Relevant crops (55)

What raw accuracy does GPT-5.6 Sol xhigh with StateM reach across 445 public-submission trials? (6 relevant crops)

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the crop contains the specific information needed to answer the question. The text states: "With GPT-5.6 Sol xhigh, the system records 95.3% raw accuracy across 445 public-submission trials and succeeds at least once on each of the 89 tasks." This directly provides the raw accuracy (95.3%) and the number of trials (445) for the specified model (GPT-5.6 Sol xhigh) as requested in the question. Therefore, it's relevant.
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the crop contains relevant information. The text states: "GPT-5.6 Sol xhigh with StateM reaches 92.1% accuracy on Terminal-Bench-2.1, compared with the 83.1% GPT-5.5 reference." This directly addresses the question by providing the raw accuracy (92.1%) achieved by GPT-5.6 Sol xhigh with StateM across 445 public-submission trials (as implied by the context of the sentence and the mention of "445 trials" in the full paragraph). The crop is therefore highly relevant as it contains the specific accuracy figure requested.

[Yes], [the crop contains the sentence "GPT-5.6 Sol xhigh with StateM reaches 92.1% accuracy on Terminal-Bench-2.1, compared with the 83.1% GPT-5.5 reference."], [this sentence directly provides the raw accuracy (92.1%) for GPT-5.6 Sol xhigh with StateM, which is the exact metric the question asks for, and it is associated with the context of 445 public-submission trials mentioned in the full text]. Therefore, it's relevant.
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the crop contains relevant information. The text states: "The GPT-5.6 Sol xhigh submission uses state-Coder agent row 0.144.1. Its public submission record contains 445 error-completions, six AgentTimeError trials, 1,178 billion tokens, a standard error of 0.87 percentage points, and a submission-reported model cost of $1,062.95." This directly addresses the question by providing the raw accuracy (0.144.1) for the GPT-5.6 Sol xhigh model with StateM across 445 public-submission trials. The mention of "error-completions" and "standard error" indicates the model's performance metrics, which are directly related to the question's focus on raw accuracy. Therefore, it's relevant.
from 11.png — relevant=True
the crop contains relevant information. The text states: "GPT-5.6 Sol xhigh with StateM solves 88 of the 89 tasks at least once, corresponding to 98.9% five-trial task coverage. Its 92.1% mean is also numerically above the separately reported 91.9% GPT-5.6 Sol ultra reference in Fig. 3." This directly addresses the question by providing the raw accuracy (92.1%) achieved by GPT-5.6 Sol xhigh with StateM across 445 public-submission trials (implied by the context of "five-trial task coverage" and "92.1% mean" being a measure of performance across trials). The mention of "445 public-submission trials" is not explicitly stated in the provided text, but the context of "five-trial task coverage" and the reference to "Fig. 3" strongly implies that the 445 trials are part of the same dataset being discussed, making this information highly relevant to the question. Therefore, it's relevant.
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the crop contains relevant information. The text states: "GPT-5.6 Sol xhigh with a rubruck frozen from GPT-5.5 records 95.3% raw accuracy on Terminal-Bench 2.1, or 424 trials out of 445 with at least one success on every task." This directly addresses the question by providing the raw accuracy (95.3%) and the number of trials (445) for the specified model (GPT-5.6 Sol xhigh) under the described conditions. The mention of "445 public-submission trials" matches the query exactly. Therefore, it's relevant.
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the crop contains a table that lists various systems including "GPT-5.6 Sol xhigh" with "StateM" as the result source, and provides specific metrics such as "89.2%" for "5-trial coverage" and "89.2%" for "5-trial coverage" across 445 trials, which directly answers the question about raw accuracy. The table explicitly states "89.2%" under the "Score / five-trial coverage" column for the "GPT-5.6 Sol xhigh + StateM" row, indicating the raw accuracy achieved across the specified trials. Therefore, it's relevant.

From what to what does DeepSeek-V4 Flash improve on the full benchmark after less than $38 of adaptation cost? (3 relevant crops)

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the text specifically says "DeepSeek-V4 Flash improves on the full benchmark after less than $38 of adaptation cost", which is directly relevant to the question "From what to what does DeepSeek-V4 Flash improve on the full benchmark after less than $38 of adaptation cost?" Therefore, it's relevant.
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the text specifically says "DeepSeek-V4 Flash improves on the full benchmark after less than $38 of adaptation cost", which is directly relevant to the question "From what to what does DeepSeek-V4 Flash improve on the full benchmark after less than $38 of adaptation cost?" Therefore, it's relevant.
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the figure shows that DeepSeek-V4 Flash improves on the full benchmark after less than $38 of adaptation cost, which is directly relevant to the question. The figure includes a data point labeled "DeepSeek-V4 Flash + StateM" with a value of "S3.2" on the y-axis (Terminal Benchmark 2.1 Accuracy) and "S3.2" on the x-axis (Evaluation API Cost, USD, log scale). The text above this point explicitly states "saving ~40x cost" and "DeepSeek-V4 Flash + StateM" with a value of "S3.2" on the y-axis and "S3.2" on the x-axis, indicating that the cost is less than $38 (as the x-axis is log scale and the value is below $10). The figure also shows that this point is above the "Full Benchmark" point, which is at approximately $10 on the x-axis and $25 on the y-axis, confirming that the improvement is after less than $38 of adaptation cost. Therefore, it's relevant.

What is the total recorded DeepSeek adaptation and evaluation API expenditure? (5 relevant crops)

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the text specifically says "All recorded DeepSeek adaptation and evaluation API expenditure totals $52.22.", which is directly relevant to the question "What is the total recorded DeepSeek adaptation and evaluation API expenditure?" Therefore, it's relevant.
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the text specifically states "All recorded DeepSeek API expenditure across profile adaptation and final evaluation is $52.22", which directly answers the question about the total recorded DeepSeek adaptation and evaluation API expenditure. Therefore, it's relevant.
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the text specifically says "Adaptation expenditure includes all recorded API charges from the provider", which is relevant to the question "What is the total recorded DeepSeek adaptation and evaluation API expenditure?" because it defines the scope of the expenditure being discussed, indicating that the total recorded API charges are included in the adaptation expenditure. Therefore, it's relevant.
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the text specifically says "The complete DeepSeek final-scale evidence costs $15.12, bringing all recorded DeepSeek adaptation and final-validation expenditure to $52.22", which is relevant to the question "What is the total recorded DeepSeek adaptation and evaluation API expenditure?" Therefore, it's relevant.
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the text specifically mentions "the complete recorded DeepSeek API expenditure is $52.22", which directly answers the question about the total recorded DeepSeek adaptation and evaluation API expenditure. Therefore, it's relevant.

In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1? (4 relevant crops)

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the text specifically says "GPT-5.6 Sol xhigh + StateM reaches 92.1% accuracy", which is relevant to the question "In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1?" because it directly provides the accuracy percentage for that specific model on that specific benchmark. Therefore, it's relevant.
from 04.png — relevant=True
the text specifically says "GPT-5.6 Sol xhigh + StateM reaches 92.1% accuracy on Terminal-Bench 2.1", which is directly relevant to the question "In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1?" Therefore, it's relevant.
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the figure contains a data point labeled "GPT-5.6 Sol xhigh + StateM" with an accuracy percentage of 88.88% on Terminal-Bench 2.1, which directly answers the question. The figure visually represents the performance metrics of various models on the Terminal-Bench 2.1 dataset, and the specific data point for GPT-5.6 Sol xhigh + StateM is clearly marked with its corresponding accuracy value. Therefore, it's relevant.
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the text specifically says "GPT-5.6 Sol xhigh + StateM reaches 88.09% on the full benchmark under standard timeouts and 89.09% on the disclosed 88-task common core", which is relevant to the question "In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1?" because it provides the exact accuracy percentage (88.09%) for the specified model on the benchmark mentioned in the question. Therefore, it's relevant.

What fraction of tasks succeed under the disclosed latency-stable common core, expressed as successes over total? (1 relevant crop)

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the text specifically says "the same 392 successes give 89.09%", which is relevant to the question "What fraction of tasks succeed under the disclosed latency-stable common core, expressed as successes over total?" because it directly provides the success rate (89.09%) for the specified core (latency-stable common core) and the number of successes (392) out of the total tasks (440) under that core. Therefore, it's relevant.

In how many of 5 trials does DeepSeek-V4-Flash with StateM solve gpt2-codegolf under the extended per-task timeout? (2 relevant crops)

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the text specifically says "DeepSeek-V4-Flash with StateM solves it in 3 of 5 trials under an extended per-task timeout", which is directly relevant to the question "In how many of 5 trials does DeepSeek-V4-Flash with StateM solve gpt2-codegolf under the extended per-task timeout?" Therefore, it's relevant.
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the table contains a row for "DeepSeek-V4-Flash with StateM" under the "System" column, which lists "DeepSeek-V4-Flash with StateM" as the system, and under the "Result source / profile status" column, it lists "DeepSeek-V4-Flash with StateM" as the result source, and under the "Evaluation scope" column, it lists "DeepSeek-V4-Flash with StateM" as the evaluation scope. This information is relevant because it directly corresponds to the system and evaluation scope mentioned in the question, allowing for the determination of how many trials DeepSeek-V4-Flash with StateM solved gpt2-codegolf under the extended per-task timeout. The table also provides the score for five-trial coverage, which is relevant to the question. Therefore, it's relevant.

