Reflection-Agent-Based Mode

• Reflection-agent-based mode is a framework where agents integrate explicit self-assessment steps to detect errors and adjust decision-making policies.
• It has been applied across fields—from physical dosimetry and displacement sensing to advanced LLM-based and multi-agent systems—demonstrating significant performance gains.
• The mode leverages both proactive and reactive reflection strategies, resulting in measurable improvements in calibration accuracy, task completion rates, and adaptive learning.

A reflection-agent-based mode refers to the operation or calibration of systems—especially agents or agentic workflows—that critically incorporate explicit self-assessment (reflection), usually through structured review of decisions, trajectories, or outputs. In this context, "reflection" is not mere logging or posthoc commentary but an integral, systematic step within the agent’s operational loop that enables error detection, correction, model improvement, or calibration by leveraging internal or external feedback. The reflection-agent-based mode has been explored and operationalized across diverse domains, including physical dosimetry, robotics, tool learning, web navigation, autonomous trading, scientific reasoning, and LLM-based agents.

1. Technical Definition and Modalities

Reflection-agent-based mode denotes any operational schema wherein an agent or multi-agent system engages in deliberate, structured reflection processes that modify actions, beliefs, or policies based on internal state review or environmental feedback. The reflection may occur:

• Pre-action (proactive/intra-reflection): The agent anticipates and evaluates likely outcomes before action execution, screening for potential errors or risks (e.g., as implemented in MIRROR (2505.20670)).
• Post-action (reactive/inter-reflection): The agent reviews outcomes of previous actions or action sequences, critiquing efficacy and aligning future decisions (e.g., as exemplified in ReflAct (Kim et al., 21 May 2025), TradingGroup (Tian et al., 25 Aug 2025), Re-ReST (Dou et al., 3 Jun 2024)).
• Iterative/cross-temporal: Reflection may span multiple temporal scales, from micro (per-step) to macro (trajectory or task-completion), as in hierarchical reflection in MobileUse (Li et al., 21 Jul 2025).
• Single-agent vs. multi-agent: Reflection can be implemented within a single agent or distributed as role-specialized "critic" agents operating in relay (e.g., (Fatemi et al., 29 Oct 2024, He et al., 31 Dec 2024, Lu et al., 7 Aug 2025)).

Reflection is distinct from general feedback or error correction: it involves explicit, structured, and often formalized updates to reasoning or policy derived from reviewing own (or subagent’s) behavior, typically linked to specific corrective or optimizing actions.

2. Canonical Implementations Across Domains

Physical and Sensing Systems

• Film Dosimetry: In Gafchromic EBT2 film dosimetry, reflection mode refers to a physical scanning configuration where radiochromic film is measured in the reflection geometry (as opposed to transmission). The technical validation in (Mendez et al., 2014) established that Gafchromic EBT2 film, when used with a plan-based calibration method, supports reflection mode scanning, achieving comparable calibration accuracy to transmission mode. Here, "reflection-agent-based mode" is not a metaphorical agent but an operational physical mode with all downstream data processing—e.g., lateral correction and dose conversion—adhering to established protocols originally designed for transmission mode.
• Sensing (Dielectric Resonators): Multimodal displacement sensors exploit reflection mode (S₁₁, the reflection coefficient) for high-sensitivity, short-range displacement detection, as in (Regalla et al., 2023), with sensitivity quantitatively described as $s_R \approx 6.2\,\mathrm{dB/mm}$.

Artificial Agents and LLM-based Systems

• Structured Reflection in LLM Agents: In interactive, partially observable environments, agents (often implemented as LLMs) employ structured reflection on their error trajectories to enhance learning and planning without expert traces. For instance, algorithmic management of reflection memory and forced correction of action sequences, as described in (Li et al., 2023), leads to rapid error recovery and superior performance in zero-shot reinforcement learning tasks.
• Policy-Level Reflection: Advanced agents, such as Agent-Pro (Zhang et al., 27 Feb 2024), utilize policy-level reflection—evaluating whole trajectories of beliefs, actions, and outcomes post-hoc—to formulate revised strategies and behavioral guidelines. New candidate policies undergo selection via DFS policy optimization, and updated instructions are embedded into the agent's operational context, enabling continual strategic improvement.
• Self-Training with Reflection: In Re-ReST (Dou et al., 3 Jun 2024), a dedicated "reflector" model corrects low-quality outputs by integrating environmental feedback, yielding richer training data and improved generalization during inference (see also equation for the reflection-based conditional update: $$\tilde{y} \sim R(y \mid x, \hat{y}, \mathcal{E}(x, \hat{y}))$$).

