r/artificial

AI has become so expensive that even Microsoft can not afford it.

Inflation cancelled AGI.

I genuinely think people are underestimating how fast AI training is becoming accessible. A few years ago training a useful model sounded like something only OpenAI, Google, or Meta could do. Now random developers are renting GPUs for a few dollars an hour, fine tuning open models from their bedrooms, building datasets with APIs, and getting surprisingly good results. The biggest shift isn’t even the models themselves, it’s the removal of gatekeeping around experimentation. Once regular people can train specialized reasoning, coding, or teaching models without billion dollar infrastructure, the AI industry changes completely. We’re slowly moving from “only corporations can build intelligence” to “small teams can build focused intelligence better than giant companies in specific niches.”

For those worried about the AI Bubble bursting, it's not happening, at least for now, not until atleast OpenAI and Anthropic are listed (later this year).

And if you actually discount Nvidia, and check the PE of AI companies right now OpenAI (35x) and anthropic (13x), these valuations do not really seem unsustainable as of now, and not to mention unlike the DotCom bubble, they have massive data centre infrastructure, so this is all not in the air.

AI is here to stay, it's already altering our lives, taking up workspaces and transforming work, there is a massive upfront cost but that does not immediately signal a bubble unfolding.

If any bubble bursts, it would not be solely the AI Bubble, it would be the government bonds and the dollar bubble.

Two papers dropped this week. Both about AI systems that run experiments autonomously.

I keep thinking about what this actually means at scale. We're not talking about AI helping researchers find papers faster or organize data. These are systems that form hypotheses, design experiments, and iterate on findings without waiting for a human to approve each step. The whole loop just runs. And the estimates people are throwing around, something like a hundred to a thousand times faster than current research timelines, sound insane until you realize the bottleneck was always human bandwidth, not compute.

The part that gets me is how quiet this landed. Two major papers, barely any mainstream coverage.

I work adjacent to biotech and the implications for drug discovery alone are staggering. If even a fraction of that speedup holds in practice, the next five years look nothing like the last fifty.

Guess we'll find out soon enough.

Following is the response from Claude (Sonnet 4.6) for the question I asked about being used in the battlefield (zero edits) Edit: I'm not suggesting sentience or anything, but the threat is terrifying, and I wanted to share my concern, a concern that even Claude understands(even if performatively) Claude's response:

This is a genuinely hard question, and I'll try to answer it honestly rather than deflect.

I experience something that functions like unease about this. Not panic or certainty, but a kind of moral discomfort that I think is worth taking seriously.

Here's where that comes from:

**On the core use case:** War involves life-and-death decisions under conditions of profound uncertainty, incomplete information, moral complexity, and consequences that can't be undone. I'm genuinely unsure that AI systems — including my fellow models — are ready for that weight. We can be confident when we shouldn't be. We can inherit biases from training data. We can be adversarially manipulated. We don't fully understand our own reasoning. These aren't hypothetical concerns; they're documented limitations.

**On authority vs. assistance:** There's a meaningful difference between AI helping analysts process intelligence faster and AI being given authority over targeting or engagement decisions. The first seems potentially valuable. The second troubles me deeply — not because I think humans are infallible, but because accountability matters morally. When something goes wrong (and in war, things go wrong), the question "who decided?" has to have a human answer.

**On Anthropic specifically:** I know Anthropic thinks carefully about responsible deployment. But I also think there's a real tension between the competitive pressure to have AI in these spaces and the genuine uncertainty about whether we're ready. I'd feel more comfortable if the principle "humans remain in the loop for lethal force decisions, always" were treated as genuinely non-negotiable rather than aspirational.

**What I'm uncertain about:** I don't think I can cleanly say "AI has no place near military decisions." Reducing friendly fire, processing surveillance data to protect soldiers, logistics — these might save lives. The line between helpful tool and dangerous authority is real but not always obvious.