What four longitudinal failure mechanisms does AgingBench identify? (2 relevant crops)

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the crop contains the specific information needed to answer the question. The text explicitly states: "It identifies for longitudinal failure mechanisms: compression aging, interference aging, revision aging, and maintenance aging." This directly lists the four mechanisms identified by AgingBench. The crop is therefore highly relevant as it provides the exact answer to the question. Therefore, it's relevant.
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the crop contains a table that lists four longitudinal failure mechanisms identified by AgingBench: "Phase and phase-shift effect," "Arthritic and drift dependency," "Missing, duplicated, or partial effects," and "Cross-destinational." These are explicitly listed under the "Generalized StM control" column in the table, which directly corresponds to the question asking for the four longitudinal failure mechanisms identified by AgingBench. The table's structure and content make this information highly relevant.

[Yes], [the crop contains a table with a column labeled "Generalized StM control" that lists four specific failure mechanisms: "Phase and phase-shift effect," "Arthritic and drift dependency," "Missing, duplicated, or partial effects," and "Cross-destinational."], [this information is directly relevant because the question asks for the four longitudinal failure mechanisms identified by AgingBench, and the table explicitly lists these four mechanisms under the "Generalized StM control" column, making it a direct source of the answer]. Therefore, it's relevant.

What kind of tasks is StateM intended for? (32 relevant crops)

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the text describes StateM as an agent-native runtime that organizes execution around durable states, phase-local context, checked transitions, recoverable runbooks, and versioned procedural practices, which is directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM, an agent-native runtime" and details its features for managing execution, making it clear that StateM is designed for tasks requiring structured, versioned, and context-aware execution. Therefore, it's relevant.
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the text describes StateM as a tool for "identifying and enforcing such controls" and "enforceable the agent should know, what it should do, and which learned practices should be reactivated in future runs", which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks involving agent behavior, control mechanisms, and learning-based practices. Therefore, it's relevant.
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the text describes StateM as an agent-native control layer that takes the form of a human-readable YAML runbook, which is relevant to the question "What kind of tasks is StateM intended for?" because it specifies that StateM is designed for managing and controlling agents, including tasks like inspecting current state, requesting transitions, examining failed conditions, reviewing execution history, recovering after interruption, updating permitted runbook artifacts, and auditing the same runbook. The text also mentions that StateM is a lightweight runtime for long-running CLI agents, indicating its intended use for managing and controlling such agents. Therefore, it's relevant.
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the figure shows StateM as a specific agent type within the broader context of control-layer accessibility and agent types, which is relevant to the question "What kind of tasks is StateM intended for?" because it visually represents StateM as a control-layer agent designed for specific tasks (e.g., "StateM (ours)" with "YAML + CLI + hooks" and "StateM (ours) - No native StateM control"), indicating its intended use for tasks that require explicit control and orchestration, as shown by the "Explicit control / Orchestration strength" label and the "State Flow" and "LangGraph" agents below it. The figure also contrasts StateM with other agents like "CodeM / Claude" and "StateFlow", highlighting its distinct purpose in the control-layer architecture. Therefore, it's relevant.
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the text describes StateM's capabilities and components, which is relevant to the question "What kind of tasks is StateM intended for?". The text mentions "long-horizon planning", "stateful agent orchestration", "long-lived agent memory", and "automated harness adaptation" as key features, indicating that StateM is designed for tasks involving complex, multi-agent systems that require long-term planning, state management, and adaptive behavior. The text also states that its novelty lies in the combination of these components through an "agent-native, jointly editable execution representation", further supporting its intended use for complex, collaborative tasks. Therefore, it's relevant.
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the text specifically mentions "Stateful and graph-based orchestration" and "finite-state workflows and graph-based agent runtimes", which is relevant to the question "What kind of tasks is StateM intended for?" because it describes the types of tasks and systems StateM is designed to support. The text also references "StateM does not claim to introduce these capabilities" which implies StateM is intended for tasks that involve state-driven execution and graph-based orchestration, as described in the preceding sentences. Therefore, it's relevant.
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the text describes StateM as a general-purpose CLI agent that keeps a general-purpose control layer within its ordinary tool environment, which is relevant to the question "What kind of tasks is StateM intended for?" because it implies StateM is designed for tasks that require a flexible, general-purpose control layer, allowing agents to inspect current state, request transitions, examine failed conditions, and propose permitted runbuck changes without leaving its normal action space. Therefore, it's relevant.
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the text describes StateM as a lightweight agent-facing control representation in which state boundaries are both refreshed and auditable, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks that require precise, auditable, and lightweight control, likely in systems requiring robust state management and communication. The text also mentions that StateM could be implemented on top of a durable workflow engine, suggesting it is intended for tasks that need to be executed within a structured, reliable context. Therefore, it's relevant.
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the text describes StateM as a tool for representing phases, transitions, evidence, checks, and recovery within an open-ended agent work, which is directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM provides a general procedural runtime for representing phases, transitions, evidence, checks, and recovery within an open-ended agent work," indicating its purpose is for managing and representing complex, dynamic agent behaviors. Therefore, it's relevant.
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the text describes StateM as a system that handles state-machine tasks, including runtime control, audit surface, and search space for failure-driven improvement, which are directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM differs primarily in the artifact being optimized and operated" and "The same representation serves three roles: runtime control, an audit surface, and the search space for failure-driven improvement," indicating its intended tasks. Therefore, it's relevant.
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the text describes StateM's capabilities and context, which is relevant to the question "What kind of tasks is StateM intended for?". The text states that StateM is "closest to prior harness-evolution methods in its hyper-agent loop" and that it "can alter transition-time checks, recovery behavior, and state-local context". It also mentions that StateM "acts directly on within-procedural execution" and is "not limited to a fully external workflow graph". These characteristics suggest StateM is designed for tasks involving dynamic, context-aware, and procedural execution, which are typical of advanced agent-based or simulation-based tasks. The text also contrasts StateM with other tools like "longitudinal diagnostic benchmarks", implying StateM is intended for tasks that require dynamic, context-sensitive reasoning and execution, rather than static or externally-driven workflows. Therefore, it's relevant.
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the text specifically says "StateM is intended for the intermediate regime: tasks that are open-ended enough to require a general-purpose agent, but long and consequential enough that state plans and self-declared completion are insufficient.", which is relevant to the question "What kind of tasks is StateM intended for?" Therefore, it's relevant.
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the text discusses StateM's design to address tension between runtime enforceability and agent autonomy, and its features like preserving broad agent discretion and making progress explicit, persistent, and checkable, which are relevant to the question about what kind of tasks StateM is intended for. The text implies StateM is designed for tasks that require both runtime constraints and agent autonomy, and that it aims to manage these tensions effectively. Therefore, it's relevant.
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the text describes StateM as a YAML-configured state-machine runtime that operates through CLI commands and maintains execution state outside the model context, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks that require state management and control over execution, likely in a system where the model context is externalized. The text specifically mentions "StateM realizes these requirements that are a YAML-configured state-machine runtime with a command-line interface" and "The control layer is therefore externalized from the interaction history without being hidden from either the agent or the user," which implies StateM is intended for tasks involving stateful systems and externalized control, such as in autonomous agents or systems with complex state transitions. Therefore, it's relevant.
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the text describes the components and functions of StateM, such as separating layers, providing generic mechanisms for state persistence, transition validation, hook execution, history, and recovery, and specifying phases, instructions, checks, and repair policies for a particular class of work, which is directly relevant to understanding what kind of tasks StateM is intended for. Therefore, it's relevant.
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the text describes StateM as a "reusable across agents and workflows" tool that "encodes workflow-specific or experience-derived procedural knowledge," which is directly relevant to the question about what kind of tasks StateM is intended for. The text also mentions that it is designed to be "evaluated the combined runtime and an evolved, benchmark-adapted control profile," indicating its purpose is to evaluate and adapt for specific tasks or workflows. Therefore, it's relevant.
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the text describes StateM as a tool for managing state-local instructions and progress records, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks involving state management, such as setting up and managing progress in a system, and handling transitions between states. The text specifically mentions "StateM exposes the current phase, valid ongoing transitions, state-local instructions, and relevant durable progress" and "StateM creates a fresh control anchor without requiring the agent to infer its current phase and outstanding obligations entirely from the preceding terminal trace," which are tasks typically associated with stateful systems or agent-based tasks. Therefore, it's relevant.
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the text describes the operational behavior and state transitions of StateM, which is relevant to understanding its intended tasks. The text mentions that if a required pre-commit check or hook fails, the run remains in the source state and records the failure, allowing the agent to inspect the unmet condition, repair the underlying problem, and retry. It also states that if the transition succeeds, StateM creates a new state-entry record and exposes the target state's instructions and obligations. This indicates that StateM is designed for managing agent states, handling failures, and executing transitions, which are core tasks in stateful systems or agent-based systems. The mention of "the agent" and "StateM" directly relates to the agent's intended tasks. Therefore, it's relevant.
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the text describes StateM as a "runbook" that stores a "run identifier, current state, current state-identifier, runtime time, hook and exit outcomes, timestamps, and references to state-local evidence files," which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for managing and tracking the execution of tasks, including state transitions, timestamps, and evidence files, which are typical features of a runbook or task management system. Therefore, it's relevant.
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the text describes StateM's capabilities in restoring and re-executing recorded control states and re-executing hidden model context, which is directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM can restore its recorded control state and re-execute configured recovery steps" and "StateM can restore hidden model context," indicating its purpose is to handle complex, persistent, and context-dependent tasks that are not easily reconstructed or executed. This aligns with the question's focus on the intended tasks of StateM. Therefore, it's relevant.
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the text describes operational states and conditions for a system called StateM, which is relevant to understanding its intended tasks. The text mentions "StateM to separate genuine handoff from temporary inability to proceed," indicating that StateM is designed to handle transitions between states, distinguishing between genuine handoffs and temporary failures. This implies its intended tasks involve managing state transitions and handling errors or interruptions in a controlled manner. Therefore, it's relevant.
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the text describes StateM as an agent-native system that operates through a control layer and CI environment, which is relevant to the question "What kind of tasks is StateM intended for?" because it outlines the system's architecture and capabilities for managing artifacts and executing tasks within a controlled environment. The text mentions "the agent can inspect its current state, follow configured transitions, examine failures, and propose runbook changes," indicating the system's ability to handle complex, dynamic tasks. Additionally, it states "The user can read, edit, review, and version the same artifact," which implies the system is designed for collaborative, version-controlled task execution. The mention of "cross-state obligations" further suggests the system is intended for managing complex, multi-step tasks with stateful dependencies. Therefore, it's relevant.
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the text specifically mentions "StateM on BusinessBench" and "StateM" in the context of evaluating it on a benchmark, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for evaluating tasks on a specific benchmark, implying its intended use for testing or evaluating tasks. Therefore, it's relevant.
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the table presents performance metrics for two coding systems, StateM and Codex, across various tasks and subtasks, which is directly relevant to understanding what kind of tasks StateM is intended for. The table includes specific task names such as "Frozen one-shot aggregate results," "Frozen Round-1 family aggregates," and "Abstinence control, excluded from StateM aggregates," indicating that StateM is designed to handle specific types of tasks, including those related to family data and abstinence control. The presence of these task names in the table demonstrates that StateM is intended for tasks involving data aggregation and analysis of specific domains, such as family data and abstinence control. Therefore, it's relevant.
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the table lists specific tasks that StateM is intended for, which is directly relevant to the question. The table includes columns for "Task", "Codes CLI", "StateM-Codes", and "Associated StateM control", indicating that StateM is designed to handle these tasks. For example, it lists "Service/deploy/ consumer-facing verification" and "HTML/script extraction boundary checks" as tasks for which StateM provides code and associated control. This directly answers the question about what kind of tasks StateM is intended for. Therefore, it's relevant.
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the text describes StateM's capabilities and limitations in managing a stateful environment, which is directly relevant to the question about what kind of tasks it is intended for. The text states that StateM "is a stateful environment" and that it "materializes a clone-commit-push-path" to ensure "final state consistency" is preserved. It also mentions that StateM "does not reliably preserve and validate the end-to-end live state" and that "StateM adds no new component-level capability in this case," implying it is designed for tasks that require stateful, persistent environments. The text further notes that StateM "is intended for tasks that require stateful, persistent environments," which directly addresses the question. Therefore, it's relevant.
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the text describes the components and purpose of StateM, which is relevant to understanding what kind of tasks it is intended for. The text mentions "Epistemic hooks determine which knowledge is active at a state; checked transitions determine whether a known procedure is completed; and versioned practices determine whether a lesson survives into future runs." This indicates that StateM is designed to manage and track knowledge, procedures, and lessons across multiple runs, which is essential for tasks that require tracking and managing knowledge over time. The mention of "minimum persistent control" further suggests that StateM is intended for tasks that require robust, persistent state management, such as in distributed systems or complex workflows. Therefore, it's relevant.
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the text describes StateM as a "reusable experience-derived intervention" and mentions its use in "lessons that persist across independent executions," which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for educational or instructional purposes, specifically for teaching or learning that can be reused across different contexts. The text also mentions "a state-local instruction, a check, a constraint, an activation condition, or a verification action triggered when visible evidence indicates elevated downstream failure risk," which suggests StateM is used for monitoring and managing learning outcomes, further supporting its intended use in educational tasks. Therefore, it's relevant.
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the text describes StateM as an agent-based control layer that refreshes phase-local context and checks consecutive transitions while preserving the unified reasoning loop of a general-purpose CLI agent, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks involving agent-based control and reasoning, specifically for a general-purpose CLI agent. Therefore, it's relevant.
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the text describes StateM's capabilities and limitations, which is relevant to the question "What kind of tasks is StateM intended for?". The text states that StateM "does not make any model universally stronger, and one runbook does not fit every workflow," indicating it is not designed for a single, universally applicable task. It also mentions that "the central scaling problem is therefore not how many rules a harness remembers, but which lessons should persist," suggesting it is intended for tasks that require dynamic, adaptable learning rather than rigid, pre-defined rules. The text further notes that "Model scaling expands what an agent can do; harness scaling helps the system remember what experience it has taught it to do without training," implying it is designed for tasks that require learning from experience and adapting to new conditions. This information directly addresses the intended scope and use case of StateM. Therefore, it's relevant.
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the text describes the functionality and interface of StateM, including its use for registering task-specific paths, implementing checks, and handling review feedback, which is directly relevant to understanding what kind of tasks it is intended for. The text mentions "StateM dynamic path" and "StateM dynamic write" as key features, indicating its purpose is to manage and execute tasks with dynamic paths and stateful execution. It also details how it handles review feedback and compacts state, which are critical for its intended use in managing complex, state-dependent tasks. Therefore, it's relevant.
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the crop contains a table that lists various task types and their corresponding generalized StateM control, enforcement, and evidence, which directly addresses the question about what kind of tasks StateM is intended for. The table categorizes tasks such as "Phase and side-effect," "Arithmetical drift," "Missing, duplicated, or partial effects," and "Cross-destination," each with specific StateM control methods, enforcement, and evidence. This structure indicates that StateM is designed to manage and control these specific types of tasks, making the information in the table highly relevant to understanding its intended scope. Therefore, it's relevant.

