# 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 --- # Notes # Doctor Prescription Re-Up Template Gentile Dott. de Ruberto, Il 02/10/2024 mi ha prescritto venlafaxina e lamotrigina (allego la documentazione). Potrebbe fornirmi un'altra prescrizione per entrambi i farmaci? il mio codice fiscale è YNGNHL81R01Z114A Cordiali saluti, Nicholas Young # hsbc IB1417924780 897432 # Draft/Plan 20 May 2025 #draft #paper/generatingphilosophy ## 1. Introduction > “Forty-two," said Deep Thought, with infinite majesty and calm. > It was a long time before anyone spoke. > Out of the corner of his eye Phouchg could see the sea of tense expectant faces down in the square outside. > "We're going to get lynched aren't we?" he whispered. > "It was a tough assignment," said Deep Thought mildly. > "Forty-two\!" yelled Loonquawl. "Is that all you've got to show for seven and a half million years' work?" > "I checked it very thoroughly," said the computer, "and that quite definitely is the answer. I think the problem, to be quite honest with you, is that you've never actually known what the question is." \-- Douglas Adams, *The Hitchhiker's Guide to the Galaxy* In *The Hitchhiker's Guide to the Galaxy*, humanity asks an AI to do some philosophy. An computer named Deep Thought is constructed and instructed to provide "The Answer to the Ultimate Question of Life, the Universe, and Everything" (REF). Humanity builds this computer only to receive the answer '42'—an answer which, while apparently correct (at least according to Deep Thought), means next to nothing at all due to humanity’s failure to know what the Ultimate Question in fact is. In 2025 we are in a position to think about the relationship between philosophy and AI *for real*. Can LLMs enhance philosophical understanding? They are happy to dispense philosophical wisdom if we ask them to, but should we listen? I suspect many would doubt that we should, notoriously prone to 'hallucinations', and the banal, hyperbolic essays churned out by ChatGPT have become the scourge of undergraduate teaching. In this paper, I argue for cautious optimism: the creators of such systems sometimes describe them as *reasoning engines*, and I suggest this description is broadly accurate. Even if LLMs do not truly reason, their ability to _simulate_ human reasoning enables them to enhance the philosophical understanding of their users - Can an LLM generate good quality philosophy? - good quality = roughly, publishable in a good quality journal - - The paper argues that an LLM could achieve this with minimal architectural changes from existing reasoning systems. The main adaptation would be a new evaluation loop designed to reward philosophical virtues. - To develop this argument, the paper will: - Define "good quality philosophy" through abduction and theoretical virtues. - Analyse DeepSeek-Prover-V2 as an example of advanced AI reasoning. - Introduce 'PhiloSeeker,' a model for AI philosophy, covering its design, training, evaluation, and potential objections. - Conclude with a summary of architectural needs. - The overall aim is to explore how LLMs might be trained to produce high-quality philosophy by targeting these virtues. ## 2. Clarifying the Scope: Producing vs. Doing Philosophy - This paper focuses on whether an LLM can _produce outputs_ that constitute good quality philosophy. This question is distinct from, and less demanding than, asking whether LLMs can 'do philosophy' in the way humans do, whether they can think, understand, or perform genuine speech acts like assertion. - The idea that LLM outputs can increase understanding, or be philosophically valuable, might be met with the objection that these systems do not themselves understand anything. They might be seen as "stochastic parrots," merely arranging words in statistically plausible patterns. - While current LLM architecture differs significantly from the human mind, we should not assume that human-like reasoning or understanding is a prerequisite for producing philosophically illuminating outputs. - Drawing on Butlin and Viebahn, it is plausible that fine-tuned LLMs can produce outputs with a "descriptive function." This is defined as "the function of conveying information to an observer or consumer system, so as to cause the consumer to behave as though some condition holds." - Pre-trained LLMs primarily predict likely word sequences. Fine-tuning, however, involves