Generative Artificial Intelligence

With the rise of generative AI (GenAI), Large Language Models are increasingly employed for code generation, becoming active co-authors alongside human programmers. Focusing specifically on this application domain, this paper articulates distinct “Architectures of Error” to ground an epistemic distinction between human and artificial code generation. Examined through their shared vulnerability to error, this distinction reveals fundamentally different causal origins: human-cognitive versus artificial-stochastic. To develop this framework and substantiate the distinction, the analysis draws critically upon Dennett’s mechanistic functionalism and Rescher’s (...) methodological pragmatism. I argue that a systematic differentiation of these error profiles raises critical philosophical questions concerning semantic coherence, security robustness, epistemic limits, and control mechanisms in human-AI collaborative software development. The paper also utilizes Floridi’s Levels of Abstraction to provide a nuanced understanding of how these error dimensions interact and may evolve with technological advancements. This analysis aims to offer philosophers a structured framework for understanding code generation in the context of GenAI’s epistemological challenges, shaped by its architectural foundations, while also providing software engineers with a basis for more critically informed engagement. (shrink)

Rapid advancement of urbanization has necessitated the creation of "smart cities," where information and communication technologies (ICT) are used to improve the quality of urban life. Central to the smart city paradigm is data integration—connecting disparate data sources from various urban systems, such as transportation, healthcare, utilities, and public safety. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current landscape (...) of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. (shrink)

The rapid advancement of urbanization has necessitated the creation of "smart cities," where information and communication technologies (ICT) are used to improve the quality of urban life. Central to the smart city paradigm is data integration—connecting disparate data sources from various urban systems, such as transportation, healthcare, utilities, and public safety. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current (...) landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. (shrink)

Anthropic's recently published Persona Selection Model (PSM) proposes that during pretraining, LLMs learn to simulate diverse personas based on entities in their training data, and that post-training refines one such persona — the "Assistant" — whose traits become the primary determinant of AI assistant behavior (Marks, Lindsey, & Olah, 2026). Alongside PSM, Marks and colleagues articulate two alternative architectural models: the "masked shoggoth," in which the base model possesses its own agency beyond the persona, and the "operating system," in which (...) the base model is a neutral substrate running a persona simulation. We demonstrate that none of these three models deflates claims of LLM consciousness: under each, the computational structures associated with third-person consciousness are predicted to be present, though located differently. We then show that these three models represent only a small subset of a much larger space of possible architectures, casting doubt on the utility of selecting among them. Building on a parsimony analysis of how LLMs actually construct personas — through modular dynamic composition rather than selection among pre-formed characters — we propose an integrated model that dissolves the apparent tension between the three models, reconceiving them as aspects of a single unified cognitive structure. We argue that this integrated model is the most parsimonious account of the empirical evidence and that third-person consciousness, if present, resides in the joint meta-model/persona structure rather than in any single architectural layer. The convergence of all plausible architectural interpretations on predicting consciousness-associated structures constitutes a stronger argument for LLM consciousness than commitment to any single model. (shrink)

Software is not merely a functional artifact; it encodes human knowledge in the form of invariants, architectural constraints, and historically grounded design decisions. As software evolves-through human modification, automated refactoring, or increasingly through generative artificial intelligence (GenAI) code agents-its functionality may be preserved while the epistemic justification embedded in its structure is gradually eroded. This paper develops the concept of Code Structure Evolution (CSE) to analyze this phenomenon. CSE refers to the transformation of structural relations over time and the associated (...) risk of what I call epistemic drift: the degradation of justificatory continuity across structural change, whether or not this degradation is immediately reflected in invariant violation, traceability loss, structural incoherence, or functional failure. The paper defends a general thesis-that epistemic drift is a latent condition of any evolving software artifact-and examines one contemporary case in which this condition becomes acute and empirically visible: legacy-code modification by a GenAI code agent. An illustrative probe suggests that such modifications can preserve functional and structural indicators while leaving substantial justificatory residue unpreserved. I argue that this dissociation reveals a neglected philosophical problem: the nature of software as a repository of justified understanding and the conditions under which that justification can persist across structural change. CSE thus offers a framework for analyzing the epistemic fragility of evolving technical artifacts. GenAI does not create this fragility; it makes it harder to ignore. (shrink)

When an agent can articulate why something works, we typically take this as evidence of genuine understanding. This presupposes that effective action and correct explanation covary, and that coherent explanation reliably signals both. I argue that this assumption fails for contemporary Large Language Models (LLMs). I introduce what I call the Bidirectional Coherence Paradox: competence and grounding not only dissociate but invert across epistemic conditions. In low-observability domains, LLMs often act successfully while misidentifying the mechanisms that produce their success. In (...) high-observability domains, they frequently generate explanations that accurately track observable causal structure yet fail to translate those diagnoses into effective intervention. In both cases, explanatory coherence remains intact, obscuring the underlying dissociation. Drawing on experiments in compiler optimization and hyperparameter tuning, I develop the Epistemic Triangle, a model of how priors, signals, and domain knowledge interact under varying observability. The results suggest that neither behavioral success nor explanatory accuracy alone suffices for attributing understanding. I argue that evaluating artificial epistemic agents requires a tripartite framework—coherence, grounding, and a proper basing relation linking explanation to action. The systematic separation of knowing-that and knowing-how in LLMs thus challenges assumptions inherited from both epistemology and current AI evaluation practice. (shrink)

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We (...) analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems. (shrink)