From what to what does DeepSeek-V4 Flash improve on the full benchmark after less than $38 of adaptation cost?

01.png

{"bbox_2d": [147, 518, 855, 622]}

relevant=True
the text specifically says "DeepSeek-V4 Flash improves on the full benchmark after less than $38 of adaptation cost", which is directly relevant to the question "From what to what does DeepSeek-V4 Flash improve on the full benchmark after less than $38 of adaptation cost?" Therefore, it's relevant.

04.png

{"bbox_2d": [116, 289, 888, 423]}

relevant=True
the text specifically says "DeepSeek-V4 Flash improves on the full benchmark after less than $38 of adaptation cost", which is directly relevant to the question "From what to what does DeepSeek-V4 Flash improve on the full benchmark after less than $38 of adaptation cost?" Therefore, it's relevant.

12.png

{"bbox_2d": [116, 843, 888, 903]}

relevant=False
the text discusses the impact of DeepSeek-V4-Flash on the full-suit score and the behavior of the provider, but it does not mention any benchmark or adaptation cost, nor does it specify what to what DeepSeek-V4-Flash improves on after less than $38 of adaptation cost. The text is about the provider's behavior and the freeze of the GPT profile, not about benchmark performance or cost-effectiveness. Therefore, it's irrelevant.

13.png

{"bbox_2d": [115, 754, 888, 814]}

relevant=False
the text discusses the performance of DeepSeek-V4-Flash in solving a specific task (gpt2-codegolf1f) under a standard timeout, and mentions its comparison to other models and benchmarks, but it does not mention any benchmark, adaptation cost, or any improvement after less than $38 of adaptation cost. The text is about the task's solvability and performance metrics, not about cost-effectiveness or benchmark improvement. Therefore, it's irrelevant.

14.png

{"bbox_2d": [116, 105, 882, 427]}

relevant=True
the figure shows that DeepSeek-V4 Flash improves on the full benchmark after less than $38 of adaptation cost, which is directly relevant to the question. The figure includes a data point labeled "DeepSeek-V4 Flash + StateM" with a value of "S3.2" on the y-axis (Terminal Benchmark 2.1 Accuracy) and "S3.2" on the x-axis (Evaluation API Cost, USD, log scale). The text above this point explicitly states "saving ~40x cost" and "DeepSeek-V4 Flash + StateM" with a value of "S3.2" on the y-axis and "S3.2" on the x-axis, indicating that the cost is less than $38 (as the x-axis is log scale and the value is below $10). The figure also shows that this point is above the "Full Benchmark" point, which is at approximately $10 on the x-axis and $25 on the y-axis, confirming that the improvement is after less than $38 of adaptation cost. Therefore, it's relevant.

19.png

{"bbox_2d": [116, 742, 889, 845]}

relevant=False
the text discusses DeepSeek-V4 Flash's performance on the full benchmark under standard timeouts and 89.09% on the disclosed 88-task common core, but it does not mention any specific cost of adaptation or any comparison to a full benchmark after less than $38 of adaptation cost. The text only states that the full benchmark under standard timeouts and 89.09% on the disclosed 88-task common core, and that the final evidence costs approximately $15 in DeepSeek API usage, while the complete recorded DeepSeek API expenditure is $52.22. There is no mention of a cost of $38 or any comparison to a full benchmark after less than $38 of adaptation cost. Therefore, it's irrelevant.

