# Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements

arXiv: https://arxiv.org/abs/2601.04235 · HTML: https://mlsanity.com/paper/2601.04235/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 4 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [Causal-learn: Causal Discovery in Python](https://mlsanity.com/paper/2307.16405/) (2023)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [A Layered Intuition -- Method Model with Scope Extension for LLM Reasoning](https://mlsanity.com/paper/2510.10592/) (2025)
- [Human-Inspired Learning for Large Language Models via Obvious Record and Maximum-Entropy Method Discovery](https://mlsanity.com/paper/2512.12608/) (2025)
- [Active Thinking Model: A Goal-Directed Self-Improving Framework for Real-World Adaptive Intelligence](https://mlsanity.com/paper/2511.00758/) (2025)

## Built on by (most cited first, 3 of 3)

- [Human Simulation Computation: A Human-Inspired Framework for Adaptive AI Systems](https://mlsanity.com/paper/2601.13887/) (2026)
- [Autonomous Question Formation for Large Language Model-Driven AI Systems](https://mlsanity.com/paper/2602.01556/) (2026)
- [Human-Inspired Continuous Learning of Internal Reasoning Processes: Learning How to Think for Adaptive AI Systems](https://mlsanity.com/paper/2602.11516/) (2026)

## Abstract (arXiv)

Obtaining reliable feedback from the environment is a fundamental capability for intelligent agents to evaluate the correctness of their actions and to accumulate reusable knowledge. However, most existing approaches rely on predefined measurements or fixed reward signals, which limits their applicability in open-ended and dynamic environments where new actions may require previously unknown forms of feedback. To address these limitations, this paper proposes an Actively Feedback Getting model, in which an AI agent proactively interacts with the environment to discover, screen, and verify feedback without relying on predefined measurements. Rather than assuming explicit feedback definitions, the proposed method exploits action-induced environmental differences to identify target feedback that is not specified in advance, based on the observation that actions inevitably produce measurable changes in the environment. In addition, a self-triggering mechanism, driven by internal objectives such as improved accuracy, precision, and efficiency, is introduced to autonomously plan and adjust actions, thereby enabling faster and more focused feedback acquisition without external commands. Experimental results demonstrate that the proposed active approach significantly improves the efficiency and robustness of factor identification.