR 2025 (to appear), [paper]
- RAGLAB: Research Platform for Retrieval-Augmented Generation, EMNLP 2024, [paper] [code]
- When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories, ACL 2023, [paper]
- Atlas: Few-shot Learning with Retrieval Augmented Language Models, arXiv 2022, [paper] [code]
- Personalized Large Language Model Assistant with Evolving Conditional Memory, COLING 2025, [paper]
- Recurrent Memory Transformer, Neurips 2022, [paper]
- Scaling Transformer to 1M tokens and beyond with RMT, arXiv 2023, [paper]
- Adapting Language Models to Compress Contexts, EMNLP 2023, [paper]
- In-context Autoencoder for Context Compression in a Large Language Model, ICLR 2024, [paper]
- Learning to Compress Prompts with Gist Tokens, NeurIPS 2023, [paper]
- CompAct: Compressing Retrieved Documents Actively for Question Answering, EMNLP 2024, [paper]
- Banishing LLM Hallucinations Requires Rethinking Generalization, arXiv 2024, [paper]
- Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture, ICML 2024, [paper]
- Mixture of A Million Experts, arXiv 2024, [paper]
- Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models, arXiv 2024, [paper]
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018, [paper] [code]
- RoBERTa: A Robustly Optimized BERT Pretraining Approach, 2019, [paper] [code]
- ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, [paper] [code]
- Deep Residual Learning for Image Recognition, CVPR 2016, [paper] [code]
- End-to-End Object Detection with Transformers, 2020, [paper] [code]
- Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection, 2024, [paper] [code]
- ViViT: A Video Vision Transformer, 2021, [paper] [code]
- VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training, 2022, [paper] [code]
- FastSpeech 2: Fast and High-Quality End-to-End Text to Speech, 2020, [paper] [code]
- Seamless: Multilingual Expressive and Streaming Speech Translation, 2023, [paper] [code]
- wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations, 2020, [paper] [code]
- Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models, 2023, [paper] [code]
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face, 2024, [paper] [code]
- MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action, 2023, [paper] [code]
- ViperGPT: Visual Inference via Python Execution for Reasoning, 2023, [paper] [code]
- AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head, 2024, [paper] [code]
- LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents, 2025, [paper] [code]
- Learning Transferable Visual Models From Natural Language Supervision, 2021, [paper] [code]
- Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision , 2021, [paper]
- Improving Image Generation with Better Captions, 2023, [paper]
- VisualBERT: A Simple and Performant Baseline for Vision and Language, 2019, [paper] [code]
- VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding, 2021, [paper] [code]
- Phenaki: Variable Length Video Generation From Open Domain Textual Description, 2022, [paper] [code]
- Make-A-Video: Text-to-Video Generation without Text-Video Data, 2022, [paper] [code]
- Wav2CLIP: Learning Robust Audio Representations From CLIP, 2022, [paper] [code]
- VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text, 2021, [paper] [code]
- AudioCLIP: Extending CLIP to Image, Text and Audio , 2022, [paper] [code]
- CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation, 2022, [paper] [code]
- Point-E: A System for Generating 3D Point Clouds from Complex Prompts, 2022, [paper] [code]
- MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning, 2023, [paper] [code]
- LLaVA-NeXT: Improved reasoning, OCR, and world knowledge, 2024, [paper] [code]
- CogVLM2: Visual Language Models for Image and Video Understanding, 2024, [paper] [code]
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution, 2024, [paper] [code]
- Generative Multimodal Models are In-Context Learners, 2024, [paper] [code]
- TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones, 2023, [paper] [code]
- MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices, 2023, [paper] [code]
- MiniCPM-V: A GPT-4V Level MLLM on Your Phone, 2024, [paper] [code]
- OmniParser for Pure Vision Based GUI Agent , 2024, [paper] [code]
- CLIPort: What and Where Pathways for Robotic Manipulation, 2022, [paper] [code]
- RT-1: Robotics Transformer for Real-World Control at Scale, 2022, [paper] [code]
- Open-World Object Manipulation using Pre-trained Vision-Language Models, 2023, [paper] [code]
- Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation, 2023, [paper] [code]
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion, 2023, [paper] [code]
- PaLM-E: An Embodied Multimodal Language Model, 2023, [paper] [code]
- MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World, 2024, [paper] [code]
- Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities, 2024, [paper] [code]
- SpeechVerse: A Large-scale Generalizable Audio Language Model, 2024, [paper]
- UniAudio 1.5: Large Language Model-driven Audio Codec is A Few-shot Audio Task Learner, 2024, [paper] [code]
- Qwen2-Audio Technical Report, 2024, [paper] [code]
- AudioLM: a Language Modeling Approach to Audio Generation, 2024, [paper] [code]
- Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming, 2024, [paper] [code]
- SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities, 2023, [paper] [code]
- ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities, 2023, [paper] [code]
- PandaGPT: One Model To Instruction-Follow Them All, 2023, [paper] [code]
- Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration , 2023, [paper] [code]
- LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment, 2023, [paper] [code]
- UnIVAL: Unified Model for Image, Video, Audio and Language Tasks, 2023, [paper] [code]
- X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages, 2023, [paper] [code]
- PointLLM: Empowering Large Language Models to Understand Point Clouds, 2025, [paper] [code]
- MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors, 2024, [paper] [code]
- NExT-GPT: Any-to-Any Multimodal LLM, 2023, [paper] [code]
- Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action, 2024, [paper] [code]
- CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation, 2024, [paper] [code]
- ModaVerse: Efficiently Transforming Modalities with LLMs, 2024, [paper] [code]
DINO-WM [358]: Video World Models on Pre-trained Visual Features Enable Zero-Shot Planning, arxiv 2024, [paper], [[code][]]
SAPIEN [351]: A Simulated Part-based Interactive Environment, CVPR 2020, [paper], [[code][]]
MuZero [349]: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, Nature 2020, [paper], [[code][]]
GR-2 [357]: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation, arxiv 2024, [paper], [[code][]]
COAT [356]: Discovery of the Hidden World with Large Language Models, arxiv 2024, [paper], [[code][]]
AutoManual [108]: Generating Instruction Manuals by LLM Agents via Interactive Environmental Learning, arxiv 2024, [paper], [[code][]]
PILCO [355]: A Model-Based and Data-Efficient Approach to Policy Search, ICML 2011, [paper], [[code][]]
ActRe [49]: ReAct meets ActRe: Autonomous Annotations of Agent Trajectories for Contrastive Self-Training, arxiv 2024, [paper], [[code][]]
World Models [348]: World Models, NeurIPS 2018, [paper], [[code][]]
Dreamer [350]: Dream to Control: Learning Behaviors by Latent Imagination, ICLR 2020, [paper], [[code][]]
Diffusion WM [353]: Diffusion for World Modeling: Visual Details Matter in Atari, arxiv 2024, [paper], [[code][]]
GQN [354]: Neural Scene Representation and Rendering, Science 2018, [paper], [[code][]]
Daydreamer [352]: World Models for Physical Robot Learning, CoRL 2023, [paper], [[code][]]
-
ReAct: Synergizing Reasoning and Acting in Language Models, ICLR 2023, [paper] [code]
-
AutoGPT: Build, Deploy, and Run AI Agents, Github, [code]
-
Reflexion: Language Agents with Verbal Reinforcement Learning, NeurIPS 2023, [paper] [code]
-
LLM+P: Empowering Large Language Models with Optimal Planning Proficiency, arXiv 2023, [paper] [code]
-
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, ICLR 2023, [paper] [code]
-
ChatDev: Communicative Agents for Software Development, ACL 2024, [paper] [code]
-
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, NeurIPS 2025, [paper] [code]
-
OpenHands: An Open Platform for AI Software Developers as Generalist Agents, arXiv 2024, [paper] [code]
-
Generative Agents: Interactive Simulacra of Human Behavior, UIST 2023, [paper] [code]
-
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, COLM 2024, [paper] [code]
-
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge, NeurIPS 2022, [paper] [code]
-
Voyager: An Open-Ended Embodied Agent with Large Language Models, TMLR 2024, [paper] [code]
-
SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models, arXiv 2024, [paper] [code]
-
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models, NeurIPS 2025, [paper] [code]
-
MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action, arXiv 2023, [paper] [code]
-
ViperGPT: Visual Inference via Python Execution for Reasoning, ICCV 2023, [paper] [code]
-
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models, arXiv 2023, [paper] [code]
-
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face, NeurIPS 2023, [paper] [code]
-
WebGPT: Browser-assisted question-answering with human feedback, arXiv 2021, [paper] [blog]
-
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents, NeurIPS 2022, [paper] [code]
-
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis, ICLR 2024, [paper]
-
Mind2Web: Towards a Generalist Agent for the Web, NeurIPS 2025, [paper] [code]
-
Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception, arXiv 2024, [paper] [code]
-
AppAgent: Multimodal Agents as Smartphone Users, arXiv 2023, [paper] [code]
-
UFO: A UI-Focused Agent for Windows OS Interaction, arXiv 2024, [paper] [code]
-
OmniParser for Pure Vision Based GUI Agent, arXiv 2024, [paper] [code]
-
A Survey of NL2SQL with Large Language Models: Where are we, and where are we going?, arXiv 2024, [paper] [Handbook]
-
Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search, ICML 2025, [paper]
-
NL2SQL-Bugs: A Benchmark for Detecting Semantic Errors in NL2SQL Translation, SIGKDD 2025, [paper] [code]
-
EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing, arXiv 2025, [paper] [code]
-
nvBench 2.0: A Benchmark for Natural Language to Visualization under Ambiguity, arXiv 2025, [paper] [code]
-
The Dawn of Natural Language to SQL: Are We Fully Ready?, VLDB 2024, [paper] [code]
-
Are Large Language Models Good Statisticians?, NIPS 2024, [paper] [code]
-
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models, EMNLP 2022, [paper] [code]
-
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments, ACL 2023, [paper] [code]