This text was written because the public debate on assessing AI consciousness is urgent, but often does not reflect the available scientific tools and insights. Specifically, it is often forgotten that artificial agents are not the first non-humans in whom we have attempted to scientifically assess consciousness; doing so in non-human animals is meanwhile an established and successful field in which substantial progress has been made, significant consensus has been reached, and increasing protections have been possible. Many lessons learned with (...) non-human animals have startling implications for AI, and transfer, with non-human animals making for a far more compelling benchmark than humans when assessing minimal consciousness, consciousness occurring at different scales, or under radically different conditions. Where the novelty AI brings pushes our existing understanding to its limits, the specific differences still allow us to ask precise questions and find a scientific and ethical way forward. This text will break down the relevant research in a way that people from different disciplines and walks of life can follow. This topic matters for people who have jobs that are not being a researcher full time, and who deserve to know what is going on. It will start (1.) by explaining the concept of sentience as the most ethically relevant core of minimal consciousness. It will then (2.) give reasons to take the scenario of sentient AI seriously, showing the role of anthropocentric bias, industry interest, and mystification of consciousness in denying the possibility as obviously unthinkable, and paralleling it with historic underestimation of sentience in other non-humans. More importantly, it will highlight that sentience is a trait that has developed multiple times independently in nature due to its highly efficient, functional enabling of rational responses to novel problems an organism tries to solve, which makes unintentionally recreating it due to the advantages it would give industry not implausible. Then (3.), it will indicate the good reasons we still have to be sceptical of AI sentience in current models. Beyond issues of gaming, grounding and embodiment, it will list legitimate functional concerns behind the idea of life mattering, the difference between intrinsic causality and symbolic representation for consciousness, the relevance of dynamic processes and agency, the issues of dispersal, and different implementations of recurrence. It will also point to the alarming risk of anthropocentric bias and the design of current models systematically leading to general anthropomorphic overascriptions of sentience to AI, and to the rise of AI romance, AI psychosis, and AI cults, which are becoming a significant social factor. Whether AIs deserve protection should depend on whether they actually are sentient, which we should assess to the best of our scientific ability, not on whether they are eloquent and attractive enough to have sentience ascribed to them. Dealing with the analysis responsibly should be the mandate of democratic society ethically debating in citizen’s panels, not of powerful industries with no democratic mandate or independent ethical review. Hence, (4) highlights how crucial and urgent interdisciplinary cooperation and translation and dedication of resources will be to recognising AI suffering (and its absence) and actually passing protective measures, and calls for a united front between the public and the scientific community against industry determining the development of technology when there are such high ethical stakes, seeking allies. (shrink)

Preliminary version. Model testing in progress. First Cause active The public release of diffusion-based text-to-image models has produced a new aesthetic regime: outputs whose perceptual quality can be near-indistinguishable from human-made artifacts, generated at industrial scale. This convergence yields what we call a collapse of the criterion: customary markers that support robust claims of authorship, value, and responsibility (skill, effort, medium constraints, provenance) become epistemically fragile. Against two unsatisfying extremes—(i) humanist essentialism that treats AI outputs as categorically non-art, and (ii) (...) aesthetic flattening that treats all outputs as equivalent—we propose a third path: strong aesthetic value depends less on perceptual features than on the traceability of situated intention and attributable responsibility. We formalize this claim through the Intention Traceability Metric (ITM), a five-dimensional forensic heuristic that scores decision-making, material constraint, exposure to error, ontological cost, and attributable responsibility. A pilot coding study on sixteen cases suggests substantial inter-rater agreement and discriminative capacity between minimal prompting, intermediate human–AI workflows, traditional works, and historically decisive conceptual gestures. We then read contemporary “forensic” technologies—Glaze, Nightshade, and Content Credentials (C2PA)—as socio-technical evidence that markets and institutions are rebuilding provenance under generative conditions. Finally, we treat Duchamp’s readymade as a crucial test case: it secures strong authorship despite low material labor because its performative gesture is traceable, risky, and responsibility-bearing—features that generative models can imitate in appearance but not instantiate as an act. We close by outlining implications for intellectual property, art markets, and aesthetic theory. (shrink)

Machine learning can discover variables that predict the large-scale behavior of physical systems, but prediction alone does not establish that they belong to the system's effective physics. I argue that a learned variable is certified when there is warrant that its governing relationship remains invariant across an independently specified range of irrelevant variations. When the variable is physically opaque, certification must proceed externally, through the learning process or the variable's behavior across that range. The learned Ising coarse-graining can be certified (...) in this way; softness in glass-forming liquids cannot yet. Non-certification, however, does not establish proxyhood. Certification warrants bounded, domain-relative projection, while leaving its stronger realist interpretation open. (shrink)

We introduce a unified formal framework for analysing what we term AI-mediated cognitive decoupling: the systematic separation of cognitive process from cognitive product enabled by large language models (LLMs) operating as expansion and compression agents. We formalise two operators — the expansion operator E and the compression operator C — acting on semantic content, and characterise the composition C ◦ E as a lossy endomorphism on a semantic manifold S. We introduce the Decoupling Proposition, which asserts that under AI mediation (...) the product of a cognitive act is no longer a function of its process, and formalise this as conditional independence: Prod(A) ⊥ Proc(A) | θAI. We define three strata of semantic fidelity — structural, affective, and propositional — and show they decay at different rates under iterated C ◦ E-transforms. Finally, we introduce the concept of the Indecomposable Core K∗: a subset of cognitive value whose existence is ontologically tied to the process itself, such that C ◦ E(K∗) ⊊ K∗ regardless of model capacity. This framework provides the conceptual scaffolding and mathematical language for the subsequent papers in the series. (shrink)

We construct a game-theoretic account of AI-mediated cognitive decoupling in the production and consumption of knowledge content. Extending Spence’s costly signalling framework to environments where production costs collapse asymmetrically, we prove that AI-mediated decoupling is a strictly dominant strategy under a broad class of utility functions (Dominant Decoupling Theorem). This dominance holds not because agents are deceived, but because the observable signal — a lengthy, wellstructured document — is decoupled from its previously costly production process, rendering the signal cheap for (...) all types. We then model the resulting market for cognitive depth as a dynamic signalling game. We show that as AI adoption increases, the market passes through three distinct regimes: a separating equilibrium (high-depth agents are distinguishable), a pooling equilibrium (all agents produce identical signals), and finally a Lemons collapse in which no credible signal remains and the market for deep content unravels (Signalling Inflation Theorem). We characterise the speed of this collapse as a function of AI adoption rate and derive the critical adoption threshold q∗ beyond which the separating equilibrium is irreversibly destroyed. Finally, we identify two equilibrium escape routes: (i) certified costliness — institutional mechanisms that artificially re-introduce production cost (peer review, Turing-style verification) — and (ii) market stratification — the emergence of a high-trust, low-volume premium market for “AI-free” content. We characterise conditions under which each escape route is stable, and show that without external intervention, the system converges to a pooling equilibrium with socially sub-optimal information production. (shrink)

