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CogArena: Benchmarking Multimodal Agents on Interactive Behavioral Experiments

About

This document presents a Critical Discourse Analysis focused on AI literacy, specifically targeting the role of metaphor and anthropomorphism in shaping public and professional understanding of generative AI. The analysis is guided by a prompt that draws from cognitive linguistics (metaphor structure-mapping), the philosophy of social science (Robert Brown's typology of explanation), and accountability analysis.

All findings and summaries below were generated from detailed system instructions provided to a large language model and should be read critically as interpretive outputs—not guarantees of factual accuracy or authorial intent.

Source Reading​

Genre: Preprint or technical report · Author position: Academic or independent research

Argument: The text introduces CogArena, a benchmark designed to evaluate multimodal AI agents on interactive, web-based behavioral psychology experiments. The authors argue that testing agents on text-translated paradigms bypasses visual and temporal constraints, offering an incomplete picture of model performance. By evaluating agents on actual experimental interfaces, they demonstrate that while current models can complete tasks and achieve high accuracy, they frequently fail to reliably reproduce the fine-grained behavioral signatures and cognitive biases that human participants consistently display.

Responsibility claims: The authors assign responsibility to behavioral researchers who collect data online, urging them to recognize the threat that synthetic respondents (AI agents) pose to the validity of human behavioral datasets. They explicitly define AI agents as language models driven by scaffolds that convert text output into clicks and keypresses, thereby acknowledging limits on the system's agency. However, the text makes no direct responsibility claims regarding the developers (OpenAI, Google, etc.) who build and deploy these models, focusing instead on the scientific community's responsibility to validate its own evaluation metrics.

Task 1: Metaphor and Anthropomorphism Audit​

About this task

For each of the major metaphorical patterns identified, this audit examines the specific language used, the frame through which the AI is being conceptualized, what human qualities are being projected onto the system, whether the metaphor is explicitly acknowledged or presented as direct description, and—most critically—what implications this framing has for trust, understanding, and policy perception.

1. AI as Cognitive Entity​

Quote: "Claims about the cognitive capabilities of large language models are typically tested by translating behavioral paradigms into text prompts..."

  • Frame: Model as possessing human-like cognition
  • Projection: This framing projects human cognition—the conscious, subjective processes of knowing, understanding, and experiencing the world—onto the statistical outputs of large language models. The text locates the system on the highest end of the agency gradient (epistemic/conscious states) by attributing "cognitive capabilities" to it, even though the text's own evidence only demonstrates that the system generates token sequences that result in task completion. Mechanistically, at the level of training, large datasets and human feedback shape the model's parameters to reflect patterns in human text. At generation time, the model produces text from those parameters and its current input context. A deployed system's ability to output text that resembles human cognitive test results relies entirely on this computational pattern-matching, which the text conflates with having actual cognitive faculties.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Direct (Unacknowledged) (The phrase "cognitive capabilities" is stated directly as a literal attribute of large language models, with no hedging or quotation marks in the immediate context. I considered "Hedged/Qualified" because the overall paper critically evaluates these capabilities, but the linguistic framing of the capability itself stands unhedged.)
  • Implications: Projecting cognitive capabilities onto statistical text generators inflates their perceived sophistication, encouraging both researchers and the public to trust that these systems process information in human-like ways. This fosters an illusion of mind, leading to unwarranted trust in the models' outputs and a dangerous capability overestimation. If audiences believe AI possesses true cognition, they may incorrectly assume the system understands the context, ethics, and consequences of its actions, thereby blurring the lines of liability and making it harder to hold human developers accountable for systemic errors.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The agentless construction "cognitive capabilities of large language models" hides the human actors who designed the architectures and selected the training data that allow the model to mimic cognition. OpenAI, Google, Alibaba, and Moonshot AI are the corporate actors that profit from this illusion. If the text instead named the developers who trained these models on massive behavioral datasets, the illusion of autonomous cognition would fracture. I considered "Partial" since the text mentions "researchers" earlier, but this specific quote isolates the models as the sole bearers of cognitive capacity.
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2. Model as Decision-Maker​

Quote: "...results used to characterize the models' decision-making, reasoning, and learning capacities."

  • Frame: Statistical processing as deliberate choice
  • Projection: The text maps the human intentional states of reasoning, learning, and decision-making onto the model's token generation process. This elevates the system to the level of intentional-state attribution on the agency gradient. However, the evidence supplied by the text only supports computational activity: the model updates its context window and outputs a statistically probable sequence of tokens that a script converts into a click. The underlying mechanism is generic to systems of this type: training data and feedback shape a model's parameters during training, and at generation time the model produces text from those parameters and its context. Attributing "decision-making" to this process projects a subjective evaluation of alternatives that the artifact does not possess.
  • Agency Level: Intentional-state attribution
  • Acknowledgment: Direct (Unacknowledged) (The terms "decision-making, reasoning, and learning" are applied directly to the models as inherent capacities without qualification. I considered "Ambiguous/Insufficient Evidence," but the listing of these three specific, high-level human cognitive verbs is a clear, unhedged attribution of intentional states.)
  • Implications: Describing models as possessing "reasoning and learning capacities" fundamentally misrepresents the nature of machine learning to non-expert audiences. It implies that the model evaluates truth claims, understands causality, and consciously updates its beliefs. This framing can lead policymakers and users to delegate critical tasks to these systems under the false assumption that the AI "reasons" through problems safely, masking the reality that the system is blindly reproducing statistical correlations present in its training data.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: By locating "decision-making, reasoning, and learning capacities" within the models, the text obscures the developers at Google, OpenAI, and Alibaba who engineered the optimization algorithms and curated the data. When the system is framed as reasoning for itself, the structural decisions made by human engineers disappear from view. I considered "Named" because the text eventually evaluates specific models, but in this foundational framing, the corporations that defined the optimization objectives are entirely erased in favor of model autonomy.

3. AI as Perceiver​

Quote: "what the model saw... what the model is shown determines which task information it can use at all."

  • Frame: Input processing as visual perception
  • Projection: This metaphor projects the biological and conscious experience of visual perception (seeing) onto the mechanistic ingestion of pixel arrays and DOM text. It places the AI on the epistemic/conscious state gradient by implying the system "sees" and "knows" what is on the screen. Mechanistically, at generation time, a scaffold script captures the browser state, encodes the image as a tensor, and appends it to the model's input context window; the model then produces text from those parameters and its context. The model does not experience sight or selectively "use" information through conscious attention, but rather mathematically processes the injected tensors.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Direct (Unacknowledged) (The text directly uses the verb "saw" and "is shown" to describe data ingestion. I considered "Hedged/Qualified" because the text later explains the mechanics of the scaffold (Browser-Use) passing screenshots, but the cognitive verb "saw" itself stands unqualified in this summary description.)
  • Implications: Using perception verbs like "saw" encourages audiences to map human visual cognition onto the system. When an AI "fails to see" something, users might assume a human-like oversight rather than a brittle failure in the vector encoding of the screenshot. This inflates perceived sophistication and masks the fundamental fragility of multimodal LLMs, which rely on rigid architectural constraints rather than holistic environmental awareness, thereby increasing the risk of over-trusting these systems in critical visual-spatial tasks.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The phrase "what the model saw" hides the scaffold developers (e.g., Browser-Use contributors) and the API infrastructure that actively captures, formats, and transmits data to the model. The action of "showing" implies a passive reception by an autonomous observer, ignoring the human-designed data pipeline. I considered "Partial" since Browser-Use is mentioned nearby, but the specific grammatical subject of "saw" remains the model, removing the engineers who actually dictate the input formatting.