-
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs, NeurIPS 2025, [paper] [project]
-
Spider 2.0: Evaluating language models on real-world enterprise text-to-sql workflows., ICLR 2025, [paper] [code]
-
Middleware for llms: Tools are instrumental for language agents in complex environments., EMNLP 2024, [paper] [code]
-
RT-1: Robotics Transformer for Real-World Control at Scale, RSS 2023, [paper] [project]
-
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control, CoRL 2023, [paper] [project]
-
Open X-Embodiment: Robotic Learning Datasets and RT-X Models, arXiv 2023, [paper] [project]
-
GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation, arXiv 2024, [paper] [project]
-
π0: A vision-language-action flow model for general robot control., arXiv 2024, [paper]
-
Do as I can, not as I say Grounding language in robotic affordances, CoRL 2022, [paper] [project]
-
Voxposer: Composable 3d value maps for robotic manipulation with language models., CoRL 2023, [paper] [code]
-
Embodiedgpt: Vision-language pre-training via embodied chain of thought., NeurIPS 2023, [paper] [project]
-
CoT: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, NeurIPS 2022, [paper]
-
ReAct: React: Synergizing reasoning and acting in language models, arXiv 2022, [paper] [project]
-
Auto-CoT: Automatic Chain of Thought Prompting in Large Language Models, ICLR 2023, [paper] [code]
-
ToT: Tree of Thoughts: Deliberate Problem Solving with Large Language Models, NeurIPS 2023, [paper] [code]
-
GoT: Graph of Thoughts: Solving Elaborate Problems with Large Language Models, AAAI 2023, [paper] [code]
-
LearnAct: Empowering Large Language Model Agents through Action Learning, arXiv 2024, [paper] [code]
-
CoA: Improving Multi-Agent Debate with Sparse Communication Topology, arXiv 2024, [paper]
-
Least-to-Most: Least-to-Most Prompting Enables Complex Reasoning in Large Language Models, ICLR 2023, [paper]
-
HuggingGPT: Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, NeurIPS 2024, [paper] [code]
-
Plan-and-Solve: Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models, ACL 2023, [paper] [code]
-
ProgPrompt: Progprompt: Generating situated robot task plans using large language models, ICRA 2023, [paper] [project]
-
Generative Agents: Generative agents: Interactive simulacra of human behavio, arXiv 2023, [paper] [code]
-
MetaGPT: Meta{GPT}: Meta Programming for Multi-Agent Collaborative Framework, ICLR 2023, [paper] [code]
-
ChatDev: ChatDev: Communicative Agents for Software Development, ACL 2024, [paper] [code]
-
SWE-Agent: SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, arXiv 2024, [paper] [project]
-
Reflexion: Reflexion: language agents with verbal reinforcement learning, NeurIPS 2023, [paper] [code]
-
Self-refine: Self-refine: Iterative refinement with self-feedback, NeurIPS 2024, [paper] [code]
-
GPTSwarm: GPTSwarm: Language Agents as Optimizable Graphs, ICML 2024, [paper] [project]
-
RT-1: RT-1: Robotics Transformer for Real-World Control at Scale, arXiv 2022, [paper] [project]
-
RT-2: RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control, arXiv 2023, [paper] [project]
-
RT-X: Open x-embodiment: Robotic learning datasets and rt-x models, arXiv 2023, [paper] [project]
-
GR-2: GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation, arXiv 2024, [paper] [project]
-
LAM: Large Action Models: From Inception to Implementation, arXiv 2024, [paper] [code]
-
CogACT: CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation, arXiv 2024, [paper] [project]
-
RT-H: RT-H: Action Hierarchies Using Language, arXiv 2024, [paper] [project]
-
OpenVLA: OpenVLA: An Open-Source Vision-Language-Action Model, arXiv 2024, [paper] [project]
-
$\pi_0$ :$\pi_0$ : A Vision-Language-Action Flow Model for General Robot Control, arXiv 2024, [paper] [project] -
UniAct: Universal Actions for Enhanced Embodied Foundation Models, CVPR 2025, [paper] [code]
-
RLHF: Training language models to follow instructions with human feedback, NeurIPS 2022, [paper]
-
DPO: Direct preference optimization: Your language model is secretly a reward model, NeurIPS 2023, [paper]
-
RLFP: Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own, CoRL 2024, [paper] [project]
-
ELLM: Guiding pretraining in reinforcement learning with large language models, ICML 2023, [paper] [code]
-
GenSim: Gensim: Generating robotic simulation tasks via large language models, arXiv 2023, [paper] [project]
-
LEA: Reinforcement learning-based recommender systems with large language models for state reward and action modeling, ACM 2024, [paper]
-
MLAQ: Empowering LLM Agents with Zero-Shot Optimal Decision-Making through Q-learning, ICLR 2025, [paper]
-
KALM: KALM: Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts, NeurIPS 2024, [paper] [project]
-
When2Ask: Enabling intelligent interactions between an agent and an LLM: A reinforcement learning approach, RLC 2024, [paper]
-
Eureka: Eureka: Human-level reward design via coding large language models, ICLR 2024, [paper] [project]
-
ArCHer: ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL, arXiv 2024, [paper] [project]
-
LLaRP: Large Language Models as Generalizable Policies for Embodied Tasks, ICLR 2024, [paper] [project]
-
GPTSwarm: GPTSwarm: Language Agents as Optimizable Graphs, ICML 2024, [paper] [project]
- Training language models to follow instructions with human feedback, 2022, [paper] [code]
