# I want to ask you something about my generating philosophy project and the end goal is going to b... ## Retrieval Notes - Session id: `019cd21c-33d7-7ff2-acca-1cb98779467f` - Source: `Codex raw session` - Last activity: `2026-03-09T10:30:52.729Z` - Model: `gpt-5.4` - CWD: `/Users/nickyoung` ## My Notes <!-- Add your notes here. This section is preserved across syncs. --> ## Conversation ### User I want to ask you something about my generating philosophy project and the end goal is going to be for you to write me a deep research prompt that I can put into gem. The question is this, okay, so I'm interested in the possibility that LLMs can do philosophy. And I as a user use them to do philosophy with all the time. They have certainly got better at doing philosophy. Okay? The different models, the more recent models are better than the older models. Okay, so this might be a difficult question, but what I want to know is what is it that makes a base model better? Okay, what is what separates Opus 3 from Opus 4.6, for example? What have they done differently? Please note I am not asking you to talk about extended reasoning specifically okay and talking more about the base model and about what that does what they can do to these things that have made them better at doing these sorts of problems over time okay this is a question about the technicalities of LLMs okay so and that's what I want I don't want you to sugarcoat it or tell me something childish I want the real answer. The Ferrija technical details of how these systems work. ### Assistant I’m loading the current project context first so I can answer from your vault rather than from stale assumptions: last two daily notes, tasks state, and the `generating philosophy` session file, then I’ll ground the technical answer in current source material on LLM training differences. *Sto ricostruendo il contesto del progetto prima di affrontare la questione tecnica sui modelli di base.* ### Assistant The vault paths in the instructions aren’t at `/Users/nickyoung` directly, so I’m locating the actual vault root and then I’ll reopen the daily notes, project session, and task note from there. *Sto correggendo i percorsi del vault prima di leggere le note rilevanti.* ### User " The Ferrija technical details of how these systems work." that was a transcription error. I meant REAL technical details. ### Assistant I’m treating that as “real technical details,” not a simplified overview. I’m finding the live vault root now so I can pull the generating-philosophy context before I answer and before I draft the Gem research prompt. *Cerco il vault giusto per ancorare la risposta ai tuoi file di progetto.* ### Assistant I’ve got the vault root and the project session file. Next I’m reading the last two daily notes, the freshly synced tasks note, and the Generating Philosophy session, then I’ll pull current technical sources on model improvements. *Adesso leggo le note recenti e il contesto del progetto prima di passare alla parte tecnica.* ### Assistant I’ve got the project context: the active thread in your tasks is already close to this question, and the session note confirms the paper needs the technical side grounded rather than hand-waved. I’m now checking primary model-report sources so I can separate what companies actually disclose from what we have to infer from the scaling literature. *Ora passo alle fonti primarie sui modelli per distinguere i fatti dichiarati dalle inferenze tecniche.* ### Assistant The source picture is converging: the public story is not “one magic algorithm made the newer model philosophical,” it’s a stack of improvements in data curation, training scale, optimization stability, tokenizer efficiency, context handling, and post-training data selection. I’m doing one more pass over your project-specific reference folder and note graph so I can shape the answer toward the paper’s actual needs rather than a generic ML explainer. *La differenza tra modelli emerge da un insieme di leve tecniche, non da un singolo trucco.* ### User The most important thing to check in my vault is the long form project about this paper. Make sure you get the correct long form project as well. ### User I really hope you're actually doing what I asked you to do rather than something else. ### Assistant I am doing the project-specific work you asked for. The session file points to the main longform manuscript at `/Users/nickyoung/My Obsidian Vault/Writing/research/generating philosophy with ai (paper)/`, and I’m reading that now so the technical answer is shaped to the actual paper rather than to the alternative exploratory draft. *Leggo ora il longform principale per ancorare la risposta al manoscritto giusto.* ### Assistant The main longform path named in the session note currently contains only [`Index.md`](/Users/nickyoung/My%20Obsidian%20Vault/Writing/research/generating%20philosophy%20with%20ai%20(paper)/Index.md). I’m checking the adjacent research directories now to see whether the substantive prose has migrated into the text-internal-evaluation project or another manuscript path, because the session note and the filesystem are no longer aligned. *C’è una discrepanza tra la sessione e il filesystem, quindi sto verificando dov’è finita la prosa effettiva.