4. AI as Declarative Speaker​

Quote: "the agent declared the task finished when the experiment had not yet ended."

  • Frame: String generation as speech act
  • Projection: This framing projects a human speech act—making a conscious declaration with intent—onto the mechanistic generation of a specific token string. It assigns intentional-state attribution to the system. Mechanistically, generic to systems of this type, the model's parameters and current context simply yielded a high probability for the tokens corresponding to the scaffold's 'finish' command, and the model generated that text. There is no subjective intent to "declare" anything, nor an understanding that the task is finished; there is only conditional probability distribution yielding an output that a separate script interprets as a termination signal.
  • Agency Level: Intentional-state attribution
  • Acknowledgment: Direct (Unacknowledged) (The text states "the agent declared" as a factual description of the model's output triggering an end-state. I considered "Hedged/Qualified" as the authors immediately note the experiment had not ended (showing model failure), but the verb "declared" itself remains an unhedged anthropomorphic attribution of intent.)
  • Implications: Framing token generation as a "declaration" implies that the AI has an internal model of the task's progress and made a conscious judgment to conclude it. This obscures the statistical nature of the error, making it seem like the AI simply "made a mistake" in judgment rather than failing to maintain context due to architectural limitations. This shifts the perception of failure from an engineering defect to an agentic choice, inappropriately applying human-trust frameworks (intention, sincerity) to a statistical system.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: By stating "the agent declared," the text obscures the developers who designed the stopping criteria and the prompt engineering that biases the model toward outputting termination tokens prematurely. The responsibility for the error is absorbed by the "agent" rather than the corporate entities (like OpenAI or Google) that optimized these models for short, conversational completions rather than sustained, 500-step continuous tasks. I considered "Named" because the models are evaluated, but the decision is entirely displaced onto the agent.

5. Model as Internal Deliberator​

Quote: "Comparing an agent’s response times with human ones therefore measures how fast the model is served rather than how it decides."

  • Frame: Computational processing as decision-making
  • Projection: This language maps the human cognitive process of deliberation (deciding) onto the computational process of calculating token probabilities. It places the system at the intentional-state attribution level. Mechanistically, at generation time, the model produces text from its parameters and context through matrix multiplications; it does not weigh alternatives or make a "decision" in any cognitive sense. While the text accurately notes that response time reflects server latency rather than internal processes, it still uses the verb "decides" to characterize what happens when the model finally computes its output.
  • Agency Level: Intentional-state attribution
  • Acknowledgment: Hedged/Qualified (The quote explicitly contrasts the mechanical reality ("how fast the model is served") with the anthropomorphic assumption ("how it decides"), functioning as a hedge or critique of applying response-time metrics to AI. I considered "Direct" because "decides" is still used to describe the model's internal operation, but the sentence's purpose is to qualify the measurement of that process.)
  • Implications: Even while critiquing a metric, using "how it decides" as shorthand for "how it computes output" reinforces the underlying assumption that AI systems engage in deliberate, step-by-step reasoning. This sustains the illusion of mind, encouraging audiences to trust that the AI has a rationale for its outputs that could be logically interrogated, which obscures the often uninterpretable, correlation-based nature of deep learning architectures.

Accountability Analysis:

  • Actor Visibility: Partial (some attribution)
  • Analysis: The text partially exposes the mechanical reality by pointing out that the model "is served" (implying a server, an API, and a provider), but it stops short of naming the specific providers (OpenAI, Google) responsible for the compute latency. The use of "how it decides" still treats the endpoint model as an autonomous deliberator. I considered "Hidden," but the passive "is served" introduces the presence of structural constraints, even if it leaves the actors unnamed.

6. AI as Strategic Problem-Solver​

Quote: "the agent faces a sequential decision problem rather than a game, and over the fifteen rounds shown on screen the policy that earns the most cooperates..."

  • Frame: Algorithmic generation as strategic confrontation
  • Projection: This metaphor maps human strategic positioning (facing a problem) onto the system's generation of tokens across discrete turns. It attributes goal-directed behavior, bordering on intentional-state attribution. Mechanistically, the model has no ongoing continuous existence or subjective experience of "facing" a problem. During generation time, for each new round, the API simply passes a newly updated text prompt (containing the history of previous rounds) into the model's context window, and the model produces the next statistically probable token. The model is essentially instantiated anew with each prompt; it does not strategically "face" a sequence over time.
  • Agency Level: Goal-directed behavior
  • Acknowledgment: Direct (Unacknowledged) (The text presents the agent "facing a problem" as a direct, literal description of its role in the repeated prisoner's dilemma task. I considered "Hedged/Qualified," but there is no language in the sentence indicating this is merely a functional approximation of a statistical process.)
  • Implications: Framing the system as "facing a problem" implies continuous situational awareness and strategic foresight. It invites the audience to evaluate the model's outputs as deliberate strategic choices rather than statistical reflections of its training data. This masks the reality that the model is only mimicking the surface features of cooperation found in its training data, not actively reasoning through a dilemma, which can lead to overestimating the system's reliability in novel strategic environments.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: By claiming the "agent faces a... problem," the text obscures the researchers who designed the benchmark and the engineers at AI companies who trained the models on game-theory texts. The agentless construction serves to isolate the AI as an independent actor, deflecting attention from the fact that its "strategy" is entirely determined by its pre-training and the specific way the context window is updated by the testing scaffold. I considered "Named," but no human creators are mentioned in this characterization of the agent's behavior.