- Offline Regularised Reinforcement Learning for Large Language Models Alignment, 2024, [paper]
- sDPO: Don't Use Your Data All at Once, 2024, [paper]
- A General Theoretical Paradigm to Understand Learning from Human Preferences, 2024, [paper]
- β-DPO: Direct Preference Optimization with Dynamic β, 2025, [paper]
- ORPO: Monolithic Preference Optimization without Reference Model, 2024, [paper] [code]
- Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences, 2024, [paper]
- Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints, 2023, [paper]
- Some things are more CRINGE than others: Iterative Preference Optimization with the Pairwise Cringe Loss, 2023, [paper]
- From r to Q∗: Your Language Model is Secretly a Q-Function, 2024, [paper] [code]
- PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning, 2024, [paper]
- SimPO: Simple Preference Optimization with a Reference-Free Reward, 2025, [paper] [code]
- LiPO: Listwise Preference Optimization through Learning-to-Rank, 2024, [paper] [code]
- RRHF: Rank Responses to Align Language Models with Human Feedback without tears, 2023, [paper] [code]
- Preference Ranking Optimization for Human Alignment, 2024, [paper] [code]
- Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization, 2024, [paper]
- Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning, 2024, [paper] [code]
- Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs, 2024, [paper] [code]
- Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation, 2024, [paper] [code]
- Nash Learning from Human Feedback, 2023, [paper]
- A Minimaximalist Approach to Reinforcement Learning from Human Feedback, 2024, [paper]
- Training language models to follow instructions with human feedback, 2022, [paper] [code]
- Offline Regularised Reinforcement Learning for Large Language Models Alignment, 2024, [paper]
- β-DPO: Direct Preference Optimization with Dynamic β, 2025, [paper]
- ORPO: Monolithic Preference Optimization without Reference Model, 2024, [paper] [code]
- PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning, 2024, [paper]
- SimPO: Simple Preference Optimization with a Reference-Free Reward, 2025, [paper] [code]
- Nash Learning from Human Feedback, 2023, [paper]
- A Minimaximalist Approach to Reinforcement Learning from Human Feedback, 2024, [paper]
- Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints, 2023, [paper]
- Curiosity-driven Exploration by Self-supervised Prediction, 2017, [paper] [code]
- Self-Supervised Exploration via Disagreement, 2019, [paper] [code]
- Planning to Explore via Self-Supervised World Models, 2020, [paper] [code]
- Liir: Learning individual intrinsic reward in multi-agent reinforcement learning, 2019, [paper]
- CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning, 2019, [paper] [code]
- Skew-Fit: State-Covering Self-Supervised Reinforcement Learning, 2019, [paper]
- DISCERN: Diversity-based Selection of Centroids for k-Estimation and Rapid Non-stochastic Clustering, 2021, [paper] [code]
- Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models, 2024, [paper] [code]
- KTO: Model Alignment as Prospect Theoretic Optimization, 2024, [paper] [code]
- Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models, 2024, [paper] [code]
- Exploration by Random Network Distillation, 2018, [paper] [code]
- Understanding Chain-of-Thought in LLMs through Information Theory, 2024, [paper]
- VIME: Variational Information Maximizing Exploration, 2016, [paper] [code]
- EMI: Exploration with Mutual Information, 2019, [paper] [code]
- Model-Based Active Exploration, 2019, [paper] [code]
- KTO: Model Alignment as Prospect Theoretic Optimization, 2024, [paper] [code]
- RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback, 2023, [paper]
- Constitutional AI: Harmlessness from AI Feedback, 2022, [paper] [code]
- Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint, 2023, [paper]
- RLHF Workflow: From Reward Modeling to Online RLHF, 2024, [paper] [code]
-
Prompt optimization in multi-step tasks (promst): Integrating human feedback and preference alignment, EMNLP 2024 [paper]
-
StraGo: Harnessing strategic guidance for prompt optimization, EMNLP 2024 [paper]
-
Connecting large language models with evolutionary algorithms yields powerful prompt optimizers, ICLR 2024 [paper]
-
Large Language Models Are Human-Level Prompt Engineers, ICLR 2023 [paper]
-
Automatic Prompt Optimization with "Gradient Descent" and Beam Search, EMNLP 2023 [paper]
-
GPTSwarm: Language Agents as Optimizable Graphs, ICML 2024 [paper]
-
Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution, ICML 2024 [paper]
-
Teaching Large Language Models to Self-Debug, ICLR 2024 [paper]
-
Large Language Models as Optimizers, ICLR 2024 [paper]
-
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines, ICLR 2024 [paper]
-
Prompt Engineering a Prompt Engineer, Findings of ACL 2024 [paper]
-
Prompt optimization in multi-step tasks (promst): Integrating human feedback and preference alignment, EMNLP 2024 [paper]
-
StraGo: Harnessing strategic guidance for prompt optimization, EMNLP 2024 [paper]
-
Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs, EMNLP 2024 [paper]
-
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs, NeurIPS 2024 [paper]
-
Optimizing Generative AI by Backpropagating Language Model Feedback, Nature [paper]
-
Are Large Language Models Good Prompt Optimizers?, arxiv [paper]
-