additional training aimed at producing outputs that are accurate, relevant, and informative, often by rewarding outputs consistent with external sources or positive human evaluations. - This fine-tuning process can equip LLM outputs with a descriptive function, making them reliable enough to help users refine their understanding of the world, analogous to how a thermometer is designed to indicate temperature. - Therefore, even if an LLM itself lacks understanding and is not performing speech acts, if its outputs are a product of fine-tuning for accuracy and informativeness (i.e., they have a descriptive function), they might provide correct or insightful philosophical claims. - Such outputs, if they possess the relevant theoretical virtues, could be considered "good quality philosophy" in terms of their content and structure, regardless of the internal states or processes of the LLM. This paper examines the potential for producing such outputs. ## 3. Conceptual Foundations: Abduction and the Systematisation of Theoretical Virtues - This section establishes the philosophical framework for the paper. - **Abductive Reasoning (following Timothy Williamson)**: - Defined as inference to the best explanation. - Williamson argues for its importance not only in natural sciences but also in mathematics and philosophy. - Theories are chosen not simply for logical entailment of evidence, but for their overall explanatory ability. - An example from mathematics: Williamson (REF) argues that even basic mathematical principles (e.g., axioms of set theory like ZFC) are supported by abductive reasoning. ZFC is accepted because it successfully and powerfully organises mathematical findings elegantly, not because its axioms are all self-evidently proven from more basic steps. - This supports the use of abduction in "armchair" disciplines like philosophy, suggesting they share a core method with empirical fields. Philosophical theories are seen as attempts to offer the best explanation for observations, conceptual differences, or intuitions. - Philosophical model-building (Williamson 2017a) is presented as a clear example of abduction in action, where preference for simpler/elegant models is justified abductively for clearer explanations and avoiding over-fitting. - **Keas's (2018) Systematisation of Theoretical Virtues**: - While Williamson identifies overall explanatory ability as key, his account does not focus extensively on a systematic breakdown of what constitutes such ability. He does mention features such as "simplicity, elegance, strength – meaning how informative and general a theory is – and its power to unify different ideas." However, a more detailed and organised understanding of what makes an explanation "best" or "virtuous" is needed for designing an LLM. Williamson's account can be _supplemented_ by Keas's research, which offers a detailed classification of theoretical virtues. - This provides a richer vocabulary for what an abductive philosophical inquiry (or an LLM emulating it) might aim for. - Keas categorises numerous theoretical virtues into four main groups: - **Evidential Virtues**: Concerning how well a theory explains evidence (e.g., _explanatory depth_, _evidential accuracy_). - **Coherential Virtues**: Relating to a theory's internal logical soundness and its fit with other knowledge (e.g., _internal coherence_, _universal coherence_). - **Aesthetic Virtues**: Pertaining to qualities like elegance and simplicity (e.g., _simplicity_, _unification_). - **Diachronic Virtues:** Concerning how a theory performs and changes over time (e.g., durability, fruitfulness). This structured taxonomy offers a comprehensive set of standards for evaluating theories. - The paper argues that Williamson’s abduction and Keas’s classification are mutually supportive. Keas articulates the standards implicit in Williamson's "best" explanation. For instance, Williamson's "simplicity" and "elegance" map to Keas's aesthetic virtues; Williamson's "strength" or "unificatory capacity" are expanded by Keas's unification and aspects of explanatory depth. - This combined understanding (philosophical thinking as abductive activity, aiming for virtues detailed by Keas) is posited as something that can, in principle, be implemented in an LLM. [add delsin, stuff here.] ## 4. The Architecture of DeepSeek-Prover-V2 (DSP-v2): An Analogous Case Study - To assess the feasibility