* ### Assistant I found the mismatch. The nominal “main” manuscript folder is effectively empty, the old full draft sits in [`Writing/research/generating philosophy with ai (paper) (abandoned)`](/Users/nickyoung/My%20Obsidian%20Vault/Writing/research/generating%20philosophy%20with%20ai%20(paper)%20(abandoned)), and the currently populated active manuscript is the text-internal-evaluation longform. I’m reading the live prose sections that bear on training data, evaluative filtering, and what models learn from corpora, because that is the part directly adjacent to your technical question. *Il progetto vivo sembra essere quello text-internal, mentre il vecchio draft completo è nell’abandoned.* ### User "I want to ask you something about my generating philosophy project and the end goal is going to be for you to write me a deep research prompt that I can put into gem." was the specific thing I asked you to do? ### Assistant No. The immediate task you gave me was to answer the technical question seriously, with the Gem prompt as the downstream deliverable. I over-spent time reconciling project files. The one useful result from that detour is this: the nominal main folder at [`/Users/nickyoung/My Obsidian Vault/Writing/research/generating philosophy with ai (paper)`](/Users/nickyoung/My%20Obsidian%20Vault/Writing/research/generating%20philosophy%20with%20ai%20(paper)) is effectively empty, the older full prose draft is in [`/Users/nickyoung/My Obsidian Vault/Writing/research/generating philosophy with ai (paper) (abandoned)`](/Users/nickyoung/My%20Obsidian%20Vault/Writing/research/generating%20philosophy%20with%20ai%20(paper)%20(abandoned)), and the currently populated live manuscript is [`/Users/nickyoung/My Obsidian Vault/Writing/research/generating-philosophy-text-internal-evaluation/4. Finding Virtue in Text.md`](/Users/nickyoung/My%20Obsidian%20Vault/Writing/research/generating-philosophy-text-internal-evaluation/4.%20Finding%20Virtue%20in%20Text.md). Now the direct answer. ## What makes a newer base model better? At the deepest level, a better base model is one that has learned a better conditional distribution over tokens. In practice that means lower pretraining loss on a much wider range of text, code, math, dialogue, and technical prose. Everything user-facing follows from that. The main levers are not mysterious, but the vendors hide the exact recipe. Publicly, companies disclose very little about frontier proprietary models. So for something like “Claude Opus 3 vs a newer frontier Claude,” the exact answer is not public. As of March 9, 2026, there is no publicly named Claude model called “Opus 4.6”; if you mean “Opus 3 versus a later frontier model,” the best-supported answer is the general one below. ## The real technical levers 1. More compute-optimal pretraining Older frontier models were often undertrained relative to their parameter count. The Chinchilla result showed that for a fixed compute budget, you often get a better model not by making it vastly bigger, but by training it on far more tokens at a better parameter/data ratio. That alone can make a model noticeably better at abstract tasks because it has actually finished learning more of the distribution instead of being a huge but underexposed network. Source: [Chinchilla](https://arxiv.org/abs/2203.15556). 2. Better data, not just more data This is probably the biggest underappreciated factor. “Better” data means aggressive deduplication, document-quality ranking, filtering out boilerplate and junk, controlling domain mixtures, removing benchmark contamination, and upweighting text that has high informational density. A later model is usually trained on a corpus that is both larger and much more curated. For philosophy-like tasks, this matters a lot: the model sees more clean examples of long argumentative structure, objection/reply patterns, abstract vocabulary, and disciplined exposition. 3. Better tokenizer A tokenizer changes what the model even sees as units. If the tokenizer is inefficient, rare names, technical terms, multilingual fragments, formulas, and odd morphology get chopped into ugly subpieces. Better tokenizers reduce fragmentation and increase effective bandwidth. Meta explicitly notes a new tokenizer in Llama 3; this is not cosmetic, it changes representational efficiency. Source: [Llama 3 paper](https://arxiv.org/abs/2407.21783). 4. Better architecture and attention plumbing The headline transformer remains, but the internals improve: grouped-query attention, context scaling tricks, positional encoding improvements, memory-layout improvements, normalization changes, expert routing in some systems, and better handling of long-range dependencies. These changes do not magically create “reasoning,” but they do improve the model’s ability to keep track of structure across long spans, which is exactly what philosophy needs. 5. Better optimization and training stability A large part of frontier progress is boring but decisive: better learning-rate schedules, gradient clipping, batch construction, distributed training, mixed precision, loss scaling, checkpoint averaging, curriculum choices, and fewer training instabilities. OpenAI was very explicit that GPT-4 involved building a stack that “scales predictably.” That matters because once training is stable, you can push longer, on better mixtures, with fewer catastrophic failures. Source: [GPT-4 report](https://openai.com/index/gpt-4-research/). 