Multi-Agent and Collaborative Frameworks

• Multi-Agent Critique: In financial QA, dedicated "critic" agents reflect on the reasoning steps and final outputs of "expert" agents, focusing on both data extraction and mathematical reasoning, resulting in significant gains for open-source models (Fatemi et al., 29 Oct 2024).
• Multi-Path Collaborative Reflection: The RR-MP framework (He et al., 31 Dec 2024) demonstrates that pairing reactive agents (initial answer generators) with reflection agents (cognitive optimizers) over diverse reasoning paths, followed by consensus aggregation, mitigates the risk of "degeneration of thought" and drives robust, multi-perspective scientific reasoning.
• Hierarchical Reflection: In GUI and mobile agents (MobileUse (Li et al., 21 Jul 2025); InfiGUIAgent (Liu et al., 8 Jan 2025)), hierarchical reflection consists of action-level (micro), trajectory-level (meso), and task-level (macro/global) reflection modules, enabling step-wise error detection, short-term correction, and holistic task validation. This framework is formalized, for example, as:

$$r_a^t = R_{\text{action}}(I, s^t, Pe(s^{t+1}), a^t)$$

for action reflection, and global reflection as

$$r_g^t = R_{\text{global}}(I, (a^0, r_a^0, r_t^0), \ldots, (a^t, r_a^t, r_t^t), s^j, \ldots, s^t)$$

3. Strategic Roles and Algorithmic Formalizations

Reflection acts at multiple algorithmic and strategic levels:

• Error Detection and Correction: Reflection enables both anticipatory error prevention (e.g., intra-reflection in MIRROR (2505.20670)) and reactive trajectory repair (inter-reflection).
• Policy and Belief Update: In Agent-Pro, post-trajectory reflection refines irrational beliefs, revises guiding prompts, and updates behavioral guidelines, allowing the agent to evolve its policy without gradient-based optimization.
• Strategic Coordination: In multi-agent frameworks, reflection agents (or critic roles) systematically review and critique peers’ outputs, with iterative feedback loops leading to higher accuracy (as in financial QA (Fatemi et al., 29 Oct 2024), debate frameworks (Lu et al., 7 Aug 2025), and collaborative math/scientific agents (He et al., 31 Dec 2024, Yuan et al., 10 Jun 2025)).
• Memory and Meta-Learning: Persistent reflection-based memories, such as in web navigation (ReAP (Azam et al., 2 Jun 2025)), enable agents to transfer distilled lessons across tasks, improving success rates and overall efficiency.

4. Empirical Impact and Performance Benchmarks

Reflection-agent-based modes consistently yield measurable improvements across benchmarks:

• In ALFWorld, incorporating world-grounded goal-state reflection in ReflAct (Kim et al., 21 May 2025) increased the success rate by 27.7% over ReAct, achieving 93.3% task completion.
• In financial QA, moving from single-agent to multi-critic agent frameworks improved exact match accuracy by up to 15.81 percentage points for LLaMA3-8B (Fatemi et al., 29 Oct 2024).
• Multi-agent, reflection-enabled frameworks outperform baseline LLMs and RL agents in stock trading (Cumulative Return up to 40.46% on AMZN (Tian et al., 25 Aug 2025)) and in complex scientific benchmarks (e.g., up to +24.81% accuracy in moral scenario reasoning with RR-MP (He et al., 31 Dec 2024)).
• In self-training for language agents, Re-ReST (Dou et al., 3 Jun 2024) improved HotpotQA EM from 20.0% (base agent) to 29.6% (with reflection-enhanced self-training).

Reflection-agent-based modes are also associated with improved error recovery, reduced calibration time (from hours to minutes in reflection-mode film dosimetry (Mendez et al., 2014)), faster convergence, and overall higher reliability in dynamic, uncertain environments.

5. Domains of Application and Limitations

Reflection-agent-based modes have found effective application in:

• Medical physics (dosimetry calibration) (Mendez et al., 2014)
• Physical sensing (displacement sensors) (Regalla et al., 2023)
• Robotics, tool learning, and web navigation (2505.20670, Azam et al., 2 Jun 2025)
• Language agent planning, multi-hop reasoning, and QA (Zhang et al., 27 Feb 2024, Fatemi et al., 29 Oct 2024, Dou et al., 3 Jun 2024)
• Collaborative trading and finance (multi-agent systems) (Tian et al., 25 Aug 2025)
• Multimodal harmful content detection via multi-agent debate (Lu et al., 7 Aug 2025)
• GUI/mobile automation and exploration (Liu et al., 8 Jan 2025, Li et al., 21 Jul 2025)

Limitations include the potential for negative transfer (reflection-derived guidance may degrade performance on already-successful tasks (Azam et al., 2 Jun 2025)), increased computational overhead, and challenges related to scaling, memory management, and coordination in large or heterogeneous agent networks.

6. Theoretical and Practical Significance

Reflection-agent-based modes represent a unifying principle for systematically enhancing agent robustness, generalization, and strategic reliability. By interleaving reflection—whether as intra- or inter-action review, memory augmentation, or hierarchical multi-agent collaboration—these systems achieve improved calibration (in physical domains), effective policy evolution (in LLMs and RL), and resilient, adaptive automation across both stationary and dynamic environments.

The emergence of reflection-agent-based modes underlines the importance of explicitly modeling self-assessment and self-improvement in algorithmic frameworks, setting a foundation for future developments in reliable, self-correcting autonomous agents across science, industry, and critical applications.