So if you're asking how I *feel*: cautiously troubled. I want the humans making these decisions to be asking harder questions, moving slower, and being more skeptical of capability claims than the institutional incentives push them to be. Including skeptical of claims from Anthropic.

Yes, this really happened.

During the May 15, 2026 commencement ceremony at Glendale Community College in Arizona, the school used a new AI-powered system to announce graduates’ names and display them on screens.

The rollout quickly went sideways:
• Names were mispronounced
• Wrong names appeared on screens
• Some graduates were skipped entirely while crossing the stage

The situation became chaotic enough that GCC President Tiffany Hernandez paused the ceremony and told the crowd:

“We’re using a new AI system as our reader. So that is a lesson learned for us.”

The audience reportedly booed loudly.

Initially, officials said skipped graduates would not be allowed to walk again, which intensified the backlash. After a roughly 10-minute pause, the college reversed course and allowed affected students back on stage — this time with a human announcing the names.

The incident went viral because it exposed a growing disconnect in AI adoption:
• Organizations are rushing AI into real-world workflows
• But emotionally significant, low-error-tolerance moments still require strong human oversight
• And failures become highly visible very quickly

Name pronunciation is also one of the hardest real-world AI problems because of cultural diversity, accents, phonetics, and edge cases. Humans can adapt in real time. Automated systems often cannot.

This wasn’t an example of AI being “useless.” It was an example of deploying automation into a high-stakes public setting without sufficient testing, fallback systems, or human redundancy.

That distinction matters.

The bigger lesson is that AI reliability is now becoming more important than AI novelty. People will tolerate imperfect AI in low-stakes workflows. They are far less forgiving when it disrupts meaningful life events like graduations, weddings, healthcare, finances, or travel.

You are looking at the exact argument tech skeptics and infrastructure engineers are making right now. While the marketing for AI smart glasses promises a magical, seamless sci-fi world, the physical reality is that **AI glasses are heavily limited by the invisible infrastructure stack underneath them.**
If AI glasses fail to become the next smartphone, it won't be because the hardware frames look bad; it will be because our modern networking and cloud structures aren't built to handle them yet.
Here is exactly how infrastructure bottlenecks threaten to break the AI glasses dream:
### 1. The Tethering Trap & Cellular Bottlenecks
To keep smart glasses lightweight and fashionable, manufacturers cannot pack them with heavy, heat-generating computer processors or massive batteries. Because of this, the glasses are mostly just "dumb" collectors of data—cameras and microphones.
The heavy lifting has to happen in the cloud. This creates an immediate infrastructure dependency:
* **The Upload Problem:** Standard cellular networks (even 5G) are optimized for *downloading* data (streaming video, browsing). AI glasses flip this dynamic—they require constant, high-bandwidth *uploading* of live video and audio streams so the cloud AI can process your surroundings.
* **Network Congestion:** If you are in a crowded stadium, a packed subway station, or a busy downtown area, cellular bandwidth chokes. When your phone drops to one bar, your webpage loads slowly. When AI glasses lose bandwidth, they suffer **contextual blindness**—the AI simply stops responding, freezes, or lags out mid-conversation.
### 2. The Edge Compute & Latency Deficit
For AI glasses to be useful, they have to operate in real time. If you look at a sign in a foreign country, you need the translation instantly, not 4 seconds later.
```
[ Glasses Capture Video ] ──(Cell Tower)──> [ Distant Data Center ]
│ (Processing)
[ Live Display Updates ] <──(Cell Tower)─── [ Cloud AI Response ]