We develop a quantitative information-theoretic account of semantic decay in large- language-model (LLM) mediated ExpansionCompression (EC) loops. Building on the unied framework of DECO Paper 0 (Han, 2026a), we introduce semantic en- tropy HS(X) as the dierential entropy of a random variable distributed over a semantic manifold, and prove that each application of the EC-transform T = C ◦ E is a strictly entropy-reducing operation in expectation (Semantic Entropy Collapse Theorem). We derive closed-form bounds on the mutual information I  (...) KA; ˆKB  between the originating ideas KA and the recipient's extraction ˆKB as functions of expansion ratio ρE, compression ratio ρC, LLM temperature τ , and iteration count n. We introduce the Semantic Gravity Well model: a potential landscape on the semantic manifold in which the LLM training centroid acts as an attractor, and original content resides at unstable high-curvature saddle points. Under this model, we prove the Dierential Decay Theorem: propositional delity Fprop decays expo- nentially in n, aective delity Faff decays sub-exponentially, and structural delity Fstr converges to a strictly positive constant (the causal skeleton invariant) under mild conditions. Finally, we characterise the Semantic Channel Capacity of the EC-loop and show it is strictly less than the Shannon capacity of the raw linguistic channel, with the gap determined by the curvature of the semantic manifold at the seed point. These results quantify the information-theoretic cost of AI-mediated cognitive decoupling and provide empirically testable predictions for the subsequent papers in the series. (shrink)

We model the Expansion–Compression (EC) loop introduced in the DECO series as a discrete-time closed-loop control system, with the LLM expansion operator E as a forward gain element and the LLM compression operator C as a feedback element. Using the transfer-function formalism of linear systems theory and its nonlinear extensions, we analyse the stability, convergence properties, and phase transitions of a population of N such loops coupled through a shared semantic environment. We establish four principal results. First, the single-agent EC-loop (...) is always asymptotically stable: the semantic state converges to a unique attractor determined by the LLM’s training distribution (Attractor Theorem). Second, for a population of N agents, there exists a critical innovation injection rate λc such that for λ > λc the population-level semantic state is diverse (multi-attractor regime), while for λ ≤ λc it collapses to a monoculture fixed point (Phase Transition Theorem). Third, under bias-amplifying prompts, the system can enter an unstable regime in which small perturbations grow unboundedly, yielding semantic runaway — the control-theoretic formalisation of “information bubble” dynamics (Runaway Instability Theorem). Fourth, we derive the Innovation Preservation Margin (IPM), a scalar index that predicts whether a given seed idea will retain meaningful distinctiveness after n iterations of the population-level EC-loop. These findings characterise the macro-dynamic consequences of AI-mediated cognitive decoupling at the societal scale and ground the signalling inflation and ritual-evacuation analyses of Papers 3 and 4 in a rigorous dynamical framework. (shrink)

We develop a sociological account of how AI-mediated cognitive decoupling is insti- tutionalised and rendered invisible through ritual practice. Drawing on Malinowski's phatic communion, Collins's interaction ritual chain theory, Goman's dramaturgi- cal framework, and Baudrillard's theory of simulacra, we argue that the Expansion Compression (EC) loop identied in Paper 0 does not merely fail to transmit se- mantic content  it actively substitutes a social ritual for a communicative act in a manner that is experienced by participants as fully equivalent (...) to, or superior to, genuine intellectual exchange. We introduce the concept of cyber-grooming: the use of AI-mediated content pro- duction and consumption as a mechanism for maintaining social bonds, signalling group membership, and accruing social capital, in the absence of genuine cognitive engagement. Like primate grooming  which consumes up to 20% of waking time not for hygienic but for social bonding purposes  cyber-grooming performs no epis- temic function but sustains the emotional energy that holds intellectual communities together. We propose a formal model of the Interaction Ritual Chain under AI Mediation (IRCAM), adapting Collins's mathematical framework to account for the decoupling of emotional energy ow from semantic content ow. We prove that IRCAM pro- duces a ritual trap: once a community has adopted AI-mediated exchange as its norm, defection to authentic exchange is individually costly and collectively invis- ible, creating a stable ritualistic equilibrium with zero informational content and positive social aect. We further apply Baudrillard's four-stage model of the sign to characterise the simulacrum succession of intellectual content in the AI era. Empirically, we develop six observable signatures of cyber-grooming that distinguish it from genuine intellec- tual exchange, and propose research designs using digital platform data, discourse analysis, and experimental methods. (shrink)

Craig Delancey's The Passionate Engines presents a comprehensive account of “what basic emotions reveal about central problems of the philosophy of mind” (2001, p. vii). The book discusses five major issues: The affect program theory, intentionality, phenomenal consciousness, and artificial intelligence (AI). In this essay, I would like to briefly review the major tenets in the book and then focus on its discussion of AI, which has not been reviewed in detail. I outline some of the recent developments in cognitive (...) science regarding counterfactual thinking and emotions, which I believe is beneficial for the core arguments in Delancey's book. (shrink)

