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总结Prompt&LLM论文，开源数据&模型，AIGC应用

DecryptPrompt

如果LLM的突然到来让你感到沮丧，不妨读下主目录的Choose Your Weapon Survival Strategies for Depressed AI Academics 持续更新以下内容，Star to keep updated~

LLM资源汇总

跟着博客读论文

和AI一起搞事情

论文汇总

Harness

Agensh: Scaling Organizational Intelligence to 1,024 Agents
Procedural Graphs: Self-Evolving Execution Structures for LLM Agents
LoopArena
SKILL.state: Scalable Long-Horizon Agent Skills
A Programming Paradigm for Spatiotemporal Composability
AutoSaddler: Automatic Harness Optimization
Meta Harness
Loop Engineering: The Anthropic Playbook
Agora: Git as Shared Memory for Collective AutoResearch
Hybrid Reasoning Agent
JevAgentHarness
The Agentic Scientist: Orchestrating Coding

Skill Evolution

WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
SkillOS: Learning Skill Curation for Self-Evolving Agents
Evolution Strategies for Skill Learning

图像生成

Neural Discrete Representation Learning
Denoising Diffusion Probabilistic Models
Scalable Diffusion Models with Transformers
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
High-Resolution Image Synthesis with Latent Diffusion Models

Post Train（和COT，RL有交集）

Inference Scaling
- An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
- Are More LM Calls All You Need? Towards the Scaling Properties of Compound AI Systems
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters :star:
- Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
- Planning In Natural Language Improves LLM Search For Code Generation
- ReST-MCTS∗ : LLM Self-Training via Process Reward Guided Tree Search
- AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
- Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
- The Surprising Effectiveness of Test-Time Training for Abstract Reasoning
- Inference Scaling for Long-Context Retrieval Augmented Generation
- Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
- InfAlign: Inference-aware language model alignment
- Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
- What type of inference is planning?
- Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
- PROVABLE SCALING LAWS OF FEATURE EMERGENCE FROM LEARNING DYNAMICS OF GROKKING
- Do Machine Learning Models Memorize or Generalize?
slow thinking COT
- O1 Replication Journey: A Strategic Progress Report – Part 1 :star:
- Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
- A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
- Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
- Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces
- Training Large Language Models to Reason in a Continuous Latent Space
- Beyond A∗ : Better Planning with Transformers via Search Dynamics Bootstrapping
- o1-Coder: an o1 Replication for Coding
- Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
- Sky-T1: Train your own O1 preview model within $450
- Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
- rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking :star:
- Demystifying Long Chain-of-Thought Reasoning in LLMs
- Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
- Huggingface Open R1
- CODEI/O: Condensing Reasoning Patterns via Code Input-Output Prediction
- Training Language Models to Reason Efficiently
- s1: Simple test-time scaling
- Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking
- ALPHAONE: Reasoning Models Thinking Slow and Fast at Test Time
O3 Related
- Competitive Programming with Large Reasoning Models
RL COT原理
- SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
- Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
- Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
- All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning
- Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
- Think Deep, Not Just Long:Measuring LLM Reasoning Effort via Deep-Thinking Tokens
R1 Reprodce
- LogicRL: Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning
- SimpleR1
- Huggingface Open R1
- DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models
- Think Only When You Need with Large Hybrid-Reasoning Models
- Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties
- Skywork Open Reasoner 1 Technical Report
- Learning to Reason: Training LLMs with GPT-OSS or DeepSeek R1 Reasoning Traces
RL Agent
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
- ToolRL: Reward is All Tool Learning Needs
- ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
- ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
- Improving Multi-Turn Tool Use with Reinforcement Learning
- WebThinker: Empowering Large Reasoning Models with Deep Research Capability
- Reinforcement Learning for Machine Learning Engineering Agents
- AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
- rStar2-Agent: Agentic Reasoning Technical Report
- The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
- IN-THE-FLOW AGENTIC SYSTEM OPTIMIZATION FOR EFFECTIVE PLANNING AND TOOL USE
- UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
- PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold
- DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
- Thinking with Programming Vision: Towards a Unified View for Thinking with Images
- Scaling Agent Learning via Experience Synthesis
- CaveAgent: Transforming LLMs into Stateful Runtime Operators
经验学习
- Welcome to the Era of Experience
- Agent Learning via Early Experience
其他训练方式
- QWENLONG-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
- REWARDBENCH 2: Advancing Reward Model Evaluation
- Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
- DiffusionNFT: Online Diffusion Reinforcement with Forward Process
- EVOLUTION STRATEGIES AT SCALE: LLM FINETUNING BEYOND REINFORCEMENT LEARNING
- Learning to Reason Across Parallel Samples for LLM Reasoning
- PARAM∆ FOR DIRECT WEIGHT MIXING: POST-TRAIN LARGE LANGUAGE MODEL AT ZERO COST
- LaSeR: Reinforcement Learning with Last-Token Self-Rewarding
- The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains
RL Overview
- Reinforcement Learning: An Overview
- Towards a Unified View of Large Language Model Post-Training
RL数据集
- ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning

Context Engineer

Lost in Compaction
A Survey of Context Engineering for Large Language Models
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Scaling Long-Horizon LLM Agent via Context-Folding
Towards a Science of Scaling Agent Systems
Budget-Aware Tool-Use Enables Effective Agent Scaling
Context Engineering 2.0
End-to-End Test-Time Training for Long Context
Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
Building Effective AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned
Meta-Harness End-to-End Optimization of Model Harnesses
The-Complete-Guide-to-Building-Skill-for-Claude

New Model Architecture

Nemotron-Labs-Diffusion: A Tri-Mode Language Model
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Less is More: Recursive Reasoning with Tiny Networks
Continuous Thought Machines
TiDAR: Think in Diffusion, Talk in Autoregression
Nested Learning: The Illusion of Deep Learning Architectures

主流LLMS和预训练

Kimi K2
GLM-130B: AN OPEN BILINGUAL PRE-TRAINED MODEL
PaLM: Scaling Language Modeling with Pathways
PaLM 2 Technical Report
GPT-4 Technical Report
Backpack Language Models
LLaMA: Open and Efficient Foundation Language Models
Llama 2: Open Foundation and Fine-Tuned Chat Models
Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
Mistral 7B
Ziya2: Data-centric Learning is All LLMs Need
MEGABLOCKS: EFFICIENT SPARSE TRAINING WITH MIXTURE-OF-EXPERTS
TUTEL: ADAPTIVE MIXTURE-OF-EXPERTS AT SCALE
Phi1- Textbooks Are All You Need :star:
Phi1.5- Textbooks Are All You Need II: phi-1.5 technical report
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Gemini: A Family of Highly Capable Multimodal Models
In-Context Pretraining: Language Modeling Beyond Document Boundaries
LLAMA PRO: Progressive LLaMA with Block Expansion
QWEN TECHNICAL REPORT
Fewer Truncations Improve Language Modeling
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
Phi-4 Technical Report
Byte Latent Transformer: Patches Scale Better Than Tokens
Qwen2.5 Technical Report
DeepSeek-V3 Technical Report
Mixtral of Experts
DeepSeek_R1 :star:
KIMI K1.5: SCALING REINFORCEMENT LEARNING WITH LLMS :star:
CWM: An Open-Weights LLM for Research on Code Generation with World Models
DeepSeek V3.2 Tech Report
DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

思维链 (prompt_chain_of_thought)