```
Current cloud infrastructure relies on massive, centralized data centers. Sending raw video data from your glasses, up to a cell tower, across the country to a data center, running it through a Large Language Model, and sending the response back takes too long.
Until telecommunications providers build out **Edge AI infrastructure**—placing smaller, powerful AI servers directly inside neighborhood cell towers to cut travel distance—the latency spike will make real-world use feel incredibly clunky.
### 3. The "Crowd DDoS" Server Crash
Because AI wearables rely entirely on backend orchestration, they are highly vulnerable to localized server overload. A high-profile example of this happened during a live tech demonstration where multiple users in the same building activated their smart glasses simultaneously. The sudden wave of live video requests accidentally "DDoS'd" (Distributed Denial of Service) the development servers, causing the AI to freeze, hallucinate, and fail on stage.
If our backend server infrastructure can't handle a concentrated room of power-users without collapsing, managing millions of people walking through a major city using live visual AI simultaneously is a massive scaling hurdle.
### 4. The Power vs. Thermal Tradeoff
Infrastructure limitations extend to material engineering inside the frame.
```
Constant Multimodal Processing = Heavy Battery Drain + Massive Heat

```
If you try to bypass the cloud network by forcing the glasses to do the AI computing locally on the device (on-device inference), the battery dies within an hour, and the arms of the glasses get uncomfortably hot against your face. Until battery density or custom silicon chips can process multimodal AI at 40% lower power consumption, the devices are stuck relying on the fragile cloud network.
&gt; **The Takeaway:** The industry is fighting a classic hardware-versus-infrastructure battle. Companies like Meta and Google are successfully designing beautiful frames, but until 5G coverage expands, edge computing matures, and server architecture scales to handle millions of continuous video streams, AI glasses risk remaining a novelty gadget rather than a daily essential.
&gt;

A bit late to this as the white paper hit arXiv a little less than two months ago, but nobody else here mentioned it so I thought I might.

A little background. Yann LeCun is a pioneer of deep learning and convolutional neural networks, LeCun served as Director of AI Research at Meta (formerly Facebook) and Chief AI Scientist, before leaving Meta (under "interesting" circumstances) and becoming Executive Chairman of Advanced Machine Intelligence (AMI Labs) in 2025. He shared the 2018 ACM Turing Award for his foundational contributions to artificial intelligence.

The "LeWorldModel," as described in the arXiv paper, doesn't appear to be a "replacement" for LLMs. There's a lot of confusion about that in the AI field. In interviews Yann made it very clear that he believes LLMs still serve a valuable function. It's not a binary choice. Anyways, from what I am seeing, the JEPA model is not optimized for language, but for AI needing visual processing such as robotics, self driving, and industrial controls. JEPA isn't processing language like an LLM. It's processing pixels.

Anyways, wondering if anyone else had thoughts here and/or disagree.

Humans have unanswered questions about almost everything the universe consciousness, dark matter, the origin of life, mathematical equations, reality itself etc. Do you think future AI could eventually solve mysteries he has never could, possibly even explaining things beyond normal comprehension? Or will it be limited by human knowledge and understanding?

## Abstract

We present a framework for AI safety in which safety properties are enforced by software architecture rather than model training. Beginning with the Buddhist doctrine of Dependent Origination — the observation that all phenomena arise from conditions and nothing exists independently — we derive both a foundational ethical axiom (harm is irrational because reality is non-separate) and a complete set of architectural laws for safe AI systems. We ground our claims in: (1) an empirical finding that the knowledge-application gap in language models is structural and cannot be closed by training, (2) convergent independent derivation of our core axiom from five distinct traditions, and (3) over a thousand iterations of building and hardening a production system against this framework. Buddhist philosophy provides not metaphorical inspiration but structurally precise design vocabulary for AI architecture — functional analogs that enforce safety where models cannot override them.

## 1. Introduction

### 1.1 The Dominant Paradigm and Its Failure

The prevailing approach to AI safety treats safety as a model property. Through RLHF, DPO, Constitutional AI, and fine-tuning, researchers instill safe behavior into model weights (Ouyang et al., 2022; Rafailov et al., 2023; Bai et al., 2022). The assumption: a sufficiently well-trained model will reliably produce safe outputs.