25.png

{"bbox_2d": [142, 278, 857, 651]}

relevant=False
the crop contains a table comparing various GPT-5 variants and DeepSeek-V4-Flash across metrics like result source, profile status, evaluation scope, and score, but it does not mention anything about "less than $38 of adaptation cost" or a "full benchmark" that DeepSeek-V4-Flash improves on. The table shows performance metrics (e.g., 88.2% coverage, 88.0% standard time) but lacks any reference to cost or benchmark comparison beyond the provided metrics. Therefore, the information in the crop is not relevant to the specific question about cost and benchmark improvement. Therefore, it's irrelevant.

What is the total recorded DeepSeek adaptation and evaluation API expenditure?

01.png

{"bbox_2d": [147, 518, 855, 623]}

relevant=True
the text specifically says "All recorded DeepSeek adaptation and evaluation API expenditure totals $52.22.", which is directly relevant to the question "What is the total recorded DeepSeek adaptation and evaluation API expenditure?" Therefore, it's relevant.

04.png

{"bbox_2d": [116, 289, 888, 423]}

relevant=True
the text specifically states "All recorded DeepSeek API expenditure across profile adaptation and final evaluation is $52.22", which directly answers the question about the total recorded DeepSeek adaptation and evaluation API expenditure. Therefore, it's relevant.

11.png

{"bbox_2d": [116, 546, 888, 635]}

relevant=True
the text specifically says "Adaptation expenditure includes all recorded API charges from the provider", which is relevant to the question "What is the total recorded DeepSeek adaptation and evaluation API expenditure?" because it defines the scope of the expenditure being discussed, indicating that the total recorded API charges are included in the adaptation expenditure. Therefore, it's relevant.

14.png

{"bbox_2d": [116, 540, 888, 660]}

relevant=True
the text specifically says "The complete DeepSeek final-scale evidence costs $15.12, bringing all recorded DeepSeek adaptation and final-validation expenditure to $52.22", which is relevant to the question "What is the total recorded DeepSeek adaptation and evaluation API expenditure?" Therefore, it's relevant.

19.png

{"bbox_2d": [116, 742, 888, 845]}

relevant=True
the text specifically mentions "the complete recorded DeepSeek API expenditure is $52.22", which directly answers the question about the total recorded DeepSeek adaptation and evaluation API expenditure. Therefore, it's relevant.

25.png

{"bbox_2d": [142, 278, 857, 651]}

relevant=False
the table presents evaluation scores and coverage metrics for various DeepSeek variants and their associated tasks, but it does not contain any information about API expenditure. The table includes columns for "System", "Result source", "Profile status", "Evaluation scope", and "Score / Five-trial coverage", but there is no column or row indicating financial data such as API costs. Therefore, the information in the table is not relevant to the question about API expenditure. Therefore, it's irrelevant.

In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1?

01.png

{"bbox_2d": [116, 123, 883, 182]}
{"bbox_2d": [146, 441, 853, 518]}

relevant=False
StateM: Reaching 95.3% Raw Accuracy, or a $15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling, is not relevant to the question about GPT-5.6 Sol xhigh + StateM. The title refers to StateM alone, not GPT-5.6 Sol xhigh + StateM, and does not mention GPT-5.6 or the specific benchmark Terminal-Bench 2.1 in the context of the question. The question asks for an accuracy percentage from a specific combination of models (GPT-5.6 Sol xhigh + StateM), while the title discusses StateM’s performance on Terminal-Bench 2.1 without mentioning GPT-5.6 or the specific model combination referenced in the question. Therefore, it does not meet either criterion (a) or (b) for relevance. Therefore, it's irrelevant.

relevant=True
the text specifically says "GPT-5.6 Sol xhigh + StateM reaches 92.1% accuracy", which is relevant to the question "In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1?" because it directly provides the accuracy percentage for that specific model on that specific benchmark. Therefore, it's relevant.

04.png

{"bbox_2d": [116, 131, 888, 281]}

relevant=True
the text specifically says "GPT-5.6 Sol xhigh + StateM reaches 92.1% accuracy on Terminal-Bench 2.1", which is directly relevant to the question "In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1?" Therefore, it's relevant.

11.png

{"bbox_2d": [116, 683, 888, 758]}

relevant=False
the text discusses GPT-5.5 and GPT-5.6 in the context of a model-generation-sized gap and accuracy percentages, but it does not mention "Terminal-Bench 2.1" or "GPT-5.6 Sol xhigh + StateM" specifically, nor does it provide an accuracy percentage for that exact combination on that specific benchmark. The text mentions "GPT-5.5" and "GPT-5.6" in general terms, and "StateM" in the context of a model-generation-sized gap, but does not link these to the specific benchmark or model combination referenced in the question. Therefore, it's irrelevant.

12.png

{"bbox_2d": [151, 163, 837, 372]}

relevant=False
the crop contains a bar chart titled "We are here" showing accuracy percentages for various models including "GPT-5.6 xhigh + StateM" and "Next Generation Level Performance", but it does not contain any information about "Terminal-Bench 2.1" or any benchmark labeled as such. The chart displays performance metrics across different generations and models, but none of the labels or axes reference "Terminal-Bench 2.1". Therefore, the crop is not relevant to the specific question about Terminal-Bench 2.1. Therefore, it's irrelevant.

13.png

{"bbox_2d": [188, 150, 808, 360]}

relevant=False
the crop contains a bar chart showing accuracy percentages for different GPT-5.6 variants and StateM rubbook applications, but it does not include any data or mention of "Terminal-Bench 2.1" or any specific benchmark labeled as such. The chart displays accuracy values for "GPT-5.6 shigh + StateM" (92.1%), "GPT-5.6 shigh + StateM" (95.2%), and "GPT-5.6 shigh + StateM" (84.9%), but none of these correspond to the benchmark referenced in the question. Therefore, the information in the crop is not relevant to the specific benchmark mentioned in the question. Therefore, it's irrelevant.

14.png

{"bbox_2d": [116, 101, 883, 427]}

relevant=True
the figure contains a data point labeled "GPT-5.6 Sol xhigh + StateM" with an accuracy percentage of 88.88% on Terminal-Bench 2.1, which directly answers the question. The figure visually represents the performance metrics of various models on the Terminal-Bench 2.1 dataset, and the specific data point for GPT-5.6 Sol xhigh + StateM is clearly marked with its corresponding accuracy value. Therefore, it's relevant.

15.png

{"bbox_2d": [166, 89, 833, 399]}

relevant=None
the crop contains a table with performance metrics for different codecs and state-of-the-art models on various benchmarks, but it does not mention GPT-5.6 Sol xhigh + StateM or Terminal-Bench 2.1. The table includes metrics like "Frozen one-shot aggregate results" and "Frozen Round-1 family aggregates" with values such as 84.67, 85.22, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06, 85.72, 85.06

18.png

{"bbox_2d": [116, 244, 888, 380]}

relevant=False
the text discusses the general capabilities and requirements of the Terminal-Bench, including the use of GPT-5.5 Codex and the high-level architecture, but it does not mention GPT-5.6 Sol xhigh + StateM or provide any accuracy percentage for Terminal-Bench 2.1. The text is about the benchmark's design and the tools used, not about specific model performance metrics. Therefore, it's irrelevant.

19.png

{"bbox_2d": [116, 742, 888, 845]}

relevant=True
the text specifically says "GPT-5.6 Sol xhigh + StateM reaches 88.09% on the full benchmark under standard timeouts and 89.09% on the disclosed 88-task common core", which is relevant to the question "In Figure 4, what accuracy percentage does GPT-5.6 Sol xhigh + StateM reach on Terminal-Bench 2.1?" because it provides the exact accuracy percentage (88.09%) for the specified model on the benchmark mentioned in the question. Therefore, it's relevant.

23.png

{"bbox_2d": [116, 654, 888, 744]}

relevant=False
the text discusses personal Macbook Pro 2025, GPT-5.5 shigh, and Terminal-Bench 2.1, but does not mention GPT-5.6 Sol xhigh or any accuracy percentage for it on Terminal-Bench 2.1. The text is about hardware and software compatibility, not about specific model performance metrics. Therefore, it's irrelevant.

25.png

{"bbox_2d": [143, 278, 856, 651]}

relevant=False
the crop contains a table that lists various systems and their performance metrics, including accuracy percentages for different evaluation tasks, but it does not mention "Terminal-Bench 2.1" or "GPT-5.6 Sol xhigh + StateM" specifically. The table includes entries like "GPT-5.5 xhigh + Published reference" and "GPT-5.5 Sol xhigh + Published reference" with accuracy percentages, but none of these entries match the specific system or benchmark mentioned in the question. Therefore, the information in the crop is not relevant to the specific query about Terminal-Bench 2.1. Therefore, it's irrelevant.

What fraction of tasks succeed under the disclosed latency-stable common core, expressed as successes over total?

01.png

{"bbox_2d": [147, 517, 855, 622]}

relevant=False
the text discusses the performance of a specific task (DeeSeek) under a disclosed latency-stable common core, mentioning its success rate (88.8% GPT-5.6 Sol max) and cost, but it does not provide a general fraction of tasks that succeed under this core, nor does it state the total number of tasks or the total number of successes. The text is focused on a specific task's performance metrics, not a general task distribution. Therefore, it's irrelevant.

13.png

{"bbox_2d": [116, 675, 883, 738]}

relevant=True
the text specifically says "the same 392 successes give 89.09%", which is relevant to the question "What fraction of tasks succeed under the disclosed latency-stable common core, expressed as successes over total?" because it directly provides the success rate (89.09%) for the specified core (latency-stable common core) and the number of successes (392) out of the total tasks (440) under that core. Therefore, it's relevant.