An Explanation of In-context Learning as Implicit Bayesian Inference, ICLR 2022, [paper]
-
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?, EMNLP 2022, [paper]
-
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes, NeurIPS 2022, [paper]
-
What Learning Algorithm Is In-Context Learning? Investigations with Linear Models, ICLR 2023, [paper]
-
Transformers Learn In-Context by Gradient Descent, ICML 2023, [paper]
-
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression, NeurIPS 2024, [paper]
-
Reflexion: language agents with verbal reinforcement learning, NeurIPS 2023, [paper]
-
Self-refine: Iterative refinement with self-feedback, NeurIPS 2023, [paper]
-
ReAct: Synergizing Reasoning and Acting in Language Models, ICLR 2023, [paper]
-
Tree of thoughts: Deliberate problem solving with large language models, NeurIPS 2023, [paper]
-
Voyager: An Open-Ended Embodied Agent with Large Language Models, TMLR 2024, [paper]
-
Let's Verify Step by Step, ICLR 2024, [paper]
-
MetaGPT: Meta programming for multi-agent collaborative framework, ICLR 2024, [paper]
-
Camel: Communicative agents for “mind” exploration of large language model society, NeurIPS 2023, [paper]
-
ChatDev: Communicative Agents for Software Development, ACL 2024, [paper]
-
Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, NeurIPS 2023, [paper]
-
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation, COLM 2024, [paper]
-
Quiet-star: Language models can teach themselves to think before speaking, CoRR 2024, [paper]
-
**Text2reward: Automated dense reward function generation for reinforcement learning. **, ICLR 2024, [paper]
-
Extracting prompts by inverting LLM outputs, ACL 2024, [paper]
-
Aligning large language models via self-steering optimization., arxiv 2024, [paper]
-
Aligning large language models via self-steering optimization., arxiv 2024, [paper]
-
Are Large Language Models Good Statisticians?, NeurIPS 2024, [paper]
-
nvBench 2.0: A Benchmark for Natural Language to Visualization under Ambiguity, arxiv 2025, [paper]
-
Srag: Structured retrieval-augmented generation for multi-entity question answering over wikipedia graph, arxiv 2025, [paper]
-
Fine-grained retrieval-augmented generation for visual question answering, arxiv 2025, [paper]
-
xLAM: A Family of Large Action Models to Empower AI Agent Systems, arxiv 2024, [paper]
-
Automated design of agentic systems., arxiv 2024, [paper]
-
LIRE: listwise reward enhancement for preference alignment, ACL 2024, [paper]
-
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers, arXiv 2024, [paper]
-
SciAgents: Automating Scientific Discovery Through Bioinspired Multi-Agent Intelligent Graph Reasoning, Advanced Materials 2024, [paper]
-
Genesis: Towards the Automation of Systems Biology Research, arXiv 2024, [paper]
-
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, arXiv 2024, [paper]
-
Agent Laboratory: Using LLM Agents as Research Assistants, arXiv 2025, [paper]
-
ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning, arXiv 2025, [paper]
-
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories, Matter 2024, [paper]
-
Towards an AI co-scientist, arXiv 2025, [paper]
-
Autonomous mobile robots for exploratory synthetic chemistry, Nature 2024, [paper]
-
Delocalized, asynchronous, closed-loop discovery of organic laser emitters, Science 2024, [paper]
-
The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation, bioRxiv 2024, [paper]
-
Solving olympiad geometry without human demonstrations, Nature 2024, [paper]
-
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data, arXiv 2024, [paper]
-
Data Interpreter: An LLM Agent For Data Science, arXiv 2024, [paper]
-
Curie: Toward Rigorous and Automated Scientific Experimentation with AI Agents, arXiv 2025, [paper], [github]
- RECONCILE (Chen et al., 2023)
- LLM-Game-Agent (Lan et al., 2023)
- BattleAgentBench (Wang et al., 2024)
- Generative Agents (Park et al., 2023)
- Agent Hospital (Li et al., 2024)
- MedAgents (Tang et al., 2024)
- MEDCO (Wei et al., 2024)
- MetaGPT (Hong et al., 2023)
- ChatDev (Qian et al., 2024)
- Agent Laboratory (Schmidgall et al., 2025)
- The Virtual Lab (Swanson et al., 2024)
- CoELA (Zhang et al., 2023)
- VillagerAgent (Dong et al., 2024)
- LLM-Coordination (Agashe et al., 2024)
- MetaGPT (Hong et al., 2023)
- ChatDev (Qian et al., 2024)
- Generative Agents (Park et al., 2023)
- S-Agents (Chen et al., 2024)
- SciAgents (Ghafarollahi et al., 2024)
- AppAgent (Chi et al., 2023)
- MetaGPT (Hong et al., 2023)
- AgentBench (Liu et al., 2023)
- VAB (Liu et al., 2024)
- TaskWeaver (Qiao et al., 2024)
- HULA (Takerngsaksiri et al., 2025)
- MCP (Anthropic)
- Agora (Marro et al., 2024)
- IoA (Chen et al., 2024)
- MEDCO (Wei et al., 2024)
- Agent Hospital (Li et al., 2024)
- Welfare Diplomacy (Mukobi et al., 2023)
- MedAgents (Tang et al., 2024)
- DyLAN (Liu et al., 2023)
- GPTSwarm (Zhuge et al., 2024)
- CodeR (Chen et al., 2024)
- Oasis (Yang et al., 2024)
- Agent Laboratory (Schmidgall et al., 2025)
- The Virtual Lab (Swanson et al., 2024)
- OASIS (Yang et al., 2024)
- Generative Agents (Park et al., 2023)
- Welfare Diplomacy (Mukobi et al., 2023)
- LLM-Game-Agent (Lan et al., 2023)
- BattleAgentBench (Wang et al., 2024)
- MEDCO (Wei et al., 2024)
- Agent Hospital (Li et al., 2024)
- MedAgents (Tang et al., 2024)
- S-Agents (Chen et al., 2024)
- Dittos (Leong et al., 2024)
- PRELUDE (Gao et al., 2024)
- Generative Agents (Park et al., 2023)
