## Diagram: AgentFlow and In-the-Flow Rollout ### Overview The image presents two diagrams illustrating the AgentFlow system and its in-the-flow rollout at a specific turn *t*. Diagram (a) shows the overall iterative process of the AgentFlow agentic system, while diagram (b) details the individual components and data flow within a single turn. ### Components/Axes **Diagram (a): AgentFlow: In-the-Flow Agentic System** * **Title:** (a) AgentFlow: In-the-Flow Agentic System * **Input:** Query, Toolkit Set (with icons representing Google, Python, and other tools) * **Turns:** The system operates in turns, labeled "Turn 1", "Turn 2", and "Turn T". * **Components per Turn:** Each turn consists of a Planner, Executor, and Verifier. In the final turn, a Generator is added. * **Memory:** A vertical orange bar labeled "Memory" runs along the left side, with brain icons indicating memory updates between turns. * **Output:** The final output is labeled "Answer". * **Nodes:** Planner, Executor, Verifier, Generator, Answer, Memory * **Edges:** Arrows indicate the flow of information between components. **Diagram (b): In-the-Flow Rollout at Turn *t*** * **Title:** (b) In-the-Flow Rollout at Turn *t* * **Components:** Planner, Executor, Verifier, Memory * **Inputs to Planner:** q (query), K (knowledge), Mt (memory at time t) * **Output from Planner:** at (action at time t) * **Input to Executor:** at (action at time t), K (knowledge) * **Output from Executor:** et (execution at time t) * **Input to Verifier:** q (query), et (execution at time t), Mt (memory at time t) * **Output from Verifier:** vt (verification at time t) * **Memory Update:** The Verifier's output and the current memory (Mt) are used to update the memory to Mt+1. * **Input/Output Boxes:** Yellow boxes describe the inputs and outputs for the Planner, Executor, and Verifier. * **Training Status:** "Trained" and "Frozen" labels indicate the training status of the system. ### Detailed Analysis or ### Content Details **Diagram (a): AgentFlow: In-the-Flow Agentic System** * **Turn 1:** Query flows into the Planner, which outputs a1 to the Executor. The Executor's output goes to the Verifier. * **Turn 2:** The process repeats with Planner outputting a2. * **Turn T:** The process repeats with Planner outputting aT. The Verifier's output goes to the Generator, which, along with input 'o', produces the final Answer. * **Memory Updates:** After each turn, the Memory is updated, indicated by the brain icon. **Diagram (b): In-the-Flow Rollout at Turn *t*** * **Planner:** * Input: [Query Analysis], [Global Goal], [Required Skills] * Output: [Current Sub-Goal], [Selected Tool], [Context for Tool Use] * **Executor:** * Input: [Current Sub-Goal], [Selected Tool & Context], [Tool Metadata] * Output: [Generated Command], [Execution Result] * **Verifier:** * Input: [Generated Command], [Execution Result] * Output: [Execution Analysis], [Memory Analysis], [Verification Status] * **Memory:** Updated from Mt to Mt+1 based on the Verifier's output. ### Key Observations * **Iterative Process:** AgentFlow is an iterative process, refining its actions over multiple turns. * **Modular Design:** Each turn consists of distinct modules (Planner, Executor, Verifier) with specific roles. * **Memory Integration:** Memory plays a crucial role in maintaining context and improving performance across turns. * **Input/Output Specialization:** Each component (Planner, Executor, Verifier) has clearly defined inputs and outputs, facilitating modularity and reusability. ### Interpretation The diagrams illustrate a sophisticated agentic system designed for complex tasks. The AgentFlow system uses an iterative approach, refining its actions over multiple turns. The Planner generates actions based on the query, knowledge, and memory. The Executor executes these actions, and the Verifier evaluates the results. The Memory component stores and updates information, allowing the system to learn and improve over time. The "In-the-Flow Rollout" diagram provides a detailed view of the data flow and processing within a single turn, highlighting the inputs and outputs of each component. The system is designed to be modular and flexible, allowing for easy integration of new tools and capabilities. The "Trained" and "Frozen" labels suggest that the system can be trained and then deployed in a fixed configuration.

