Must‑Read AI Agent and LLM Research Papers for Deep Understanding

Interesting AI Agents

Generative Agents: Interactive Simulacra of Human Behavior – https://arxiv.org/abs/2304.03442 RoleLLM: Benchmarking, Eliciting, and Enhancing Role‑Playing Abilities of Large Language Models – https://arxiv.org/abs/2310.00746 Role play with large language models – https://www.nature.com/articles/s41586-023-06647-8 Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf – https://arxiv.org/abs/2309.04658 MemGPT: Towards LLMs as Operating Systems – https://arxiv.org/abs/2310.08560 Augmenting Language Models with Long‑Term Memory – https://arxiv.org/abs/2306.07174 Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models –

https://arxiv.org/pdf/2307.16180.pdf

Useful AI Agents

The Rise and Potential of Large Language Model‑Based Agents: A Survey – https://arxiv.org/abs/2309.07864 MetaGPT: Meta Programming for A Multi‑Agent Collaborative Framework – https://arxiv.org/abs/2308.00352 Communicative Agents for Software Development – https://arxiv.org/pdf/2307.07924.pdf Large Language Models Can Self‑Improve – https://arxiv.org/abs/2210.11610 Evaluating Human‑Language Model Interaction – https://arxiv.org/abs/2212.09746 Large Language Models can Learn Rules – https://arxiv.org/abs/2310.07064 AgentBench: Evaluating LLMs as Agents – https://arxiv.org/abs/2308.03688 WebArena: A Realistic Web Environment for Building Autonomous Agents – https://arxiv.org/abs/2307.13854 TableGPT: Towards Unifying Tables, Natural Language and Commands into One GPT –

https://arxiv.org/abs/2307.08674

Task Planning and Decomposition

Chain‑of‑Thought Prompting Elicits Reasoning in Large Language Models – https://arxiv.org/abs/2201.11903 Tree of Thoughts: Deliberate Problem Solving with Large Language Models – https://arxiv.org/abs/2305.10601 Implicit Chain of Thought Reasoning via Knowledge Distillation – https://arxiv.org/abs/2311.01460 ReAct: Synergizing Reasoning and Acting in Language Models – https://arxiv.org/abs/2210.03629 ART: Automatic Multi‑step Reasoning and Tool‑use for Large Language Models – https://arxiv.org/abs/2303.09014 Branch‑Solve‑Merge Improves Large Language Model Evaluation and Generation – https://arxiv.org/abs/2310.15123 WizardLM: Empowering Large Language Models to Follow Complex Instructions –

https://arxiv.org/pdf/2304.12244.pdf

Hallucination

Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models – https://arxiv.org/pdf/2309.01219.pdf Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback – https://arxiv.org/abs/2302.12813 SelfCheckGPT: Zero‑Resource Black‑Box Hallucination Detection for Generative Large Language Models – https://arxiv.org/abs/2303.08896 WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus –

https://arxiv.org/abs/2304.04358

Multimodal Learning

Learning Transferable Visual Models From Natural Language Supervision (CLIP) – https://arxiv.org/abs/2103.00020 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT) – https://arxiv.org/abs/2010.11929 MiniGPT‑v2: Large Language Model as a Unified Interface for Vision‑Language Multi‑task Learning – https://arxiv.org/abs/2310.09478 MiniGPT‑4: Enhancing Vision‑Language Understanding with Advanced Large Language Models – https://arxiv.org/abs/2304.10592 NExT‑GPT: Any‑to‑Any Multimodal LLM – https://arxiv.org/pdf/2309.05519.pdf Visual Instruction Tuning (LLaVA) – https://arxiv.org/pdf/2304.08485.pdf LLaVA‑1.5: Improved Baselines with Visual Instruction Tuning – https://arxiv.org/abs/2310.03744 Sequential Modeling Enables Scalable Learning for Large Vision Models (LVM) – https://arxiv.org/pdf/2312.00785.pdf CoDi‑2: In‑Context, Interleaved, and Interactive Any‑to‑Any Generation – https://arxiv.org/pdf/2311.18775.pdf Neural Discrete Representation Learning (VQ‑VAE) – https://browse.arxiv.org/pdf/1711.00937.pdf Taming Transformers for High‑Resolution Image Synthesis (VQ‑GAN) – https://arxiv.org/abs/2012.09841 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows – https://arxiv.org/abs/2103.14030 BLIP‑2: Bootstrapping Language‑Image Pre‑training with Frozen Image Encoders and Large Language Models – https://browse.arxiv.org/pdf/2301.12597.pdf InstructBLIP: Towards General‑purpose Vision‑Language Models with Instruction Tuning – https://browse.arxiv.org/pdf/2305.06500.pdf ImageBind: One Embedding Space To Bind Them All – https://arxiv.org/abs/2305.05665 Meta‑Transformer: A Unified Framework for Multimodal Learning –

