# created ```dataview LIST WITHOUT ID file.link FROM -"windsurf" WHERE file.cday = date(this.file.name) AND !startswith(file.folder, "windsurf") SORT file.cday ASC ``` # modified ```dataview LIST WITHOUT ID file.link FROM -"windsurf" WHERE file.mday = date(this.file.name) AND !startswith(file.folder, "windsurf") SORT file.mday ASC ``` --- # [[diary and thoughts]] #thought #diary --- # Plan for Section 5 of [[the aesthetics]] of llms paper #paper/environmentalaestheticsofai Here is the [[full plan]] for Section 5 in one place, verbatim. **Section 5: Plan** **5.1 The Objects of Appreciation (3 paragraphs)** **Paragraph 1** What are we appreciating when we appreciate LLMs? Not the model in abstraction, but specific episodes of generation—individual LLM‑mediated chats, particular outputs. These specific episodes, understood as “[[generative environments]],” are the objects of appreciation. **Paragraph 2** These specific episodes are appreciated as manifestations of a model’s [[characteristic generative order]]. The particular instantiates the general. This is analogous to appreciating a particular cliff face as a manifestation of geological processes that operate generally—the particular cliff shows us what those processes produce. **Paragraph 3** This framing—particular episodes manifesting general patterns—determines what kind of “story” we need. We need a story that makes visible the order in particular episodes by connecting them to the [[generative process]] that produces them. The story must provide categories that function as ways of reading. **5.2 [[The Question]] of Level (4 paragraphs)** **Paragraph 4** Different levels of description could in principle provide such a story. [[The question]] is which level provides categories that connect to what readers actually perceive—categories that can function as ways of reading, guiding acts of aspection. **Paragraph 5** The parallel to [[nature appreciation]] is instructive. Chemical physics could in principle provide a story for appreciating cliff faces—it explains the molecular interactions that constitute rock. But geological categories (strata, faults, erosion channels) are more useful because they map directly onto perceptible features. When we look at a cliff, we can see strata as horizontal bands, trace fault lines with our eyes, notice erosion channels cutting through the rock. These geological categories function as ways of looking—they guide perception. Chemical physics, while true, does not provide categories at this perceptual level. **Paragraph 6** Similarly for LLMs. Mechanistic interpretability could in principle provide a story—it explains how internal structures (attention heads, circuits, features) produce outputs. But its categories do not map onto what readers perceive when reading text. We do not perceive attention patterns; we perceive meaning, coherence, voice. Mechanistic interpretability, like chemical physics for cliffs, operates at a level disconnected from ordinary appreciation. **Paragraph 7** What we need is something between the technical level (too disconnected from reading) and the personal/intentional level (which Section 3 [[ruled out]] as misrepresenting [[what LLMs are]]). We need categories at a level that connect to textual patterns available to readers—patterns we actually perceive when we read. **5.3 The Semiotic Level (5 paragraphs)** **Paragraph 8** The relevant level is the semiotic level—[[the level]] of signs and their meaningful relationships. This is where reading operates. When we read LLM outputs, we perceive tokens combining into words, words into sentences, sentences into arguments or narratives. We perceive meaningful structure, not probability distributions or weight matrices. **Paragraph 9** Why “semiotic” rather than “semantic”? In philosophy of language, “semantic” typically implies truth‑conditional meaning—the relationship between expressions and what they refer to or represent. LLM outputs do not straightforwardly have semantics in this sense. They are not generated by representing reality but by statistical patterns over signs. “Semiotic” captures something more apt: tokens function as signs, carrying meaning through interpretation and through their relationships to other signs, not through reference to the world. **Paragraph 10** The term “semiotic” is used here in a minimal sense—to indicate [[the level]] of signs and their meaningful relationships—without committing to any particular semiotic theory. We need not engage with Saussure’s signifier/signified distinction, Peirce’s triadic sign theory, or Barthes’s cultural semiotics. The point is simply that the relevant level of description concerns signs and how they combine into meaningful structures. **Paragraph 11** The [[semiotic physics]] framework, developed in recent work on [[language models]], provides categories at this level. It describes LLM outputs in terms of the dynamics of sign‑production: how tokens are generated sequentially, how meaningful structures emerge from this process, how characters and voices arise within generated text. It provides vocabulary for both the order that appears (trajectories, emergent phenomena, simulacra) and the forces that produce this order. **Paragraph 12** This framework functions as a “story” in Carlson’s sense. It is general, applying to LLM outputs across different models and contexts. It is nonaesthetic—a descriptive and explanatory framework, not an aesthetic theory prescribing what is beautiful. It is nonartistic—not derived from art appreciation or artistic categories. Most importantly, it makes order visible and intelligible: it reveals patterns in