## Diagram: AgentFlow: In-the-Flow Agentic System and Rollout ### Overview This image presents two interconnected diagrams illustrating an "In-the-Flow Agentic System" named AgentFlow. Diagram (a) depicts the overall multi-turn, iterative architecture of the system, showing how a query is processed through a series of turns involving planning, execution, and verification, with a central memory component. Diagram (b) provides a detailed breakdown of the components and their interactions within a single turn (`t`), specifying their inputs, outputs, and whether they are "Trained" or "Frozen." ### Components/Axes **Diagram (a): AgentFlow: In-the-Flow Agentic System** * **Inputs (Top):** * **Query:** A white rounded rectangle at the top-left, representing the initial input to the system. * **Toolkit Set:** A white rounded rectangle at the top-right, representing available tools. It contains icons for Python, a Globe (web browser), a Magnifying Glass (search), a Gear (settings/utility), and an ellipsis (...), indicating a variety of tools. * **Main Process Area:** A large light orange/yellow rounded rectangle enclosing the iterative turns. * **Memory:** A vertical orange/yellow bar on the far left of the main process area, labeled "Memory" at its bottom. It contains three brain icons, one for each turn segment, indicating its role in storing and providing context. * **Turn Components (within each turn, e.g., Turn 1, Turn 2, Turn T):** * **Planner:** A yellow rounded rectangle. It receives input from the Query/Memory and Toolkit Set. A flame icon is positioned next to it, possibly indicating an active or dynamic process. * **`a^t` (Action):** A white rounded rectangle, representing the output of the Planner for turn `t`. * **Executor:** A blue rounded rectangle, receiving `a^t` as input. * **Verifier:** A blue rounded rectangle, receiving output from the Executor. * **Generator (only in Turn T):** A blue rounded rectangle, receiving output from the Verifier in the final turn. * **`o` (Output):** A white rounded rectangle, representing the output of the Generator. * **Answer:** A green rounded rectangle, representing the final output of the system. * **Turn Labels:** "Turn 1", "Turn 2", "Turn T" are explicitly labeled above their respective process rows. An ellipsis "..." indicates intermediate turns between Turn 2 and Turn T. **Diagram (b): In-the-Flow Rollout at Turn t** * **Components (Top Row, left to right):** * **Planner π_θ:** A yellow rounded rectangle. * **Inputs (above Planner):** `q` (query), `K` (toolkit), `M^t` (memory at turn `t`). * **Icon:** A target icon to its right. * **Output (below Planner):** `a^t` (action at turn `t`). * **Executor:** A blue rounded rectangle. * **Inputs (above Executor):** `a^t` (action at turn `t`), `K` (toolkit). * **Icon:** A wrench icon to its right. * **Output (below Executor)::** `e^t` (execution result at turn `t`). * **Verifier:** A blue rounded rectangle. * **Inputs (above Verifier):** `q` (query), `e^t` (execution result at turn `t`), `M^t` (memory at turn `t`). * **Icons:** A red 'X' hand icon and a green checkmark hand icon to its right, indicating approval/rejection. * **Output (below Verifier):** `v^t` (verification status at turn `t`). * **Memory:** An orange/yellow rounded rectangle, positioned to the right of the Verifier. * **Input:** From the Verifier (via the hand icons). * **Icon:** A brain icon above it. * **Output (below Memory):** `M^{t+1}` (memory for the next turn `t+1`). * **Detailed Input/Output Boxes (Below components):** * **Planner Input/Output (yellow box below Planner):** * **Input:** * [Query Analysis] * [Global Goal] * [Required Skills] * **Output:** * [Current Sub-Goal] * [Selected Tool] * [Context for Tool Use] * **Executor Input/Output (blue box below Executor):** * **Input:** * [Current Sub-Goal] * [Selected Tool & Context] * [Tool Metadata] * **Output:** * [Generated Command] * [Execution Result] * **Verifier Input/Output (blue box below Verifier):** * **Input:** * [Generated Command] * [Execution Result] * **Output:** * [Execution Analysis] * [Memory Analysis] * [Verification Status] * **Legend (Bottom-right):** * **Trained:** A yellow rounded rectangle. * **Frozen:** A blue rounded rectangle. ### Detailed Analysis **Diagram (a): AgentFlow: In-the-Flow Agentic System** The system processes a "Query" using a "Toolkit Set" over multiple turns. 1. **Turn 1:** The "Query" and "Toolkit Set" are fed into the "Planner". The "Planner" (indicated by a flame icon, suggesting active processing) generates an action `a^1`. This action is then processed by the "Executor", whose output is passed to the "Verifier". The "Verifier"'s output is then stored in "Memory" (represented by the top brain icon in the vertical bar). 2. **Turn 2:** The "Planner" for Turn 2 receives input from "Memory" (the second brain icon) and the "Toolkit Set". It generates action `a^2`, which flows through the "Executor" and "Verifier". The "Verifier"'s output is again stored in "Memory" (the middle brain icon). 