Mirhoseini, Azalia

last modified on Fri, 11 Jul 2025

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Jul 11, 2025	»	CS 329A: Self-Improving AI Agents 2 min; updated Jul 11, 2025 Stanford CS329A \| Self-Improving AI Agents. Azalia Mirhoseini; Aakanksha Chowdhery; Mert Yuksekgonul; Jon Saad-Falcon. cs329a.stanford.edu . Accessed Jul 11, 2025. Test-time Compute Scaling Large Language Monkeys: Scaling Inference Compute with Repeated Sampling Archon; An Architecture Search Framework for Inference-Time Techniques Scaling LLM Test-Time Compute Optimally Can Be More Effective Than Scaling Model Parameters Self-Improvement Techniques with Verifiers: Training Verifiers to Solve Math Word Problems Let’s Verify Step by Step Math-Shepherd: Verify and Reinforce LLMs Step-by-step Without Human Annotations Self-Improvement Techniques with RL Constitutional AI: Harmlessness from AI Feedback STaR: Bootstrapping Reasoning With Reasoning Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models Self-Improvement Techniques with Search Thinking Fast and Slow with Deep Learning and Tree Search Competitive-level Code Generation with AlphaCode AlphaCode 2 Technical Report Open-ended Agent Learning in the Era of Foundation Models The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery Automated Design of Agentic Systems Augmenting LLMs with Tool Use/Actions ReAct: Synergizing Reasoning and Acting in Language Models Toolformer: Language Models Can Teach Themselves to Use Tools RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning Planning and Multi-Step Reasoning Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models LLMs Still Can’t Plan; Can LRMs? A Preliminary Evaluation of OpenAI’s o1 on PlanBench ADaPT: As-Needed Decomposition and Planning with Language Models Reasoning Across Modalities Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents Developing a Computer Use Model The Dawn of GUI Agent: A Preliminary Study with Claude 3.5 Computer Use Benchmarks and Challenges in Evaluating Agents SWE-bench: Can Language Models Resolve Real-World GitHub Issues? KernelBench: Can LLMs Write GPU Kernels? RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Models Against Human Experts MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering \(\tao\)-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains GAIA: A Benchmark for General AI Assistants AI Coding Agents SWE-Agent: Agent-Computer Interfaces Enable Automated Software Engineering SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? Agent Orchestration Frameworks AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Augmenting LLMs with Retrieval/Memory Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? Contextual Retrieval MemGPT: Towards LLMs as Operating Systems Multimodal AI Agents Multi-agent Systems and Future Research Areas Multi-agent Fine-tuning: Self Improvement with Diverse Reasoning Chains Mixture-of-Agents Enhances Large Language Model Capabilities CodeMonkeys: Scaling Test-Time Compute for Software Engineering