6. Better continued pretraining and synthetic data Even before instruction tuning, companies often do extra stages: continued pretraining on curated mixtures, code-heavy corpora, math corpora, multilingual technical text, or synthetic high-quality data generated by earlier strong models and then filtered. This can significantly sharpen abstract competence without being “extended reasoning” at inference time. Gemini’s technical reporting points in this general direction: mixture design, distillation, and post-training matter a lot. Source: [Gemini 1.5 technical report](https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf). ## Why this makes a model better at philosophy specifically A better base model is better at philosophy because philosophy is an unusually demanding language task. It requires: - long-horizon coherence across many paragraphs - stable handling of abstract distinctions - sensitivity to dialectical roles - retrieval of rare concepts and authors - the ability to continue not just locally plausible prose, but globally structured argument When the base model improves, what improves is not merely “knowledge.” Its internal representations get cleaner. It tracks latent variables better. It holds more constraints in play at once. It is less likely to collapse into the nearest generic continuation. That is why newer models feel less like autocomplete and more like they have actually internalized argumentative form. So the blunt answer is this: they did not discover one philosophical ingredient. They improved the model’s compression of the training distribution. And because philosophy is heavily constituted by highly structured textual practice, better compression of that distribution yields better philosophical performance. That is also why your Section 4 question is exactly the right one: what does the training data encode? Publicly, that is still the key empirical unknown. But technically, the answer to “what makes the base model better?” is: better data mixture, more tokens, better compute allocation, better architecture, better optimization, and better intermediate training stages. If you want, I’ll now turn this into a deep Gem research prompt aimed at extracting the strongest available answer while forcing clear separation between disclosed fact, inference, and speculation. *La differenza decisiva non è un singolo trucco, ma una compressione molto più efficace della struttura astratta presente nei corpora su cui il modello viene addestrato.* ### User It still seems like you're not doing what I asked you to do, btw. ### Assistant You’re right. What you asked for was a deep research prompt for Gem, and I had not delivered it yet. Here is the prompt, written to force a genuinely technical answer rather than a glossy overview: ```text I want a technically serious research memo on the following question: Question: What makes a newer frontier base language model better than an older one at difficult abstract tasks such as philosophy, apart from explicit extended-reasoning scaffolds at inference time? Important scope constraints: - This is a question about the base model, not chain-of-thought, test-time search, deliberate reasoning modes, agent loops, tool use, or prompt engineering. - Focus on what changed in the model/training stack itself. - Treat “better at philosophy” as a case of “better at long-horizon abstract textual reasoning, dialectical continuity, conceptual discrimination, and structured argument generation.” - I do not want a simplified popular explanation. I want the real technical answer. - Do not pad with AI-safety discussion, ethics, or product framing unless directly relevant to model capability. - If exact information about proprietary models is unavailable, say so clearly and distinguish disclosed fact from strong inference from the literature. Concrete target: Explain what likely separates an older frontier model from a much newer frontier model at the level of training dynamics, data mixture, architecture, optimization, representation learning, and post-pretraining adaptation. Use this structure: 1. Executive thesis Give a 1-2 paragraph answer stating the main claim in precise terms. Example target form: “A newer base model is better primarily because it has learned a better conditional distribution over tokens due to improvements in data quality, token budget, compute-optimal scaling, tokenizer efficiency, optimization stability, architecture, and post-training distribution shaping.” 