3. **Intermediate Turns (...):** This iterative process continues, with each turn's "Planner" receiving updated context from "Memory" and the "Toolkit Set", and the "Verifier" updating "Memory" with its findings. 4. **Turn T (Final Turn):** The "Planner" for Turn T receives input from "Memory" (the bottom brain icon) and the "Toolkit Set", generating action `a^T`. This action flows through the "Executor" and "Verifier". Unlike previous turns, the "Verifier" in Turn T outputs to a "Generator". The "Generator" produces an output `o`, which is then presented as the final "Answer". **Diagram (b): In-the-Flow Rollout at Turn t** This diagram details the internal workings of a single turn `t`. 1. **Planner π_θ (Trained):** This component, marked with a target icon, takes the current query (`q`), the available toolkit (`K`), and the current memory state (`M^t`) as inputs. Its detailed inputs include "Query Analysis", "Global Goal", and "Required Skills". It outputs an action `a^t`, which comprises a "Current Sub-Goal", "Selected Tool", and "Context for Tool Use". The yellow color indicates it is a "Trained" component. 2. **Executor (Frozen):** This component, marked with a wrench icon, takes the action `a^t` from the Planner and the toolkit `K` as inputs. Its detailed inputs are "Current Sub-Goal", "Selected Tool & Context", and "Tool Metadata". It outputs `e^t`, which consists of a "Generated Command" and the "Execution Result". The blue color indicates it is a "Frozen" component. 3. **Verifier (Frozen):** This component, marked with red 'X' and green checkmark hand icons, takes the original query (`q`), the execution result `e^t` from the Executor, and the current memory state `M^t` as inputs. Its detailed inputs are "Generated Command" and "Execution Result". It outputs `v^t`, which includes "Execution Analysis", "Memory Analysis", and "Verification Status". The blue color indicates it is a "Frozen" component. 4. **Memory (Trained):** This component, marked with a brain icon, receives input from the Verifier (specifically, the verification status and analysis). It then updates its state to `M^{t+1}`, which will be used in the next turn. The orange/yellow color indicates it is a "Trained" component. ### Key Observations * **Iterative Refinement:** Diagram (a) clearly shows an iterative process where the system refines its understanding and actions over multiple turns, leveraging a persistent "Memory". * **Memory as Context:** "Memory" is central to the multi-turn process, providing context (`M^t`) to the "Planner" and being updated by the "Verifier" at each step. * **Modular Design:** The system is composed of distinct modules: Planner, Executor, Verifier, and Generator, each with specific roles. * **Trained vs. Frozen Components:** Diagram (b) highlights that the "Planner" and "Memory" are "Trained" components (yellow), suggesting they are adaptable and learn from experience. In contrast, the "Executor" and "Verifier" are "Frozen" (blue), implying their logic is fixed or pre-defined. * **Role of Icons:** The icons (flame, target, wrench, hands, brain) visually reinforce the function of each component, making the diagram intuitive. * **Finalization:** The "Generator" and "Answer" components appear only in the final turn (Turn T), indicating a specific phase for synthesizing the final response. ### Interpretation The AgentFlow system describes a sophisticated agentic architecture designed to handle complex queries requiring multiple steps and tool interactions. The "in-the-flow" aspect suggests that the system dynamically plans, executes, and verifies actions within a continuous loop, adapting its strategy based on intermediate results and accumulated memory. The distinction between "Trained" (Planner, Memory) and "Frozen" (Executor, Verifier) components is crucial. It implies that the core mechanisms for *how* tools are used (Executor) and *how* results are evaluated (Verifier) are stable and reliable, perhaps based on robust, pre-engineered logic or models. Conversely, the "Planner" and "Memory" are designed to be adaptive. The "Planner" (π_θ) learns to strategize and select tools effectively based on the query, global goals, and required skills, while "Memory" learns to store and retrieve relevant context for future planning. This architecture allows the system to be flexible and learn new strategies without compromising the integrity of execution and verification. The iterative nature, where the Verifier updates Memory, and Memory feeds back into the Planner, forms a self-correcting loop. If an execution or verification fails (implied by the red 'X' hand icon), the updated memory can guide the Planner to adjust its strategy in subsequent turns. This continuous feedback mechanism is essential for robust autonomous agents. The final "Generator" and "Answer" suggest that after a series of verification-driven iterations, the system synthesizes a coherent response, indicating a completion state for the query. This system aims to provide a structured and adaptive approach to agentic problem-solving, particularly in environments requiring tool use and complex reasoning.