of an LLM generating good quality philosophy, this section introduces DeepSeek-Prover-V2 (DSP-v2). DSP-v2 is not presented merely as a recent LLM advancement, but as an analogous case study for several specific reasons: - **Demonstrating Capacity for Complex Reasoning**: DSP-v2's success in formal mathematical theorem proving shows that current LLMs can perform complex, goal-directed, and highly structured reasoning. This establishes a baseline capability relevant to the demands of philosophical argumentation. - **Illustrating Operationalisable Abduction-like Processes**: The way DSP-v2 generates high-level proof sketches and then decomposes these into verifiable sub-steps provides a model for how abductive-like reasoning (forming a hypothesis and then testing its components) can be implemented in an LLM. - **Showing Implicit "Virtue-Seeking" in a Formal Domain**: DSP-v2's training, particularly its use of a consistency reward, implicitly guides it towards outputs that exhibit formal analogues of theoretical virtues, such as internal coherence with an initial plan and logical consistency. This suggests that LLMs can be oriented towards desirable output characteristics beyond mere statistical likelihood. - **Providing an Architectural and Conceptual Precedent**: The specific architecture of DSP-v2 (e.g., hierarchical decomposition, feedback loops based on verification) offers a concrete starting point for conceptualising PhiloSeeker. It makes the proposal for adapting such principles to the less formal domain of philosophy appear as a plausible extension of existing AI methodologies. - **DSP-v2 Overview**: - An open-source LLM for formal theorem proving in Lean 4 (Ren et al. 2025). - Objective: Generate formal mathematical proofs, linking informal intuition to formal verification. - **Core Strategy**: Hierarchical decomposition, guided by informal reasoning. - A general LLM (DeepSeek-V3) generates a natural language ‘proof sketch’. - This sketch acts as a blueprint for decomposing the problem into formal subgoals (lemmas). - **Proof Generation Process**: - The sketch is translated into formal Lean 4 statements (e.g., `have` clauses with `sorry` placeholders). - Subgoals are tackled individually by a specialised prover model (e.g., a 7B parameter model mentioned in their research). - Successful sub-proofs are synthesised into a complete, verified proof. - **Learning and Refinement Mechanisms**: - **‘Cold-start’ reasoning data**: Pairs successful informal sketches with their verified formal proofs for training. - **[[Reinforcement Learning]] (RL)**: - Primary reward: Binary (correct/incorrect Lean verification). - ‘Consistency reward’: Encourages structural alignment between the final proof and the initial sketch. - **Expert Iteration**: Incorporates successfully generated and verified proofs into the supervised fine-tuning (SFT) dataset. - **Operational Modes**: - **‘non-CoT’ (non-Chain-of-Thought) mode**: For rapid, concise formal proof generation. - **‘CoT’ (Chain-of-Thought) mode**: Articulates intermediate reasoning steps in natural language for transparency. - These architectural and learning features of DSP-v2 provide a concrete foundation for considering how similar principles might be adapted for the distinct challenges of philosophical reasoning, as explored in the following sections. ## 5. DSP-v2: Operationalising Abduction and Implicitly Seeking Theoretical Virtues - This section analyses DSP-v2's operational processes, as described in Section 4, through the conceptual lens of abduction and theoretical virtues (from Section 3). The aim is to demonstrate how its mechanisms can be interpreted as formal analogues of these philosophical concepts. - **DSP-v2's Core Methodology as an Analogue to Abductive Process**: - The generation of an initial informal proof sketch by DeepSeek-V3 is analogous to the abductive step of formulating a high-level hypothesis or potential explanation (i.e., "This theorem is true, and this sequence of major steps offers a plausible way to demonstrate its truth"). - The subsequent decomposition of this sketch into formal subgoals, and the systematic attempt to prove each one, mirrors the elaboration and rigorous testing of an initial abductive hypothesis. Each successfully proven subgoal lends support to the overarching sketch. - A fully verified proof, therefore, confirms the initial sketch as a "good" explanation or a successful strategic outline within the formal system. - **Through its training (RL and expert iteration), DSP-v2 learns to generate sketches that are more likely to lead to successful proofs, akin to an abductive reasoner learning to propose better, more tractable hypotheses.