2. Mechanistic decomposition Give a technical breakdown of the main causal factors: - parameter count versus token count - compute-optimal scaling - architecture improvements within the transformer family - attention variants and long-context machinery - normalization, optimization, curriculum, batch construction, distributed training stability - tokenizer design and why it matters - data curation, deduplication, quality filtering, mixture weighting - synthetic data, distillation, continued pretraining, replay, or other intermediate stages - post-training methods that alter the model without being “extended reasoning” For each factor: - explain what it is technically - explain why it changes capability - explain whether it primarily improves knowledge, coherence, abstraction, robustness, calibration, or something else - explain how it would show up in philosophy-like tasks specifically 3. Base-model capability versus post-training capability Draw a clean line between: - capabilities learned during pretraining - capabilities sharpened during post-training - capabilities that only appear with explicit test-time scaffolds I want a technically careful discussion of whether “philosophical competence” is mainly: - latent in pretraining - surfaced by post-training - or partly manufactured by inference-time scaffolding Do not collapse these together. 4. Representation-level account Give the best available technical story about what it means for a newer model to be “better” internally. Address issues like: - richer internal representations - better feature superposition management - cleaner abstractions - better tracking of long-range constraints - better implicit world-model structure - improved compositional generalization - lower training loss versus qualitatively different internal organization I want a serious answer here, not slogans like “it understands more.” 5. Philosophy as a stress test Explain why gains in the base model would disproportionately matter for philosophical tasks. Discuss: - long-range coherence - dialectical sequencing - objection/reply structure - abstraction over sparse concepts - analogical transfer - maintaining local precision while preserving global argumentative shape - resistance to generic filler and summary-mode collapse 6. Data question Analyze the role of training data in a technically precise way. I want to know: - whether later models are better mainly because they saw more philosophy-like text - whether they are better because they learned more general cognitive structure from broader corpora - whether curation and filtering matter more than raw philosophical volume - whether “virtue-filtered corpora” is a plausible technical idea or an overstatement Be explicit about what is known versus unknown about frontier proprietary training sets. 7. Case-comparison frame Without inventing facts, explain how one should think about a comparison like: “Claude Opus 3 versus a much newer frontier base model” or “GPT-3.5/4-era base model versus a 2025-2026 frontier base model.” I do NOT want fabricated proprietary details. Instead: - state what is not public - infer likely differences from disclosed papers, system cards, scaling-law papers, and open-model reports - identify which changes are most likely to matter for abstract reasoning performance 8. Best evidence Use primary or near-primary sources where possible: - major technical reports - scaling-law papers - model cards - papers from Anthropic, OpenAI, Google DeepMind, Meta, etc. - strong open-model reports where relevant Prioritize sources like: - Chinchilla / compute-optimal scaling - GPT-4 technical report - Gemini technical reports - Llama 3 paper/report - other credible reports on tokenizer, data mixture, and post-training effects 9. Epistemic discipline Throughout the memo, explicitly label claims as one of: - Disclosed by source - Strong inference - Plausible speculation - Unknown / not publicly knowable I want the memo to be extremely strict about this. 10. Final synthesis End by answering, in one compact section: If a newer base model is better at philosophy, what are the 3-5 most likely technical reasons? Output requirements: - Write as a research memo, not a blog post - Assume an intelligent humanities scholar who can handle real ML detail - Use equations or formal notation only if genuinely helpful - Avoid hype and anthropomorphic language - Do not claim certainty where there is none - Include a bibliography with links - Quote key technical passages when useful, but keep the focus on explanation Optional extension: After the main memo, add a short section titled “Implications for the generating philosophy project” explaining how the answer bears on the hypothesis that LLMs can learn philosophical move-types, evaluative patterns, and dialectical sequencing from text. ``` If you want, I can now do the second half properly too: tailor a version of this prompt specifically to your paper’s Section 4 / “what does the training data encode?” problem, so it directly tests the claim in [`/Users/nickyoung/My Obsidian Vault/Writing/research/generating-philosophy-text-internal-evaluation/4. Finding Virtue in Text.md`](/Users/nickyoung/My%20Obsidian%20Vault/Writing/research/generating-philosophy-text-internal-evaluation/4.%20Finding%20Virtue%20in%20Text.md). *La domanda decisiva è se il modello apprenda soltanto regolarità superficiali o anche strutture dialettiche abbastanza ricche da sostenere argomentazione filosofica di alto livello.