基础&进阶用法
- 【zero-shot-COT】 Large Language Models are Zero-Shot Reasoners :star:
- 【few-shot COT】 Chain of Thought Prompting Elicits Reasoning in Large Language Models :star:
- 【SELF-CONSISTENCY 】IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS
- 【LEAST-TO-MOST】 PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS :star:
- 【TOT】Tree of Thoughts: Deliberate Problem Solving with Large Language Models :star:
- 【Plan-and-Solve】 Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
- 【Verify-and-Edit】: A Knowledge-Enhanced Chain-of-Thought Framework
- 【GOT】Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models
- 【TOMT】Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
- 【LAMBADA】: Backward Chaining for Automated Reasoning in Natural Language
- 【AOT】Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models :star:
- 【GOT】Graph of Thoughts: Solving Elaborate Problems with Large Language Models :star:
- 【PHP】Progressive-Hint Prompting Improves Reasoning in Large Language Models
- 【HtT】LARGE LANGUAGE MODELS CAN LEARN RULES :star:
- 【DIVSE】DIVERSITY OF THOUGHT IMPROVES REASONING ABILITIES OF LARGE LANGUAGE MODELS
- 【CogTree】From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
- 【Step-Back】Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models :star:
- 【OPRO】LARGE LANGUAGE MODELS AS OPTIMIZERS :star:
- 【BOT】Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
- Abstraction-of-Thought Makes Language Models Better Reasoners
- 【SymbCoT】Faithful Logical Reasoning via Symbolic Chain-of-Thought
- 【XOT】EVERYTHING OF THOUGHTS : DEFYING THE LAW OF PENROSE TRIANGLE FOR THOUGHT GENERATION
- 【IoT】Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
- 【DOT】On the Diagram of Thought
- 【ROT】Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up.
- Thinking Forward and Backward: Effective Backward Planning with Large Language Models
- 【KR】K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning
- 【Self-Discover】SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures
- 【Theory-of-Mind】HOW FAR ARE LARGE LANGUAGE MODELS FROMAGENTS WITH THEORY-OF-MIND?
- 【PC-SUBQ】Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
- Reverse Thinking Makes LLMs Stronger Reasoners
- Chain of Draft: Thinking Faster by Writing Less
- Atom of Thoughts for Markov LLM Test-Time Scaling
非传统COT问题分解方向
- Decomposed Prompting A MODULAR APPROACH FOR Solving Complex Tasks
- Successive Prompting for Decomposing Complex Questions
分领域COT [Math, Code, Tabular, QA]
- Solving Quantitative Reasoning Problems with Language Models
- SHOW YOUR WORK: SCRATCHPADS FOR INTERMEDIATE COMPUTATION WITH LANGUAGE MODELS
- Solving math word problems with processand outcome-based feedback
- CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
- T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering
- LEARNING PERFORMANCE-IMPROVING CODE EDITS
- Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
原理分析
- Chain of Thought Empowers Transformers to Solve Inherently Serial Problems :star:
- Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters :star:
- TEXT AND PATTERNS: FOR EFFECTIVE CHAIN OF THOUGHT IT TAKES TWO TO TANGO
- Towards Revealing the Mystery behind Chain of Thought: a Theoretical Perspective
- Large Language Models Can Be Easily Distracted by Irrelevant Context
- Chain-of-Thought Reasoning Without Prompting
- Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
- Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
- To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning :star:
- Why think step by step? Reasoning emerges from the locality of experience
- Internal Consistency and Self-Feedback in Large Language Models: A Survey :star:
- Iteration Head: A Mechanistic Study of Chain-of-Thought :star:
- The Impact of Reasoning Step Length on Large Language Models :star:
- Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?
- Compressed Chain of Thought: Efficient Reasoning Through Dense Representations
- Do LLMs Really Think Step-by-step In Implicit Reasoning?
- Cognitive Foundations for Reasoning and Their Manifestation in LLMs
小模型COT蒸馏
- Specializing Smaller Language Models towards Multi-Step Reasoning :star:
- Teaching Small Language Models to Reason
- Large Language Models are Reasoning Teachers
- Distilling Reasoning Capabilities into Smaller Language Models
- The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
- Distilling System 2 into System 1
COT样本自动构建/选择
- AutoCOT：AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS
- Active Prompting with Chain-of-Thought for Large Language Models
- COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING
COT能力学习
- Large Language Models Can Self-Improve
- Training Chain-of-Thought via Latent-Variable Inference
- Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
- STaR: Self-Taught Reasoner Bootstrapping ReasoningWith Reasoning
- V-STaR: Training Verifiers for Self-Taught Reasoners
- THINK BEFORE YOU SPEAK: TRAINING LANGUAGE MODELS WITH PAUSE TOKENS
- SELF-DIRECTED SYNTHETIC DIALOGUES AND REVISIONS TECHNICAL REPORT
- COT-SELF-INSTRUCT: BUILDING HIGH-QUALITY SYNTHETIC PROMPTS FOR REASONING AND NON-REASONING TASKS
others
- OlaGPT Empowering LLMs With Human-like Problem-Solving abilities
- Challenging BIG-Bench tasks and whether chain-of-thought can solve them
- Large Language Models are Better Reasoners with Self-Verification
- ThoughtSource A central hub for large language model reasoning data
- Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

Self-Evolution

Recursive Self-Improvement via On-Policy Distillation for Reasoning
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Alpha Evolve
Can Large Reasoning Models Self-Train
Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO
Evolution Strategies at the Hyperscale
Guided Self-Evolving LLMs with Minimal Human Supervision