outputs as products of identifiable processes, rather than as accidents, as magic, or as evidence of a designing mind. **5.4 The Order in LLM Outputs (5 paragraphs)** **Paragraph 13** What order appears at the semiotic level? The most general answer is: meaningful structure. LLM outputs exhibit coherence—text that hangs together grammatically, semantically, pragmatically. They exhibit consistency—patterns that persist across the output, voices that remain stable, topics that develop rather than scatter. They exhibit plausibility—text that seems like something that could have been written, that follows recognizable patterns of human language use. **Paragraph 14** This order takes various forms depending on the type of output. In narratives: plot structure, character consistency, thematic development, appropriate pacing. In explanations: argumentative structure, logical progression, appropriate qualification and hedging. In dialogue: turn‑taking patterns, responsiveness to what came before, maintenance of conversational coherence. In all cases: stylistic patterns, characteristic phrasings, recognizable registers and genres. **Paragraph 15** The crucial point—and what distinguishes this from design appreciation—is that this order is emergent. No one designed the specific coherence of a specific output. The engineers who built the model did not specify that this particular explanation should have this particular argumentative structure. The order arises from the generative process itself: from learned patterns operating under constraints, producing meaningful structure without anyone intending that specific structure. **Paragraph 16** This emergent order is perceptible to readers. We notice when text is coherent or incoherent, when arguments hang together or fall apart, when a voice is consistent or shifts unexpectedly, when a narrative develops satisfyingly or meanders pointlessly. The semiotic level is not an abstraction imposed by theory; it is the level at which we actually experience LLM outputs when we read them. **Paragraph 17** The shift from design appreciation to order appreciation is precisely the shift from asking “did the designer succeed in their intention?” to asking “what order appears and how did it arise?” For designed artefacts, the first question is appropriate—we assess the chair by whether it achieves what the designer intended. For LLM outputs, only the second question is appropriate. There is no designer of specific outputs, no intention for specific outputs to succeed or fail at. There is only emergent order—order that we can attend to, understand, and appreciate. **5.5 The Forces That Produce Order (6 paragraphs)** **Paragraph 18** If the order is emergent rather than designed, what produces it? The answer is: forces operating at the semiotic level. These forces shape the order without designing it, without intending any particular outcome. They are analogous to the geological, biological, and meteorological forces that shape natural environments—forces that produce order without planning it. **Paragraph 19** The first force is learned patterns of co‑occurrence. Through training on vast amounts of human‑generated text, the model has learned patterns of how signs combine. It has learned which tokens tend to follow which other tokens, in which contexts, in which genres and registers. It has learned patterns of how sentences follow sentences, how arguments are structured, how narratives unfold, how different voices sound. These learned patterns are statistical regularities—not rules explicitly programmed, but regularities extracted from the training distribution. When the model generates text, these patterns constrain what tokens are probable at each step, producing text that exhibits the coherence characteristic of the training data. **Paragraph 20** The second force is the autoregressive constraint. Each token is generated based on what came before—the prompt plus all previously generated tokens. The growing sequence constrains what can coherently follow. This creates path‑dependence: earlier tokens constrain later possibilities. If a topic is established in the first sentence, later sentences are constrained to relevance. If a character is introduced with certain traits, later text is constrained to consistency with those traits. The autoregressive constraint is a force for coherence and continuity—it ensures that text hangs together, that later parts are responsive to earlier parts. **Paragraph 21** The third force is stochastic sampling. At each step, the model produces a probability distribution over possible next tokens, and one token is sampled from this distribution. This introduces contingency into generation. The same prompt, run twice, will typically yield different outputs—different tokens sampled, different paths taken. The particular trajectory that results is not determined but is one path among many possible paths through the space of possibilities. This force produces variation and singularity: each output is a particular realization, a specific path that happened to be taken. **Paragraph 22** The fourth force is post‑training shaping. After initial training, models typically undergo further shaping through processes like reinforcement learning from human feedback (RLHF). This biases the model toward certain patterns—helpfulness, appropriate hedging, characteristic structures of explanation and refusal, avoidance of certain outputs. Post‑training produces what users recognize as a model’s characteristic “style” or “vibe”—the patterns that make one model feel different from another. Different models, with different post‑training