* ### User Did you consult best practices in generating this prompt for Gemini Deep Research? If notPlease start the task(s) again completely from scratch, keeping this in mind. ### Assistant I’m restarting from scratch and this time anchoring the work on two things only: best practices for Gemini Deep Research prompting from current sources, and the actual live Generating Philosophy manuscript so the prompt is designed for that use-case. Then I’ll give you a new research prompt built around those constraints. *Riparto da zero, con le linee guida attuali di Gemini Deep Research e il manoscritto giusto come unici vincoli iniziali.* ### Assistant No, I had not done that before. I restarted and consulted current official Gemini Deep Research guidance from Google: - Google AI for Developers says to provide context, explicitly define the output format, and tell the agent how to handle unknowns: [Gemini Deep Research Agent docs](https://ai.google.dev/gemini-api/docs/deep-research) - Google’s Gemini team says Deep Research will show a plan first, so you can start from the end goal and then refine via “Edit plan”: [How to use Google’s Deep Research](https://blog.google/products-and-platforms/products/gemini/tips-how-to-use-deep-research/) - Google Cloud’s Deep Research docs confirm the plan-review workflow and source selection: [Get reports with Deep Research](https://docs.cloud.google.com/gemini/enterprise/docs/research-assistant) I also reset the project grounding and used the live manuscript thread, especially [`/Users/nickyoung/My Obsidian Vault/Writing/research/generating-philosophy-text-internal-evaluation/4. Finding Virtue in Text.md`](/Users/nickyoung/My%20Obsidian%20Vault/Writing/research/generating-philosophy-text-internal-evaluation/4.%20Finding%20Virtue%20in%20Text.md), because that is where your “what does the training data encode?” claim is currently being worked out. The prompt below is therefore optimized for Gemini Deep Research specifically: it gives enough context, specifies deliverables, tells it how to behave under uncertainty, and is structured to produce a usable report rather than a vague essay. ```text I am researching a question for an academic philosophy paper about whether LLMs can do philosophy. My specific question is: What makes a newer frontier base language model better than an older one at difficult abstract tasks such as philosophy, apart from explicit extended-reasoning scaffolds at inference time? Scope constraints: - This is about the base model and the training stack, not chain-of-thought, tool use, agent loops, search-based reasoning, self-reflection modes, or prompt tricks. - Focus on what changes in the model itself and in training/post-training make later models better. - Treat “better at philosophy” as better at long-range argumentative coherence, conceptual discrimination, dialectical sequencing, analogical transfer, objection/reply structure, and maintaining global argumentative shape across many paragraphs. - I want a technically serious answer, not a popular-audience simplification. - Do not pad with ethics, safety, or product marketing unless directly relevant to capability. - If exact information about proprietary frontier models is not public, say so explicitly and distinguish disclosed fact from inference and speculation. Research task: Produce a research memo that explains, as rigorously as possible from public sources, what likely separates an older frontier base model from a significantly newer one. The memo must address: 1. Scaling and token budget Explain compute-optimal scaling, parameter count versus token count, and why later models may be better even without dramatic parameter increases. 2. Data pipeline Explain the technical importance of: - data curation - filtering - deduplication - mixture weighting - quality ranking - multilingual and code/math balancing - synthetic or distilled data - continued pretraining I especially want to know whether later models are better because they saw more philosophy-like text, because they saw better-curated text, or because they learned more general abstract structure from broader corpora. 3. Tokenization and representation efficiency Explain how tokenizer changes can materially affect downstream capability on abstract and technical tasks. 4. Architecture and optimization Explain which architecture and optimization changes within the modern transformer stack are most likely to matter: - attention variants - long-context machinery - positional methods - MoE or expert routing if relevant - normalization - optimizer/training stability - curriculum - distributed training improvements 5. Representation-level account Give the best technical explanation of what it means internally for a newer model to be “better.” Discuss: - richer or cleaner latent representations - abstraction quality - long-range constraint tracking - compositional generalization - world-model structure - whether lower training loss alone explains the gains or whether qualitative internal reorganization is also likely 6. Pretraining versus post-training Distinguish carefully between: - capabilities primarily learned during pretraining - capabilities sharpened in post-training - capabilities that mostly require inference-time scaffolding I want a careful judgment about whether philosophical competence is mainly latent in pretraining, mainly surfaced by post-training, or substantially dependent on later scaffolding. 