** - [the above is wrong] - [Enrico says theorems to be proved by proofs, corresponds to hypothesis supported by arguments] - **DSP-v2's Architecture and Learning Mechanisms as Implicitly Seeking Formal Analogues of Theoretical Virtues**: - **Evidential Accuracy and Internal Consistency (Formal Analogues)**: The Lean 4 proof assistant acts as an objective arbiter. Any proof verified by DSP-v2 is, by definition, formally correct (analogous to ‘evidential accuracy’ relative to the axioms of the formal system) and internally consistent (containing no detectable logical contradictions). These are non-negotiable requirements of its operational environment. - **Internal Coherence (Formal Analogue)**: The ‘consistency reward’ in DSP-v2’s RL phase directly promotes a formal counterpart to this virtue. By rewarding proofs that maintain structural alignment with the initial lemma decomposition, the system is encouraged to produce outputs where sub-proofs fit together cohesively and follow the strategic plan. This is analogous to _internal coherence_, which values the integrated and non-ad hoc arrangement of a theory's components. - **Simplicity (Formal Analogue)**: The ‘non-CoT’ mode, optimised for concise formal proofs, aims for a form of syntactic or structural simplicity—achieving the deductive goal with less complex formal output. This parallels the theoretical virtue of _simplicity_. - **Clarity and Explanatory Depth (Formal Analogues)**: DSP-v2’s ‘CoT’ mode, by explicitly articulating intermediate reasoning steps in natural language alongside formal code, enhances transparency and understandability. This provides a form of ‘explanatory depth’ by revealing the inferential structure and rationale, analogous to the philosophical desire for clear and insightful explanations. - **Learning from "Virtuous" Examples**: Through its training on "cold-start" data (its own successfully verified proofs, which are inherently accurate, consistent, and often coherent due to the consistency reward), DSP-v2 learns from exemplars that already possess these desirable formal characteristics. - In essence, while DSP-v2 does not engage in philosophical deliberation, its design illustrates how an LLM can be structured to navigate a complex reasoning space using abduction-like strategies and be guided by feedback mechanisms that implicitly select for outputs possessing characteristics analogous to theoretical virtues. This analysis provides the crucial groundwork for proposing that similar principles can be adapted for the philosophical domain in the PhiloSeeker model. ## 6. PhiloSeeker: A Hypothetical Adaptation of DSP-v2 for Virtuous Philosophy - This section proposes 'PhiloSeeker,' a hypothetical LLM adapted from DSP-v2's principles to aim for theoretically virtuous philosophical outputs. - **Objective and Domain**: - Primary Objective: Generate philosophical texts (theories, arguments, conceptual analyses, responses to problems) that are cogent, internally consistent, and exhibit high theoretical virtue (as per Keas, 2018). - [just say 'arguments'] - Operational Domain: Natural language philosophical discourse, with its nuances and lack of formal verifiability (unlike Lean 4). - **Core Strategy (adapting DSP-v2's hierarchical decomposition)**: - A sophisticated general LLM, ‘PhiloSketcher’ (analogous to DeepSeek-V3), would produce an initial high-level ‘theory sketch’ or ‘argumentative blueprint’ in response to a philosophical problem/prompt. [this needs to be worked out a lot more carefully] - This blueprint would articulate main claims, define [[key concepts]], outline sub-argument structures, and potentially flag objections or areas needing clarification, decomposing the task. - **Process of Philosophical Construction (mirroring DSP-v2's sketch-to-subgoal translation)**: - ‘PhiloSketcher’ translates its blueprint into a structured outline (assertions, questions, tasks with placeholders for elaboration). - These individual philosophical sub-tasks (e.g., "provide three arguments for claim X," "define '[[phenomenal consciousness]]'," "formulate response to Gettier problem") would be processed