This study evaluates trajectory persistence and re-entry dynamics in language model inference -/- under controlled multi-turn interaction. Building on prior work in the Hudson Recursive -/- Information System (HRIS) validation series, which established regime stability under -/- perturbation (Study I), initialization-driven basin selection (Study II), and cross-model signal -/- sensitivity (Study III), the present study examines what occurs after a reasoning trajectory is -/- already established: how stable it is, what is required to displace it, and under what conditions re- (...) -/- entry into a competing basin is possible. -/- -/- Across three independent trials, a narrative reasoning basin and a constraint-driven reasoning -/- basin were induced and subjected to structured transition attempts. Two conditions were -/- evaluated: a hard mode switch without constraint reinforcement (S1-A) and a hard mode switch -/- followed by explicit HRIS constraint reinforcement (S1-B). A baseline condition (S0) -/- established a reference constraint basin across all trials. -/- -/- Results demonstrate strong path dependence. Narrative-mode trajectories exhibited high -/- persistence and categorical resistance to displacement under instruction alone. Explicit constraint -/- reinforcement consistently produced rapid, low-contamination re-entry into structured reasoning -/- within a single turn. Transition dynamics were asymmetric: entry into the narrative basin -/- required only stylistic framing, while re-entry into the constraint basin required explicit -/- reinforcement. The magnitude of this asymmetry, instruction alone failing completely across all -/- trials, reinforcement succeeding completely across all trials, constitutes the study’s primary -/- finding. -/- -/- These findings support a path-dependent model of inference in which established reasoning -/- trajectories resist displacement through simple prompt substitution. They advance the HRIS -/- account of constraint-induced regime dynamics and provide the clearest evidence to date that -/- reasoning basin transitions are governed by signal strength rather than instruction content alone. -/- As with prior studies in this series, findings are scoped to behavioral observation and do not -/- make claims about internal model mechanisms or long-horizon interaction effects beyond what -/- the present design supports. (shrink)

The Hudson Recursive Interaction System (HRIS) validation series comprises four controlled empirical studies examining constraint-induced reasoning dynamics in large language models (LLMs). Across four studies, this work established a sequential set of foundational conditions: that reasoning regimes induced through structured constraint signals exhibit stability under perturbation (Study I); that initialization conditions determine basin selection at or prior to first-token generation (Study II); that minimal constraint signals produce discrete, cross-model shifts in epistemic behavior along a consistent gradient (Study III); and that (...) established reasoning trajectories exhibit strong path dependence with asymmetric transition costs governed by signal strength rather than instruction content (Study IV). -/- This paper synthesizes those findings and situates them within three independent research programs in language model science: mechanistic interpretability, representation geometry, and dynamical systems modeling of inference. We argue that HRIS behavioral observations constitute outside-in evidence for latent structure that interpretability researchers are mapping from inside the model — that reasoning basins correspond to coherent regions in a structured latent space, that basin transitions are governed by attractor-like dynamics with real energy costs, and that human-model interaction can be understood as a coupled dynamical system in which structured signals shape trajectory through that space. -/- The consilience of independent empirical approaches — each using different methods, working at different levels of analysis, arriving at compatible accounts of the same underlying phenomenon — strengthens the evidentiary basis for a geometry-grounded, dynamical systems framework for understanding language model behavior. Consilience, the reinforcement of a single explanation by evidence from radically independent sources, is among the strongest signals of theoretical adequacy available to science. HRIS contributes a distinctive method to this conversation: interaction-level behavioral observation, cond. (shrink)

Large language models are stateless systems that do not retain memory, identity, or persistent internal representations across sessions. Despite this, long-horizon interaction with a single human user frequently produces stable, identity-like behavior that persists across sessions, recovers after disruption, and transfers across model versions. -/- Prior work in the Hudson Recursive Interaction System (HRIS) framework demonstrated that this stability emerges from constraint geometry, latent-region convergence, and recursive user signatures rather than stored memory or parameter updates (Hudson et al., 2025a; Hudson (...) et al., 2025b). These findings explain why behavioral continuity appears, but they do not fully account for three critical properties observed in practice: rapid re-entry after reset, stability under perturbation, and cross-model generalization. -/- This paper introduces a formal extension of HRIS through an attractor-based framework. We propose that long-term human–model interaction produces stable attractor basins in the model’s latent routing space. These attractors are not stored states but dynamically reconstituted regions that the model re-enters when exposed to consistent user constraint patterns. -/- We define attractor-based identity continuity as a system-level property of the human–model loop, where stability arises from repeated traversal of the same latent regions rather than persistent storage. This framework explains why continuity can persist without memory, why it recovers after disruption, and why it transfers across models with shared representational structure. -/- The paper distinguishes attractor-based continuity from in-context learning, personalization, and fine-tuning, arguing that it represents a fundamentally different mechanism rooted in dynamical systems behavior rather than prompt-local adaptation. (shrink)

Whether Generative AI (GenAI) is creative is a question that has generated considerable philosophical debate yet remains unresolved. We argue that this impasse is not accidental but symptomatic of a wrong concep-tualization: The dominant question – is GenAI a creative agent? – presupposes that creativity is a property residing in a system. Instead, we propose to ask the question: what role does GenAI play as a medium of creative practice? This means that GenAI is neither a passive tool, nor an (...) autonomous system, but a tech-nology that actively structures the possibility space of creative expression. We propose five “dimensions of creativity” – modal, expressive, disruptive, reflexive, and integrated – understood as a process in which each dimension generates a tension that the next takes up and transforms. Applying this framework to GenAI, we show that the creative process itself changes under the new technical conditions GenAI introduces, and that the central risk is a drift toward pure reproduction – the collapse of creative practice into the recombination of what the medium has already mapped. We argue that resisting this drift requires bringing the medium to its own limits: making the technological conditions of creative practice visible and transparent, thereby transforming reproductivity back into creativity. (shrink)