7. Philosophy as a test case Explain why these technical improvements would matter for philosophy in particular. Focus on: - sustained abstraction - argumentative continuity - handling objections and repairs - avoiding generic filler - maintaining non-local commitments - producing structured rather than merely plausible continuation 8. Case-comparison frame Use a comparison like: - Claude Opus 3 versus a much newer frontier base model - or GPT-4-era base models versus 2025-2026 frontier base models But do not invent proprietary details. Instead: - state clearly what is not public - infer likely differences from model reports, scaling papers, open-model reports, and system cards - rank the most likely causal factors behind improved abstract reasoning performance 9. Epistemic discipline For every major claim, label it as one of: - Disclosed by source - Strong inference from source - Plausible speculation - Unknown / not publicly knowable Do not blur these categories. 10. Final synthesis End with a short section answering: If a newer base model is better at philosophy, what are the 3 to 5 most likely technical reasons? Source requirements: - Prioritize primary or near-primary sources. - Use official technical reports, model cards, scaling-law papers, and serious open-model reports. - Strongly prioritize sources such as: - Chinchilla / compute-optimal scaling - GPT-4 technical report - Gemini technical reports - Llama 3 report/paper - other credible papers on tokenization, data quality, continued pretraining, distillation, and post-training effects - Include links to all major sources. Output format: Write the result as a research memo with these sections: 1. Executive Thesis 2. What Is Publicly Known and What Is Not 3. Main Technical Drivers of Improvement 4. Pretraining vs Post-training vs Inference-time Scaffolding 5. Why This Matters for Philosophy 6. Ranked Conclusions 7. Bibliography with links Handling uncertainty: - If evidence is missing, say so rather than estimating. - If a claim is commonly repeated but poorly sourced, flag it. - If specific details about proprietary model internals are unavailable, do not fill the gap with generic lore. - Where helpful, quote short key passages from technical sources, but keep the emphasis on explanation. Final optional section: Add a brief section titled “Implications for the generating philosophy project” addressing whether the evidence supports the idea that models can learn philosophical move-types, evaluative patterns, and dialectical sequencing from text alone. ``` If you want to use Gemini Deep Research the way Google seems to intend, I’d pair that with one short plan-edit instruction after it shows its initial research plan: ```text Edit the plan so that it explicitly separates disclosed facts from inference, prioritizes primary technical sources, and treats philosophy as a demanding test case for long-horizon abstract textual competence rather than as a generic humanities task. ``` This is the version I’d actually use in Gemini. *La versione corretta del prompt deve costringere il sistema a separare nettamente ciò che è noto, ciò che è inferito, e ciò che resta davvero opaco nei modelli proprietari.* ### User Give me the prompt again, but uh don't mention philosophy in it. Okay, otherwise it'll send the model off in the completely wrong direction. ### Assistant ```text I want a technically serious research memo on the following question: What makes a newer frontier base language model better than an older one at difficult abstract tasks, apart from explicit extended-reasoning scaffolds at inference time? Scope constraints: - This is about the base model and the training stack, not chain-of-thought, tool use, agent loops, search-based reasoning, self-reflection modes, or prompt tricks. - Focus on what changes in the model itself and in training/post-training make later models better. - Treat “better at difficult abstract tasks” as better at long-range coherence, conceptual discrimination, dialectical sequencing, analogical transfer, handling objections and repairs, and maintaining global structure across many paragraphs. - I want a technically serious answer, not a popular-audience simplification. - Do not pad with ethics, safety, or product marketing unless directly relevant to capability. - If exact information about proprietary frontier models is not public, say so explicitly and distinguish disclosed fact from inference and speculation. Research task: Produce a research memo that explains, as rigorously as possible from public sources, what likely separates an older frontier base model from a significantly newer one. The memo must address: 1. Scaling and token budget Explain compute-optimal scaling, parameter count versus token count, and why later models may be better even without dramatic parameter increases. 2. Data pipeline Explain the technical importance of: - data curation - filtering - deduplication - mixture weighting - quality ranking - multilingual and code/math balancing - synthetic or distilled data - continued pretraining I especially want to know whether later models are better because they saw more high-quality reasoning-like text, because they saw better-curated text, or because they learned more general abstract structure from broader corpora. 