by a more specialised ‘[[Philosophical Argumentation]] Model’ (analogous to DSP-v2’s smaller prover model). [some of this wrong, needs to be tightened up considerably] - ['provide an argument' seems to be the best way to go] - **Adapted Learning and Refinement Mechanisms**: - **‘[[Synthetic data]] for a philosophical cold start’**: - When PhiloSeeker successfully constructs a complete philosophical theory/argument deemed highly virtuous by evaluators (human or AI), this entire output is paired with the initial argumentative blueprint. - This creates training examples linking high-level philosophical strategy with detailed, virtuous execution in natural language. - **[[Reinforcement Learning]] (RL)**: - Central to development, but with a different reward structure than DSP-v2's binary reward. - Guided by a composite ‘virtue score’ derived from evaluating the text against Keas’s theoretical virtues (via automated metrics and [[human feedback]], detailed in §7). - A critical component: a ‘blueprint-consistency reward’ (paralleling DSP-v2) to incentivise the output to faithfully instantiate the initial sketch, promoting internal coherence. - **Expert Iteration (adapted)**: - PhiloSeeker learns from its own most ‘virtuous’ outputs (those receiving high ratings from human evaluators or a virtue-discriminator model). - These are incorporated back into its training set. - **Dual Generation Modes (similar to DSP-v2)**: - **‘non-CoT’ mode**: Might produce concise, polished philosophical essays or arguments. - **‘CoT’ mode**: Would explicitly articulate the underlying argumentative blueprint, rationale, definitions, argument development, counter-argument consideration, and connections between theory parts, enhancing transparency and scrutiny. - The section suggests DSP-v2's core architectural loop (sketch, decompose, solve, synthesise, refine) is robustly applicable to philosophy. The primary adaptation lies in [[the nature]] of the ‘solved state’ (philosophically virtuous output) and the evaluation/reward mechanisms. ## 7. Training and Evaluating PhiloSeeker - This section details methods for training and evaluating PhiloSeeker, emphasising [[operationalising theoretical virtues]] (Keas 2018) as the guiding principle. - **Core Training Loop Adaptations from DSP-v2**: - **Initial Data Seeding**: - A substantial corpus of high-quality philosophical texts, selected for philosophical significance and exemplification of virtues (clarity, rigour, coherence, etc.). - Corpus might require annotation (human or LLM-driven) to highlight argumentative structures, definitions, or virtue instantiations for initial supervised fine-tuning of ‘PhiloSketcher’ and ‘[[Philosophical Argumentation]] Model’. - **‘Cold-start’ Data Generation (Philosophical)**: - When PhiloSeeker produces a complete philosophical work (essay, theory fragment) from a blueprint, and it is deemed highly virtuous by the evaluation framework, this blueprint-to-output sequence becomes a training instance. - **Curriculum Learning**: - ‘PhiloSketcher’ could generate numerous smaller, focused [[philosophical tasks]] (e.g., "define '[[free will]]' compatibly with determinism," "formulate three objections to utilitarianism"). - These provide denser, targeted training signals for the ‘[[Philosophical Argumentation]] Model’. - **Expert Iteration (Philosopher-in-the-loop refinement)**: - PhiloSeeker learns from its own generated philosophical outputs that achieve a high ‘virtue score’ (determined by the evaluation process). These are added back to [[the training]] set. - **[[Reinforcement Learning]] (RL) with a ‘Virtue Score’**: - This is the most critical adaptation from DSP-v2's binary reward. The score is multi-faceted, derived from Keas’s taxonomy. - **Operationalising Evidential Virtues**: Rewards for consistency with specified empirical data/scientific theories (if prompt demands), or accurate representation/citation of texts. Judging ‘causal adequacy’ or ‘explanatory depth’ of novel claims would likely initially require significant human feedback to train a discriminator. - **Operationalising Coherential Virtues**: NLP tools for detecting overt logical contradictions or inconsistent terminology (for ‘internal consistency’). The ‘blueprint-alignment reward’ strongly promotes ‘internal coherence’. ‘Universal coherence’ could be partially checked against a curated knowledge base. - **Operationalising Aesthetic