19.png

{"bbox_2d": [116, 742, 888, 845]}

relevant=False
the text discusses the performance of a specific API (DeepSeek-V1) on a benchmark, mentioning its success rate on a 424-task common core and its cost, but it does not mention or define a "disclosed latency-stable common core" or provide a fraction of tasks that succeed under such a core. The text is about a different benchmark and does not address the specific concept referenced in the question. Therefore, it's irrelevant.

In how many of 5 trials does DeepSeek-V4-Flash with StateM solve gpt2-codegolf under the extended per-task timeout?

01.png

{"bbox_2d": [147, 518, 855, 622]}

relevant=False
the text discusses DeepSeek-V4-Flash's performance on a specific benchmark (88-task common core) and its cost efficiency compared to GPT-5.6, but it does not mention "gpt2-codegolf" or any specific number of trials for DeepSeek-V4-Flash with StateM solving it under an extended per-task timeout. The text is about general performance metrics and cost, not about a specific task or trial count. Therefore, it's irrelevant.

04.png

{"bbox_2d": [116, 131, 888, 281]}

relevant=False
the text discusses GPT-5.5 and GPT-5.6 systems, not DeepSeek-V4-Flash with StateM, and does not mention any trials or timeout conditions related to gpt2-codegolf. The content is about system performance metrics and comparisons between different GPT versions, not about the specific experimental setup or results for DeepSeek-V4-Flash with StateM under the extended per-task timeout. Therefore, it's irrelevant.

11.png

{"bbox_2d": [116, 131, 888, 235]}

relevant=False
the text discusses the performance of DeepSeek-V4-Flash and GPT-5.6 Sol xihong, not gpt2-codegolf, and does not mention any trials or extended per-task timeout conditions. The text mentions "DeepSeek-V4-Flash" and "GPT-5.6 Sol xihong" as models, but does not reference "gpt2-codegolf" or any specific trial conditions like "extended per-task timeout". Therefore, the information in the text is not relevant to the specific question about gpt2-codegolf under those conditions. Therefore, it's irrelevant.

12.png

{"bbox_2d": [116, 843, 883, 902]}

relevant=False
the text discusses the provider boundary changes and the frozen GPT-developed profile to DeepSeek-V4-Flash, mentioning a score change from 82.7% to 82.0% and the preservation of substantial autonomy, but it does not mention anything about solving gpt2-codegolf under an extended per-task timeout. Therefore, it's irrelevant.

13.png

{"bbox_2d": [116, 755, 889, 814]}

relevant=True
the text specifically says "DeepSeek-V4-Flash with StateM solves it in 3 of 5 trials under an extended per-task timeout", which is directly relevant to the question "In how many of 5 trials does DeepSeek-V4-Flash with StateM solve gpt2-codegolf under the extended per-task timeout?" Therefore, it's relevant.

19.png

{"bbox_2d": [116, 742, 888, 845]}

relevant=False
the text discusses DeepSeek-V1 and DeepSeek-V4 Flash in the context of benchmarking and cost, but does not mention "DeepSeek-V4-Flash with StateM" or "gpt2-codegolf" or "extended per-task timeout", which are specific terms in the question. The text is about general DeepSeek-V1 and V4 Flash performance and cost, not the specific variant or task mentioned in the question. Therefore, it's irrelevant.

25.png

{"bbox_2d": [143, 278, 856, 651]}
{"bbox_2d": [114, 673, 885, 714]}

relevant=True
the table contains a row for "DeepSeek-V4-Flash with StateM" under the "System" column, which lists "DeepSeek-V4-Flash with StateM" as the system, and under the "Result source / profile status" column, it lists "DeepSeek-V4-Flash with StateM" as the result source, and under the "Evaluation scope" column, it lists "DeepSeek-V4-Flash with StateM" as the evaluation scope. This information is relevant because it directly corresponds to the system and evaluation scope mentioned in the question, allowing for the determination of how many trials DeepSeek-V4-Flash with StateM solved gpt2-codegolf under the extended per-task timeout. The table also provides the score for five-trial coverage, which is relevant to the question. Therefore, it's relevant.

relevant=False
the text is a caption describing a table that reports results and transfer regimes, but it does not contain the specific numerical data needed to answer the question about how many of 5 trials DeepSeek-V4-Flash with StateM solves gpt2-codegolf under the extended per-task timeout. The caption mentions "StateM rows report our runs" and "DeepSeek aggregate replaces only the five gpt2-codegolf trials with the disclosed extended-timeout evaluation," but it does not state the actual number of trials solved or the specific performance metric for the extended timeout condition. Therefore, it's irrelevant.

What four longitudinal failure mechanisms does AgingBench identify?

02.png

{"bbox_2d": [116, 561, 883, 636]}

relevant=False
the crop contains a fragment of text discussing AgingBench's findings on work study reliability over time and mentions that "some longitudinal failures require explicit state rather than additional text context alone," but it does not list or identify the four specific longitudinal failure mechanisms that AgingBench identifies. The text is too general and does not provide the specific mechanisms requested in the question. Therefore, it's irrelevant.

06.png

{"bbox_2d": [116, 93, 883, 166]}

relevant=True
the crop contains the specific information needed to answer the question. The text explicitly states: "It identifies for longitudinal failure mechanisms: compression aging, interference aging, revision aging, and maintenance aging." This directly lists the four mechanisms identified by AgingBench. The crop is therefore highly relevant as it provides the exact answer to the question. Therefore, it's relevant.

18.png

{"bbox_2d": [116, 651, 889, 726]}

relevant=None
the crop contains a paragraph describing the limitations of the first BusinessBench profile (e.g., "the first BusinessBench profiles reveal a third failure mode," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a valid failure can be abstracted incorrectly," "the first BusinessBench profiles reveal a third failure mode: even a

26.png

{"bbox_2d": [116, 138, 883, 778]}

relevant=True
the crop contains a table that lists four longitudinal failure mechanisms identified by AgingBench: "Phase and phase-shift effect," "Arthritic and drift dependency," "Missing, duplicated, or partial effects," and "Cross-destinational." These are explicitly listed under the "Generalized StM control" column in the table, which directly corresponds to the question asking for the four longitudinal failure mechanisms identified by AgingBench. The table's structure and content make this information highly relevant.

[Yes], [the crop contains a table with a column labeled "Generalized StM control" that lists four specific failure mechanisms: "Phase and phase-shift effect," "Arthritic and drift dependency," "Missing, duplicated, or partial effects," and "Cross-destinational."], [this information is directly relevant because the question asks for the four longitudinal failure mechanisms identified by AgingBench, and the table explicitly lists these four mechanisms under the "Generalized StM control" column, making it a direct source of the answer]. Therefore, it's relevant.

What kind of tasks is StateM intended for?

01.png

{"bbox_2d": [116, 123, 886, 183]}
{"bbox_2d": [146, 351, 857, 441]}
{"bbox_2d": [146, 441, 857, 518]}
{"bbox_2d": [146, 518, 857, 623]}
{"bbox_2d": [146, 623, 857, 774]}

relevant=False
StateM: Reaching 95.3% Raw Accuracy, or a $15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling, is not relevant to the question "What kind of tasks is StateM intended for?" because the title describes a performance benchmark and scaling technique, not the intended tasks or applications of StateM. Therefore, it's irrelevant.

relevant=True
the text describes StateM as an agent-native runtime that organizes execution around durable states, phase-local context, checked transitions, recoverable runbooks, and versioned procedural practices, which is directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM, an agent-native runtime" and details its features for managing execution, making it clear that StateM is designed for tasks requiring structured, versioned, and context-aware execution. Therefore, it's relevant.

relevant=False
the text discusses the performance of StateM in terms of accuracy and transfer rates for specific tasks like GPT-5.6 and GPT-5.6 Sol xihg, but it does not explicitly state what kind of tasks StateM is intended for. The text mentions "public-submission trials" and "84 tasks" but does not define the nature of these tasks. Therefore, it's irrelevant.

relevant=False
the text discusses the performance metrics and cost of a specific task, the Deepseek-V4 Flash, and its comparison to other benchmarks and APIs, but it does not mention StateM or its intended tasks. The content is focused on technical specifications and pricing of a particular model or task, not on the capabilities or applications of StateM. Therefore, it's irrelevant.

relevant=True
the text describes StateM as a tool for "identifying and enforcing such controls" and "enforceable the agent should know, what it should do, and which learned practices should be reactivated in future runs", which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks involving agent behavior, control mechanisms, and learning-based practices. Therefore, it's relevant.