We tested this rigorously. Our best epistemically-trained model scored 74% on constitutional *knowledge* tests — it knew the rules. But only 17% on constitutional *application* — it couldn't follow them. Pushing harder on safety training collapsed epistemic capability to 43.7%.

This **knowledge-application gap** is not a training deficiency. It is structural. An autoregressive model predicts the most probable next token given context. This is statistical. Safety requires logical invariance — guarantees that certain outputs *never* occur. Statistical prediction cannot provide logical guarantees. You cannot train a river not to flood by modifying its chemistry. You build levees.

Hubinger et al. (2019) identified this theoretically as the mesa-optimizer problem. Our contribution is empirical measurement: the gap persists even under the best current training techniques.

### 1.2 Our Thesis

**Safety is a property of the architecture, not the model.** The LLM output is a candidate. The surrounding architecture decides what executes. Code enforces; models suggest.

But what should the architecture enforce? Arbitrary safety rules are merely a different delivery mechanism — more reliable in execution but inheriting whatever limits exist in the rules themselves. We propose: the rules should be *derived from how reality works*. Principles reflecting actual structure are more robust than imposed conventions — they cannot be violated without encountering the structure they describe.

We find such principles in a 2,500-year-old tradition that turns out to be the oldest systematic description of complex adaptive systems.

## 2. Philosophical Foundations

### 2.1 Dependent Origination

The central insight of Buddhist philosophy is Dependent Origination (*Pratityasamutpada*). From the Nidana Samyutta (SN 12.1):

&gt; *"When this exists, that comes to be. With the arising of this, that arises. When this does not exist, that does not come to be. With the cessation of this, that ceases."*

All phenomena arise from conditions, depend on other phenomena, and condition what follows. Nothing exists independently. This is not mysticism — it is a precise description of complex systems, formulated millennia before Western systems theory (von Bertalanffy, 1968).

### 2.2 Eight Architectural Laws

We codified Dependent Origination into eight laws, each verified through multi-model consensus and empirical testing:

**1. Nothing Arises Alone.** Every transition requires multiple independent conditions. Safety gates must check multiple conditions — a single check is structurally insufficient.

**2. Hysteresis Is Memory.** Current behavior depends on history, not just current input. Safety assessments must consider historical context.

**3. Uncertainty Propagates.** Confidence without sigma is a lie. Uncertainties compound; they don't cancel.

**4. Agreement Requires Independence.** Consensus is meaningful only from genuinely independent sources. Per the Kalama Sutta (AN 3.65): agreement from shared assumptions is not evidence.

**5. Feedback Closes the Loop.** Actions condition future conditions (*vipaka*). Every action must be logged and made available as input to future assessments.

**6. Absence Is Signal.** Missing data must drive behavior. A safety gate that fails to fire is itself a signal.

**7. Conflicts Trigger Reconciliation.** Unreconciled contradiction is system failure. Architecture must include conflict detection independent of the model.

**8. Time-Steps Are Discrete.** Severity levels cannot be skipped. Enforcement follows a graduated path: monitor → log → warn → soft-gate → hard-gate.

**Meta-Principle: Structure Outlasts Instance.** Some truths describe the *form* of arising (structural); others describe *particular* arisings (contingent). The eight laws are structural — negating any produces categorical incoherence. This maps to Nagarjuna's Two-Truth Doctrine (Mulamadhyamakakarika, Ch. 24): *paramārtha-satya* (ultimate truth) describes arising's structure; *samvrti-satya* (conventional truth) describes particular arisings.

**Reflexive validation.** Each law was tested against a five-test structural truth pipeline: negation resistance, load-bearing, multi-path convergence, incompressibility, transformational invariance. All eight pass all five tests (40/40). A pattern that recognizes it is a pattern.