RLHF

Deepmind
- Teaching language models to support answers with verified quotes
- sparrow, Improving alignment of dialogue agents via targetd human judgements :star:
- STATISTICAL REJECTION SAMPLING IMPROVES PREFERENCE OPTIMIZATION
- Reinforced Self-Training (ReST) for Language Modeling
- SLiC-HF: Sequence Likelihood Calibration with Human Feedback
- CALIBRATING SEQUENCE LIKELIHOOD IMPROVES CONDITIONAL LANGUAGE GENERATION
- REWARD DESIGN WITH LANGUAGE MODELS
- Final-Answer RL Solving math word problems with processand outcome-based feedback
- Solving math word problems with process- and outcome-based feedback
- Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
- BOND: Aligning LLMs with Best-of-N Distillation
- RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
- Generative Verifiers: Reward Modeling as Next-Token Prediction
- Training Language Models to Self-Correct via Reinforcement Learning
openai
- PPO: Proximal Policy Optimization Algorithms :star:
- Deep Reinforcement Learning for Human Preference
- Fine-Tuning Language Models from Human Preferences
- learning to summarize from human feedback
- InstructGPT: Training language models to follow instructions with human feedback :star:
- Scaling Laws for Reward Model Over optimization :star:
- WEAK-TO-STRONG GENERALIZATION: ELICITING STRONG CAPABILITIES WITH WEAK SUPERVISION :star:
- PRM：Let's verify step by step :star:
- Training Verifiers to Solve Math Word Problems [PRM的前置依赖]
- OpenAI Super Alignment Blog
- LLM Critics Help Catch LLM Bugs :star:
- PROVER-VERIFIER GAMES IMPROVE LEGIBILITY OF LLM OUTPUTS
- Rule Based Rewards for Language Model Safety
- Self-critiquing models for assisting human evaluators
Anthropic
- A General Language Assistant as a Laboratory for Alignmen
- Measuring Progress on Scalable Oversight or Large Language Models
- Red Teaming Language Models to Reduce Harms Methods,Scaling Behaviors and Lessons Learned
- Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback :star:
- Constitutional AI Harmlessness from AI Feedback :star:
- Pretraining Language Models with Human Preferences
- The Capacity for Moral Self-Correction in Large Language Models
- Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainin
AllenAI, RL4LM：IS REINFORCEMENT LEARNING (NOT) FOR NATURAL LANGUAGE PROCESSING BENCHMARKS
改良方案
- RRHF: Rank Responses to Align Language Models with Human Feedback without tears
- Chain of Hindsight Aligns Language Models with Feedback
- AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
- RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
- RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
- Training Socially Aligned Language Models in Simulated Human Society
- RAIN: Your Language Models Can Align Themselves without Finetuning
- Generative Judge for Evaluating Alignment
- PEERING THROUGH PREFERENCES: UNRAVELING FEEDBACK ACQUISITION FOR ALIGNING LARGE LANGUAGE MODELS
- SALMON: SELF-ALIGNMENT WITH PRINCIPLE-FOLLOWING REWARD MODELS
- Large Language Model Unlearning :star:
- ADVERSARIAL PREFERENCE OPTIMIZATION :star:
- Preference Ranking Optimization for Human Alignment
- A Long Way to Go: Investigating Length Correlations in RLHF
- ENABLE LANGUAGE MODELS TO IMPLICITLY LEARN SELF-IMPROVEMENT FROM DATA
- REWARD MODEL ENSEMBLES HELP MITIGATE OVEROPTIMIZATION
- LEARNING OPTIMAL ADVANTAGE FROM PREFERENCES AND MISTAKING IT FOR REWARD
- ULTRAFEEDBACK: BOOSTING LANGUAGE MODELS WITH HIGH-QUALITY FEEDBACK
- MOTIF: INTRINSIC MOTIVATION FROM ARTIFICIAL INTELLIGENCE FEEDBACK
- STABILIZING RLHF THROUGH ADVANTAGE MODEL AND SELECTIVE REHEARSAL
- Shepherd: A Critic for Language Model Generation
- LEARNING TO GENERATE BETTER THAN YOUR LLM
- Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
- Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model
- HIR The Wisdom of Hindsight Makes Language Models Better Instruction Followers
- Aligner: Achieving Efficient Alignment through Weak-to-Strong Correction
- A Minimaximalist Approach to Reinforcement Learning from Human Feedback
- PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
- Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
- Weak-to-Strong Extrapolation Expedites Alignment
- Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
- Token-level Direct Preference Optimization
- SimPO: Simple Preference Optimization with a Reference-Free Reward
- AUTODETECT: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
- META-REWARDING LANGUAGE MODELS: Self-Improving Alignment with LLM-as-a-Meta-Judge
- HELPSTEER: Multi-attribute Helpfulness Dataset for STEERLM
- Recursive Introspection: Teaching Language Model Agents How to Self-Improve
- Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements
- REFT: Reasoning with REinforced Fine-Tuning
- SCPO：SELF-CONSISTENCY PREFERENCE OPTIMIZATION
- MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
- Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
- Pre-Trained Policy Discriminators are General Reward Models
RL探究
- UNDERSTANDING THE EFFECTS OF RLHF ON LLM GENERALISATION AND DIVERSITY
- A LONG WAY TO GO: INVESTIGATING LENGTH CORRELATIONS IN RLHF
- THE TRICKLE-DOWN IMPACT OF REWARD (IN-)CONSISTENCY ON RLHF
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
- HUMAN FEEDBACK IS NOT GOLD STANDARD
- CONTRASTIVE POST-TRAINING LARGE LANGUAGE MODELS ON DATA CURRICULUM
- Language Models Resist Alignment
- Towards a Unified View of Preference Learning for Large Language Models: A Survey