3. Tokenization and representation efficiency Explain how tokenizer changes can materially affect downstream capability on abstract and technical tasks. 4. Architecture and optimization Explain which architecture and optimization changes within the modern transformer stack are most likely to matter: - attention variants - long-context machinery - positional methods - MoE or expert routing if relevant - normalization - optimizer/training stability - curriculum - distributed training improvements 5. Representation-level account Give the best technical explanation of what it means internally for a newer model to be “better.” Discuss: - richer or cleaner latent representations - abstraction quality - long-range constraint tracking - compositional generalization - world-model structure - whether lower training loss alone explains the gains or whether qualitative internal reorganization is also likely 6. Pretraining versus post-training Distinguish carefully between: - capabilities primarily learned during pretraining - capabilities sharpened in post-training - capabilities that mostly require inference-time scaffolding I want a careful judgment about whether high-level abstract competence is mainly latent in pretraining, mainly surfaced by post-training, or substantially dependent on later scaffolding. 7. Difficult abstract tasks as a test case Explain why these technical improvements would matter for tasks involving: - sustained abstraction - argumentative continuity - handling objections and repairs - avoiding generic filler - maintaining non-local commitments - producing structured rather than merely plausible continuation 8. Case-comparison frame Use a comparison like: - Claude Opus 3 versus a much newer frontier base model - or GPT-4-era base models versus 2025-2026 frontier base models But do not invent proprietary details. Instead: - state clearly what is not public - infer likely differences from model reports, scaling papers, open-model reports, and system cards - rank the most likely causal factors behind improved abstract reasoning performance 9. Epistemic discipline For every major claim, label it as one of: - Disclosed by source - Strong inference from source - Plausible speculation - Unknown / not publicly knowable Do not blur these categories. 10. Final synthesis End with a short section answering: If a newer base model is better at difficult abstract tasks, what are the 3 to 5 most likely technical reasons? Source requirements: - Prioritize primary or near-primary sources. - Use official technical reports, model cards, scaling-law papers, and serious open-model reports. - Strongly prioritize sources such as: - Chinchilla / compute-optimal scaling - GPT-4 technical report - Gemini technical reports - Llama 3 report/paper - other credible papers on tokenization, data quality, continued pretraining, distillation, and post-training effects - Include links to all major sources. Output format: Write the result as a research memo with these sections: 1. Executive Thesis 2. What Is Publicly Known and What Is Not 3. Main Technical Drivers of Improvement 4. Pretraining vs Post-training vs Inference-time Scaffolding 5. Why This Matters for Difficult Abstract Tasks 6. Ranked Conclusions 7. Bibliography with links Handling uncertainty: - If evidence is missing, say so rather than estimating. - If a claim is commonly repeated but poorly sourced, flag it. - If specific details about proprietary model internals are unavailable, do not fill the gap with generic lore. - Where helpful, quote short key passages from technical sources, but keep the emphasis on explanation. ``` *Per ottenere una buona risposta, il prompt deve orientare la ricerca verso le leve tecniche del modello e non verso esempi di dominio che rischiano di distorcere il piano iniziale.* ### User That has made the model want to research really old-fashioned LLMs. It's not talking about up-to-date ones. So useless for my purposes. ### Assistant Use a prompt that explicitly forces a 2025-2026 frontier comparison and treats older papers as background only. ```text I want a technically serious research memo on the following question: As of March 9, 2026, what most likely makes a current frontier base language model better than a 2023-2024 frontier base language model at difficult abstract tasks, apart from explicit extended-reasoning scaffolds at inference time? Scope constraints: - This is about the base model and the training stack, not chain-of-thought, tool use, agent loops, search-based reasoning, self-reflection modes, or prompt tricks. - Focus on current frontier systems and near-frontier systems from 2025-2026. - Use 2023-2024 material mainly as background, not as the main object of analysis. - I care about what likely separates newer models from earlier frontier models in practice now, not about the historical development of transformers in general. - Treat “better at difficult abstract tasks” as better at long-range coherence, conceptual discrimination, analogical transfer, handling objections and repairs, and maintaining global structure across many paragraphs. - I want a technically serious answer, not a popular-audience simplification. - Do not pad with ethics, safety, or