Virtues**: ‘Beauty’ would likely rely on human ratings to train a discriminator. ‘Simplicity’ might use proxy metrics (ontological parsimony via entity counts; conceptual parsimony via undefined primitive terms; syntactic simplicity via conciseness relative to scope). ‘Unification’ rewarded for connecting disparate problems or deriving many consequences from few principles. - **Operationalising Diachronic Virtues**: ‘Durability’ tested by subjecting theories to ‘objection-bot’ LLMs. ‘Fruitfulness’ might be rewarded for theories identifying unresolved questions or suggesting novel lines of inquiry. - Balancing these virtues would likely require multi-objective RL with careful weighting. - **Hybrid Evaluation Framework (in absence of formal checker)**: - **Automated proxy metrics**: First-pass signal for certain virtues (consistency, some simplicity forms). - **Human expert evaluation**: Indispensable for qualitative virtues (explanatory depth, beauty, fruitfulness, plausibility). Panels of philosophers with diverse specialisations rating outputs on virtue dimensions using clear rubrics and inter-rater reliability checks. - **RLHF (Reinforcement Learning from Human Feedback)** techniques: Iterative sampling and focused feedback. - **‘Virtue-discriminator’ LLM**: Trained on human ratings to serve as a scalable proxy for direct human judgment, guiding RL and flagging outputs for human review, with periodic recalibration. ## 8. Potential Objections and Pluralism - This section considers potential objections to the PhiloSeeker project, as outlined in the original draft. - **Objection: Need for a unique human 'creative spark'**: - The paper's proposal of a system generating complex, virtuous philosophical content implicitly challenges this. - **Objection: Risk of homogenising philosophical thought**: - The draft does not detail specific safeguards in this section, but the broader emphasis on diverse virtues and training data might be relevant. - **Objection: Subjectivity of aesthetic virtues**: - The paper proposes addressing this by using human ratings to train discriminator models for these virtues. - **Safeguard Mentioned: Universal coherence with empirical science**: - This suggests a grounding mechanism to ensure generated theories align with established knowledge where applicable. ## 9. Summary of Architectural Adaptations - This section summarises the key architectural changes proposed for transitioning an LLM from mathematical proof generation to philosophical theory generation. - **Core LLM Architecture Largely Retained**: - GPT-style models. - Hierarchical prompting strategies. - Reinforcement learning (RL) infrastructure. - **Critical Swap**: - The Lean-verification filter (used in DSP-v2) is replaced with a 'philosophical virtue-evaluation module.' - **Additions for the New Evaluation Module**: - A virtue-discriminator LLM (trained on human judgments of philosophical virtue). - A human rating pipeline. - Potentially 'objection-bots' to test theory durability. - **Retuned RL Rewards**: - Shift from binary correctness to multi-objective optimisation for Keas's theoretical virtues. - Maintenance of a blueprint-consistency reward to ensure coherence with the initial plan. ## 10. Conclusion - The paper reiterates its main argument: few fundamental architectural changes are needed to transition from mathematical proof generation to philosophical theory generation. - **Primary Adaptation**: The replacement of a formal verifier with a complex system for evaluating philosophical virtue. This evaluation system is central to the PhiloSeeker concept. - **Challenges of Philosophical Language**: - The paper acknowledges that the nuances, ambiguities, and lack of formal verifiability in philosophical language present challenges. - However, these are framed as primarily affecting training data creation and evaluation mechanisms, not necessarily posing insurmountable obstacles for the core LLM architecture. - **Prospect of 'PhiloSeeker'**: - Presented as a conceptually coherent and challenging, yet potentially near-term, research project. - **Broader Contribution**: - The endeavour to build such a system would not only advance AI capabilities but also contribute to a deeper and more operational understanding of philosophical virtue itself, by necessitating the clear articulation and implementation of criteria for what constitutes good philosophical work.