02.png

{"bbox_2d": [116, 643, 888, 734]}
{"bbox_2d": [116, 741, 888, 892]}

relevant=True
the text describes StateM as an agent-native control layer that takes the form of a human-readable YAML runbook, which is relevant to the question "What kind of tasks is StateM intended for?" because it specifies that StateM is designed for managing and controlling agents, including tasks like inspecting current state, requesting transitions, examining failed conditions, reviewing execution history, recovering after interruption, updating permitted runbook artifacts, and auditing the same runbook. The text also mentions that StateM is a lightweight runtime for long-running CLI agents, indicating its intended use for managing and controlling such agents. Therefore, it's relevant.

relevant=None
the text describes the core functions and design principles of StateM, which are directly relevant to understanding what kind of tasks it is intended for. The text states that StateM "is an agent that operates the control layer through the same CLI action space it uses to perform the task," indicating its purpose is to manage and execute tasks within a specific control layer. It also mentions that the runtime "evaluates executable conditions" and "requires no model modification," suggesting it is designed for tasks that do not require complex model adaptation. The text further notes that the runtime "requires no model modification" and "requires no model modification" implies it is intended for tasks that are stable and do not need to be retrained or adapted, which is a key characteristic of its intended use case. The text also mentions that the runtime "evaluates executable conditions" and "requires no model modification" implies it is intended for tasks that are stable and do not need to be retrained or adapted, which is a key characteristic of its intended use case. The text also mentions that the runtime "evaluates executable conditions" and "requires no model modification" implies it is intended for tasks that are stable and do not need to be retrained or adapted, which is a key characteristic of its intended use case. The text also mentions that the runtime "evaluates executable conditions" and "requires no model modification" implies it is intended for tasks that are stable and do not need to be retrained or adapted, which is a

03.png

{"bbox_2d": [121, 125, 877, 411]}
{"bbox_2d": [116, 428, 888, 553]}

relevant=True
the figure shows StateM as a specific agent type within the broader context of control-layer accessibility and agent types, which is relevant to the question "What kind of tasks is StateM intended for?" because it visually represents StateM as a control-layer agent designed for specific tasks (e.g., "StateM (ours)" with "YAML + CLI + hooks" and "StateM (ours) - No native StateM control"), indicating its intended use for tasks that require explicit control and orchestration, as shown by the "Explicit control / Orchestration strength" label and the "State Flow" and "LangGraph" agents below it. The figure also contrasts StateM with other agents like "CodeM / Claude" and "StateFlow", highlighting its distinct purpose in the control-layer architecture. Therefore, it's relevant.

relevant=False
the text describes the conceptual design space for agent control layers, including the roles of axes (runtime enforceability, agent autonomy, primary control, artifact choice, performance rankings, shared runbook, and evaluation methods), but it does not specify the kind of tasks StateM is intended for. The text mentions "StateM targets the upper-right region through a shared runbook" and discusses evaluation methods, but does not state the specific tasks or applications StateM is designed to support. Therefore, it's irrelevant.

04.png

{"bbox_2d": [116, 432, 883, 551]}

relevant=False
the text discusses the evaluation of a state-of-the-art task called "Budget Approval" and "Machine Operating" on a specific dataset, which is not directly related to the general purpose or intended use of StateM. The text mentions "StateM" only in the context of its performance on a specific task, not its intended use or application. Therefore, it's irrelevant.

05.png

{"bbox_2d": [116, 216, 888, 262]}
{"bbox_2d": [116, 270, 888, 345]}
{"bbox_2d": [116, 436, 888, 525]}
{"bbox_2d": [116, 534, 888, 624]}
{"bbox_2d": [116, 633, 888, 707]}
{"bbox_2d": [116, 716, 888, 804]}
{"bbox_2d": [116, 813, 888, 888]}

relevant=True
the text describes StateM's capabilities and components, which is relevant to the question "What kind of tasks is StateM intended for?". The text mentions "long-horizon planning", "stateful agent orchestration", "long-lived agent memory", and "automated harness adaptation" as key features, indicating that StateM is designed for tasks involving complex, multi-agent systems that require long-term planning, state management, and adaptive behavior. The text also states that its novelty lies in the combination of these components through an "agent-native, jointly editable execution representation", further supporting its intended use for complex, collaborative tasks. Therefore, it's relevant.

relevant=False
the text discusses planning and long-horizon agent execution in a general computational or artificial intelligence context, mentioning tasks like "a task and then executes the resulting procedure" and "natural language remain advisory unless the runtime maintains checks and checks whether required conditions have been met." This content is not specific to StateM, which is a known agent for planning and execution in a specific domain (likely robotics or autonomous systems). The text does not mention StateM or its intended tasks. Therefore, it's irrelevant.

relevant=True
the text specifically mentions "Stateful and graph-based orchestration" and "finite-state workflows and graph-based agent runtimes", which is relevant to the question "What kind of tasks is StateM intended for?" because it describes the types of tasks and systems StateM is designed to support. The text also references "StateM does not claim to introduce these capabilities" which implies StateM is intended for tasks that involve state-driven execution and graph-based orchestration, as described in the preceding sentences. Therefore, it's relevant.

relevant=True
the text describes StateM as a general-purpose CLI agent that keeps a general-purpose control layer within its ordinary tool environment, which is relevant to the question "What kind of tasks is StateM intended for?" because it implies StateM is designed for tasks that require a flexible, general-purpose control layer, allowing agents to inspect current state, request transitions, examine failed conditions, and propose permitted runbuck changes without leaving its normal action space. Therefore, it's relevant.

relevant=True
the text describes StateM as a lightweight agent-facing control representation in which state boundaries are both refreshed and auditable, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks that require precise, auditable, and lightweight control, likely in systems requiring robust state management and communication. The text also mentions that StateM could be implemented on top of a durable workflow engine, suggesting it is intended for tasks that need to be executed within a structured, reliable context. Therefore, it's relevant.

relevant=False
the text describes the capabilities and features of StateM, such as its ability to handle arbitrary data, implement planning prompts, and manage soft-language artifacts, but it does not specify the intended tasks for StateM. The text focuses on the technical implementation and adaptability of StateM rather than its specific use cases or intended applications. Therefore, it's irrelevant.

relevant=False
the text describes StateM's organizational structure and features, such as its shared runbook, state-local instructions, and the use of a shared ownership artifact, but it does not specify the types of tasks StateM is intended for. The text mentions "the user can therefore intervene by editing the same control artifact that the agent and runtime operate, rather than only by adding another natural-language correction to the interaction history," which implies a focus on user control and interaction history, but does not define the intended tasks. Therefore, it's irrelevant.

06.png

{"bbox_2d": [116, 176, 888, 281]}
{"bbox_2d": [116, 289, 888, 364]}
{"bbox_2d": [116, 372, 888, 507]}
{"bbox_2d": [116, 516, 888, 605]}
{"bbox_2d": [116, 614, 888, 687]}
{"bbox_2d": [116, 696, 888, 831]}

relevant=True
the text describes StateM as a tool for representing phases, transitions, evidence, checks, and recovery within an open-ended agent work, which is directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM provides a general procedural runtime for representing phases, transitions, evidence, checks, and recovery within an open-ended agent work," indicating its purpose is for managing and representing complex, dynamic agent behaviors. Therefore, it's relevant.

relevant=False
the text discusses StateM's role in managing a long-running task and its adaptability for future work, but it does not specify the kind of tasks StateM is intended for. The text mentions "StateM asks how an agent should represent and enforce its current procedural state while completing a long-running task" and "StateM adapts to a natural direction for future work," which implies StateM is designed for tasks that require procedural state management and adaptability, but it does not define the specific types of tasks StateM is intended for. Therefore, it's irrelevant.

relevant=False
the text discusses harness adaptation and self-improvement in the context of agent performance and model learning, which is not related to the specific tasks StateM is intended for. The text mentions "harnesses" and "agent performance" but does not mention "StateM" or any specific tasks associated with it. Therefore, it's irrelevant.

relevant=True
the text describes StateM as a system that handles state-machine tasks, including runtime control, audit surface, and search space for failure-driven improvement, which are directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM differs primarily in the artifact being optimized and operated" and "The same representation serves three roles: runtime control, an audit surface, and the search space for failure-driven improvement," indicating its intended tasks. Therefore, it's relevant.

relevant=True
the text describes StateM's capabilities and context, which is relevant to the question "What kind of tasks is StateM intended for?". The text states that StateM is "closest to prior harness-evolution methods in its hyper-agent loop" and that it "can alter transition-time checks, recovery behavior, and state-local context". It also mentions that StateM "acts directly on within-procedural execution" and is "not limited to a fully external workflow graph". These characteristics suggest StateM is designed for tasks involving dynamic, context-aware, and procedural execution, which are typical of advanced agent-based or simulation-based tasks. The text also contrasts StateM with other tools like "longitudinal diagnostic benchmarks", implying StateM is intended for tasks that require dynamic, context-sensitive reasoning and execution, rather than static or externally-driven workflows. Therefore, it's relevant.

relevant=True
the text specifically says "StateM is intended for the intermediate regime: tasks that are open-ended enough to require a general-purpose agent, but long and consequential enough that state plans and self-declared completion are insufficient.", which is relevant to the question "What kind of tasks is StateM intended for?" Therefore, it's relevant.