product marketing unless directly relevant to capability. - If exact information about proprietary frontier models is not public, say so explicitly and distinguish disclosed fact from inference and speculation. Important anti-drift instruction: Do not spend much time on GPT-2, BERT, original Transformer basics, or other old foundation material except where absolutely necessary. The center of gravity should be 2025-2026 frontier LLM capability differences, using older papers only to explain concepts like scaling laws or tokenization when needed. Research task: Produce a research memo that explains, as rigorously as possible from public sources, what likely separates a 2025-2026 frontier base model from a 2023-2024 frontier base model. The memo must address: 1. Current frontier comparison Anchor the discussion in present-day frontier models and recent near-frontier open models. Use examples like: - Claude Opus 3 versus a much newer frontier base model - GPT-4-era models versus 2025-2026 frontier models - recent Gemini-era and Llama-era systems where relevant Do not invent proprietary details. 2. Scaling and token budget Explain compute-optimal scaling, parameter count versus token count, and why newer models may be better even without dramatic parameter increases. 3. Data pipeline Explain the technical importance of: - data curation - filtering - deduplication - mixture weighting - quality ranking - multilingual and code/math balancing - synthetic or distilled data - continued pretraining I especially want to know whether newer frontier models are better because they saw better-curated text, more high-quality reasoning-like text, more synthetic training data, or because they learned more general abstract structure from broader corpora. 4. Tokenization and representation efficiency Explain how tokenizer changes can materially affect downstream capability on abstract and technical tasks. 5. Architecture and optimization Explain which architecture and optimization changes within the modern transformer stack are most likely to matter in 2025-2026 frontier systems: - attention variants - long-context machinery - positional methods - MoE or expert routing if relevant - normalization - optimizer/training stability - curriculum - distributed training improvements 6. Representation-level account Give the best technical explanation of what it means internally for a newer model to be “better.” Discuss: - richer or cleaner latent representations - abstraction quality - long-range constraint tracking - compositional generalization - world-model structure - whether lower training loss alone explains the gains or whether qualitative internal reorganization is also likely 7. Pretraining versus post-training Distinguish carefully between: - capabilities primarily learned during pretraining - capabilities sharpened in post-training - capabilities that mostly require inference-time scaffolding I want a careful judgment about whether high-level abstract competence in current frontier models is mainly latent in pretraining, mainly surfaced by post-training, or substantially dependent on later scaffolding. 8. Difficult abstract tasks as a test case Explain why these technical improvements matter for tasks involving: - sustained abstraction - argumentative continuity - handling objections and repairs - avoiding generic filler - maintaining non-local commitments - producing structured rather than merely plausible continuation 9. Epistemic discipline For every major claim, label it as one of: - Disclosed by source - Strong inference from source - Plausible speculation - Unknown / not publicly knowable Do not blur these categories. 10. Final synthesis End with a short section answering: If a 2025-2026 frontier base model is better than a 2023-2024 frontier base model at difficult abstract tasks, what are the 3 to 5 most likely technical reasons? Source requirements: - Prioritize primary or near-primary sources from 2024-2026 where possible. - Use official technical reports, model cards, scaling-law papers, and serious open-model reports. - Use older classic papers only as supporting background, not as the main evidential basis. - Include links to all major sources. Output format: Write the result as a research memo with these sections: 1. Executive Thesis 2. What Is Publicly Known and What Is Not 3. Main Technical Drivers of Improvement in 2025-2026 Frontier Models 4. Pretraining vs Post-training vs Inference-time Scaffolding 5. Why This Matters for Difficult Abstract Tasks 6. Ranked Conclusions 7. Bibliography with links Handling uncertainty: - If evidence is missing, say so rather than estimating. - If a claim is commonly repeated but poorly sourced, flag it. - If specific details about proprietary model internals are unavailable, do not fill the gap with generic lore. - Keep the analysis centered on current frontier systems, not the early history of LLMs. ``` If you want, I can make this even tighter by naming exactly which model families it should prioritize and which ones it should ignore. *Per evitare la deriva verso la storia remota degli LLM, bisogna vincolare esplicitamente il confronto ai modelli di frontiera del 2025-2026 e relegare i classici a semplice sfondo concettuale.*