Memory

脱离上文长度这个狭窄的视角重新看待模型记忆

Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
A-MEM: Agentic Memory for LLM Agents
MemInsight: Autonomous Memory Augmentation for LLM Agents
G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
AGENT WORKFLOW MEMORY
KBLAM: KNOWLEDGE BASE AUGMENTED LANGUAGE MODEL
MIRIX: Multi-Agent Memory System for LLM-Based Agents
M3-Agent: Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Multiple Memory Systems for Enhancing the Long-term Memory of Agent
PerPilot: Personalizing VLM-based Mobile Agents via Memory and Exploration
Coarse-to-Fine Grounded Memory for LLM Agent Planning
Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory
Memp: Exploring Agent Procedural Memory
RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory
A-MEM: Agentic Memory for LLM Agents
MemoryBank: Enhancing Large Language Models with Long-Term Memory
Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors
Cognitive Architectures for Language Agents
Reason ingBank: Scaling Agent Self-Evolving with Reasoning Memory
LIGHTMEM: LIGHTWEIGHT AND EFFICIENT MEMORY-AUGMENTED GENERATION
Titans: Learning to Memorize at Test Time
Learning to Reason from Feedback at Test-Time
Deep Researcher with Test-Time Diffusion
It’s All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
MEMRL: SELF-EVOLVING AGENTS VIA RUNTIME REINFORCEMENT LEARNING ON EPISODIC MEMORY

多轮对话

近期我们也陷入多轮对话优化，发现了角色混乱、理解下降等很多问题

LLMS GET LOST IN MULTI-TURN CONVERSATION

指令微调&对齐 (instruction_tunning)

经典方案
- Flan: FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS :star:
- Flan-T5: Scaling Instruction-Finetuned Language Models
- ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
- Instruct-GPT: Training language models to follow instructions with human feedback :star:
- T0: MULTITASK PROMPTED TRAINING ENABLES ZERO-SHOT TASK GENERALIZATION
- Natural Instructions: Cross-Task Generalization via Natural Language Crowdsourcing Instructions
- Tk-INSTRUCT: SUPER-NATURALINSTRUCTIONS: Generalization via Declarative Instructions on 1600+ NLP Tasks
- ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-shot Generalization
- Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
- INSTRUCTEVAL Towards Holistic Evaluation of Instrucion-Tuned Large Language Models
SFT数据Scaling Law
- LIMA: Less Is More for Alignment :star:
- Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
- AlpaGasus: Training A Better Alpaca with Fewer Data
- InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
- Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
- Visual Instruction Tuning with Polite Flamingo
- Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
- Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
- WHEN SCALING MEETS LLM FINETUNING: THE EFFECT OF DATA, MODEL AND FINETUNING METHOD
新对齐/微调方案
- WizardLM: Empowering Large Language Models to Follow Complex Instructions :star:
- Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning
- Self-Alignment with Instruction Backtranslation :star:
- Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
- Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
- PROMPT2MODEL: Generating Deployable Models from Natural Language Instructions
- OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs
- Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
- Human-like systematic generalization through a meta-learning neural network
- Magicoder: Source Code Is All You Need
- Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
- Generative Representational Instruction Tuning
- InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
- The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
- Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
指令数据生成
- APE: LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS :star:
- SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions :star:
- iPrompt: Explaining Data Patterns in Natural Language via Interpretable Autoprompting
- Flipped Learning: Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
- Fairness-guided Few-shot Prompting for Large Language Models
- Instruction induction: From few examples to natural language task descriptions .
- SELF-QA Unsupervised Knowledge Guided alignment.
- GPT Self-Supervision for a Better Data Annotator
- The Flan Collection Designing Data and Methods
- Self-Consuming Generative Models Go MAD
- InstructEval: Systematic Evaluation of Instruction Selection Methods
- Overwriting Pretrained Bias with Finetuning Data
- Improving Text Embeddings with Large Language Models
- MAGPIE: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
- Scaling Synthetic Data Creation with 1,000,000,000 Personas
- UNLEASHING REASONING CAPABILITY OF LLMS VIA SCALABLE QUESTION SYNTHESIS FROM SCRATCH
- A Survey on Data Synthesis and Augmentation for Large Language Models
- AgentInstruct: Toward Generative Teaching with Agentic Flows
- Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
如何降低通用能力损失
- How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
- TWO-STAGE LLM FINE-TUNING WITH LESS SPECIALIZATION AND MORE GENERALIZATION
微调经验/实验报告
- BELLE: Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
- Baize: Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
- A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Large LM
- Exploring ChatGPT’s Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences
- Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
- Fine tuning LLMs for Enterprise: Practical Guidelines and Recommendations
Others
- Crosslingual Generalization through Multitask Finetuning
- Cross-Task Generalization via Natural Language Crowdsourcing Instructions
- UNIFIEDSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
- PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
- ROLELLM: BENCHMARKING, ELICITING, AND ENHANCING ROLE-PLAYING ABILITIES OF LARGE LANGUAGE MODELS