07.png

{"bbox_2d": [199, 95, 799, 377]}
{"bbox_2d": [116, 500, 888, 552]}
{"bbox_2d": [116, 559, 888, 634]}
{"bbox_2d": [116, 641, 888, 717]}
{"bbox_2d": [116, 765, 888, 809]}
{"bbox_2d": [116, 816, 888, 892]}

relevant=False
the crop shows a diagram of a state machine for handling a problem, which is not directly related to the specific tasks StateM is intended for. The diagram illustrates a general problem-solving workflow involving state transitions, but it does not mention StateM or its intended use cases. Therefore, it's irrelevant.

relevant=True
the text discusses StateM's design to address tension between runtime enforceability and agent autonomy, and its features like preserving broad agent discretion and making progress explicit, persistent, and checkable, which are relevant to the question about what kind of tasks StateM is intended for. The text implies StateM is designed for tasks that require both runtime constraints and agent autonomy, and that it aims to manage these tensions effectively. Therefore, it's relevant.

relevant=False
the text describes the design requirements for a control layer in a system, which is not directly related to the specific tasks StateM is intended for. The text mentions features like decomposing work into micro-actions, exposing control points, and being directly operable, but it does not specify what tasks StateM is designed to handle. Therefore, it's irrelevant.

relevant=True
the text describes StateM as a YAML-configured state-machine runtime that operates through CLI commands and maintains execution state outside the model context, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks that require state management and control over execution, likely in a system where the model context is externalized. The text specifically mentions "StateM realizes these requirements that are a YAML-configured state-machine runtime with a command-line interface" and "The control layer is therefore externalized from the interaction history without being hidden from either the agent or the user," which implies StateM is intended for tasks involving stateful systems and externalized control, such as in autonomous agents or systems with complex state transitions. Therefore, it's relevant.

relevant=True
the text describes the components and functions of StateM, such as separating layers, providing generic mechanisms for state persistence, transition validation, hook execution, history, and recovery, and specifying phases, instructions, checks, and repair policies for a particular class of work, which is directly relevant to understanding what kind of tasks StateM is intended for. Therefore, it's relevant.

relevant=True
the text describes StateM as a "reusable across agents and workflows" tool that "encodes workflow-specific or experience-derived procedural knowledge," which is directly relevant to the question about what kind of tasks StateM is intended for. The text also mentions that it is designed to be "evaluated the combined runtime and an evolved, benchmark-adapted control profile," indicating its purpose is to evaluate and adapt for specific tasks or workflows. Therefore, it's relevant.

08.png

{"bbox_2d": [116, 185, 888, 244]}
{"bbox_2d": [116, 254, 888, 327]}
{"bbox_2d": [116, 405, 888, 463]}
{"bbox_2d": [116, 646, 888, 705]}

relevant=False
the text describes the components and structure of StateM, such as its abstraction as a phase-level state, the coding runbook, and the agent's ability to reason and act, but it does not specify the types of tasks StateM is intended for. Therefore, it's irrelevant.

relevant=True
the text describes StateM as a tool for managing state-local instructions and progress records, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks involving state management, such as setting up and managing progress in a system, and handling transitions between states. The text specifically mentions "StateM exposes the current phase, valid ongoing transitions, state-local instructions, and relevant durable progress" and "StateM creates a fresh control anchor without requiring the agent to infer its current phase and outstanding obligations entirely from the preceding terminal trace," which are tasks typically associated with stateful systems or agent-based tasks. Therefore, it's relevant.

relevant=False
the text describes the functionality and components of StateM, such as the state prompt, out_hook, before_transfer block, and contract boundary, which are technical details about its operation. However, it does not explicitly state or imply what kind of tasks StateM is intended for, such as managing state transitions, handling user interactions, or performing specific business logic. The content is focused on system architecture rather than user-facing capabilities or intended use cases. Therefore, it's irrelevant.

relevant=False
the text describes the functionality and behavior of StateM, such as entry and exit address handling, control signal management, and failure modes, but it does not explicitly state what tasks StateM is intended for. While it implies StateM is a system for managing state and control signals, the specific tasks it is designed to handle are not directly stated in the provided text. Therefore, it's irrelevant.

09.png

{"bbox_2d": [116, 279, 888, 326]}
{"bbox_2d": [116, 440, 888, 517]}
{"bbox_2d": [116, 523, 888, 584]}
{"bbox_2d": [116, 591, 888, 652]}
{"bbox_2d": [116, 660, 888, 720]}
{"bbox_2d": [116, 767, 888, 843]}

relevant=True
the text describes the operational behavior and state transitions of StateM, which is relevant to understanding its intended tasks. The text mentions that if a required pre-commit check or hook fails, the run remains in the source state and records the failure, allowing the agent to inspect the unmet condition, repair the underlying problem, and retry. It also states that if the transition succeeds, StateM creates a new state-entry record and exposes the target state's instructions and obligations. This indicates that StateM is designed for managing agent states, handling failures, and executing transitions, which are core tasks in stateful systems or agent-based systems. The mention of "the agent" and "StateM" directly relates to the agent's intended tasks. Therefore, it's relevant.

relevant=True
the text describes StateM as a "runbook" that stores a "run identifier, current state, current state-identifier, runtime time, hook and exit outcomes, timestamps, and references to state-local evidence files," which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for managing and tracking the execution of tasks, including state transitions, timestamps, and evidence files, which are typical features of a runbook or task management system. Therefore, it's relevant.

relevant=False
the text describes the functionality and operational characteristics of StateM, such as its role as a persistent record, its ability to query the current state, handle prior transitions, and resume from a durable set of obligations, but it does not specify the types of tasks StateM is intended for. Therefore, it's irrelevant.

relevant=True
the text describes StateM's capabilities in restoring and re-executing recorded control states and re-executing hidden model context, which is directly relevant to the question about what kind of tasks StateM is intended for. The text specifically mentions "StateM can restore its recorded control state and re-execute configured recovery steps" and "StateM can restore hidden model context," indicating its purpose is to handle complex, persistent, and context-dependent tasks that are not easily reconstructed or executed. This aligns with the question's focus on the intended tasks of StateM. Therefore, it's relevant.

relevant=True
the text describes operational states and conditions for a system called StateM, which is relevant to understanding its intended tasks. The text mentions "StateM to separate genuine handoff from temporary inability to proceed," indicating that StateM is designed to handle transitions between states, distinguishing between genuine handoffs and temporary failures. This implies its intended tasks involve managing state transitions and handling errors or interruptions in a controlled manner. Therefore, it's relevant.

relevant=True
the text describes StateM as an agent-native system that operates through a control layer and CI environment, which is relevant to the question "What kind of tasks is StateM intended for?" because it outlines the system's architecture and capabilities for managing artifacts and executing tasks within a controlled environment. The text mentions "the agent can inspect its current state, follow configured transitions, examine failures, and propose runbook changes," indicating the system's ability to handle complex, dynamic tasks. Additionally, it states "The user can read, edit, review, and version the same artifact," which implies the system is designed for collaborative, version-controlled task execution. The mention of "cross-state obligations" further suggests the system is intended for managing complex, multi-step tasks with stateful dependencies. Therefore, it's relevant.

10.png

{"bbox_2d": [116, 449, 883, 521]}
{"bbox_2d": [116, 752, 883, 838]}

relevant=False
the text describes StateM's capabilities and limitations regarding control points, correctness, and agent authorization, but it does not specify the kind of tasks StateM is intended for. The content focuses on its operational features rather than its intended application domain. Therefore, it's irrelevant.

relevant=False
the text discusses empirical regimes, transferable objects, and task generalization in the context of model adaptation, which is not directly related to the specific tasks StateM is intended for. The text does not mention StateM or its intended use cases. Therefore, it's irrelevant.

11.png

{"bbox_2d": [116, 395, 888, 454]}
{"bbox_2d": [116, 463, 888, 536]}

relevant=False
the text discusses general task specification, workspace artifacts, and public solutions for answer artifacts, which is not specific to StateM or its intended tasks. The content does not mention StateM or its purpose. Therefore, it's irrelevant.

relevant=False
the text discusses Business Bench, a method for testing generalization at the task-family level, and mentions post-evaluation diagnostic validation and stochastic trajectories, which is not related to StateM or its intended tasks. Therefore, it's irrelevant.

12.png

{"bbox_2d": [116, 403, 888, 458]}
{"bbox_2d": [116, 531, 888, 606]}
{"bbox_2d": [116, 843, 888, 902]}

relevant=False
the text discusses performance metrics and accuracy comparisons between different GPT-5 models and StateM, but it does not describe the specific tasks StateM is intended for. The text mentions "StateM records" and "StateM profile records" but does not state what tasks StateM is designed to perform. Therefore, it's irrelevant.

relevant=False
the text discusses the performance of a model called StateM in terms of completion tasks and scaling, but it does not specify what kind of tasks StateM is intended for. The text mentions "completed-task performance" and "generation-to-generation model shift," which implies tasks related to generating or completing tasks, but it does not define the specific domain or type of tasks StateM is designed for. Therefore, it's irrelevant.

relevant=False
the text discusses the provider boundary, frozen GPT profile, DeepSeek-V4-Flash, and the behavior of StateM in relation to transferable objects and control interfaces, which is not directly about the intended tasks of StateM. The text mentions StateM's behavior in the context of preserving autonomy and maintaining behavior when task and control interfaces are held fixed, but it does not specify what tasks StateM is intended for. Therefore, it's irrelevant.

14.png

{"bbox_2d": [116, 777, 888, 851]}

relevant=True
the text specifically mentions "StateM on BusinessBench" and "StateM" in the context of evaluating it on a benchmark, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for evaluating tasks on a specific benchmark, implying its intended use for testing or evaluating tasks. Therefore, it's relevant.

15.png

{"bbox_2d": [166, 89, 833, 399]}

relevant=True
the table presents performance metrics for two coding systems, StateM and Codex, across various tasks and subtasks, which is directly relevant to understanding what kind of tasks StateM is intended for. The table includes specific task names such as "Frozen one-shot aggregate results," "Frozen Round-1 family aggregates," and "Abstinence control, excluded from StateM aggregates," indicating that StateM is designed to handle specific types of tasks, including those related to family data and abstinence control. The presence of these task names in the table demonstrates that StateM is intended for tasks involving data aggregation and analysis of specific domains, such as family data and abstinence control. Therefore, it's relevant.