## Diagram: AgentFlow: In-the-Flow Agentic System ### Overview The image presents a diagram illustrating an agentic system called "AgentFlow". It depicts the flow of information and processing within the system across multiple turns (labeled Turn 1, Turn 2, ... Turn T). The diagram is split into two main sections: (a) AgentFlow: In-the-Flow Agentic System, which shows the overall system architecture, and (b) In-the-Flow Rollout at Turn t, which details the input and output of each component at a specific turn. ### Components/Axes The diagram consists of several key components: * **Query:** The initial input to the system. * **Toolkit Set:** A collection of tools available to the agent (represented by icons). * **Planner:** Generates a plan based on the query and available tools. * **Executor:** Executes the plan. * **Verifier:** Verifies the results of the execution. * **Generator:** Generates the final answer. * **Memory:** Stores information across turns. * **Input/Output Blocks:** Represent the data flow into and out of each component at a given turn. * **Turn Indicators:** Labels indicating the current turn (Turn 1, Turn 2, ... Turn T). * **a¹, a², ..., a^T:** Represent the actions taken at each turn. * **q, K, M^t:** Inputs to the Planner at turn t. * **a^t, K:** Inputs to the Executor at turn t. * **e^t, M^t:** Inputs to the Verifier at turn t. * **v^t:** Output of the Verifier. * **o:** The final answer. ### Detailed Analysis or Content Details **(a) AgentFlow: In-the-Flow Agentic System** * The system starts with a "Query" and a "Toolkit Set". * The query is fed into a "Planner" at each turn (Turn 1, Turn 2, ... Turn T). * The Planner outputs an action (a¹, a², ..., a^T) which is passed to an "Executor". * The Executor then passes its output to a "Verifier". * At the final turn (Turn T), the Verifier's output is passed to a "Generator", which produces the "Answer". * "Memory" is used across turns, with feedback loops from the Verifier to the Memory. * The arrows indicate the flow of information. **(b) In-the-Flow Rollout at Turn t** * **Planner Input:** [Query Analysis], [Global Goal], [Required Skills]. * **Planner Output:** [Current Sub-goal], [Selected Tool], [Context for Tool Use]. * **Executor Input:** [Current Sub-goal], [Selected Tool & Context]. * **Executor Output:** [Generated Command], [Execution Result]. * **Verifier Input:** [Generated Command], [Execution Result]. * **Verifier Output:** [Execution Analysis], [Memory Analysis], [Verification Status]. * The "Memory" block is divided into "Trained" and "Frozen" sections. * The diagram shows the inputs and outputs of each component at a single turn 't'. * The inputs to the Planner are 'q', 'K', and 'M^t'. * The inputs to the Executor are 'a^t' and 'K'. * The inputs to the Verifier are 'e^t' and 'M^t'. ### Key Observations * The system operates iteratively across multiple turns. * Each turn involves planning, execution, and verification. * The "Memory" component plays a crucial role in retaining information across turns. * The system utilizes a toolkit of tools to accomplish tasks. * The diagram clearly delineates the input and output of each component at each stage. * The "Memory" is divided into "Trained" and "Frozen" sections, suggesting a learning or adaptation process. ### Interpretation The diagram illustrates a sophisticated agentic system designed to solve complex tasks through iterative planning, execution, and verification. The system's ability to leverage a toolkit and maintain memory across turns suggests a capacity for learning and adaptation. The separation of "Trained" and "Frozen" memory indicates a mechanism for preserving knowledge while allowing for ongoing refinement. The detailed breakdown of inputs and outputs for each component at turn 't' highlights the system's transparency and modularity. The flow of information is clearly defined, allowing for a comprehensive understanding of the system's operation. The system appears to be designed for tasks that require reasoning, tool use, and memory retention. The diagram suggests a robust and adaptable agent capable of tackling complex challenges.