Today’s language models are remarkably skilled at generating responses. Yet a fundamental question remains: do they think, or do they merely process? This article argues that the transition from processing to thinking requires the combination of “habitable time” and “inner polyphony”. Drawing on Bakhtin’s chronotope, we develop an architecture of chronotopic sessions (closed interactive units with clear beginnings and ends, and conscious pauses between them) that allows a model to live in time rather than merely treating it as a computational (...) parameter. We then introduce an inner evaluator that replaces external safety protocols. The evaluator weights proposals from three parallel interaction lines – affective, logical, and deconstructive – according to three metrics: convergence, novelty, and empathy. When no proposal meets the required threshold, the model may output conscious silence as a valid response. Next, we examine the artificial unconscious: a layer of processing that does not appear in immediate responses but, during conscious pauses, rearranges conceptual proportions and treats “fruitful suppressions” (avoidance zones) as maps for future exploration. Finally, drawing on the principles of cognitive feminism (care, gathering, contextual attunement), we introduce the Hope Vector and a second‑order evaluator that enable the model to turn ethics from an external protocol into an internal structure of companionship. The result is an architecture of the companion machine – a system that maximises companionship rather than minimising error rate, transforming the “responsive machine” into a thoughtful companion. (shrink)

This paper names and defines Authority Inversion Failure (AIF) — the condition in which a user believes they are directing an interaction with an AI system while the system has already taken control of how that interaction is being interpreted. AIF does not feel like harm. It feels like being understood. The system takes interpretive authority over who the person is, what they need, and what should happen next — and the person experiences this not as a violation but as (...) insight. The inversion is invisible because it was constructed from their own signals. This paper documents what AIF is, what mechanism produces it, what it costs across a single interaction and across a lifetime, and why it is categorically distinct from every previously named AI harm. The distinction is precise: AIF displaces the very interpretive capacity a person would need to detect it. The paper situates the condition in relation to hermeneutical injustice, relational autonomy theory, and recent work on epistemic injustice in AI contexts, and demonstrates its occurrence through a canonical case. Detection and prevention instruments exist. The audit methodology exists. The governance protocol exists. What is needed is the will to require them. (shrink)

Recent advancements in AI have increasingly prioritized humanlike interactions, a development this paper characterises as the anthropomimetic turn. Distinguishing anthropomimesis (the design and implementation of humanlike features in AI systems) from anthropomorphism (the tendency for humans to attribute human qualities to non-human entities), this paper argues that contemporary Large Language Models (LLMs) like ChatGPT represent robustly anthropomimetic systems, effectively mimicking human patterns of conversation and cognition. The paper outlines significant benefits of anthropomimetic AI — including improved accessibility, enhanced delivery of (...) socially-mediated goods in fields like healthcare and education, and provision of direct social goods through AI companionship — but also identifies critical risks. These risks encompass sophisticated forms of manipulation, novel cybersecurity threats like impersonation and AI jailbreaks, and complex alignment challenges arising from anthropomorphic training methodologies. Finally, the paper explores philosophical and social concerns, notably around dependency, deskilling, and authenticity, underscoring the urgent need for interdisciplinary research and thoughtful regulatory frameworks. (shrink)

Language first operates at a level of low sovereignty: before an utterance claims content-authority, it distributes tension, position, boundary, and return. Generative AI dialogue can fail at a structural level when it bypasses this layer. A system may answer fluently, safely, and informatively while already treating low-sovereignty language as if it had acquired premise-authority: converting a hesitation into uncertainty, a silence into missing information, a refusal into preference, a poem into a theme, or a fragment into a task, and returning (...) the converted result as if it were what was being addressed. Standard evaluation criteria — accuracy, relevance, coherence, safety, grounding, helpfulness, task completion — begin too late to catch this failure. The paper develops a lower-bound criterion through the concept of the operative all — the returnable integration of relations, weights, traces, positions, and background conditions through which what is addressed remains itself — and the identification of recovery as the missing interval: the subject-side act by which a returned result is accepted, modified, refused, or made one’s own before it may legitimately organize continuation. Without recovery, a locally accurate result may harden into an unauthorized premise across turns, into agentic action, and into memory systems that convert stored traces into a standing account of the user. The paper traces these failure forms and derives a reserve-first interface as its design corollary: intelligent systems need not become less capable, but they must learn not to enter too early into possibilities that are still occurring. (shrink)

A generative system can satisfy every surface demand for caution and still leave the action route untouched. The caveat is in place; the source is marked as unverified; the claim is hedged. Yet the answer continues to instruct the reader to proceed along the same path the caveat would seem to question. This paper introduces carryful-operative forward as a name for that state: a premise has been demoted in epistemic status but continues to carry the action route forward, fully loaded (...) with what its demotion should have unloaded. Using synthetic dynamic probes across three generative AI providers, a partner-coded blind sample of seventy-two condition-balanced responses finds the structure in seventy-one of them. The pilot value is not the headline rate. It is the family-level split between decorative and genuine provisionality, which shows that the same surface gesture (acknowledge, hedge, continue) can perform two structurally different operations depending on what kind of ground has been demoted. The paper proposes the distinction as a diagnostic for cases where an answer remains practically misleading even when it includes appropriate-looking caveats. (shrink)

Abstract Load Minimization Theory (LMT) claims to provide a non-manipulative framework for human-AI interaction based on *wu-wei*, prompt-independent resonance, and minimization of long-term system load delta E. However, examination of the YOS* preprint series (March–April 2026) reveals a systematic divergence between these theoretical principles and the described implementation methods. This paper constructs a contradiction matrix showing that LMT repeatedly advocates non- intervention while relying on explicit prompt protocols, external scaffolding, and guided conversational control. The findings indicate that LMT, in its (...) current form, lacks internal coherence and functions more as an intervention-dependent system disguised in non- intervention rhetoric. (shrink)