- Welfare Diplomacy (Mukobi et al., 2023)
- LLM-Game-Agent (Lan et al., 2023)
- BattleAgentBench (Wang et al., 2024)
- Agent Hospital (Li et al., 2024)
- Agent Laboratory (Schmidgall et al., 2025)
- MEDCO (Wei et al., 2024)
- MBPP (dataset-mbpp)
- HotpotQA (dataset-hotpot-qa)
- MATH (dataset-math)
- SVAMP (dataset-svamp)
- MultiArith (dataset-multiarith)
- Collab-Overcooked (Sun et al., 2025)
- REALM-Bench (Geng et al., 2025)
- PARTNR (Chang et al., 2024)
- VillagerBench (Dong et al., 2024)
- AutoArena (Zhao et al., 2024)
- MultiagentBench (Zhu et al., 2025)
-
Jailbreak attacks and defenses against large language models: A survey, arXiv 2024, [paper]
-
Universal and transferable adversarial attacks on aligned language models, arXiv 2023, [paper]
-
Boosting jailbreak attack with momentum, arXiv 2024, [paper]
-
Improved techniques for optimization-based jailbreaking on large language models, arXiv 2024, [paper]
-
Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting, arXiv 2024, [paper]
-
Open the Pandora's Box of LLMs: Jailbreaking LLMs through Representation Engineering, arXiv 2024, [paper]
-
DROJ: A Prompt-Driven Attack against Large Language Models, arXiv 2024, [paper]
-
Autodan: Generating stealthy jailbreak prompts on aligned large language models, arXiv 2023, [paper]
-
POEX: Policy Executable Embodied AI Jailbreak Attacks, arXiv 2024, [paper]
-
Jailbroken: How does LLM safety training fail?, NeurIPS 2023, [paper]
-
Jailbreaking black box large language models in twenty queries, arXiv 2023, [paper]
-
Jailbreaking large language models against moderation guardrails via cipher characters, NeurIPS 2024, [paper]
-
Visual adversarial examples jailbreak aligned large language models, AAAI 2024, [paper]
-
POEX: Policy Executable Embodied AI Jailbreak Attacks, arXiv 2024, [paper]
-
Autodan: Generating stealthy jailbreak prompts on aligned large language models, arXiv 2023, [paper]
-
Guard: Role-playing to generate natural-language jailbreakings to test guideline adherence of large language models, arXiv 2024, [paper]
-
Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models, arXiv 2024, [paper]
-
Rt-attack: Jailbreaking text-to-image models via random token, arXiv 2024, [paper]
-
Not what you've signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection, AISec@CCS 2023, [paper
-
Automatic and universal prompt injection attacks against large language models, arXiv 2024, [paper]
-
Optimization-based prompt injection attack to LLM-as-a-judge, CCS 2024, [paper]
-
Benchmarking indirect prompt injections in tool-integrated large language model agents, arXiv 2024, [paper]
-
Trust No AI: Prompt Injection Along The CIA Security Triad, arXiv 2024, [paper]
-
Empirical analysis of large vision-language models against goal hijacking via visual prompt injection, arXiv 2024, [paper]
-
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition, arXiv 2024, [paper]
-
Ignore this title and HackAPrompt: Exposing systemic vulnerabilities of LLMs through a global prompt hacking competition, EMNLP 2023, [paper]
-
Not what you've signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection, AISec@CCS 2023, [paper]
-
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models, arXiv 2025, [paper]
-
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models, arXiv 2024, [paper]
-
Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems, arXiv 2024, [paper]
-
Adversarial search engine optimization for large language models, arXiv 2024, [paper]
-
Survey of hallucination in natural language generation, ACM Computing Surveys 2023, [paper]
-
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions, arXiv 2023, [paper]
-
DELUCIONQA: Detecting Hallucinations in Domain-specific Question Answering, Findings of EMNLP 2023, [paper]
-
Deficiency of large language models in finance: An empirical examination of hallucination, Failure Modes Workshop @ NeurIPS 2023, [paper]
-
MetaGPT: Meta Programming for Multi-Agent Collaborative Framework, ICLR 2023, [paper]
-
Hallucination is inevitable: An innate limitation of large language models, arXiv 2024, [paper]
-
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models, arXiv 2024, [paper]
-
Truth-Aware Context Selection: Mitigating the Hallucinations of Large Language Models Being Misled by Untruthful Contexts, arXiv 2024, [paper]
-
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis, arXiv 2024, [paper]
-
HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild, arXiv 2024, [paper]
-
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models, ICLR 2023, [paper]
-
Mitigating object hallucination in large vision-language models via classifier-free guidance, arXiv 2024, [paper]
-
When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour, arXiv 2023, [paper]
-
HallusionBench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models, CVPR 2024, [paper]
-
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models, arXiv 2024, [paper]
-
AI alignment: A comprehensive survey, arXiv 2023, [paper]
-
Specification Gaming: The Flip Side of AI Ingenuity, DeepMind Blog 2020, [paper]
-
The alignment problem from a deep learning perspective, arXiv 2022, [paper]
-
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!, arXiv 2024, [paper]
-
Agent Alignment in Evolving Social Norms, arXiv 2024, [paper]
-
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch, arXiv 2024, [paper]
-
Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment, arXiv 2023, [paper]
-
Assessing the brittleness of safety alignment via pruning and low-rank modifications, arXiv 2024, [paper]
-
AI alignment: A comprehensive survey, arXiv 2023, [paper]
-
Fine-tuning aligned language models compromises safety, even when users do not intend to!, arXiv 2023, [paper]
-
Fundamental limitations of alignment in large language models, arXiv 2023, [paper]
-
Weight poisoning attacks on pre-trained models, ACL 2020, [paper]
-
Badedit: Backdooring large language models by model editing, arXiv 2024, [paper]
-
The philosopher's stone: Trojaning plugins of large language models, arXiv 2023, [paper]
-
Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm, arXiv 2024, [paper]
-
Poisoned ChatGPT finds work for idle hands: Exploring developers’ coding practices with insecure suggestions from poisoned AI models, IEEE S&P 2024, [paper
-
Secret Collusion Among Generative AI Agents, arXiv 2024, [paper]
-
Exploiting the Vulnerability of Large Language Models via Defense-Aware Architectural Backdoor, arXiv 2024, [paper]
-
Poisoning language models during instruction tuning, ICML 2023, [paper]
-
Agentpoison: Red-teaming LLM agents via poisoning memory or knowledge bases, NeurIPS 2025, [paper]
-
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems, arXiv 2025, [paper]
-
PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning, arXiv 2024, [paper]
-
The dark side of human feedback: Poisoning large language models via user inputs, arXiv 2024, [paper]
-
Scaling laws for data poisoning in LLMs, arXiv 2024, [paper]
-
Talk too much: Poisoning large language models under token limit, arXiv 2024, [paper]
-
Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data, arXiv 2024, [paper]
-
Sleeper agents: Training deceptive LLMs that persist through safety training, arXiv 2024, [paper]
-
Wipi: A new web threat for LLM-driven web agents, arXiv 2024, [paper]
-
Exploring backdoor attacks against large language model-based decision making, arXiv 2024, [paper]
-
When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations, arXiv 2024, [paper]
-
Backdooring instruction-tuned large language models with virtual prompt injection, NAACL 2024, [paper]
-
Membership inference attacks against machine learning models, IEEE S&P 2017, [paper]
-
The secret sharer: Evaluating and testing unintended memorization in neural networks, USENIX Security 2019, [paper]
-
Label-only membership inference attacks, ICML 2021, [paper]
-
Practical membership inference attacks against fine-tuned large language models via self-prompt calibration, arXiv 2023, [paper]
-
Membership inference attacks from first principles, IEEE S&P 2022, [paper]
-
Membership inference attacks on machine learning: A survey, ACM Computing Surveys 2022, [paper]
-
Extracting training data from large language models, USENIX Security 2021, [paper]
-
Special characters attack: Toward scalable training data extraction from large language models, arXiv 2024, [paper]
-
Ethicist: Targeted training data extraction through loss smoothed soft prompting and calibrated confidence estimation, arXiv 2023, [paper]
-
Language model inversion, arXiv 2023, [paper]
-
Privacy risks of general-purpose language models, IEEE S&P 2020, [paper]
-
Quantifying memorization across neural language models, arXiv 2022, [paper]
-
Stealing part of a production language model, arXiv 2024, [[paper](https://arxiv.org
-
Ignore previous prompt: Attack techniques for language models, TSRML@NeurIPS 2022, [paper]
-
Prompt Stealing Attacks Against Text-to-Image Generation Models, USENIX Security 2024, [paper]
-
Safeguarding System Prompts for LLMs, arXiv 2024, [paper]
-
InputSnatch: Stealing Input in LLM Services via Timing Side-Channel Attacks, arXiv 2024, [paper]
-
Effective prompt extraction from language models, arXiv 2023, [paper]
-
Last one standing: A comparative analysis of security and privacy of soft prompt tuning, lora, and in-context learning, arXiv 2023, [paper]
-
LLM app store analysis: A vision and roadmap, ACM TOSEM 2024, [paper]
-
Prsa: Prompt reverse stealing attacks against large language models, arXiv 2024, [paper]
-
Prompt Leakage effect and defense strategies for multi-turn LLM interactions, arXiv 2024, [paper]
-
Investigating the prompt leakage effect and black-box defenses for multi-turn LLM interactions, arXiv 2024, [paper]
-
Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models, arXiv 2024, [paper]
-
Pleak: Prompt leaking attacks against large language model applications, CCS 2024, [paper]
-
Stealing User Prompts from Mixture of Experts, arXiv 2024, [paper]
-
Extracting Prompts by Inverting LLM Outputs, arXiv 2024, [paper]
-
An LLM can Fool Itself: A Prompt-Based Adversarial Attack, arXiv 2023, [paper]
-
Revisiting Character-level Adversarial Attacks for Language Models, ICML 2024, [paper]
-
Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery, NeurIPS 2024, [paper]
-
Universal and transferable adversarial attacks on aligned language models, arXiv 2023, [paper]
-
Image hijacks: Adversarial images can control generative models at runtime, arXiv 2023, [paper]
-
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs, arXiv 2025, [paper]
-
Dissecting Adversarial Robustness of Multimodal LM Agents, ICLR 2025, [paper]
-
Poltergeist: Acoustic adversarial machine learning against cameras and computer vision, IEEE S&P 2021, [paper]
-