https://arxiv.org/abs/2307.10802

Image/Video Generation

High‑Resolution Image Synthesis with Latent Diffusion Models – https://arxiv.org/pdf/2112.10752.pdf Structure and Content‑Guided Video Synthesis with Diffusion Models (RunwayML Gen1) – https://browse.arxiv.org/pdf/2302.03011.pdf Hierarchical Text‑Conditional Image Generation with CLIP Latents (DaLLE‑2) – https://arxiv.org/pdf/2204.06125.pdf AnimateDiff: Animate Your Personalized Text‑to‑Image Diffusion Models without Specific Tuning – https://arxiv.org/abs/2307.04725 ControlNet: Adding Conditional Control to Text‑to‑Image Diffusion Models – https://arxiv.org/abs/2302.05543 SDXL: Improving Latent Diffusion Models for High‑Resolution Image Synthesis – https://arxiv.org/abs/2307.01952 Zero‑1‑to‑3: Zero‑shot One Image to 3D Object – https://arxiv.org/abs/2303.11328 Scaling Vision Transformers to 22 Billion Parameters – https://arxiv.org/abs/2302.05442 Glow: Generative Flow with Invertible 1×1 Convolutions – https://browse.arxiv.org/pdf/1807.03039.pdf Language Model Beats Diffusion – Tokenizer is Key to Visual Generation – https://arxiv.org/pdf/2310.05737.pdf InstaFlow: One Step is Enough for High‑Quality Diffusion‑Based Text‑to‑Image Generation – https://arxiv.org/pdf/2309.06380.pdf Perceptual Losses for Real‑Time Style Transfer and Super‑Resolution – https://arxiv.org/pdf/1603.08155.pdf CogView: Mastering Text‑to‑Image Generation via Transformers – https://arxiv.org/abs/2105.13290 Diffusion Models for Video Prediction and Infilling –

https://arxiv.org/abs/2206.07696

Foundational Large‑Model Papers

Attention Is All You Need – https://arxiv.org/abs/1706.03762 Sequence to Sequence Learning with Neural Networks – https://arxiv.org/abs/1409.3215 Neural Machine Translation by Jointly Learning to Align and Translate – https://arxiv.org/abs/1409.0473 BERT: Pre‑training of Deep Bidirectional Transformers for Language Understanding – https://arxiv.org/abs/1810.04805 Scaling Laws for Neural Language Models – https://arxiv.org/pdf/2001.08361.pdf Emergent Abilities of Large Language Models – https://openreview.net/pdf?id=yzkSU5zdwD Training Compute‑Optimal Large Language Models (Chinchilla scaling law) – https://arxiv.org/abs/2203.15556 Scaling Instruction‑Fine‑tuned Language Models – https://arxiv.org/pdf/2210.11416.pdf Direct Preference Optimization: Your Language Model is Secretly a Reward Model – https://arxiv.org/pdf/2305.18290.pdf Progress Measures for Grokking via Mechanistic Interpretability – https://arxiv.org/abs/2301.05217 Language Models Represent Space and Time – https://arxiv.org/abs/2310.02207 GLaM: Efficient Scaling of Language Models with Mixture‑of‑Experts – https://arxiv.org/abs/2112.06905 Adam: A Method for Stochastic Optimization – https://arxiv.org/abs/1412.6980 Word2Vec: Efficient Estimation of Word Representations in Vector Space – https://arxiv.org/abs/1301.3781 Distributed Representations of Words and Phrases and Their Compositionality –

https://arxiv.org/abs/1310.4546

GPT Series

Language Models are Few‑Shot Learners (GPT‑3) – https://arxiv.org/abs/2005.14165 Language Models are Unsupervised Multitask Learners (GPT‑2) –