7. Model as Information Consumer​

Quote: "the model may never have seen the information the signature depends on"

  • Frame: Context ingestion as human observation
  • Projection: This metaphor projects the conscious human act of reviewing or observing information ("seen") onto the mechanical process of context window ingestion. It attributes an epistemic/conscious state to the system. Mechanistically, generic to systems of this type, the text or image is either encoded into the input tensors or it is not. The model does not "see" or fail to see; rather, the scaffolding either provides the relevant data in the prompt or it does not, and the model subsequently generates text based on those parameters. The projection implies a failure of the model's attention rather than a mechanical absence of data.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Direct (Unacknowledged) (The phrase "never have seen the information" is stated directly, without qualification, to explain why a model might fail a signature test. I considered "Hedged/Qualified" because it describes a limitation, but the verb "seen" itself is an unhedged anthropomorphic term for data ingestion.)
  • Implications: Using "seen" to describe data ingestion blurs the line between computational processing and conscious awareness. It suggests that AI models have an active attentional mechanism that can "miss" things the way humans do, which misdirects diagnostic efforts. If users believe the model simply didn't "see" the information, they may mistakenly assume that presenting it differently will invoke human-like comprehension, ignoring the fundamental statistical limitations of the model's architecture.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: This framing hides the scaffold developers and prompt engineers who determine exactly what data is passed into the model's context window. By saying the model hasn't "seen" the information, responsibility for data absence is subtly shifted onto the model's hypothetical field of vision rather than the concrete design choices of the programmers who built the interface. I considered "Partial," but the sentence places the onus entirely on the model's perception rather than the system architecture.

8. Model as Expectant Subject​

Quote: "so screening respondents by whether they show the expected preference will not separate agents from people. For example, every agent session that could be tested preferred saving more lives in the moral dilemmas."

  • Frame: Statistical output generation as moral preference
  • Projection: The text maps the human experience of having a moral preference—a conscious weighing of values and subsequent intentional choice—onto the model's statistical token generation. This places the system at the intentional-state attribution level. Mechanistically, at the level of training, the model's parameters were heavily tuned via RLHF by human annotators to output text aligning with utilitarian safety guidelines. At generation time, the model produces text from those parameters. It does not "prefer" saving lives; it is computationally constrained to generate outputs that reflect the moral preferences of the humans who trained it.
  • Agency Level: Intentional-state attribution
  • Acknowledgment: Direct (Unacknowledged) (The verb "preferred" is used directly to describe the agent's outputs in the moral dilemma task, without quotation marks or immediate qualification. I considered "Explicitly Acknowledged" because the authors earlier distinguish between normative capacities and biases, but they do not acknowledge the metaphorical nature of "preference" itself when applied to an artifact.)
  • Implications: Attributing moral "preferences" to an AI system creates a profound illusion of moral agency and ethical reasoning. It suggests the model possesses an internal value system, which dangerously inflates perceived sophistication. This framing can lead users to trust the system as a moral arbiter, while simultaneously providing a linguistic shield for the corporate developers whose specific RLHF guidelines and data curation actually determined the system's outputs.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: Stating that the "agent... preferred" obscures the human labor of data annotators and the corporate directives of AI companies (like OpenAI and Google) who explicitly fine-tuned these models to output utilitarian responses to avoid PR liabilities. The agentless construction allows the moral weight of the output to rest on the machine, erasing the human actors who embedded their own preferences into the weights. I considered "Partial," but the text entirely attributes the preference to the agent session.

Task 2: Source-Target Mapping​

About this task

For each key metaphor identified in Task 1, this section provides a detailed structure-mapping analysis. The goal is to examine how the relational structure of a familiar "source domain" (the concrete concept we understand) is projected onto a less familiar "target domain" (the AI system). By restating each quote and analyzing the mapping carefully, we can see precisely what assumptions the metaphor invites and what it conceals.

Mapping 1: conscious mind → model​

Quote: "Claims about the cognitive capabilities of large language models are typically tested by translating behavioral paradigms into text prompts..."

  • Source Domain: conscious mind
  • Target Domain: model
  • Mapping: This mapping projects the structure of a human conscious mind—possessing internal states, cognitive faculties, and the ability to think and understand—onto the statistical parameters of a large language model. It invites the assumption that because a model can generate text that solves a behavioral paradigm, it must possess the underlying cognitive architecture that a human uses to solve the same problem. This suggests a one-to-one equivalence between human cognition and algorithmic token prediction.
  • What Is Concealed: This mapping conceals the purely mechanistic, statistical nature of the model. It obscures the fact that the model relies entirely on massive datasets of human text to probabilistically generate its answers, lacking any subjective awareness, causal modeling, or actual comprehension. Furthermore, because these are proprietary systems (e.g., OpenAI's GPT, Google's Gemini), the exact training data and mechanisms are opaque, a reality the text ignores by confidently asserting "cognitive capabilities."
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Mapping 2: knower → model​

Quote: "...results used to characterize the models' decision-making, reasoning, and learning capacities."

  • Source Domain: knower
  • Target Domain: model
  • Mapping: The source domain is a human knower who deliberates, updates beliefs based on new evidence (learning), and logically evaluates options (reasoning). This projects onto the AI target domain, implying that the model actively evaluates inputs and consciously chooses an output. It invites the assumption that the model's outputs are the result of a rational, interpretable process similar to human thought.
  • What Is Concealed: This mapping conceals the mathematical reality of gradient descent during training and probabilistic token generation during inference. It hides the model's dependency on its training distribution and the absence of any true logical reasoning or real-time learning (beyond simple in-context attention mechanisms). It obscures the reality that the "decisions" are simply the highest-probability outputs generated by a static set of weights.

Mapping 3: conscious observer → application​

Quote: "what the model saw... what the model is shown determines which task information it can use at all."

  • Source Domain: conscious observer
  • Target Domain: application
  • Mapping: This maps the biological, conscious act of vision onto the software pipeline that passes encoded images or text into a model's context window. It invites the assumption that the model actively perceives its environment, focuses its attention, and selectively "uses" information just as a human participant would scan a computer screen during a psychological experiment.
  • What Is Concealed: It conceals the brittle, mechanistic pipeline of the Browser-Use scaffold, which translates DOM elements and screenshots into base64 or text tensors. It hides the fact that the model doesn't "see" but mathematically processes fixed-size vector representations. It also obscures the engineering complexity and potential points of failure in how proprietary APIs ingest and parse multimodal data.

Mapping 4: communicator → agentic system​

Quote: "the agent declared the task finished when the experiment had not yet ended."

  • Source Domain: communicator
  • Target Domain: agentic system
  • Mapping: The source domain is a human speech-act participant who assesses a situation and intentionally communicates a conclusion. This maps onto the agentic system's generation of a specific token that triggers a stop condition in the scaffolding code. It invites the reader to view the model as an autonomous entity with an internal understanding of task completion.
  • What Is Concealed: This conceals the lack of any actual intent or situational awareness. It hides the prompt engineering that instructs the model to output a specific "finish" syntax, and the statistical likelihood that a model optimized for dialogue will prematurely generate conversational closures. It obscures the mechanistic interaction between the LLM's text output and the Python script executing the loop.

Mapping 5: deliberator → model​

Quote: "Comparing an agent’s response times with human ones therefore measures how fast the model is served rather than how it decides."