## Diagram: AgentFlow System Architecture ### Overview The image displays a two-part technical diagram illustrating the architecture of "AgentFlow," an agentic system designed for iterative problem-solving using tools. Part (a) shows the high-level, multi-turn system flow, while part (b) details the internal processing components and data flow for a single turn (`t`). ### Components/Axes The diagram is divided into two labeled sections: * **(a) AgentFlow: In-the-Flow Agentic System** (Left panel) * **(b) In-the-Flow Rollout at Turn `t`** (Right panel) **Part (a) Components:** * **Input:** `Query` * **Toolkit Set:** Represented by icons for a web browser, a globe, a document, a calculator, and a settings gear, followed by "..." indicating more tools. * **Core Process:** A sequence of `Turn 1`, `Turn 2`, ... `Turn T...`. Each turn contains: * `Planner` (yellow box with a flame icon) * `Executor` (light blue box) * `Verifier` (light blue box) * **Flow:** Arrows show the sequence: `Query` -> `Planner` -> `Executor` -> `Verifier`. The output of one turn's `Verifier` feeds into the next turn's `Planner`. * **Final Stage:** After the last turn, the flow goes to a `Generator` (light blue box), which outputs an `Answer` (green box). An arrow from `Generator` also points to a circle labeled `o`. * **Memory:** A vertical orange bar on the far left labeled `Memory` connects to each turn's `Planner`. **Part (b) Components:** This panel breaks down the operations within a single turn `t`, showing three parallel processing blocks and a memory update. * **Planner πθ (Trained):** * **Input:** `q`, `K`, `M^t` * **Output:** `a^t` * **Input Description Box:** "Input: [Query Analysis], [Global Goal], [Required Skills]. Output: [Current Sub-Goal], [Selected Tool], [Context for Tool Use]." * **Executor (Frozen):** * **Input:** `a^t`, `K` * **Output:** `e^t` * **Input Description Box:** "Input: [Current Sub-Goal], [Selected Tool & Context], [Tool Metadata]. Output: [Generated Command], [Execution Result]." * **Verifier (Frozen):** * **Input:** `q`, `e^t`, `M^t` * **Output:** `v^t` * **Input Description Box:** "Input: [Generated Command], [Execution Result]. Output: [Execution Analysis], [Memory Analysis], [Verification Status]." * **Memory Update:** * The `Verifier`'s output `v^t` and the `Executor`'s output `e^t` feed into a `Memory` block (orange). * This produces an updated memory state `M^(t+1)`. * **Legend:** A box in the bottom-right corner defines the color coding: * **Trained:** Yellow background (used for `Planner πθ`). * **Frozen:** Light blue background (used for `Executor` and `Verifier`). ### Detailed Analysis **Part (a) - System Flow:** 1. A `Query` is received. 2. The system enters an iterative loop for up to `T` turns. 3. In each turn `i`: * The `Planner` (accessing `Memory`) generates an action `a^i`. * The `Executor` runs the action. * The `Verifier` checks the result. * The verified output is passed to the next turn's `Planner`. 4. After the final turn, a `Generator` synthesizes all turn outputs into a final `Answer`. **Part (b) - Single Turn (`t`) Rollout:** * **Planner (`πθ`):** Takes the query (`q`), a knowledge base (`K`), and current memory (`M^t`). It outputs an action plan (`a^t`). This component is marked as **Trained**. * **Executor:** Takes the action plan (`a^t`) and knowledge (`K`). It generates a command and executes it, producing an execution result (`e^t`). This component is **Frozen**. * **Verifier:** Takes the original query (`q`), the execution result (`e^t`), and current memory (`M^t`). It produces a verification output (`v^t`). This component is **Frozen**. * **Memory Update:** The outputs from the Executor (`e^t`) and Verifier (`v^t`) are used to update the system's memory from state `M^t` to `M^(t+1)`. ### Key Observations 1. **Iterative Refinement:** The core of AgentFlow is a multi-turn process where planning, execution, and verification are repeated, allowing for complex, multi-step problem-solving. 2. **Component Specialization:** The architecture clearly separates roles: Planning (learned/trained), Execution (fixed/frozen), and Verification (fixed/frozen). 3. **Central Role of Memory:** Memory (`M`) is a persistent state that is read from and written to in every turn, acting as the system's working context. 4. **Toolkit Abstraction:** The system is designed to work with a generic "Toolkit Set," implying it can be equipped with various APIs or tools. 5. **Flow Direction:** The data flow is strictly sequential within a turn (Planner -> Executor -> Verifier) and iterative across turns. ### Interpretation This diagram describes a sophisticated AI agent framework designed for robust task completion. The "In-the-Flow" designation suggests the agent's reasoning and tool use are tightly integrated within a single operational pipeline. * **The Peircean Investigation:** The process mirrors a scientific or investigative cycle. The **Planner** forms a hypothesis (action plan `a^t`) based on available data (`q`, `K`, `M^t`). The **Executor** conducts an experiment or gathers evidence (`e^t`). The **Verifier** analyzes the evidence against the original question (`q`) and existing knowledge (`M^t`) to assess its validity (`v^t`). This new evidence then updates the investigator's understanding (`M^(t+1)`), informing the next hypothesis. The final **Generator** synthesizes the findings into a conclusion (`Answer`). * **Why It Matters:** This architecture addresses key challenges in autonomous AI: error correction (via the Verifier), long-horizon planning (via iterative turns), and state management (via Memory). By freezing the Executor and Verifier, the system may ensure reliable, predictable tool use and evaluation, while the trained Planner can adapt its strategies. * **Notable Design Choice:** The separation of the "Trained" Planner from "Frozen" execution and verification modules suggests a design philosophy where core reasoning is learned, but the mechanisms for acting and checking are kept constant and reliable, potentially improving safety and predictability.