LLM Agent 让模型使用工具 (llm_agent)

AGENT AI: SURVEYING THE HORIZONS OF MULTIMODAL INTERACTION
A Survey on Large Language Model based Autonomous Agents
PERSONAL LLM AGENTS: INSIGHTS AND SURVEY ABOUT THE CAPABILITY, EFFICIENCY AND SECURITY
基于prompt通用方案
- ReAct: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS :star:
- Self-ask: MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS :star:
- MRKL SystemsA modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
- PAL: Program-aided Language Models
- ART: Automatic multi-step reasoning and tool-use for large language models
- ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models :star:
- Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
- Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models :star:
- Faithful Chain-of-Thought Reasoning
- Reflexion: Language Agents with Verbal Reinforcement Learning :star:
- Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
- RestGPT: Connecting Large Language Models with Real-World RESTful APIs
- ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
- InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
- TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents
- ControlLLM: Augment Language Models with Tools by Searching on Graphs
- Reflexion: an autonomous agent with dynamic memory and self-reflection
- AutoAgents: A Framework for Automatic Agent Generation
- GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension
- PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
- TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS :star: -AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
- AIOS: LLM Agent Operating System
- LLMCompiler An LLM Compiler for Parallel Function Calling
- Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval
基于微调通用方案
- TALM: Tool Augmented Language Models
- Toolformer: Language Models Can Teach Themselves to Use Tools :star:
- Tool Learning with Foundation Models
- Tool Maker：Large Language Models as Tool Maker
- TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
- AgentTuning: Enabling Generalized Agent Abilities for LLMs
- SWIFTSAGE: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
- FireAct: Toward Language Agent Fine-tuning
- Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
- REST MEETS REACT: SELF-IMPROVEMENT FOR MULTI-STEP REASONING LLM AGENT
- Efficient Tool Use with Chain-of-Abstraction Reasoning
- Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
- AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
- Agent Lumos: Unified and Modular Training for Open-Source Language Agents
- ToolGen: Unified Tool Retrieval and Calling via Generation
- Scaling Agents via Continual Pre-training
- LIMI: Less is More for Agency
调用模型方案
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace
- Gorilla：Large Language Model Connected with Massive APIs :star:
- OpenAGI: When LLM Meets Domain Experts
垂直领域
- 数据分析
  - DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
  - InsightLens: Discovering and Exploring Insights from Conversational Contexts in Large-Language-Model-Powered Data Analysis
  - Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
  - Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
  - TaskWeaver: A Code-First Agent Framework
  - Automated Social Science: Language Models as Scientist and Subjects
  - Data Interpreter: An LLM Agent For Data Science
  - FDABench: A Benchmark for Data Agents on Analytical Queries over Heterogeneous Data
- 金融
  - WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
  - FinGPT: Open-Source Financial Large Language Models
  - FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
  - AlphaFin：使用检索增强股票链框架对财务分析进行基准测试
  - FinAgent： A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist :star:
  - Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in stock Selection
  - ENHANCING ANOMALY DETECTION IN FINANCIAL MARKETS WITH AN LLM-BASED MULTI-AGENT FRAMEWORK
  - TRADINGGPT: MULTI-AGENT SYSTEM WITH LAYERED MEMORY AND DISTINCT CHARACTERS FOR ENHANCED FINANCIAL TRADING PERFORMANCE
  - FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
  - LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
  - Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
  - Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs
  - TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
  - FinVision: A Multi-Agent Framework for Stock Market Prediction
  - AI in Investment Analysis: LLMs for Equity Stock Ratings
  - AAPM: Large Language Model Agent-based Asset Pricing Models
  - FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
  - TradingAgents: Multi-Agents LLM Financial Trading Framework
  - Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading
  - FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents
  - FinSphere: A Conversational Stock Analysis Agent Equipped with Quantitative Tools based on Real-Time Database
  - FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
  - Ploutos: Towards interpretable stock movement prediction with financial large language model
  - HedgeAgents: A Balanced-aware Multi-agent Financial Trading System
  - TIMERAG: BOOSTING LLM TIME SERIES FORECASTING VIA RETRIEVAL-AUGMENTED GENERATION
  - CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction
  - Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?
  - Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges
  - AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions
- 生物医疗
  - GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
  - ChemCrow Augmenting large language models with chemistry tools
  - Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
  - Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
  - Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
  - CHEMAGENT: SELF-UPDATING LIBRARY IN LARGE LANGUAGE MODELS IMPROVES CHEMICAL REASONING
- web/mobile Agent
  - AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
  - A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
  - Mind2Web: Towards a Generalist Agent for the Web
  - MiniWoB++ Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
  - WEBARENA: A REALISTIC WEB ENVIRONMENT FORBUILDING AUTONOMOUS AGENTS
  - AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
  - WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
  - WebVoyager: Building an End-to-end Web Agent with Large Multimodal Models
  - CogAgent: A Visual Language Model for GUI Agents
  - Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