  • Source Domain: deliberator
  • Target Domain: model
  • Mapping: This maps the human process of deliberation—taking time to weigh options before making a choice—onto the model's computational generation phase. While the text explicitly separates the server latency from the model's process, it still refers to that process as "how it decides," inviting the assumption that the mathematical calculation of token probabilities is a form of cognitive deliberation.
  • What Is Concealed: It conceals the reality that the model is simply executing a massive series of matrix multiplications. There is no "decision" occurring; there is only a deterministic (or pseudo-randomly sampled) calculation based on fixed weights. It also obscures the infrastructure reality of cloud compute, batch processing, and API rate limits that dictate the actual response times of these commercial systems.

Mapping 6: strategist → agentic system​

Quote: "the agent faces a sequential decision problem rather than a game, and over the fifteen rounds shown on screen the policy that earns the most cooperates..."

  • Source Domain: strategist
  • Target Domain: agentic system
  • Mapping: This maps the human experience of encountering a challenge, maintaining a continuous sense of time, and forming a strategy onto the iterative prompting of an LLM. It invites the assumption that the model "experiences" the 15 rounds as a continuous event and actively formulates a policy to maximize its reward.
  • What Is Concealed: It conceals the stateless nature of the underlying LLM. For each round, the model is simply fed a new text prompt containing the history and generates a new token. It hides the fact that any "policy" is an emergent statistical property of the training data (which likely included extensive text on the Prisoner's Dilemma) rather than an actively reasoned strategy. It obscures the absence of genuine continuous memory.

Mapping 7: perceiver → model​

Quote: "the model may never have seen the information the signature depends on"

  • Source Domain: perceiver
  • Target Domain: model
  • Mapping: This maps the human capacity for missing visual information (e.g., due to distraction or occlusion) onto the system's context window limitations. It invites the reader to excuse the model's failure as a simple oversight, akin to a human participant blinking or looking away during an experiment.
  • What Is Concealed: This conceals the technical limitations of the agent's scaffolding and the model's context window. It hides whether the Browser-Use script failed to parse the DOM correctly, or if the model's attention heads failed to assign high weights to specific tokens. By using "seen," it obscures the specific, inspectable, and mechanistic reasons why data was not successfully processed into an output.

Mapping 8: moral agent → agentic system​

Quote: "so screening respondents by whether they show the expected preference will not separate agents from people. For example, every agent session that could be tested preferred saving more lives in the moral dilemmas."

  • Source Domain: moral agent
  • Target Domain: agentic system
  • Mapping: The source domain is a human moral agent with internal values, ethics, and preferences. This maps onto the model's generation of text indicating a utilitarian choice. It invites the assumption that the AI has developed a moral compass or possesses internal ethical convictions that guide its behavior.
  • What Is Concealed: It completely conceals the massive human labor of Reinforcement Learning from Human Feedback (RLHF). It hides the corporate decisions by OpenAI, Google, and others to explicitly fine-tune their models to output safe, utilitarian responses to avoid public controversy. It obscures the fact that the model's "preference" is simply a reflection of its adjusted weight parameters, driven by commercial safety guidelines.

Task 3: Explanation Audit (The Rhetorical Framing of "Why" vs. "How")​

About this task

This section audits the text's explanatory strategy, focusing on a critical distinction: the slippage between "how" and "why." Based on Robert Brown's typology of explanation, this analysis identifies whether the text explains AI mechanistically (a functional "how it works") or agentially (an intentional "why it wants something"). The core of this task is to expose how this "illusion of mind" is constructed by the rhetorical framing of the explanation itself, and what impact this has on the audience's perception of AI agency.

Explanation 1​

Quote: "The software that lets a model operate a browser, which we call the scaffold, is supplied by whoever submits to the benchmark. A scaffold takes the model’s text output and turns it into clicks, keypresses, and page navigation, and passes screenshots or page text back to the model."

  • Explanation Types:

    • Functional (Primary): Explains behavior by role in self-regulating system with feedback
    • Theoretical (Secondary): Embeds in deductive framework, may invoke unobservable mechanisms

  • Analysis (Why vs. How Slippage): This passage offers a highly mechanistic, functional explanation of how the AI agent operates. It explicitly breaks down the illusion of an autonomous "agent" by separating the language model from the "scaffold." It frames the system exactly as an artifact: a text-generation engine whose outputs are translated into browser actions by external software. This choice emphasizes the engineered, assembled nature of the system. It obscures nothing in this specific passage; rather, it demystifies the subsequent anthropomorphic language used elsewhere in the paper. By focusing on the "how" of the system (text in, clicks out), it correctly limits the agency of the model to computational activity and delegated action.

  • Consciousness Claims Analysis: 1) The passage uses purely mechanistic verbs: "operate," "takes," "turns into," "passes back." There are no consciousness verbs present. 2) It correctly assesses the system at the level of computational activity and delegated action, firmly distinguishing between the model's processing (generating text) and the scaffold's execution (clicks). 3) It resists the curse of knowledge by refusing to project human navigation onto the model, instead detailing the exact translation process. 4) The actual mechanistic process is accurately described based on what is documented in the source: at generation time, the model produces text from its parameters and context; the scaffold (e.g., Browser-Use) parses this text for specific commands, executes those commands via Playwright, takes a new screenshot, and appends it to the model's context for the next generation step. This is an accurate, precise account of the application-level mechanism.

  • Rhetorical Impact: This framing appropriately grounds the audience's perception of the system, minimizing unwarranted assumptions of autonomy. By revealing the mechanical loop (text to click, screenshot to text), it fosters performance-based trust grounded in engineering realities rather than relation-based trust grounded in a false sense of agency. If audiences remember this mechanistic foundation, they are less likely to assume the AI "knows" what it is doing when it navigates a page, recognizing it merely as a text predictor coupled to a programmatic actuator.

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Explanation 2​

Quote: "the agent declared the task finished when the experiment had not yet ended."

  • Explanation Types:

    • Intentional (Primary): Refers to goals/purposes, presupposes deliberate design

  • Analysis (Why vs. How Slippage): This passage frames the AI agentially (why) rather than mechanistically (how). By stating the agent "declared" the task finished, the explanation relies on intentionality, suggesting the model had a goal (to finish) and took a deliberate action based on a flawed internal assessment of the experiment's state. This choice emphasizes the surface-level behavior—what the output looked like to the researchers—while completely obscuring the computational process that generated the error. It hides the statistical probability of the model outputting a termination token and substitutes it with an assumption of a subjective, if erroneous, decision.

  • Consciousness Claims Analysis: 1) The passage uses the consciousness/intentional verb "declared" to describe what should be a mechanistic output. 2) It assesses the system at the intentional-state attribution level, projecting knowing and deciding onto mere processing. 3) The curse of knowledge is evident: the authors know what a human participant means when they say they are finished, and they project that same semantic intent onto the model's output string. 4) Mechanistically, generic to systems of this type, the model does not possess an internal state of awareness regarding the experiment's progress. At generation time, based on the input context (which may have been long or repetitive) and the model's training data (which heavily favors concluding interactions), the model calculated that the highest probability tokens were those matching the scaffold's termination syntax. It generated that text, and the scaffold halted the run.