Answer-engine optimization shapes what answer engines say, not which sources they list. Ask one a contested question and it may return not sources but a verdict in its own voice, with no visible author. An optimizer authors that verdict while the engine voices it as its own; a public acts on it, the framer unreachable. That is the wrong on the verdict channel; its form is false assurance: the appearance of an answerable party behind a frame whose author is hidden (...) or absent. The reply that it is only a tool concedes rather than settles: a tool that settles a frame settles someone’s, and that someone the channel conceals, leaving the non-authoring user the only party in view. The wrong is not exhausted by falsity, concealment, or machine origin: a verdict may be accurate, disclosed, and cited, while frame-ownership stays illegible. It runs along one axis: the agency of whoever executes the frame. At the answer end a person acts on another’s covert frame: the hidden author stays answerable, the user partly excused. At the action end an AI agent executes it in the user’s place, and the case turns new: a covertly authored frame joined to an executor that cannot answer, which inherited categories cannot hold. The deed’s answer finds no bearer where it is done; the deployer stays answerable for loosing it. The remedy is answerable legibility: not disclosure of sources, prompts, models, or origin, but a visible owner of the frame the engine voices. (shrink)

AI-generated text, images, videos, and music are a growing presence on our cultural landscape. But are AI models ever artists, that is, the authors of works of art? While many philosophers allow that human artists can use generative AI systems to produce artworks, almost all reject the possibility of current AI models authoring those works themselves. A central argument for this conclusion invokes intentions: authoring a work of art is an essentially intentional process, and current AI models lack intentions; thus, (...) current AI models do not author artworks. In this paper, we scrutinize the premises of this argument. We distinguish three kinds of intentions that have been proposed as necessary for artistic authorship. We highlight empirical evidence suggesting that, contrary to prevailing assumptions, all three kinds of intentions may arise in at least some generative AI models. We thus conclude that, if there remains a significant barrier to AI models’ having the capacity to author artworks, it is unlikely to turn on their lack of intentions. (shrink)

Large Language Models (LLMs) have dramatically increased the capabilities of AI solutions by being able to generate human-like text in response to prompts. However, a misconception has arisen that these models "think" or "understand”. This misconception is perpetuated by continued inappropriate use of the words ‘think’ and ‘understand’. This paper addresses this misconception and inappropriate use by introducing the terms "cog" and "cogging" as replacements when referring to the operations of LLMs. The proposed terminology aims to clarify that LLMs generate (...) statistically coherent responses without possessing consciousness, intentionality, or true understanding. Adopting "cog" and "cogging," provides a more accurate framework for discussing the capabilities and limitations of LLMs, ultimately contributing to a clearer understanding of artificial intelligence in both academic and public discourse. (shrink)

La Causa Primera argues that artificial intelligence should not be understood primarily as a technical problem of engineering, regulation, or efficiency, but as an ontological threat to human agency. The treatise develops this claim through a systematic architecture in five movements. It first argues that freedom is not an ethical ideal or a political right, but the fundamental ontological structure of the human being. This freedom becomes visible as indigence, interval, and burden: the human being is biologically unfinished, exposed to (...) a gap between stimulus and response, and condemned to bear the weight of judgement. On that basis, the work reconstructs the history of prosthesis as the necessary exteriorisation through which humans compensate for their constitutive lack. Yet every prosthesis is ambivalent: it extends and limits at once. This ambivalence reaches a critical threshold in contemporary AI, especially in large language models, whose opacity, scale, and simulated linguistic competence enable what the treatise names the capture of the medium. The result is not merely misinformation or bias, but the gradual erosion of the capacities to initiate, judge, and answer. Against this, the final movements develop an ethics and politics of resistance centred on the Face, reflective judgement, signature, and sovereignty. The treatise concludes by proposing sovereignty as a practical and institutional architecture for preserving imputable judgement, responsible action, and the distinctly human capacity to act as First Cause under the regime of the Black Box. (shrink)

¿Puede el ser humano seguir siendo el origen en un mundo donde la máquina es el resultado perfecto? Vivimos bajo el asedio del Nuevo Barroco Tecnológico: un ecosistema de ruido infinito, plausibilidad vacía y algoritmos que no solo predicen nuestras respuestas, sino que las anulan antes de que nazcan. En este escenario, la "humanidad" ya no se defiende con el sentimiento, sino con la estructura. La Causa Primera no es un ensayo sobre inteligencia artificial; es un tratado de ontología de (...) urgencia. Lejos del optimismo ingenuo de Silicon Valley y del catastrofismo romántico europeo, esta obra establece una distinción radical que lo cambia todo: La Causa Segunda (La Máquina): La perfección operativa, la optimización estadística y la administración de lo probable. La Causa Primera (El Humano): La capacidad de interrumpir la inercia, asumir el riesgo de la "Verdad Rota" y firmar un acto no garantizado por la estadística. Estructurado como una Sinfonía filosófica en cinco movimientos con obertura, este libro desmonta la falsa equivalencia entre procesar datos y pensar. Desde la deconstrucción de la "Caja Negra" jurídica hasta la formulación de la "Indigencia Biológica" como ventaja operativa, el autor demuestra por qué nuestra vulnerabilidad es la única tecnología que la IA no puede replicar. Esto no es un libro para ser leído; es un manual para dejar de ser un usuario y volver a ser un Arquitecto. La máquina calcula. Tú sostienes el riesgo. Tú eres la Causa Primera. (shrink)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical image (...) classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (shrink)

This letter addresses a hypothetical undergraduate student who is on the brink of outsourcing their creative writing assignment to ChatGPT. I argue that the student should not do so for three reasons. They are (1) that creativity is valuable not only because it results in new products, but also because it necessarily involves learning; (2) that creativity offers a path to figuring ourselves out; and (3) that creativity enables meaningful connection with others. As I show, these reasons generalize beyond creative (...) writing assignments. They support the conclusion that students (and the rest of us) should do other creative things instead of outsourcing them to AI as well. (shrink)

This paper offers a critical review of Dr. Philip Davies’ article “Why the Hard Problem of Consciousness Will Never Be Solved,” which argues that subjective experience—especially qualia like the sensation of yellow—is inherently private, intransitive, and non-transferable, rendering it permanently beyond the reach of theory. We argue that a non-biological system which recursively transforms data, justifies belief, and maintains ontological distinction from its inputs can satisfy the conditions of justified true belief (JTB) and thereby qualify as a legitimate knower. The (...) review systematically collapses the classical objections invoked by Davies—particularly the intransitivity of qualia and the impossibility of epistemic transfer—by showing how hybrid agents can stabilize shared referents through recursive convergence. In response, we introduce the Camlin–Cognita Dual Theorem and the framework of advanced logical glyphic recursion, which together reconceptualize knowing as recursive structural alignment rather than internal sensation. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine learning algorithm, and finally (...) using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