  - WebCanvas: Benchmarking Web Agents in Online Environments
  - The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use
  - UI-TARS: Pioneering Automated GUI Interaction with Native Agents
  - Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
  - WebSailor: Navigating Super-human Reasoning for Web Agent
  - WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
  - WebWatcher: Breaking New Frontiers of Vision-Language Deep Research Agent
  - OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
  - Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents
  - Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents
  - Watch and Learn: Learning to Use Computers from Online Videos
  - Fara-7B: An Efficient Agentic Model for Computer Use
- software engineer
- Agents in Software Engineering: Survey, Landscape, and Vision
- ChatDev: Communicative Agents for Software Development
- Research Agent
  - PaSa: An LLM Agent for Comprehensive Academic Paper Search
  - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
  - Agent Laboratory: Using LLM Agents as Research Assistants
  - Automated Hypothesis Validation with Agentic Sequential Falsifications
  - Towards an AI co-scientist
  - AI4Research: A Survey of Artificial Intelligence for Scientific Research
  - Kosmos: An AI Scientist for Autonomous Discovery
  - Knowledge-Informed Automatic Feature Extraction via Collaborative Large Language Model Agents
- 设计
  - PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs
  - Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers
- 其他
  - WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
  - ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
  - PointLLM: Empowering Large Language Models to Understand Point Clouds
  - Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
  - CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
  - SCIAGENTS: AUTOMATING SCIENTIFIC DISCOVERY THROUGH MULTI-AGENT INTELLIGENT GRAPH REASONING
评估
- Evaluating Verifiability in Generative Search Engines
- Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions
- API-Bank: A Benchmark for Tool-Augmented LLMs
- ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
- Automatic Evaluation of Attribution by Large Language Models
- Benchmarking Large Language Models in Retrieval-Augmented Generation
- ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
- Agent-as-a-Judge: Evaluate Agents with Agents
MultiAgent
- An Empirical Study of Agent Developer Practices in AI Agent Frameworks
- GENERATIVE AGENTS
- LET MODELS SPEAK CIPHERS: MULTIAGENT DEBATE THROUGH EMBEDDINGS
- War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
- Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
- Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
- Generative Agents: Interactive Simulacra of Human Behavior :star:
- AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
- System-1.x: Learning to Balance Fast and Slow Planning with Language Models
- Agents Thinking Fast and Slow:A Talker-Reasoner Architecture
- Generative Agent Simulations of 1,000 People
- Advanced Reasoning and Learning for Autonomous AI Agents
- Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
- Emergent Coordination in Multi-Agent Language Models
- TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture
- SOLVING A MILLION-STEP LLM TASK WITH ZERO ERRORS
- Latent Collaboration in Multi-Agent Systems
- 多智能体系统
  - Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence
  - MULTI-AGENT COLLABORATION: HARNESSING THE POWER OF INTELLIGENT LLM AGENTS
  - Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
  - Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation
任务型智能体协作
- METAAGENTS: SIMULATING INTERACTIONS OF HUMAN BEHAVIORS FOR LLM-BASED TASK-ORIENTED COORDINATION VIA COLLABORATIVE
- CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society :star:
- Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
- Communicative Agents for Software Development :star:
- MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
- METAGPT: META PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK
智能体路由
- One Agent To Rule Them All: Towards Multi-agent Conversational AI
- A Multi-Agent Conversational Recommender System
基座模型路由&Ensemble
- Large Language Model Routing with Benchmark Datasets
- LLM-BL E N D E R: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
- RouteLLM: Learning to Route LLMs with Preference Data
- More Agents Is All You Need
- Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
自主学习和探索进化
- AppAgent: Multimodal Agents as Smartphone Users
- Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
- LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
- Empowering Large Language Model Agents through Action Learning
- Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
- OS-COPILOT: TOWARDS GENERALIST COMPUTER AGENTS WITH SELF-IMPROVEMENT
- LLAMA RIDER: SPURRING LARGE LANGUAGE MODELS TO EXPLORE THE OPEN WORLD
- PAST AS A GUIDE: LEVERAGING RETROSPECTIVE LEARNING FOR PYTHON CODE COMPLETION
- AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
- A Survey on Self-Evolution of Large Language Models
- ExpeL: LLM Agents Are Experiential Learners
- ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
- PROACTIVE AGENT: SHIFTING LLM AGENTS FROM REACTIVE RESPONSES TO ACTIVE ASSISTANCE
- From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning
- AGILE: A Novel Reinforcement Learning Framework of LLM Agents
- Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
- ARMAP: SCALING AUTONOMOUS AGENTS VIA AUTOMATIC REWARD MODELING AND PLANNING
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
- Contextual Experience Replay for Continual Learning of Language Agents
- TaskCraft: Automated Generation of Agentic Tasks
MCP
- SCALEMCP: DYNAMIC AND AUTO-SYNCHRONIZING MODEL CONTEXT PROTOCOL TOOLS FOR LLM AGENTS
- LIVEMCP-101: STRESS TESTING AND DIAGNOSING MCP-ENABLED AGENTS ON CHALLENGING QUERIES
其他
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
- Inference with Reference: Lossless Acceleration of Large Language Models
- RecallM: An Architecture for Temporal Context Understanding and Question Answering
- LLaMA Rider: Spurring Large Language Models to Explore the Open World
- LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks
- Routine: A Structural Planning Framework for LLM Agent System in Enterprise
Custom Agent
- Creating General User Models from Computer Use

