Agent Papers
1.Agent Architecture
- Cognitive Architecture
2.Execution Paradigms
Plan-and-Execute:
- Paper:
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models - Year:
ACL 2023 - Link: https://arxiv.org/abs/2305.04091
- Description: 提出了
Plan-and-Solve的零样本提示方法，来引导LLM首先制定一个计划，将整个任务划分为若干较小的子任务，然后按照该计划逐步完成这些子任务。
对比：
| Method | Trigger Sentence |
|---|---|
| CoT | Let’s think step by step. |
| PS | Let’s first understand the problem and devise a plan to solve the problem. Then, let’s carry out the plan to solve the problem step by step. |
| PS+ | Let’s first understand the problem, extract relevant variables and their corresponding numerals, and devise a plan. Then, let’s carry out the plan, calculate intermediate variables (pay attention to correct numeral calculation and commonsense), solve the problem step by step, and show the answer. |
3. Planning
- Task Decomposition
- Hierarchical Planning
- Long-horizon Planning
- Dynamic Replanning
4. Tool Use
SpaceTools:
- Paper:
Tool-Augmented Spatial Reasoning via Double Interactive RL - Year:
CVPR 2026 - Link: https://arxiv.org/abs/2512.04069
- Description: 提出了一个名为
DIRL双重交互强化学习的两阶段训练框架。第一个阶段即教学阶段，为VLM建立基础的工具使用能力；第二个阶段是探索阶段，主要通过持续强化学习进一步完善VLM多工具协调能力。本质上是通过这个训练框架教VLM在空间推理时如何自主协调多种视觉/机器人工具。
5. Memory
- Short-term Memory
- Long-term Memory
- Episodic Memory
- Retrieval-Augmented Memory
- Memory Management
6. Multi-Agent
- Cooperation
- Debate
- Role-playing
- Communication Protocols
- Swarm / Society
7. Learning and Improvement /Reflection
- Self-Refinement
- Preference Learning
- RL for Agents
- Experience Replay
8. Evaluation / 评测
- Benchmarks
- Metrics
- Failure Analysis
- Agent Robustness