regimes, have different characteristic patterns. **Paragraph 23** These four forces—learned patterns, autoregressive constraint, stochastic sampling, post‑training shaping—operate at the semiotic level. They are forces that shape the production of signs, that constrain and enable the emergence of meaningful structure. They are not intentions or plans; they do not aim at any particular outcome. They are dynamics and constraints that, operating together, produce the order we perceive in LLM outputs. **Paragraph 24** Understanding these forces is part of what makes the order intelligible. Without this understanding, the coherence of LLM outputs might seem magical or might be mistaken for evidence of a designing mind. With this understanding, we see coherence as the product of learned patterns operating under autoregressive constraints—as emergent order, not executed design. The forces are part of the “story” that makes order visible. **5.6 Key Concepts: Trajectories (4 paragraphs)** **Paragraph 25** The semiotic physics framework provides several key concepts that function as categories for appreciation. The first and most fundamental is the concept of a trajectory. A trajectory is the unfolding sequence of tokens generated through the autoregressive process—the text as it develops over time, each token depending on what came before, the sequence growing step by step until completion. **Paragraph 26** The trajectory is the basic unit of output for appreciation. We do not appreciate isolated tokens—a single token has no meaningful structure to appreciate. Nor do we appreciate the model in abstraction—the model is a generative process, not an object with appreciable features. What we appreciate are specific trajectories: particular sequences of generation that exhibit particular patterns of order. Each trajectory is a specific episode, a particular path through the space of what the model could generate. **Paragraph 27** The concept of trajectory highlights two important features of LLM outputs. First, path‑dependence: the trajectory accumulates meaning as it unfolds, with earlier parts constraining later parts. The coherence of the text is not a static property but a dynamic one—it is the coherence of a path, a sequence of steps each constrained by what came before. Second, contingency: the trajectory could have gone otherwise at any point. Each token is a branch point where a different sample would have produced a different continuation. The particular trajectory we are reading is one path among countless possible paths—a singular realization of the model’s generative potential. **Paragraph 28** This concept of trajectory differs from how we typically think about texts. We usually encounter texts as finished products, as static objects to be read. But for LLM outputs, attending to the trajectory—the unfolding sequence, the path taken—reveals something essential about the order. The text we are reading did not have to be this way; it emerged step by step through a process that could have gone differently at every step. Appreciating the trajectory means appreciating this particular path, this particular unfolding, as one realization among many possible. **5.7 Key Concepts: Emergent Phenomena (4 paragraphs)** **Paragraph 29** The second key concept is emergent phenomena—meaningful structures that arise within trajectories without being designed. The semiotic physics framework describes these as analogous to emergent phenomena in physical nature: just as stars and organisms emerge from physical processes without being designed, stories and characters emerge from the semiotic process without being designed. **Paragraph 30** Emergent phenomena in LLM outputs include structures at various scales. At the local scale: well‑formed sentences, appropriate word choices, coherent phrases. At the intermediate scale: paragraph structure, argumentative moves, narrative beats, turns in dialogue. At the global scale: overall argumentative structure, narrative arc, thematic development, consistency of voice across an entire output. All of these are meaningful structures that emerge from the generative process. **Paragraph 31** What makes these phenomena “emergent” is that they arise from the process without being specified in advance. No one designed the particular argumentative structure of a particular explanation generated by an LLM. The structure emerges from learned patterns of how arguments are typically structured, operating under the autoregressive constraint that each sentence must follow coherently from the last. The emergence is real—the structure is really there, perceptible to readers—but it is not the product of design. **Paragraph 32** The concept of emergent phenomena is crucial for appreciation because it allows us to attend to meaningful structure without attributing it to a designing mind. We can appreciate the elegance of an argument’s structure, the satisfying development of a narrative, the consistency of a voice—all while understanding these as emergent from the generative process. This is the core of order appreciation applied to LLMs: appreciating order as emergent rather than as designed. **5.8 Key Concepts: Simulacra (4 paragraphs)** **Paragraph 33** The third key concept is simulacra—characters, voices, personas that emerge within trajectories. The semiotic physics framework describes these as “dynamic representations of extremely complex, sometimes arguably intelligent entities” that “have trajectories of their own, distinct from the textual ones they supervene on.” Simulacra are a special case of emergent phenomena: emergent entities with character‑like