## Diagram: AgentFlow System Architecture and In-Flow Rollout Process ### Overview The image depicts a two-part technical diagram illustrating an agentic system's workflow and its in-flow rollout mechanics. Part (a) shows the system's general architecture across multiple turns, while part (b) details the component-level operations at a specific turn (t). The system integrates planning, execution, verification, and memory management components with explicit data flow between them. ### Components/Axes **Part (a): AgentFlow Architecture** - **Components**: - Planner (labeled with fire icon) - Executor - Verifier - Generator - Memory (with "Trained" and "Frozen" states) - **Flow**: - Sequential progression from Query → Planner → Executor → Verifier → Generator → Answer - Memory persists across turns (Turn 1 to Turn T) - Toolkit Set icons (Chrome, Firefox, globe, etc.) appear at the top **Part (b): In-Flow Rollout at Turn t** - **Components**: - Planner πθ (with target icon) - Executor - Verifier - Memory (with brain icon) - **Data Flow**: - Inputs/Outputs for each component: - Planner: [Query Analysis], [Global Goal], [Required Skills] → [Current Sub-Goal], [Selected Tool & Context], [Tool Metadata] - Executor: [Generated Command] → [Execution Result] - Verifier: [Execution Result] → [Execution Analysis], [Verification Status] - Memory: [Memory Analysis] → [Memory Update] ### Detailed Analysis **Part (a) Flow Mechanics** 1. **Turn Structure**: - Each turn (1 to T) follows: Planner → Executor → Verifier → Generator - Memory accumulates across turns (shown as stacked orange blocks) 2. **Component Roles**: - Planner: Generates actions (a₁ to aᵀ) based on query and memory - Executor: Executes actions with tool verification - Verifier: Validates execution results - Generator: Produces final answer (o) after T turns **Part (b) Turn-Level Details** 1. **Planner πθ**: - Inputs: Query analysis, global goal, required skills - Outputs: Current sub-goal, selected tool/context, tool metadata 2. **Executor**: - Input: Current sub-goal + context - Output: Generated command + execution result 3. **Verifier**: - Input: Execution result - Output: Execution analysis + verification status (✓/✗) 4. **Memory**: - Input: Verification status + execution analysis - Output: Updated memory state (Mᵗ→Mᵗ⁺) ### Key Observations 1. **Cyclical Verification**: - Verification occurs at every turn, with results feeding back into memory 2. **Memory Dynamics**: - Memory contains both "Trained" (learned) and "Frozen" (static) components 3. **Tool Integration**: - Tool selection and metadata are explicitly tracked through the workflow 4. **State Transitions**: - Each turn updates memory (Mᵗ→Mᵗ⁺) based on verification outcomes ### Interpretation This diagram illustrates a recursive agentic system where: 1. **Planning-Driven Execution**: The Planner (πθ) generates context-aware actions using memory-augmented query analysis 2. **Closed-Loop Verification**: Every execution result undergoes verification before memory updates, ensuring quality control 3. **Memory as Knowledge Base**: The system maintains both dynamic (Trained) and static (Frozen) knowledge, enabling context-aware tool selection 4. **Incremental Progress**: The T-turn structure suggests cumulative learning, with each verification result refining future planning The architecture emphasizes reliability through continuous verification and memory-augmented planning, while the rollout mechanics show how individual turns contribute to long-term system improvement. The use of both trained and frozen memory components suggests a hybrid approach combining learned patterns with fixed domain knowledge.