https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

Improving Language Understanding by Generative Pre‑Training (GPT‑1) –

https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf

Training language models to follow instructions with human feedback (InstructGPT) – https://arxiv.org/pdf/2203.02155.pdf Evaluating Large Language Models Trained on Code – https://arxiv.org/pdf/2107.03374.pdf Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond – https://arxiv.org/abs/2304.13712 Instruction Tuning with GPT‑4 – https://arxiv.org/pdf/2304.03277.pdf The Dawn of LMMs: Preliminary Explorations with GPT‑4V (Vision) – https://arxiv.org/abs/2309.17421 Sparks of Artificial General Intelligence: Early Experiments with GPT‑4 – https://arxiv.org/abs/2303.12712 Weak‑to‑Strong Generalization: Eliciting Strong Capabilities With Weak Supervision –

https://arxiv.org/abs/2312.09390

Open‑Source Large Models

LLaMA: Open and Efficient Foundation Language Models – https://arxiv.org/abs/2302.13971 Llama 2: Open Foundation and Fine‑Tuned Chat Models – https://arxiv.org/pdf/2307.09288.pdf Vicuna: An Open‑Source Chatbot Impressing GPT‑4 with 90% ChatGPT Quality – https://lmsys.org/blog/2023-03-30-vicuna/ LMSYS‑Chat‑1M: A Large‑Scale Real‑World LLM Conversation Dataset – https://arxiv.org/abs/2309.11998 Judging LLM‑as‑a‑Judge with MT‑Bench and Chatbot Arena – https://arxiv.org/abs/2306.05685 How Long Can Open‑Source LLMs Truly Promise on Context Length? – https://lmsys.org/blog/2023-06-29-longchat/ Mixtral of Experts – https://mistral.ai/news/mixtral-of-experts/ OpenChat: Advancing Open‑source Language Models with Mixed‑Quality Data – https://arxiv.org/abs/2309.11235 RWKV: Reinventing RNNs for the Transformer Era – https://arxiv.org/abs/2305.13048 Mamba: Linear‑Time Sequence Modeling with Selective State Spaces – https://arxiv.org/ftp/arxiv/papers/2312/2312.00752.pdf Retentive Network: A Successor to Transformer for Large Language Models – https://arxiv.org/abs/2307.08621 Baichuan 2: Open Large‑scale Language Models – https://arxiv.org/abs/2309.10305 GLM‑130B: An Open Bilingual Pre‑trained Model – https://arxiv.org/abs/2210.02414 Qwen Technical Report – https://arxiv.org/abs/2309.16609 Skywork: A More Open Bilingual Foundation Model –

https://arxiv.org/abs/2310.19341

Fine‑Tuning Techniques

Learning to Summarize from Human Feedback – https://arxiv.org/abs/2009.01325 Self‑Instruct: Aligning Language Model with Self‑Generated Instruction – https://arxiv.org/abs/2212.10560 Scaling Down to Scale Up: A Guide to Parameter‑Efficient Fine‑Tuning – https://arxiv.org/abs/2303.15647 LoRA: Low‑Rank Adaptation of Large Language Models – https://arxiv.org/abs/2106.09685 Vera: Vector‑Based Random Matrix Adaptation – https://arxiv.org/pdf/2310.11454.pdf QLoRA: Efficient Finetuning of Quantized LLMs – https://arxiv.org/abs/2305.14314 Chain of Hindsight Aligns Language Models with Feedback – https://arxiv.org/abs/2302.02676 Beyond Human Data: Scaling Self‑Training for Problem‑Solving with Language Models –

https://arxiv.org/pdf/2312.06585.pdf

Performance Optimization

Efficient Memory Management for Large Language Model Serving with PagedAttention (vLLM) – https://arxiv.org/abs/2309.06180 FlashAttention: Fast and Memory‑Efficient Exact Attention with IO‑Awareness – https://arxiv.org/abs/2205.14135 S‑LoRA: Serving Thousands of Concurrent LoRA Adapters – https://arxiv.org/abs/2311.03285 GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism –

https://proceedings.neurips.cc/paper/2019/file/093f65e080a295f8076b1c5722a46aa2-Paper.pdf

Megatron‑LM: Training Multi‑Billion Parameter Language Models Using Model Parallelism – https://arxiv.org/pdf/1909.08053.pdf ZeRO: Memory Optimizations Toward Training Trillion Parameter Models –

https://arxiv.org/pdf/1910.0205.pdf