TLDR: InfoSeeker is a new LLM framework that significantly improves decision-making in uncertain environments by integrating active information seeking with task-oriented planning. Unlike previous models that react to failures, InfoSeeker proactively gathers information to align its internal understanding with the real world, leading to a 74% performance gain on new benchmarks featuring unpredictable dynamics and generalizing well to existing tasks.
In the complex and often unpredictable real world, making robust decisions is a significant challenge, especially when information is incomplete or environmental dynamics are uncertain. While humans excel at navigating such scenarios by actively seeking information to update their understanding, Large Language Models (LLMs) have historically struggled with these discrepancies between their internal models and reality.
A new research paper introduces InfoSeeker, an innovative LLM decision-making framework designed to bridge this gap. InfoSeeker integrates task-oriented planning with explicit information seeking, allowing LLMs to proactively gather knowledge and align their internal dynamics with the actual environment before making critical decisions.
The Challenge of Partial Observability and Uncertain Dynamics
Many real-world tasks are partially observable, meaning agents don’t have a complete picture of their environment. Observations can be noisy, and the way the environment responds to actions (its dynamics) might be unpredictable. For instance, a robot arm might not move exactly as commanded due to calibration errors, or a software function might yield unexpected results due to faulty implementation. Existing LLM planning agents often overlook these mismatches, leading to flawed plans based on inaccurate beliefs.
Humans, on the other hand, instinctively combine task-oriented planning (selecting actions to achieve a goal) with information seeking (proactively gathering data to refine beliefs). If a plan goes awry, we don’t just react; we investigate, test hypotheses, and update our understanding of how things work. InfoSeeker aims to imbue LLMs with this crucial human-like ability.
How InfoSeeker Works: A Loop of Learning and Planning
InfoSeeker operates on an iterative decision-making loop. Instead of blindly executing a plan and reacting to failures, it first prompts the LLM to actively gather information. This involves:
- Analyzing past interactions to identify uncertainties.
- Designing and executing targeted exploratory actions to validate its understanding, detect environmental changes, or test hypotheses.
- Extracting key insights from these information-seeking trials.
- Using these refined insights to update its internal dynamics and belief states.
- Finally, generating or revising task-oriented plans based on this improved understanding.
This proactive approach contrasts sharply with prior methods that rely solely on reactive adaptation after a failure has occurred. By seeking evidence first, InfoSeeker uncovers the root causes of problems and adjusts its plans accordingly, leading to more robust and effective behavior.
A New Benchmark for Real-World Uncertainty
To rigorously evaluate InfoSeeker, the researchers introduced a novel benchmark suite of text-based simulation tasks. Crucially, this benchmark goes beyond traditional evaluations that only consider uncertainty in observations. It incorporates environments with uncertain dynamics, where actions may yield unexpected results due to unmodeled factors. This better reflects the complexities of real-world scenarios.
The benchmark includes tasks such as:
- Robot arm control: A robot arm with a constant offset in its movements, requiring the agent to infer and adapt to this miscalibration.
- Robot navigation: A mobile robot with inverted action mappings (e.g., ‘left’ moves right), demanding the agent to detect and adjust to these inconsistencies.
- Mix colors: A task where paint tubes might be mislabeled or containers pre-contaminated.
- Block stacking: Classic BlocksWorld scenarios with initially unknown inventory states.
Each task is presented in two configurations: a ‘Basic’ version with predictable dynamics and a ‘Perturbed’ version with noisy, uncertain dynamics.
Impressive Performance Gains and Generalization
Experiments demonstrated InfoSeeker’s remarkable effectiveness. On the challenging ‘perturbed’ settings of the new benchmark, InfoSeeker achieved an absolute performance gain of 74% over prior methods. For example, in the robot arm control task with a miscalibrated controller, InfoSeeker achieved an 80% success rate, while the best baseline’s performance plummeted from 100% (in the basic setting) to just 6%.
The framework also proved efficient, acquiring information without sacrificing sample efficiency and generating optimal plans faster than baselines. Furthermore, InfoSeeker showed strong generalization capabilities, outperforming existing approaches on established benchmarks like LLM3 (for robotic manipulation) and TravelPlanner (for web navigation). This versatility highlights InfoSeeker’s potential across diverse domains.
Ablation studies confirmed that both the explicit information-seeking behavior and the information extraction module are critical for InfoSeeker’s success, demonstrating that simply providing uncertainty descriptions to other LLMs does not yield similar benefits.
Also Read:
- Sda-Planner: Enhancing Embodied Agents with Adaptive and Error-Aware Task Planning
- Efficient Decision-Making: How AI Learns with Just Enough Context
Looking Ahead
InfoSeeker represents a significant step forward in enabling LLM agents to operate robustly in complex, uncertain environments. By embedding active information seeking directly into the decision-making loop, it allows agents to adapt their internal understanding and generate more reliable plans. While the current benchmark is hand-crafted, the findings underscore the importance of integrating planning and information seeking for truly intelligent and adaptive AI systems. You can read the full research paper here.