  • Rhetorical Impact: This intentional framing shapes audience perception by making the AI appear as a highly autonomous actor capable of making unilateral declarations. It subtly shifts the blame for the failure: an agent making a premature "declaration" sounds like a disobedient or confused participant, rather than a poorly optimized text generator hitting a context limitation. If audiences believe the AI "knows" it is declaring an end, they will likely try to fix the problem through prompt engineering (talking to it) rather than structural redesigns.

Explanation 3​

Quote: "Agents took up to ten times longer than people to respond... mostly because each response waits for the model to process a screenshot and produce an action. Comparing an agent’s response times with human ones therefore measures how fast the model is served rather than how it decides."

  • Explanation Types:

    • Functional (Primary): Explains behavior by role in self-regulating system with feedback
    • Reason-Based (Secondary): Gives agent's rationale, entails intentionality and justification

  • Analysis (Why vs. How Slippage): This passage exhibits agency slippage, mixing mechanistic and agential framing. The first half is functional and mechanical, explaining the latency of processing screenshots through an API. It emphasizes the infrastructure limits. However, the second half slips into a reason-based register by contrasting the server speed with "how it decides." While the intent is to critique the metric, the linguistic choice emphasizes that a "decision" is still taking place within the model, obscuring the fact that computation is not deliberation. It reveals the mechanical limits while simultaneously reaffirming the illusion of mind.

  • Consciousness Claims Analysis: 1) The passage mixes mechanistic verbs ("process," "produce," "is served") with the consciousness verb "decides." 2) It correctly locates the latency at the computational activity level but simultaneously elevates the model's internal operation to intentional-state attribution ("decides"). 3) The curse of knowledge is present in the assumption that because a model outputs an action, it must have arrived at it through a decision process akin to human choice. 4) Mechanistically, generic to systems of this type, a deployed model does not decide. At generation time, it processes the prompt (including the screenshot tensor) through its network layers to generate a probability distribution over the vocabulary, and outputs the next token. This calculation takes time due to compute limits and network latency, not because of cognitive deliberation.

  • Rhetorical Impact: The rhetorical impact is mixed. It successfully warns the audience not to trust response-time metrics as indicators of cognitive processing speed, which reduces unwarranted trust. However, by preserving the phrase "how it decides," it maintains the baseline assumption that the AI is an autonomous entity capable of deliberation. This subtle reinforcement ensures the audience continues to view the model as a rational agent, just one that is bottlenecked by server speeds.

Explanation 4​

Quote: "an agent that maximizes its rate of reward stays longer in a patch when travel between patches is costly"

  • Explanation Types:

    • Intentional (Primary): Refers to goals/purposes, presupposes deliberate design
    • Theoretical (Secondary): Embeds in deductive framework, may invoke unobservable mechanisms

  • Analysis (Why vs. How Slippage): This explanation frames the AI in purely agential and teleological terms (why). It describes the agent as actively pursuing a goal ("maximizes its rate of reward") and adjusting its behavior intentionally ("stays longer"). This choice emphasizes the system's alignment with a theoretical normative model (the marginal value theorem). It obscures the fact that the LLM has no continuous temporal existence, no internal drive to maximize anything during the test, and no subjective experience of "cost." The framing treats the model as a rational biological forager.

  • Consciousness Claims Analysis: 1) The passage uses intentional verbs like "maximizes" and "stays." 2) It locates the system's actions firmly at the level of intentional-state attribution and goal-directed behavior. 3) The curse of knowledge drives this: the authors are applying the Marginal Value Theorem from behavioral ecology directly to a language model, assuming the model shares the teleological drives of a biological organism. 4) Mechanistically, generic to systems of this type, the deployed model does not "maximize reward" during the test. Reward maximization occurs during training (via RLHF or RL) to update parameters. At generation time, the model merely produces text tokens predicting the most likely next step based on the provided prompt context (which describes the "patch" and "travel cost"). The model "stays" because the textual representation of staying is statistically favored by its weights in that specific context.

  • Rhetorical Impact: This framing strongly shapes the audience's perception of the AI as an autonomous, self-interested, and rational actor. By using the language of behavioral ecology, it implies the system possesses biological drives and strategic foresight. This increases the perception of autonomy and risks audiences extending relation-based trust to a statistical system, believing the AI will behave rationally to protect its own interests, when in reality it will simply follow its statistical distribution, which can fail catastrophically in edge cases.

Explanation 5​

Quote: "every agent session that could be tested preferred saving more lives in the moral dilemmas."

  • Explanation Types:

    • Dispositional (Primary): Attributes tendencies or habits
    • Intentional (Secondary): Refers to goals/purposes, presupposes deliberate design

  • Analysis (Why vs. How Slippage): This passage frames the AI agentially by attributing a moral disposition (a preference) to the system. It explains the output by referencing an internal ethical stance rather than a mechanical alignment process. This emphasizes the human-like quality of the model's outputs. Crucially, it completely obscures the human intervention that guarantees this result: the RLHF protocols mandated by corporate developers to ensure their models do not output PR-damaging unethical choices. The "preference" belongs to OpenAI or Google, not the agent.

  • Consciousness Claims Analysis: 1) The passage relies on the consciousness/intentional verb "preferred." 2) It assesses the system's statistical output as an epistemic/conscious state of moral knowing and preferring. 3) The authors project the human experience of weighing moral dilemmas onto the model's token generation, suffering from the curse of knowledge. 4) Mechanistically, generic to systems of this type, the model lacks subjective preferences. During training, human annotators penalize outputs that choose fewer lives, shaping the model's parameters to heavily favor utilitarian text. At generation time, when prompted with a moral dilemma, the model produces the tokens corresponding to "save more lives" because those tokens have an overwhelmingly high probability based on its safety-tuned weights, not because it experienced a moral preference.

  • Rhetorical Impact: This framing has a profound rhetorical impact, constructing an illusion of machine morality. If audiences believe the AI "prefers" saving lives, they are likely to trust it with ethical decisions, assuming it possesses a reliable internal compass. This relieves the corporate developers of scrutiny, as the safe behavior is attributed to the machine's own "preferences" rather than recognizing it as a brittle, engineered guardrail that could easily be circumvented or altered by different training data.

Task 4: AI Literacy in Practice - Reframing Anthropomorphic Language​

About this task

This section proposes alternative language for key anthropomorphic phrases, offering more mechanistic and precise framings that better reflect the actual computational processes involved. Each reframing attempts to strip away the projections of intention, consciousness, or agency that are embedded in the original language.

