GPT-4V

How to Improve Your Agents: Academic Lit Review

Feb 22, 2025 · 39:02

Joe from Columbia University and founder of Arklex AI explains research on AI agents, focusing on improving their reasoning and planning through self-reflection, test-time compute, and tree search methods, without relying on human supervision. He details the TriPath method, which uses larger models to edit smaller models' feedback for better self-improvement, achieving up to 48% accuracy on math benchmarks. He shows how multi-color tree search (MCTS) with contrastive reflection and multi-agent debate (RMCTS) outperforms other search methods on Visual Web Arena and OS World, achieving top non-trained results. He proposes exploratory learning, where models learn from search trajectories rather than optimal actions, improving performance under compute budgets. Finally, he presents the Arklex open-source agent framework, combining machine learning, systems, and security for practical multi-agent orchestration.

Building Context-Aware Reasoning Applications with LangChain and LangSmith: Harrison Chase

Nov 1, 2023 · 18:54

Harrison Chase, CEO of LangChain, argues that building context-aware reasoning applications on LLMs requires treating the model as part of a larger system, and he outlines key approaches and challenges. He categorizes context provision as instruction prompting, few-shot examples, retrieval-augmented generation (RAG), and fine-tuning. For reasoning, he describes a spectrum from single LLM calls to chains, routers, agents with cycles, and autonomous agents like Auto-GPT. Chase then details engineering challenges: choosing the right cognitive architecture, data engineering for context, prompt engineering across multi-step systems, evaluation using LLM-assisted metrics and user feedback, and enabling collaboration between technical and non-technical team members. He emphasizes that the field remains early and that LangChain and LangSmith help prototype, debug, and iterate on these systems.