A review of Agentic bemchmarks for LLMs. What are the challenges for current gen algorithms?

Overview of agentic benchmarks for LLMs

Key benchmarks

• AgentBench: A multi-dimensional evolving benchmark with 8 distinct environments to assess LLM agents' capabilities in multi-turn open-ended generation settings 15
• SmartPlay: Consists of 6 games with 20+ evaluation settings and infinite environment variations, challenging 9 important capabilities of LLM agents 3
• LLM-Coordination Benchmark: Evaluates LLMs in multi-agent coordination through Agentic Coordination and Coordination Question Answering tasks 23
• LMRL-Gym: A benchmark for evaluating multi-turn reinforcement learning with LLMs, featuring 8 language tasks requiring multiple rounds of interaction 13
• GoodAI LTM Benchmark: Focuses on evaluating long-term memory and continual learning capabilities of LLM-based conversational agents 24

Capabilities assessed

• Reasoning with object dependencies
• Planning ahead
• Spatial reasoning
• Learning from history
• Understanding randomness
• Theory of Mind (ToM) reasoning
• Joint planning
• Instruction following
• Long-term memory management

Challenges for current gen algorithms

Reasoning and decision-making limitations

• Poor long-term reasoning: LLMs struggle with tasks requiring extended logical chains or maintaining context over long periods 15
• Limited decision-making abilities: Difficulty in making optimal choices in complex, multi-step scenarios 15
• Struggles with spatial reasoning: Challenges in understanding and manipulating spatial relationships in environments like Minecraft 3
• Difficulty in understanding randomness: LLMs often fail to grasp probabilistic concepts fully 3

Instruction following and task execution

• Weak instruction following abilities: Especially evident in open-source models compared to top commercial LLMs 15
• Challenges in multi-turn interactions: LLMs rarely ask clarifying questions or engage in explicit information gathering 13
• Inefficient task decomposition: Difficulty in breaking down complex tasks into manageable subtasks 35

Memory and learning challenges

• Limited context window: Restricts the ability to process long documents or maintain context over extended conversations 27
• Ineffective long-term memory management: Struggles with retaining and utilizing information over extended periods 24
• Challenges in continual learning: Difficulty in integrating new information and adapting strategies over time 24

Multi-agent coordination issues

• Weak Theory of Mind reasoning: Difficulty in understanding and predicting other agents' intentions and knowledge states 23
• Joint planning limitations: Struggles in coordinating actions with other agents towards a common goal 23
• Challenges in adapting to dynamic environments: Difficulty in adjusting strategies based on changing conditions or partner behaviors 23

Benchmark-specific challenges

• Reproducibility issues: Non-reproducible model scores and arena rankings due to changing API-served models 17
• Cost-related performance discrepancies: Expensive models may outperform others due to multiple API calls rather than superior architecture 32
• Overfitting to specific benchmarks: Risk of models being optimized for benchmark performance rather than real-world applications 28

Potential improvements and future directions

• Training on code and high-quality multi-turn alignment data: Could enhance long-term reasoning and decision-making capabilities 15
• Implementing more advanced planning techniques: To improve performance on open-ended or ambiguous tasks 6
• Developing better memory architectures: To enhance long-term information retention and retrieval 24
• Improving instruction following through fine-tuning: To bridge the gap between open-source and commercial models 15
• Enhancing multi-agent coordination abilities: Through specialized training in Theory of Mind and joint planning scenarios 23