properties. **Paragraph 34** Simulacra are not properties of the model itself. They are patterns that emerge in specific outputs, in specific trajectories. The “helpful assistant” that appears in a ChatGPT conversation is a simulacrum—a character‑pattern that emerges in that particular trajectory, shaped by the prompt, the conversation history, and the model’s learned patterns of how helpful assistants behave. A different prompt might yield a different simulacrum—a different voice, a different persona. The model is capable of generating countless different simulacra; none of them is the model itself. **Paragraph 35** This distinction between model and simulacra is crucial, and it connects directly to Section 3’s argument. We cannot appreciate LLMs as persons because they are not persons—they lack the temporal extension, the stable dispositions, the life history that person‑appreciation requires. But LLMs generate simulacra that have person‑like features: consistency of voice, characteristic responses, something like personality. We can appreciate these simulacra as emergent patterns—as phenomena that arise in specific trajectories—without making the mistake of attributing them to the model as properties of an underlying subject. **Paragraph 36** Simulacra have their own coherence and development within a trajectory. A character introduced early in a generated story will, if the generation is coherent, behave consistently later—responding in character, maintaining their voice, developing in ways consistent with their established traits. This consistency is emergent: it arises from the model’s learned patterns of how characters behave in stories, how voices are maintained, how personas respond. But it is real consistency, perceptible to readers, available for appreciation. **5.9 Acts of Aspection (7 paragraphs)** **Paragraph 37** These concepts—trajectory, emergent phenomena, simulacra—are not merely theoretical apparatus. They function as categories for appreciation, guiding what Carlson calls “acts of aspection”: the ways of attending to objects that constitute appreciation. Just as geological categories guide how we look at cliff faces (which bands to treat as distinct strata, which lines to trace as faults), these semiotic categories guide how we read LLM outputs. **Paragraph 38** Attending to trajectory means reading with awareness of the text’s temporal structure—its character as an unfolding sequence. It means noticing how the text develops: how earlier parts set up later parts, how themes are introduced and developed, where transitions occur, how the argument or narrative progresses. It means appreciating the particular path taken—this sequence of tokens, this development of meaning—while being aware that different paths were possible. Attending to trajectory is reading with sensitivity to development, to progression, to the way meaning accumulates over the course of the text. **Paragraph 39** Attending to emergent order means noticing coherence, consistency, and structure while understanding these as emergent rather than designed. It means appreciating how an argument hangs together—its logical structure, its use of evidence, its qualifications—without asking whether an author succeeded in their intention. It means seeing the order as a product of forces (learned patterns, autoregressive constraints) rather than as evidence of a planning mind. Attending to emergent order is reading that appreciates structure without attributing it to a designer. **Paragraph 40** Attending to simulacra means noticing the voices, characters, and personas that emerge in text while maintaining the distinction between simulacra and model. It means appreciating consistency of voice—the way a particular persona responds, the characteristic patterns of a particular character—while understanding this consistency as an emergent pattern in this trajectory, not as a property of an underlying subject. It means being able to appreciate character‑like phenomena without personifying the system that generates them. **Paragraph 41** Attending to characteristic patterns means noticing what is typical of a particular model—its characteristic style, its typical moves, its recognizable ways of handling uncertainty or structuring explanations. Different models have different characteristic patterns, different “vibes,” and appreciating a particular output includes recognizing it as a manifestation of this model’s characteristic generative order. Attending to characteristic patterns is reading that sees the particular as an instance of the general—this output as a manifestation of how this model typically generates. **Paragraph 42** Attending to contingency means maintaining awareness that the text could have gone otherwise. It means noticing moments where the trajectory takes a surprising turn, where an unexpected word choice opens new possibilities, where the path diverges from what might have been predicted. It means appreciating the singularity of this particular output—this specific realization of the model’s generative potential, this path through possibility space that will never be exactly repeated. Attending to contingency is reading that appreciates the particular as particular, as one actualization among many possible. **Paragraph 43** These five acts of aspection—attending to trajectory, emergent order, simulacra, characteristic patterns, and contingency—are not separate activities performed in sequence. They are aspects of a unified appreciative stance, different dimensions of attention that together constitute appreciation of LLM outputs. When we appreciate an LLM‑generated text using