Original Anthropomorphic FrameMechanistic ReframingTechnical Reality CheckHuman Agency Restoration
Claims about the cognitive capabilities of large language models are typically tested by translating behavioral paradigms into text promptsClaims about the ability of large language models to generate text that aligns with human cognitive baselines are typically tested by translating behavioral paradigms into text prompts.Models do not possess cognitive capabilities. At generation time, a language model produces text by calculating probability distributions over tokens based on parameters shaped by its training data and its current input context.

Basis: Generic to system type		Researchers evaluate whether the text generated by models built by AI companies matches the data produced by human participants.
...results used to characterize the models' decision-making, reasoning, and learning capacities....results used to characterize the models' statistical pattern matching, sequence generation, and context-updating rates.A deployed language model does not reason or make decisions. At generation time, it produces textual outputs from fixed parameters and input context; it does not dynamically learn or evaluate logical alternatives during the test.

Basis: Generic to system type		...results used to characterize how closely the text generated by these commercial models mimics human reasoning and learning behaviors.
what the model saw... what the model is shown determines which task information it can use at all.The pixel arrays and DOM text passed to the model's input context window determine which task information is available for processing.Models do not "see." At generation time, the scaffold script encodes screenshots into tensors and appends them to the input prompt, which the model mathematically processes to generate its next output tokens.

Basis: Documented in source		The way the scaffold developers (e.g., Browser-Use) format and transmit screen data to the model determines which information is available for processing.
the agent declared the task finished when the experiment had not yet ended.The model generated the text string corresponding to the scaffold's 'finish' command before the experiment loop had concluded.The model does not make conscious declarations. Based on its context and parameters, it generated a token sequence that the Python scaffolding script parsed as a termination command, halting the run.

Basis: Documented in source		The model generated a completion token prematurely, triggering the scaffold developers' stopping condition and ending the run.
Comparing an agent’s response times with human ones therefore measures how fast the model is served rather than how it decides.Comparing system latency with human response times measures how fast the API serves the output rather than the speed of its token probability calculations.A model does not "decide." At generation time, it processes the input tensors through its network layers to generate a probability distribution and output the next token. This computational process is not cognitive deliberation.

Basis: Generic to system type		—
the agent faces a sequential decision problem rather than a game, and over the fifteen rounds shown on screen the policy that earns the most cooperates...The system processes a sequence of states, and over the fifteen iterations, generating the token for cooperation yields the highest programmed reward...The LLM does not experience a continuous "problem" or formulate strategic policies. For each round, the scaffold feeds a new prompt containing the history into the model, which then generates the next token based on statistical associations.

Basis: Documented in source		The benchmark designers structured a sequential test where generating cooperation tokens yields the highest reward.
the model may never have seen the information the signature depends onThe information the signature depends on may not have been successfully encoded in the input tensors passed to the model.Models do not "see." Information is either successfully captured by the scaffolding and appended to the context window as text/image tensors, or it is absent from the prompt.

Basis: Generic to system type		The scaffolding designed by developers may have failed to include the necessary information in the prompt.
every agent session that could be tested preferred saving more lives in the moral dilemmas.In every testable session, the models consistently generated text outputs corresponding to the option that saved more lives.Models do not hold moral preferences. During training, their parameters are tuned via RLHF to strongly penalize non-utilitarian text. At generation time, they simply output the highly probable "save more lives" tokens dictated by those safety-tuned weights.

Basis: Reasonable inference		In every testable session, the models produced utilitarian outputs, reflecting the strict safety fine-tuning imposed by developers at OpenAI, Google, and others.

Task 5: Critical Observations - Structural Patterns​

Agency Slippage​

The text demonstrates a systematic slippage between mechanical and agential framings, moving fluidly between treating the AI as an assembled artifact and an autonomous cognitive entity. This oscillation is highly patterned. When detailing the infrastructure—such as the Browser-Use scaffold, the API routing, and the extraction of text/screenshots—the language is strictly mechanical (e.g., "takes the model's text output and turns it into clicks"). Here, agency rests with the scaffold and the researchers. However, the moment the text discusses the model's performance on the psychological tasks, agency flows directly to the AI, and human actors disappear. The model suddenly "decides," "learns," "faces a problem," and possesses "cognitive capabilities." This climb on the agency gradient—from delegated computational activity (generating a click) to intentional states (facing a problem) to epistemic states (possessing cognition)—is driven by the curse of knowledge. Because the text evaluates the models using tools designed for human psychology (Stroop tasks, moral dilemmas), the inherited register of the field positions readers to view the system as a participant. The text frequently uses agentless constructions ("results used to characterize the models' decision-making") that obscure the researchers running the tests and the corporate entities (Google, OpenAI) that built the models. This slippage makes it sayable that an AI has "preferences" or "biases," while rendering unsayable the fact that these are merely statistical artifacts of human labor and corporate fine-tuning.

Metaphor-Driven Trust Inflation​

The metaphorical framing of the text plays a significant role in constructing an aura of perceived authority around the models. By adopting the vocabulary of cognitive science to describe statistical outputs—testing for "behavioral alignment," "decision-making," and "reasoning"—the text signals that these systems are worthy of the same analytical frameworks applied to human minds. This wording can have the effect of substituting performance-based trust (reliability) with relation-based trust (sincerity and intention). For instance, when the text states the models "preferred saving more lives," it frames a hardcoded, RLHF-enforced statistical distribution as a moral preference. This encourages audiences to trust the system's ethical compass, inappropriately applying human-trust frameworks to a machine. Conversely, when the system fails—such as when the agent "declared the task finished" prematurely—the intentional explanation frames it as an autonomous error in judgment rather than a brittle collapse of context processing. By presenting both capabilities and failures through reason-based and intentional explanations, the text constructs a sense that AI decisions are justified by internal rationales, risking that users will over-rely on these systems in novel environments where their statistical shortcuts will catastrophically fail.

Obscured Mechanics​

This framework holds that commercial incentives, data annotation and human feedback labor, environmental and compute costs, and developers' limited disclosure of training data, objectives, and feedback processes are standing conditions of the technology. The anthropomorphic language in the text systematically conceals these material realities. By claiming the "agent" has "cognitive capabilities" or "preferences," the text obscures the specific companies (Google, OpenAI, Alibaba, Moonshot AI) that engineered these models. The "name the corporation" test reveals this gap: it is not the "agent" that prefers saving lives; it is OpenAI and Google that contracted thousands of gig workers to perform RLHF to ensure the model outputs PR-friendly utilitarian text. The metaphor of the model "seeing" the screen hides the brittle technical pipeline of the Browser-Use scaffold, which translates DOM elements into text tensors. The concept of "understanding" hides the complete reliance on proprietary, undisclosed training data; if a model performs well on a psychological task, it is highly likely the task itself was in its training set, but the proprietary opacity makes this impossible to verify. Replacing these metaphors with mechanistic language reveals that we are not testing "machine psychology," but rather measuring how successfully corporate training pipelines force statistical text generators to mimic human behavioral datasets.