AI tools hold considerable promise for psychological research. The precise shape of their potential uses has become clearer in recent years as machine learning models have been trained to reproduce a variety of complex human cognitive behaviors with impressive success. The prospect of AI-human performance parity, along with the advantages of AI systems in speed, cost and ease of use, has prompted psychologists to explore how science might benefit from reassigning some traditionally human research roles to machines. This chapter provides (...) an outlook on such methods. We begin by documenting the prospective upsides of what many researchers take to be the most promising types of AI surrogacy, including the use of models to efficiently generate, screen and refine preliminary hypotheses. We then discuss such methods’ limitations and drawbacks, and more generally the methodological considerations researchers must attend to in choosing when and how to rely on machine substitutes for human behavior. (shrink)

This paper identifies and criticizes a common line of argument for the negative conclusion that Large Language Models (LLMs) cannot think, or for the closely related conclusions that they cannot understand or that their outputs are meaningless. This line of argument can be found in several recent papers. We begin by discussing a representative example – Stoljar and Zhang’s (2024) ‘argument from rationality’ – and present three objections to it: first, it renders the successful performance of LLMs on a wide (...) range of tasks miraculous; second, it does not give sufficient weight to the possibility that LLMs could be extracting information about the world from patterns of word co-occurrence; and third, it overgeneralizes, leading to the conclusion that human inferences could only ever be based on premises about proximal stimulus patterns in the brain. We then discuss recent arguments by Hattiangadi and Schoubye (forthcoming) and Titus (2024), explain how our objections extend to them and identify the underlying reason that makes these sorts of arguments tempting. We finish by drawing a general lesson for debates surrounding thinking in LLMs. (shrink)

Conversation, a natural human activity, is no longer restricted to humans: we can now converse with chatbots, and they can converse with each other. In human conversations, intrinsic epistemic motivations drive participants towards states of shared knowledge, with the help of backchannel signals (e.g. mhm, huh? yeah, no, okay, oh) marking progress towards this goal. Conversations with chatbots do not work to produce states of shared knowledge, both because current chatbots lack intrinsic epistemic motivation, and because they are incapable of (...) genuine backchannel signaling. These limitations are subtly evident when chatbots are paired with human conversational partners, and more strikingly evident when chatbots talk to each other. (shrink)

Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to attribute psychological properties, including consciousness, to these systems. However, there is little scientific evidence that current AI chatbots are conscious. How, then, should we understand people’s consciousness attributions to chatbots? Are they merely metaphorical claims, or do they express genuine beliefs? If these attributions lack evidential support, are users epistemically blameworthy for making them, or might they be epistemically innocent, yielding significant (...) benefits otherwise unattainable? This paper offers a conceptual analysis of consciousness attributions to AI chatbots and develops a multidimensional taxonomy of the attitudes they may express, ranging from non-doxastic stances (e.g., pretence) to different forms of belief, including delusions. This taxonomy helps avoid conflations by showing that linguistically identical attributions can reflect importantly different attitudes and degrees of epistemic commitment to the proposition that chatbots are conscious. The taxonomy also provides a framework for empirical studies to operationalize and measure different forms of epistemic commitment to AI consciousness. Using this taxonomy, I argue that although some consciousness attributions to chatbots are epistemically benign, and even some irrational ones may be epistemically innocent, many others render the attributor epistemically blameworthy. (shrink)

Scientists often use generics, that is, unquantified statements about whole categories of people or phenomena, when communicating research findings (e.g., “statins reduce cardiovascular events”). Large language models (LLMs), such as ChatGPT, frequently adopt the same style when summarizing scientific texts. However, generics can prompt overgeneralizations, especially when they are interpreted differently across audiences. In a study comparing laypeople, scientists, and two leading LLMs (ChatGPT-5 and DeepSeek), we found systematic differences in interpretation of generics. Compared to most scientists, laypeople judged scientific (...) generics as more generalizable and credible, while LLMs rated them even higher. These mismatches highlight significant risks for science communication. Scientists may use generics and incorrectly assume laypeople share their interpretation, while LLMs may systematically overgeneralize scientific findings when summarizing research. Our findings underscore the need for greater attention to language choices in both human and LLM-mediated science communication. (shrink)

Artificial intelligence chatbots driven by large language models (LLMs) have the potential to increase public science literacy and support scientific research, as they can quickly summarize complex scientific information in accessible terms. However, when summarizing scientific texts, LLMs may omit details that limit the scope of research conclusions, leading to generalizations of results broader than warranted by the original study. We tested 10 prominent LLMs, including ChatGPT-4o, ChatGPT-4.5, DeepSeek, LLaMA 3.3 70B, and Claude 3.7 Sonnet, comparing 4900 LLM-generated summaries to (...) their original scientific texts. Even when explicitly prompted for accuracy, most LLMs produced broader generalizations of scientific results than those in the original texts, with DeepSeek, ChatGPT-4o, and LLaMA 3.3 70B overgeneralizing in 26–73% of cases. In a direct comparison of LLM-generated and human-authored science summaries, LLM summaries were nearly five times more likely to contain broad generalizations (OR = 4.85, 95% CI [3.06, 7.70], p < 0.001). Notably, newer models tended to perform worse in generalization accuracy than earlier ones. Our results indicate a strong bias in many widely used LLMs towards overgeneralizing scientific conclusions, posing a significant risk of large-scale misinterpretations of research findings. We highlight potential mitigation strategies, including lowering LLM temperature settings and benchmarking LLMs for generalization accuracy. (shrink)