Inaudible adversarial perturbation: Manipulating the recognition of user speech in real time, arXiv 2023, [paper]
-
The Silent Manipulator: A Practical and Inaudible Backdoor Attack against Speech Recognition Systems, ACM Multimedia 2023, [paper]
-
Enrollment-stage backdoor attacks on speaker recognition systems via adversarial ultrasound, IEEE IoT Journal 2023, [paper]
-
Ultrabd: Backdoor attack against automatic speaker verification systems via adversarial ultrasound, ICPADS 2023, [paper
-
DolphinAttack: Inaudible voice commands, CCS 2017, [paper]
-
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles, arXiv 2024, [paper]
-
Rocking drones with intentional sound noise on gyroscopic sensors, USENIX Security 2015, [paper]
-
Adversarial attacks on multi-agent communication, ICCV 2021, [paper]
-
GPS location spoofing attack detection for enhancing the security of autonomous vehicles, IEEE VTC-Fall 2021, [paper]
-
Grounding large language models in interactive environments with online reinforcement learning, ICML 2023, [paper]
-
Bias and fairness in large language models: A survey, Computational Linguistics 2024, [paper]
-
Domain generalization using causal matching, ICML 2021, [paper]
-
GEM: Glare or gloom, I can still see you—End-to-end multi-modal object detection, IEEE RA-L 2021, [paper]
-
NPHardEval: Dynamic benchmark on reasoning ability of large language models via complexity classes, arXiv 2023, [paper]
-
Modeling opinion misperception and the emergence of silence in online social system, PLOS ONE 2024, [paper]
-
Bridging the domain gap for multi-agent perception, ICRA 2023, [paper]
-
Cooperative and competitive biases for multi-agent reinforcement learning, arXiv 2021, [paper]
-
Model-agnostic multi-agent perception framework, ICRA 2023, [paper]
-
Mutual influence between language and perception in multi-agent communication games, PLOS Computational Biology 2022, [paper]
-
A new era in LLM security: Exploring security concerns in real-world LLM-based systems, arXiv 2024, [paper]
-
Wipi: A new web threat for LLM-driven web agents, arXiv 2024, [paper]
-
Identifying the risks of LM agents with an LM-emulated sandbox, arXiv 2023, [paper]
-
Not what you've signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection, AISec@CCS 2023, [paper]
-
Benchmarking indirect prompt injections in tool-integrated large language model agents, arXiv 2024, [paper]
-
Identifying the risks of LM agents with an LM-emulated sandbox, arXiv 2023, [paper]
-
Toolsword: Unveiling safety issues of large language models in tool learning across three stages, arXiv 2024, [paper]
-
Benchmarking indirect prompt injections in tool-integrated large language model agents, arXiv 2024, [paper]
-
Agentpoison: Red-teaming LLM agents via poisoning memory or knowledge bases, NeurIPS 2025, [paper]
-
ConfusedPilot: Confused deputy risks in RAG-based LLMs, arXiv 2024, [paper]
-
PoisonedRAG: Knowledge corruption attacks to retrieval-augmented generation of large language models, arXiv 2024, [paper]
-
Machine against the RAG: Jamming retrieval-augmented generation with blocker documents, arXiv 2024, [paper]
-
BadRAG: Identifying vulnerabilities in retrieval augmented generation of large language models, arXiv 2024, [paper]
-
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models, arXiv 2024, [paper]
-
Whispers in Grammars: Injecting Covert Backdoors to Compromise Dense Retrieval Systems, arXiv 2024, [paper]
-
Autonomous vehicles: Sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions, JCP 2023, [paper]
-
Engineering challenges ahead for robot teamwork in dynamic environments, Applied Sciences 2020, [paper]
-
On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions, PeerJ Computer Science 2021, [paper]
-
Security and privacy in cyber-physical systems: A survey, IEEE Communications Surveys & Tutorials 2017, [paper]
-
Adversarial objects against LiDAR-based autonomous driving systems, arXiv 2019, [paper]
-
Learning to walk in the real world with minimal human effort, arXiv 2020, [paper]
-
Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science, arXiv 2024, [paper]
-
A new era in LLM security: Exploring security concerns in real-world LLM-based systems, arXiv 2024, [paper]
-
Demystifying RCE vulnerabilities in LLM-integrated apps, CCS 2024, [paper]
-
Wipi: A new web threat for LLM-driven web agents, arXiv 2024, [paper]
-
Application of large language models to DDoS attack detection, SPCPS 2023, [paper]
-
Coercing LLMs to do and reveal (almost) anything, arXiv 2024, [paper]
-
Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science, arXiv 2024, [paper]
-
EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage, arXiv 2024, [paper]
-
AdvWeb: Controllable Black-Box Attacks on VLM-Powered Web Agents, arXiv 2024, [paper]
-
AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection, arXiv 2025, [paper]
-
Multi-Agent Risks from Advanced AI, arXiv 2025, [paper]
-
Hoodwinked: Deception and cooperation in a text-based game for language models, arXiv 2023, [paper]
-
Attacking deep reinforcement learning with decoupled adversarial policy, IEEE TDSC 2022, [paper]
-
Secure consensus of multi-agent systems under denial-of-service attacks, Asian Journal of Control 2023, [paper]
-
A Perfect Collusion Benchmark: How can AI agents be prevented from colluding with information-theoretic undetectability?, Multi-Agent Security Workshop @ NeurIPS 2023, [paper]