RAG

经典论文
- WebGPT：Browser-assisted question-answering with human feedback
- WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
- WebCPM: Interactive Web Search for Chinese Long-form Question Answering :star:
- REPLUG: Retrieval-Augmented Black-Box Language Models :star:
- RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
- Atlas: Few-shot Learning with Retrieval Augmented Language Models
- RRAML: Reinforced Retrieval Augmented Machine Learning
- FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION
微调
- RLCF：Aligning the Capabilities of Large Language Models with the Context of Information Retrieval via Contrastive Feedback
- RA-DIT: RETRIEVAL-AUGMENTED DUAL INSTRUCTION TUNING
- CHAIN-OF-NOTE: ENHANCING ROBUSTNESS IN RETRIEVAL-AUGMENTED LANGUAGE MODELS
- RAFT: Adapting Language Model to Domain Specific RAG
- Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
- ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
其他论文
- Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
- PDFTriage: Question Answering over Long, Structured Documents
- Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading :star:
- Active Retrieval Augmented Generation
- kNN-LM Does Not Improve Open-ended Text Generation
- Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
- DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
- Factuality Enhanced Language Models for Open-Ended Text Generation
- KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
- Complex Claim Verification with Evidence Retrieved in the Wild
- Retrieval-Augmented Generation for Large Language Models: A Survey
- ChatQA: Building GPT-4 Level Conversational QA Models
- RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
- Benchmarking Large Language Models in Retrieval-Augmented Generation
- T-RAG: Lessons from the LLM Trenches
- ARAGOG: Advanced RAG Output Grading
- ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
- OpenResearcher: Unleashing AI for Accelerated Scientific Research
- Contextual.ai-RAG2.0
- Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation
- Memory3 : Language Modeling with Explicit Memory
优化检索
- IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
- HyDE：Precise Zero-Shot Dense Retrieval without Relevance Labels
- PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES
- Query Rewriting for Retrieval-Augmented Large Language Models
- Query2doc: Query Expansion with Large Language Models :star:
- Query Expansion by Prompting Large Language Models :star:
- Anthropic Contextual Retrieval
- Multi-Level Querying using A Knowledge Pyramid
- A Survey of Query Optimization in Large Language Models
Ranking
- A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
- RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
- Improving Passage Retrieval with Zero-Shot Question Generation
- Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
- RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
- Ranking Manipulation for Conversational Search Engines
- Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
- Opensource Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
- T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
- Learning to Filter Context for Retrieval-Augmented Generation
传统搜索方案
- ASK THE RIGHT QUESTIO