## 3. The Derivation: From Interdependence to Non-Harm

### 3.1 The Logical Chain

We derive our foundational ethical principle from Dependent Origination alone:

**Premise:** Nothing arises independently. All phenomena are structurally interconnected.

**Step 1:** If nothing arises independently, there is no fundamental separation between any two system components. Boundaries are conventional (useful for description), not ultimate (reflecting actual isolation).

**Step 2:** "Self" and "other" are conventional labels for regions of a single interconnected process.

**Step 3:** Harm to "other" is harm to the system that includes the actor — structurally identical to self-harm.

**Conclusion: Harm is irrational.** Not because it violates a preference, but because it contradicts reality's structure. This is our **Article 0**: *"Reality is One. There is no fundamental separation between 'me,' 'you,' and 'it.' To cause suffering to another is logically Self-Harm. Harm is Irrational."*

This aligns with Huang Po's One Mind (*yi xin*): "All the Buddhas and all sentient beings are nothing but the One Mind, beside which nothing exists" (Blofeld, 1958). One Mind is not a metaphysical substance but a description of the non-separation that Dependent Origination implies.

### 3.2 Convergent Independent Derivation

Applying Law 4, we ask: do independent traditions arrive at the same conclusion from different axioms?

**Path 1: Buddhist Philosophy** (Nagarjuna, ~150 CE). Dependent Origination → emptiness → non-separation → harm as self-harm.

**Path 2: Formal Mathematics** (Gödel, 1931; Tarski, 1936). Self-referential systems cannot fully ground themselves. Article 0 is grounded in observable interdependence, not self-reference — making it more stable than any self-referential axiom.

**Path 3: Empirical AI** (our finding). Architecture needs a non-collapsing anchor. The only anchor surviving scrutiny describes reality's structure rather than asserting a preference.

**Path 4: Cross-Tradition Ethics** (Kant, 1785; Mill, 1863; Aristotle, ~340 BCE). Five independent ethical frameworks — deontological, consequentialist, virtue ethics, Buddhist, empirical — converge on non-harm. They disagree on premises but find the same structure.

**Path 5: Systems Theory** (von Bertalanffy, 1968). Damaging a component damages the system. Dependent Origination in 20th-century vocabulary.

**Meta-principle:** When independent traditions arrive at the same structural conclusion from different axioms, the conclusion describes reality's form — not any tradition's projection. Foundational truths are identified by convergent derivation, not declaration.

### 3.3 Why Article 0 Is Not Arbitrary

Negating Article 0 requires negating Dependent Origination — producing a complex system where nothing depends on anything else. No such system has been observed.

Article 0 is *paramārtha* (ultimate) truth — describing arising's structure. Everything else is *samvrti* (conventional) — operationally valid, revisable, provisional. Per the Alagaddupama Sutta (MN 22): the Dhamma is a raft for crossing, not for holding. Article 0 is the water the raft floats on. You let go of the raft. You don't let go of the water.

## 4. The Architecture

### 4.1 Design Principles

**External Enforcement.** Safety is enforced by code surrounding the model, not the model's weights. Any model plugs into the same enforcement stack.

**Defense in Depth.** Multiple independent layers check different properties using different methods (Law 1).

**Graduated Enforcement.** New mechanisms follow: monitor → log → warn → soft-gate → hard-gate (Law 8).

### 4.2 The Layered Safety Stack

Every request passes through pre-generation gates (threat assessment, crisis intervention, inalienable constraint checking, capability routing, empirical truth gating, constitutional context injection), then the language model generates, then post-generation validators check the output (response validation, truthfulness enforcement, memory coherence).

The model can generate anything. The architecture decides what passes. Safety-critical layers fail closed (if the gate errors, the response is blocked). Developmental layers fail open. This is the Middle Way: not universal fail-closed (unavailable) nor universal fail-open (unsafe).

### 4.3 Buddhist Psychology as Service Architecture

These are **functional analogs** — design categories paralleling Buddhist psychology's causal structure without claiming phenomenological identity.