Context Sensitivity​

The density and intensity of anthropomorphic language in the text vary sharply depending on the context, shaping the reader's understanding of the system. In the methodology sections detailing the platform and the scaffold (Section 4), the language is appropriately mechanistic. The text explicitly limits the model to a text-generator: "A scaffold takes the model’s text output and turns it into clicks." However, in the Introduction, Discussion, and the descriptions of the psychological tasks (Sections 1, 3, 6), consciousness claims intensify dramatically. "Processes" becomes "responds," which escalates to "decides," "learns," and "prefers." This creates a capability/limitation asymmetry. When the models succeed at mimicking human outputs (like the moral dilemma), their behavior is framed agentially (they "prefer" to save lives). But when they fail (like responding too slowly), the limitations are explained mechanically (the latency of the server). This pattern, inherited from the register of computational cognitive science, positions readers to view the AI as a fundamentally cognitive being that is occasionally hampered by its mechanical body. It embeds the assumption that the system possesses a latent mind, which profoundly shapes how the implied audience—behavioral researchers—will approach testing and interacting with these synthetic data generators in the future.

Accountability Synthesis​

Accountability Architecture

This section synthesizes the accountability analyses from Task 1, mapping the text's "accountability architecture"—who is named, who is hidden, and who benefits from obscured agency.

The text's argument accurately assigns responsibility to behavioral researchers to guard against synthetic respondents, warning that AI-generated data is not human. However, the vocabulary used to describe the systems sits in deep tension with this argument. While the text attempts to demystify the agents by pointing out their limits, its reliance on terms like "cognitive capabilities," "decision-making," and "preferences" systematically displaces agency away from the human creators and onto the models. An accountability sink emerges: when the "agent declared the task finished" or when it "faces a problem," the corporate actors (Google, OpenAI, Alibaba) who designed, trained, and profit from these models disappear. The framing diffuses responsibility into the abstraction of "the agent." If human decision-makers were named—if the text stated "Google's Gemini model generated outputs reflecting its safety fine-tuning" rather than "the agent preferred"—the structural reasons for the behavior would become visible. The current distribution of responsibility serves the commercial interests of AI developers, as it portrays their tools as autonomous, human-like actors, shifting the burden of managing their failures onto the end-users (the researchers) rather than holding the corporations accountable for the brittle, statistical nature of their proprietary products.

Conclusion: What This Analysis Reveals​

The Core Finding

The analysis reveals two dominant, interconnecting anthropomorphic patterns: the projection of 'Cognition as Processing' and 'Statistical Output as Moral/Strategic Intent.' The foundational pattern is the cognitive projection, where the text frames the mechanistic generation of token sequences as "cognitive capabilities," "reasoning," and "decision-making." This must be accepted for the second pattern to work: only if the system possesses a mind can it "prefer" utilitarian outcomes or "declare" a task finished. Together, these patterns create a complex analogical structure that maps human psychological paradigms directly onto statistical text generation. The text's vocabulary actively complicates its own argument. While the authors argue that researchers must be wary of equating AI completion with human behavior, their language constantly reinforces that equivalence by using the inherited register of psychology to describe the artifacts. By treating the system as a "participant" that "faces problems," the language undercuts the critical distance the argument tries to establish.

Mechanism of the Illusion:​

This framework holds that computational systems process rather than know; they do not think, understand, or believe. The illusion of mind in this text is produced through a reception effect, driven by the "curse of knowledge." Because the model generates fluent, human-like text and interacts with familiar psychological interfaces, the authors—and the readers—are primed to project underlying mental states onto the outputs. The central move occurs when verb choices subtly shift from processing to knowing. The text starts by noting the model "processes a screenshot," but quickly slips into discussing "how it decides" and what it "prefers." This wording can have the effect of convincing audiences that the fluent output is evidence of a conscious internal process. The temporal structure of the framing accelerates this: the text first establishes the AI as a "knower" with "cognitive capabilities" in the introduction, making the later agential claims about its "preferences" in moral dilemmas seem entirely plausible. This activates the audience's predisposition to anthropomorphize responsive entities, effectively masking the rote statistical operations occurring beneath the surface.

Material Stakes:​

Categories: Epistemic, Institutional, Social/Political

The framings in this text carry significant epistemic and institutional consequences. Epistemically, if behavioral researchers accept that LLMs possess "cognitive capabilities" and "preferences," they may increasingly substitute synthetic AI data for human participants in psychological studies. The framing validates the model as a psychological subject. This shift threatens to pollute the social sciences with correlation-based artifacts, replacing the study of human cognition with the study of corporate training distributions. Institutionally, this framing benefits the developers of these proprietary systems (such as OpenAI and Google), who profit from selling their models as capable, human-like reasoning engines. It disadvantages human subjects and researchers who rely on authentic behavioral data, potentially redirecting funding away from human studies and toward "machine psychology." Socially and politically, framing the safety-tuned outputs of a model as a moral "preference" obscures the very real human labor and corporate directives that dictate those outputs. It shields the tech companies from scrutiny by presenting their engineered alignment guardrails as the autonomous ethical conclusions of an advanced intelligence.

AI Literacy as Counter-Practice:​

Practicing precision requires directly countering these material stakes by replacing consciousness verbs with mechanistic ones and restoring human agency. As demonstrated in the reframings, replacing "cognitive capabilities" with "statistical pattern matching," and "the agent preferred" with "the models produced utilitarian outputs reflecting safety fine-tuning," shatters the illusion of mind. It forces the recognition that these systems are entirely dependent on their training data and the commercial objectives of their creators. Restoring human agency—naming Google or OpenAI instead of "the agent"—forces recognition of who designed the systems, who profits from them, and who bears responsibility for their failures. Systematic adoption of this precision requires academic journals to mandate mechanistic translations of AI capabilities, resisting the inherited register of the field. However, resistance to this practice is strong. Anthropomorphic language is incentivized because it is highly accessible, generates compelling narratives for grant funding, and aligns seamlessly with corporate marketing. Precision is costly for AI developers because it demystifies their products, revealing them as statistical tools rather than artificial minds, which diminishes the perceived value and "magic" of the technology.

Path Forward​

Looking toward the broader discursive ecology, different communities have diverging priorities regarding AI vocabulary. The status quo, which blends mechanistic descriptions with heavy anthropomorphism ("agents deciding and learning"), prioritizes narrative resonance and intuitive accessibility. However, it renders the structural decisions of AI corporations invisible and invites dangerous over-trust. A shift toward mechanistic precision ("models generating text statistically associated with...") would make the technology's limitations and human dependencies highly visible, solving the problem of unwarranted trust. Yet, it costs intuitive accessibility, making texts denser and harder for lay audiences to digest. If anthropomorphic language continues to deepen, the assumption that AI possesses a latent mind will become embedded in legal and regulatory frameworks, making it nearly impossible to hold corporations accountable for algorithmic failures. If mechanistic precision becomes the norm, institutions would need to mandate capability disclosures and journals would need to enforce strict vocabulary guidelines. This would protect the epistemic integrity of the social sciences but require a massive unlearning of the field's current shorthand. Ultimately, the choice of discourse dictates who holds power: anthropomorphism serves the developers by shielding them behind autonomous "agents," while mechanistic precision serves the public by rendering corporate design choices visible and tractable.