this framework, we are simultaneously following its trajectory, noticing its emergent order, attending to its simulacra, recognizing its characteristic patterns, and appreciating its contingency. The acts are analytically distinguishable but practically integrated. **5.10 The Relationship Between Knowledge and Perception (4 paragraphs)** **Paragraph 44** A question might arise at this point: how does background knowledge about LLMs connect to the actual experience of reading their outputs? Carlson faces a similar question regarding nature appreciation: how does scientific knowledge connect to perceptual experience of landscapes? His answer is that knowledge shapes perception—it determines what we notice, what we attend to, what patterns become visible. **Paragraph 45** The same applies here. Knowledge of the generative process—understanding that text is produced token by token, that coherence emerges from learned patterns, that simulacra are emergent rather than properties of a subject—shapes how we read. It does not replace the experience of reading with theoretical abstraction; it informs the experience, making certain patterns visible that might otherwise be invisible or misunderstood. **Paragraph 46** Without this knowledge, a reader might experience LLM outputs as simply “text”—to be evaluated for accuracy, helpfulness, or style, much as one might evaluate any piece of writing. Or they might experience outputs as the utterances of a subject—attributing the coherence to a mind, the voice to a personality, the arguments to beliefs. The semiotic framework provides an alternative: experiencing outputs as emergent order in a generative environment, appreciating the patterns that arise without misattributing their source. **Paragraph 47** This is not to say that appreciation requires constant conscious attention to the theoretical framework. Just as a geologist can appreciate a cliff face without consciously rehearsing geological theory, a reader informed by the semiotic framework can appreciate LLM outputs without constantly thinking about autoregressive generation. The knowledge becomes background—it shapes perception without dominating conscious attention. The concepts become ways of seeing, or rather ways of reading, that inform appreciation without replacing it with analysis. **5.11 The Particular and the General (3 paragraphs)** **Paragraph 48** One more dimension of this framework deserves explicit attention: the relationship between particular outputs and general patterns. Each LLM output is singular—a specific trajectory, a particular path through possibility space, never to be exactly repeated. Yet each output is also a manifestation of general patterns—the model’s learned regularities, its characteristic style, the dynamics of autoregressive generation. **Paragraph 49** Appreciation involves both dimensions. We appreciate the particular output in its particularity—this specific development, this specific voice, this specific argument. But we also appreciate it as a manifestation of the general—as an instance of how this model generates, as an example of what emerges from these dynamics. The particular and the general are not in tension; they are two aspects of the same appreciation. We appreciate this cliff face both as this particular configuration of rock and as a manifestation of geological processes operating over millennia. **Paragraph 50** This dual attention—to particular and general—is what makes the framework genuinely appreciative rather than merely analytical. Pure analysis might attend only to the general: what patterns does this output exemplify? Pure aesthetic response might attend only to the particular: what is this specific text like? Appreciation, as Carlson understands it, involves both: attending to the particular as a manifestation of the general, understanding the general through attention to the particular. **5.12 Synthesis and Transition (3 paragraphs)** **Paragraph 51** The framework for appreciating LLMs can now be stated in full. The objects of appreciation are specific episodes of generation—individual trajectories, particular outputs—understood as generative environments in which order emerges. The order we appreciate is emergent meaningful structure at the semiotic level: coherence, consistency, narrative and argumentative structure, simulacra, stylistic patterns. This order is produced by forces operating at the semiotic level: learned patterns of co‑occurrence, the autoregressive constraint, stochastic sampling, and post‑training shaping. **Paragraph 52** The story that makes this order visible and intelligible is semiotic physics—a framework providing concepts (trajectory, emergent phenomena, simulacra) that function as categories for appreciation. These concepts guide acts of aspection: attending to trajectory, emergent order, simulacra, characteristic patterns, and contingency. Together, these acts constitute appreciation of LLM outputs as manifestations of a model’s characteristic generative order. **Paragraph 53** This framework enables appreciation that is neither personifying nor merely technical. It does not treat the model as a subject with intentions and a life history; Section 3 showed why that approach fails. It does not reduce appreciation to analysis of computational mechanisms disconnected from the experience of reading. It operates at the semiotic level—the level of signs and meaningful relationships—where appreciation of text actually occurs. The following section will demonstrate this framework in practice, showing how the concepts and acts of aspection apply to specific examples of LLM‑generated text.