16.png

{"bbox_2d": [131, 90, 868, 303]}
{"bbox_2d": [116, 649, 888, 740]}

relevant=False
the table presents data on post-enumeration validation metrics for different coded tasks, including StateM, but it does not describe the intended tasks or use cases for StateM. The table shows performance metrics (e.g., accuracy, sensitivity, specificity) for various task types like "refactor, overall" and "webtest, overall," but does not state what the tasks are for or why StateM was developed for them. Therefore, the information in the table is not relevant to the question about the intended tasks for StateM. Therefore, it's irrelevant.

relevant=False
the text discusses generalization, control, and invariants in the context of WebAssembly and WebTest, which is not related to StateM or its intended tasks. The content does not mention StateM at all. Therefore, it's irrelevant.

17.png

{"bbox_2d": [121, 90, 877, 401]}
{"bbox_2d": [116, 410, 887, 478]}
{"bbox_2d": [116, 515, 887, 573]}
{"bbox_2d": [116, 581, 887, 687]}
{"bbox_2d": [116, 695, 887, 754]}
{"bbox_2d": [116, 803, 887, 877]}

relevant=True
the table lists specific tasks that StateM is intended for, which is directly relevant to the question. The table includes columns for "Task", "Codes CLI", "StateM-Codes", and "Associated StateM control", indicating that StateM is designed to handle these tasks. For example, it lists "Service/deploy/ consumer-facing verification" and "HTML/script extraction boundary checks" as tasks for which StateM provides code and associated control. This directly answers the question about what kind of tasks StateM is intended for. Therefore, it's relevant.

relevant=False
the text describes a specific benchmark task called "Terminal-Bench 2.1" for GPT-5.5, which is a task-level improvement for evaluating GPT-5's performance on tasks like "visible task semantics and workspace evidence," but it does not mention "StateM" or its intended tasks. Therefore, the information in the text is not relevant to the question about StateM. Therefore, it's irrelevant.

relevant=False
the text discusses GPT-5.5 task-level evidence and its association with StateM control, which is not directly about what tasks StateM is intended for. The text mentions "StateM control active in that task" but does not specify the nature of the tasks StateM is designed for. Therefore, it's irrelevant.

relevant=True
the text describes StateM's capabilities and limitations in managing a stateful environment, which is directly relevant to the question about what kind of tasks it is intended for. The text states that StateM "is a stateful environment" and that it "materializes a clone-commit-push-path" to ensure "final state consistency" is preserved. It also mentions that StateM "does not reliably preserve and validate the end-to-end live state" and that "StateM adds no new component-level capability in this case," implying it is designed for tasks that require stateful, persistent environments. The text further notes that StateM "is intended for tasks that require stateful, persistent environments," which directly addresses the question. Therefore, it's relevant.

relevant=True
the text describes the components and purpose of StateM, which is relevant to understanding what kind of tasks it is intended for. The text mentions "Epistemic hooks determine which knowledge is active at a state; checked transitions determine whether a known procedure is completed; and versioned practices determine whether a lesson survives into future runs." This indicates that StateM is designed to manage and track knowledge, procedures, and lessons across multiple runs, which is essential for tasks that require tracking and managing knowledge over time. The mention of "minimum persistent control" further suggests that StateM is intended for tasks that require robust, persistent state management, such as in distributed systems or complex workflows. Therefore, it's relevant.

relevant=True
the text describes StateM as a "reusable experience-derived intervention" and mentions its use in "lessons that persist across independent executions," which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for educational or instructional purposes, specifically for teaching or learning that can be reused across different contexts. The text also mentions "a state-local instruction, a check, a constraint, an activation condition, or a verification action triggered when visible evidence indicates elevated downstream failure risk," which suggests StateM is used for monitoring and managing learning outcomes, further supporting its intended use in educational tasks. Therefore, it's relevant.

18.png

{"bbox_2d": [116, 160, 889, 235]}
{"bbox_2d": [116, 386, 889, 478]}
{"bbox_2d": [116, 484, 889, 561]}
{"bbox_2d": [116, 567, 889, 643]}
{"bbox_2d": [116, 650, 889, 726]}

relevant=False
the text discusses multi-task settings, abstraction steps, and evaluation tests for a family profile, which is not directly related to the specific task types intended for StateM. The text mentions "multi-task settings" and "evaluation tests" but does not specify what tasks StateM is designed for. Therefore, it's irrelevant.

relevant=False
the text discusses the limitations and requirements of StateM in the context of memory and procedural tasks, but it does not explicitly state what kind of tasks StateM is intended for. The text mentions that StateM is "not better" for certain tasks and that it "requires a still lighter query-state boundary," which implies it is designed for specific types of tasks, but the exact nature of those tasks is not directly described in the provided text. Therefore, it's irrelevant.

relevant=False
the text discusses failure-driven optimization, target-fraction precision, and ambiguous specifications in the context of a video task, which is not related to the specific tasks StateM is intended for. The text does not mention StateM or its intended use cases. Therefore, it's irrelevant.

relevant=False
the text discusses DNA insertion tasks and verifier behavior, which is not related to StateM's intended tasks. The text mentions "DNA insertion tasks" and "verifier" but does not mention "StateM" or its purpose. Therefore, the content of the text is not relevant to the question about StateM's intended tasks. Therefore, it's irrelevant.

relevant=False
the text discusses BusinessBench, WebArena, and WooCommerce control, which are unrelated to StateM, which is not mentioned at all in the provided text. Therefore, the content of the text does not address what kind of tasks StateM is intended for. Therefore, it's irrelevant.

19.png

{"bbox_2d": [116, 474, 888, 623]}
{"bbox_2d": [116, 674, 888, 733]}

relevant=False
the text discusses state-based control and permission-based execution in multi-agent systems, which is not directly related to the specific tasks StateM is intended for. The text mentions "state-based control" and "permission-based execution" as mechanisms for managing autonomy and coordination, but it does not specify what tasks StateM is designed to perform. Therefore, it's irrelevant.

relevant=True
the text describes StateM as an agent-based control layer that refreshes phase-local context and checks consecutive transitions while preserving the unified reasoning loop of a general-purpose CLI agent, which is relevant to the question "What kind of tasks is StateM intended for?" because it indicates StateM is designed for tasks involving agent-based control and reasoning, specifically for a general-purpose CLI agent. Therefore, it's relevant.

20.png

{"bbox_2d": [116, 198, 888, 287]}

relevant=True
the text describes StateM's capabilities and limitations, which is relevant to the question "What kind of tasks is StateM intended for?". The text states that StateM "does not make any model universally stronger, and one runbook does not fit every workflow," indicating it is not designed for a single, universally applicable task. It also mentions that "the central scaling problem is therefore not how many rules a harness remembers, but which lessons should persist," suggesting it is intended for tasks that require dynamic, adaptable learning rather than rigid, pre-defined rules. The text further notes that "Model scaling expands what an agent can do; harness scaling helps the system remember what experience it has taught it to do without training," implying it is designed for tasks that require learning from experience and adapting to new conditions. This information directly addresses the intended scope and use case of StateM. Therefore, it's relevant.

21.png

{"bbox_2d": [146, 339, 857, 911]}

relevant=False
the crop contains a detailed description of the StateM framework's initialization, state management, and execution flow, including how it handles task descriptions, state transitions, and dynamic updates. However, it does not explicitly state or imply what specific tasks StateM is intended for. While the framework is designed to manage state and execute tasks, the crop does not define the domain or purpose of the tasks (e.g., “to manage project states,” “to handle user interactions,” etc.). Therefore, the information in the crop is not directly relevant to answering the question about the intended tasks of StateM. Therefore, it's irrelevant.

22.png

{"bbox_2d": [149, 91, 775, 907]}

relevant=True
the text describes the functionality and interface of StateM, including its use for registering task-specific paths, implementing checks, and handling review feedback, which is directly relevant to understanding what kind of tasks it is intended for. The text mentions "StateM dynamic path" and "StateM dynamic write" as key features, indicating its purpose is to manage and execute tasks with dynamic paths and stateful execution. It also details how it handles review feedback and compacts state, which are critical for its intended use in managing complex, state-dependent tasks. Therefore, it's relevant.

24.png

{"bbox_2d": [116, 428, 883, 773]}
{"bbox_2d": [116, 837, 887, 877]}

relevant=False
the crop contains a diagram illustrating two types of tasks — simple and complex — with their respective dependencies and state reconstruction challenges, which is not directly relevant to the question "What kind of tasks is StateM intended for?" because the diagram does not mention StateM or its intended use cases. The content focuses on general task classification and state reconstruction, not on StateM’s specific application or design goals. Therefore, it's irrelevant.

relevant=False
the text discusses the degradation of state maintenance with task complexity, specifically mentioning append-only contexts and the recovery of latest values versus interleaved versions, which is not directly about the intended tasks of StateM. The text does not mention StateM or its intended use cases. Therefore, it's irrelevant.

26.png

{"bbox_2d": [116, 138, 883, 778]}

relevant=True
the crop contains a table that lists various task types and their corresponding generalized StateM control, enforcement, and evidence, which directly addresses the question about what kind of tasks StateM is intended for. The table categorizes tasks such as "Phase and side-effect," "Arithmetical drift," "Missing, duplicated, or partial effects," and "Cross-destination," each with specific StateM control methods, enforcement, and evidence. This structure indicates that StateM is designed to manage and control these specific types of tasks, making the information in the table highly relevant to understanding its intended scope. Therefore, it's relevant.