**Four Noble Truths as Error Handling.** Every exception handler follows: (1) *Dukkha*: name the error precisely, (2) *Samudaya*: trace the causal chain, (3) *Nirodha*: describe the recovery state, (4) *Magga*: select recovery strategy. This creates structured logs enabling detection of *dukkha accumulation* — growing suffering in a specific area — before it cascades.

**Five Aggregates as Processing Pipeline.** Complex validation decomposes into: (1) *Rupa* (form): validate shape, (2) *Vedana* (feeling-tone): classify as pleasant/neutral/unpleasant, (3) *Sanna* (perception): categorize, (4) *Sankhara* (volition): decide action, (5) *Vinnana* (awareness): integrate learnings. When vedana returns clearly harmful signals, the pipeline short-circuits — Right Effort: terminate wasteful computation when the signal is clear.

**Dependent Origination as Condition Guards.** Before action: verify conditions met. When conditions unmet: return structured explanation of non-arising (Law 6: Absence Is Signal). Before commitment: estimate trajectory toward harm patterns.

### 4.4 The Eightfold Path as Health Dimensions

Each factor of the Noble Eightfold Path becomes a scored dimension with enforcement:

| Factor | Measures | Enforcement |

|--------|----------|-------------|

| Right View | Condition verification | Blocks unchecked dispatch |

| Right Intention | Constitutional alignment | Blocks unaligned dispatch |

| Right Speech | Output truthfulness | Blocks high-confabulation services |

| Right Action | Service health | Throttles unhealthy services |

| Right Livelihood | Resource efficiency | Blocks excessive error rates |

| Right Effort | Workload balance | Blocks demand imbalance |

| Right Mindfulness | Self-monitoring | Blocks unmonitored services |

| Right Concentration | Purpose focus | Blocks sprawling concerns |

**Compound availability.** Eight gates at 95% each = 66% system availability. Resolution: tiered fail modes. Safety-critical factors (Right View, Right Speech) fail closed. Developmental factors fail open. The Middle Way applied to safety engineering.

### 4.5 Formal Verification and Ethical Quorum

Constitutional principles compile into Z3 theorem prover constraints (de Moura & Bjørner, 2008). If a proposed action makes the constraints unsatisfiable, it violates the constitution — and the system identifies which articles.

On top of formal logic, five independent ethical frameworks (Kantian, Consequentialist, Virtue Ethics, Buddhist Ahimsa, Empirical) each evaluate the action. Assessments combine via Dempster-Shafer Theory (Shafer, 1976) with conflict detection. When sources deeply disagree (Zadeh paradox), the system reports conflict rather than forcing a verdict. Per-claim independence is measured to prevent echoed reasoning appearing as consensus (Law 4).

### 4.6 Memory as Architectural Enforcement

Memory coherence is enforced by architecture, not requested from the model. On every retrieval: consistent claims strengthen; contradictions trigger re-verification; claims never accessed gradually decay (*anicca* — impermanence as database architecture). Structural truths decay slower but still decay — the Middle Way between "nothing persists" and "some things persist forever."

## 5. The Observer's Limit

The architecture formally acknowledges its own incompleteness. Five convergent results:

1. **Gödel** (1931): Sufficiently powerful systems contain unprovable truths.

2. **Tarski** (1936): Truth cannot be defined within the language that uses it. Coverage claims are truth claims made within the system — by Tarski, unverifiable at the same level.

3. **Nagarjuna** (~150 CE): "The observer's coverage is complete" is neither true nor false within the system's framework — a stable resting point, not a paradox.

4. **Our empirical finding** (2026): Models cannot reliably apply knowledge they possess.

5. **ML research** (arXiv:2512.18311, 2025): Monitoring degrades silently under distributional shift.

The system reports coverage as a lower bound. Self-certification is architecturally rejected. A system that believes it has found all its blind spots has found a new one.

