Survey - Agents Applications
In this session, our readings cover:
Required Readings: AGENT APPLICATIONS
Core Component: Translating Agent Architectures into Real-World Systems
Focus on how agent capabilities are adapted to specific domains and product workflows, including user experience, operational constraints, and measurable impact.
Key Concepts: Domain adaptation, workflow integration, human-in-the-loop design, reliability in production, evaluation in context, compliance and governance, and case studies (software, education, healthcare, finance, science, and robotics)
Here are the related slide deck from the previous two course offerings:
| Topic | Slide Deck | Previous Semester |
|---|---|---|
| Survey: LLMs and Multimodal FMs | S1-LLM | 24course |
| Agent - In Healthcare | W9.1-HealthAI-agenticHealth | 25course |
| LLM Agents | W12-Team2-LLMAgents | 24course |
2025 HIGH-IMPACT PAPERS on a related topic:
- a. Deep Research: A Survey of Autonomous Research Agents (August 2025)
- Link: https://arxiv.org/html/2508.12752v1
Research Agent Architecture:
- Planning strategies: World model simulation, modular design search, human-like reasoning synthesis, self-refinement
- World models: LLMs as implicit world models, graph-based structured knowledge
- Meta-learning: MPO (Meta-Plan Optimization) - adaptive tuning across environments
- Architecture search: AgentSquare for automatic pipeline assembly
DeepResearchBench: Evaluates report fidelity, citation accuracy, comprehensive coverage
Key Challenge: Plan brittleness, lack of robustness to ambiguous queries, evaluation coarseness
- b. Towards Scientific Intelligence: LLM-based Scientific Agents (2025)
- Roadmap for scientific discovery with LLM agents
- c. A Survey of Data Science Agents (Published October 2025)
- Venue: Journal of the American Statistical Association
- Link: https://www.tandfonline.com/doi/full/10.1080/00031305.2025.2561140
- Comprehensive review of LLM agents for data analysis, visualization, ML workflows
- d. CitySim: Modeling Urban Behaviors with LLM-Driven Agents (2025)
- Urban simulation using recursive value-driven approach
- Scalable agent-based modeling for city dynamics
- e. From Single-Agent to Multi-Agent: Legal Agents Review (November 2025)
- Venue: AI Agent Journal 2025
- Link: https://www.oaepublish.com/articles/aiagent.2025.06
- Core tasks: Legal information retrieval, QA, judgment prediction, text generation
- Evaluation benchmarks: LAiW (Chinese practical), UCL-Bench (user-centric), JuDGE (judgment documents)
- Single-agent challenges: Trustworthiness, explainability, factuality
- Multi-agent systems: Collaborative reasoning, specialized roles (researcher, analyst, writer)
- Future directions: Cross-jurisdictional interoperability via legal knowledge graphs, ethical governance
- f. LitMOF: LLM-Driven Multi-Agent Curation of Materials Database (December 2025)
- Link: https://arxiv.org/abs/2512.01693
- Problem: Nearly half of Metal-Organic Framework (MOF) database entries contain structural errors
- Solution: Multi-agent framework validating crystallographic information from literature
- Results:
- Curated LitMOF-DB: 118,464 computation-ready structures
- Corrected 69% (6,161 MOFs) of invalid entries in CoRE MOF database
- Discovered 12,646 experimentally reported MOFs absent from existing resources
- Paradigm: Self-correcting scientific databases through LLM-driven curation
- g. LongVideoAgent: Multi-Agent Reasoning with Long Videos (December 2025)
- Link: https://arxiv.org/abs/2512.20618 Architecture:
- Master agent: Coordinates with step limit, trained via RL
- Grounding agent: Localizes question-relevant segments
- Vision agent: Extracts targeted textual observations from video
- Training: Reinforcement learning to encourage concise, correct, efficient cooperation
- Benchmark: LongTVQA and LongTVQA+ (episode-level datasets from TVQA/TVQA+)
- Results: Significantly outperforms non-agent baselines on hour-long video reasoning
- h. CitySim: Modeling Urban Behaviors with LLM-Driven Agents (2025)
- Urban simulation using recursive value-driven approach
- Scalable agent-based modeling for city dynamics
- Applications in urban planning and policy analysis