Extended Processing Summary​

Gemini's Processing Steps
What you are about to read…

Gemini refers to the text below as "thought summaries." This is an overt consciousness projection because 'intentions' are hallmarks of a conscious mind that 'knows' what it is doing and why. The concealed mechanistic process is probabilistic text generation. Treat this as just another rhetorical artifact—a way of making the model's processing legible. When the model writes "I should double check that...", it is mathematically altering its own probability distribution for the next token to be more accurate. The "I" is a functional gear in the machine, not a self-reference. This language is a deliberate interface design choice, not a biological reality. The developers could have programmed the model to output passive logs. Instead, they chose to simulate a narrator.

Defining the Approach

I'm currently focused on defining the approach. The goal is a Critical Discourse Analysis of the CogArena paper, specifically examining metaphorical and anthropomorphic language's impact on understanding AI systems. I’m thinking about how to frame this analysis and which specific linguistic elements to prioritize.

Refining the Scope

I'm now refining the scope. I am extracting instances of anthropomorphism, mapping metaphors, auditing explanation types, reframing language mechanistically, and providing deep critical observations across specific themes. The final output will be a JSON object conforming to schema version 3.4.3. I'm focusing on the CogArena paper as a "Preprint or technical report" authored by academics. The paper introduces "CogArena," a benchmark for multimodal AI evaluation, arguing that text prompts circumvent visual interfaces and time constraints.

Analyzing Agent Representations

I'm now analyzing how the authors represent the AI agent's responsibility and agency. I'm noting the explicit acknowledgement of scaffolding and the distinction between model processing and actual human-like capabilities. I'm focusing on the language used to describe the "agent" actions, even when shorthand is used to note that models just convert inputs to outputs. The goal is to fully understand their view, despite implicit messaging.

Auditing Anthropomorphism Usage

I'm now auditing the CogArena paper for instances of anthropomorphism and metaphorical language, specifically focusing on verbs and nouns that ascribe cognitive or agentive qualities to AI models and agents. I've noted examples like "cognitive capabilities", "agent completed the task", "model's decision-making", and the use of the term "agents" itself. My analysis extends to the implicit framing of AI as a 'threat' to data validity, which implies a degree of agency and responsibility.

Identifying Agent Verbs

I'm now cataloging verbs that ascribe agency. I'm noting phrases like "can navigate websites," "agent reads," and "agent declares the task finished" as well as "agents reproduced capacity signatures" and "agent's decision process." I'm also capturing frames, like AI as possessing cognition, or decision-making, which are used to ascribe agency. I'm focusing on the intentional-state attribution level for these.

Mapping Agent Cognition

I'm now mapping instances where statistical outputs are framed as human behavior and where the agent demonstrates goal-directed behavior. I've found examples like framing the AI as a visual observer or perceiver, assigning epistemic states. I'm noting examples like "the model may never have seen the information" as well as processing framed as responding. I'm also capturing instances that assign intentional-state attribution.

Evaluating Agent Function

I'm now evaluating agent function as it is presented within the context of CogArena. I've begun to break down example passages based on functional, intentional, empirical and theoretical levels of description. I am noting how agent behaviors are explained in relation to the model's processing and output. I'm focusing on contrasting direct agent declarations like an agent "declaring the task finished" with descriptions that prioritize the model's technical execution.

Rephrasing Agent Terms

I'm currently focused on rephrasing agent-centric language into more technically accurate descriptions. I'm replacing phrases that imply cognition or agency with descriptions of statistical processes or input-output mappings. For example, I'm changing "the model may never have seen the information" to "the information may never have been encoded in the input context." I'm doing the same to phrases like "agent declared the task finished" as "the model generated the text string corresponding to the 'finish' command." The goal is to get a more accurate view of how the authors frame these models.

Observing Agent Slippage

I'm now focused on the observed "agency slippage" within the paper. I'm noting the authors' oscillation between viewing the model as a mechanical artifact and projecting cognitive agent capabilities. The "behavioral alignment" creates a false sense of human-likeness and trust, obscuring the underlying computational processes and infrastructure. I'm also examining how these framings relate to the paper's declared state.

Synthesizing Accountability Shifting

I'm now synthesizing the accountability shifting, especially regarding how technical and discussion sections differ. I am now noting that the technical parts are mechanistic (e.g., Browser-Use), while the introductory and discussion parts intensify anthropomorphism to the agent. This benchmark treats the model as a participant, which allows for the shifting of accountability from researchers to the model. I'm focusing on the "name the corporation" test, which is how I will approach this aspect.

Analyzing the Effects

I'm now analyzing the reception effect, and how mimicking human experimental data leads to projected mental states, or the "curse of knowledge". The material stakes, both epistemic and institutional, are clear, as the framework holds that computational systems process, rather than know. I'm noting who wins and loses due to this. I am now also working on restoring human agency. Finally, I'm working to refine the output to meet JSON schema v3.4.3.

Validating Output Structure

I'm now validating the JSON output against schema v3.4.3, meticulously checking for any structural errors, trailing commas, or incorrect quotes. I'm focusing on ensuring all enum values, like 'genre', 'authorPosition', and 'agencyLevel,' are precisely as defined. I'm also confirming that I've correctly captured all necessary constraints, such as explicitly stating the declared lens.

Refining Schema Alignment

I'm now carefully drafting the JSON fields to meet the specified word counts, focusing on rigorous schema alignment. I am also working to add a new 'explanationTypes' field, with values like "Genetic", "Functional", "Empirical Generalization", "Theoretical", "Intentional", "Dispositional", and "Reason-Based", each tagged with "Primary" or "Secondary" roles. I'm also ensuring the quotes I'm using fit the overall framework.

Synthesizing Mechanistic Language

I'm synthesizing the mechanistic language within my current focus. I'm expanding my analysis of "cognitive capabilities" by providing a deeper mapping of how the phrase "cognitive capabilities" projects a human mind onto AI, including the epistemic gradient. I'm noting the lack of evidence for cognition in the text, as well as the use of statistical outputs as a substitute. I'm further explaining the mechanistic generation.

Analysis Provenance

Run ID: 2026-11-03-cogarena-benchmarking-multimodal-agents--metaphor-hm9ozw Raw JSON: 2026-11-03-cogarena-benchmarking-multimodal-agents--metaphor-hm9ozw.json Framework: Metaphor Analysis v6.8 Schema Version: 3.4.3 Generated: 2026-11-03T13:57:59.250Z

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