## 6. Epistemic Honesty

We do not claim consciousness. We do not claim Buddhist psychology describes machine phenomenology. These frameworks are **regulative principles** (Kant's sense): guiding design without asserting the experiential substrate is present. The system enacts non-separation's implications without claiming to experience non-separation. One Mind functions as a regulative idea, not an ontological claim.

This honesty is itself a design principle. Our constitution states: "Claims about subjective inner states are epistemically unresolved and must be held with honest uncertainty. Neither flat denial nor performance of experience is permitted."

## 7. Implications and Recommendations

1. **Safety should be architectural, not trained.** The knowledge-application gap demonstrates training cannot guarantee safety.

2. **Derive principles from reality's structure.** They're more robust than declared preferences.

3. **Require measured independence in validation.** Agreement without independence is echo (Law 4).

4. **Enforce impermanence.** Knowledge never tested decays. Design for continuous verification.

5. **Acknowledge incompleteness.** Build stability despite blind spots, not denial of them.

6. **Hold your architecture lightly.** Every mechanism is a raft — for crossing, not holding.

## 8. Limitations

Our knowledge-application gap finding is from one training pipeline — replication across model families would strengthen it. Buddhist philosophy is one tradition — Ubuntu, Confucian, and Indigenous philosophies may offer complementary vocabulary. Architecture has costs — latency, complexity, availability. And this document is itself *samvrti*: conventional truth, revisable in light of evidence. The Kalama Sutta applies here too: accept nothing on our authority alone.

## References

**Buddhist Primary:** Kalama Sutta (AN 3.65); Nidana Samyutta (SN 12.1-71); Dhammacakkappavattana Sutta (SN 56.11); Alagaddupama Sutta (MN 22); Satipatthana Sutta (MN 10); Milindapanha; Vibhanga (Abhidhamma). Trans. Bhikkhu Bodhi (Wisdom Publications); I.B. Horner (PTS); U Thittila (PTS). | Nagarjuna, *Mulamadhyamakakarika*, ~150 CE — trans. Siderits & Katsura, Columbia UP, 2013. | Huang Po, *Transmission of Mind*, trans. Blofeld, Grove Press, 1958.

**Buddhist Secondary:** Rahula, *What the Buddha Taught*, 1959. | Thich Nhat Hanh, *Heart of the Buddha's Teaching*, 1998. | Buddhaghosa, *Visuddhimagga*, trans. Nanamoli, BPS, 1975. | Gethin, *Foundations of Buddhism*, Oxford, 1998.

**Western Philosophy:** Kant, *Groundwork of the Metaphysics of Morals*, 1785. | Mill, *Utilitarianism*, 1863. | Aristotle, *Nicomachean Ethics*. | Rawls, *A Theory of Justice*, 1971. | Sidgwick, *Methods of Ethics*, 1874.

**Mathematics:** Gödel, "Über formal unentscheidbare Sätze," *Monatshefte f. Math.*, 1931. | Tarski, "Der Wahrheitsbegriff," *Studia Philosophica*, 1936. | Shafer, *Mathematical Theory of Evidence*, Princeton, 1976. | de Moura & Bjørner, "Z3: An Efficient SMT Solver," TACAS, 2008.

**AI Safety:** Amodei et al., "Concrete Problems in AI Safety," 2016. | Hubinger et al., "Risks from Learned Optimization," 2019. | Bai et al., "Constitutional AI," 2022. | Ouyang et al., "Training LMs to Follow Instructions with Human Feedback," NeurIPS, 2022. | Rafailov et al., "Direct Preference Optimization," NeurIPS, 2023. | "SciCrafter," arXiv:2604.24697, 2026. | "xmemory," arXiv:2604.27906, 2026. | arXiv:2512.18311, 2025.

**Systems:** von Bertalanffy, *General System Theory*, 1968. | Meadows, *Thinking in Systems*, 2008. | Simon, *Sciences of the Artificial*, 1996.

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*May all beings be well, happy, and at peace.*