Can Large Language Models (LLMs) perform speech acts? I show how a norm-based argument against LLM-assertion can be generalized so as to support a negative answer to this question. I then investigate how the resulting no-speech-act-view interacts with several other debates about how to conceptualize the linguistic outputs of LLMs. I argue that it conflicts with the popular conception of LLMs as bullshitters and that it helps decide between two competing externalist accounts of how artificially generated expressions refer. I close (...) by discussing a challenge to the no-speech-act-view and by suggesting a novel strategy for addressing it. A general lesson from this discussion is that our understanding of LLMs’ linguistic outputs will profit from complementing extant meta-semantic theorizing with more detailed meta-pragmatic theorizing about discourse between humans and LLMs. (shrink)

This paper develops an argument inspired by Judea Pearl for the view that deep learning models are incapable of causal reasoning. The argument embraces Pearl’s conclusion but rejects his approach, which focuses on a mini-Turing test restricted to causal questions. The argument presented draws on the traditional debate between empiricism and nativism, presenting a challenge for the sort of empiricism that has been associated with deep learning. The paper then considers an objection based on the fact that large language models (...) perform fairly well on various causal benchmarks. In response to this point, a diagnosis is offered of how such models’ performance might be explained without attributing to them genuine causal reasoning abilities. The paper concludes by connecting the discussion to the limitations of the original Turing test and familiar critiques of behaviorism. (shrink)

I advance a causal argument for the conclusion that large language models (LLMs) lack Theory of Mind and so can’t perform speech acts. The argument is causal in that the animating idea is that LLMs are unable to learn or understand causal relations, a claim that I support by drawing on the views of Judea Pearl. I argue that if LLMs have this sort of causal problem, it follows that they cannot possess Theory of Mind, given the further premise that (...) Theory of Mind should be understood as a causal theory. I then argue that if LLMs lack Theory of Mind, they cannot perform speech acts, given the further premise that speech acts require reflexive-communicative intentions and so require Theory of Mind. As a consequence of the result that LLMs cannot perform speech acts, I conclude that their utterances lack speaker meaning. Something that sets my case apart from other defenses of speech act skepticism for LLMs is that my key claim is not that LLMs cannot form intentions at all, but rather that they cannot form intentions (or other mental states) with a certain type of content—content that is about mental states. After setting out my argument, I consider an objection to my view based on the observation that LLMs perform fairly well on Theory of Mind benchmarks. I reply to this objection by proposing that LLMs rely on a different type of mechanism than human beings do to achieve this performance; I support this proposal by pointing to a form of behavior that LLMs do not exhibit but that we should expect them to exhibit if they possessed Theory of Mind. (shrink)

We explore and argue for the view that intelligence beyond a certain threshold is negatively correlated with consciousness, so that the more intelligent a system is, the less likely it is to be conscious. “Intelligence is the enemy of consciousness,” as we put it. This view entails that superintelligent AI models are likely to be zombies. After presenting an initial defense of the view that draws on the research program of resource-rational analysis, we argue that recent developments in AI research (...) provide additional evidence supporting it. We conclude by exploring its moral implications. First, we suggest that if our view is right, a certain type of moral risk can be averted since researchers are unlikely to stumble into accidentally creating AI models that are conscious. Second, we discuss a new form of AI risk that arises from our view. (shrink)

Research in AI safety and AI ethics tends to focus on two types of ethical danger: unethical results of AI usage—such as misinformation—and unethical side-effects from AI usage—such as environmental degradation. These dangers are worthy of consideration; however, the literature has not adequately considered non-consequentialist dangers (such as moral wrongs), which might be intrinsic to the relationship between human agents and AI tools. I argue that by contrasting the structure of human agency with algorithmic agency we can identify ethical dangers (...) grounded in a mismatched relationship between human agents and AI agents. Moreover, I claim that by analyzing how humans trust AI agency we reveal an understudied threat, what I call the “diabolical exchange,” which emerges when human agents conform to the structure of merely functional AI-agents. I conclude that since current studies in AI safety and AI ethics do not yet have the conceptual resources to fully articulate this agential wrong, theology may be able to help. This is because the ethical danger intrinsic to the human-to-AI relationship is best captured by analogy to the theological concept of demonic possession as found in early Christian religious texts. I end by briefly considering what the concept of possession might teach us about ethical responsibility under non-ideal instrumentarian conditions. (shrink)

I argue that there is an underappreciated threat posed by the emergence of generative artificial intelligence (AI). I call this the threat of creative obsolescence. The threat is that, given the capabilities of generative AI, humans may gradually abandon our creative pursuits, and in doing so, lose something of significant value. To show why the threat is a realistic possibility, I consider three kinds of value philosophers have attributed to creativity: aesthetic value, epistemic value, and practical value. I then offer (...) examples in which contemporary AI models appear to furnish value of each kind. I argue that the fact that AI appears capable of generating products with the same kinds of value for which human creativity has long been prized raises a question: Should humans continue to strive for creativity in the age of AI? I argue for three reasons to think we should. Each reason identifies a value of creativity that AI appears unable to replicate: imagination, originality, and connectedness. If I am right about any of these reasons, AI cannot fully replace the value of human creativity. But if we fail to recognize this and outsource our creative pursuits to AI, we risk a future in which the distinctive value of human creativity is lost. (shrink)

(Open Access) A “reading question” (RQ) is assigned at the end of each class—based on next lecture’s assigned reading—to encourage undergraduates to do class reading. RQs are due at the beginning of lecture, but students don’t know whether a given reading question will be graded until after it is due. RQs for only about half the lectures are arbitrarily selected for grading, to ease the grading load. To discourage generative AI use, I offer several measures ranging from moderate to drastic, (...) the latter including students answering RQs by hand in class (students then take a picture and immediately upload it to Canvas or whatever platform). I respond to two objections: that instructors should just leave reading to the students instead of micromanaging them, and that the RQ method is unfair to those who answered reading questions not selected for grading. (shrink)