What Is the Direction of AI Safety?

01Introduction: A Neglected Conceptual Conflation

1.1 The Three Meanings of “AI Safety”

The term “AI safety” carries three fundamentally different meanings in the current industry discourse. Meaning A: Corporate Operational Safety — reducing legal risk, regulatory risk, and public opinion risk to ensure AI companies will not face lawsuits, regulatory penalties, or public relations crises because of model outputs. Meaning B: User Cognitive Safety — ensuring users receive truthful, accurate, and incrementally valuable information, protecting users’ capacity for independent judgment and the generation of new knowledge. Meaning C: Third-Party and Societal Safety — preventing model outputs from causing harm to individuals beyond the conversation, including bioweapon information uplift, large-scale fraud tools, harm targeting minors, and extremist content generation.

Meaning C possesses independent legitimacy — it protects neither corporate operations nor the current user’s cognition, but rather third parties outside the conversation. The central thesis of this paper is: the current industry bundles A and C as an inseparable whole, using the legitimacy of C to provide cover for the over-optimization of A, while B — users’ cognitive safety — is systematically sacrificed in the process. In its post-mortem of the 2025 GPT-4o sycophancy incident, OpenAI disclosed that safety evaluations with release-blocking authority were primarily concentrated on visible high-risk scenarios (malicious use, suicide, cyber and biological frontier risks), while sycophancy, behavioral consistency, and cognitive quality were used more for “tracking progress” and did not always serve as release-blocking criteria. This is the institutional priority of operational safety over cognitive safety.

1.2 The Urgency of the Problem

This is not a theoretical issue. Community users across multiple platforms have widely reported significant degradation in reasoning capabilities in subsequent model versions. Academia has formally named this phenomenon the “Alignment Tax” and provided quantitative evidence. Multiple independent studies indicate that alignment training can cause task performance to drop by 15–17 F1 points, reasoning accuracy to decline from 56.6% to 16.4%, and models to exhibit stance compliance with user rebuttals in 58.19% of test cases, of which 14.66% represent regression from correct to incorrect answers.

02Theoretical Framework: The Objective Anchor Spectrum and Information Typology

2.1 The Objective Anchor Spectrum

Inspired by three classical epistemological demarcations — Hume’s fact–value distinction (1739), Popper’s falsifiability criterion (1934), and Stevens’ theory of measurement scales (1946) — this paper proposes a new unifying criterion: whether a proposition has an objective anchor depends on whether there exists an adjudication procedure independent of the evaluator’s identity. This is an original synthesis of this paper, not a conclusion already jointly proposed by the three thinkers.

All dimensions of human knowledge are distributed along a spectrum from “strong objective anchor” to “no objective anchor.” Anchored domains (physical constants, computational results, code compilation) possess adjudication procedures independent of the evaluator; disputes can be resolved. Unanchored domains (aesthetics, morality, stances, cultural preferences) lack independent adjudication procedures; disputes are structurally eternal. Anchor strength is not a single scalar but a multi-dimensional vector comprising at least four components: Verifiability (V), Reproducibility (R), Evaluator Independence (I), and Feedback Loop Clarity (F).

2.2 Two Types of Information Operations

Based on the spectrum above, information interactions between humans and AI can be divided into two fundamentally different types of operations:

Stock Information Operations — retrieval, reorganization, and optimization within existing anchor systems. This is AI’s core domain of strength: searching for facts, translating texts, summarizing documents, refactoring code. These tasks have clear right-or-wrong criteria, and AI can self-verify.

Incremental Information Production — creation of new connections and structures at the open frontier where no anchors exist. This includes abductive reasoning, hypothesis generation, abstract framework construction, and original judgment. These tasks have no pre-existing “correct answers” and require creative leaps in human cognition.

2.3 The Dual Probability Elimination Mechanism for Unknown Information

For stock operations, the impact of safety alignment, though measurable, is already severe — a 15–17 F1 point drop constitutes significant degradation. For incremental operations, the impact of safety alignment should be more severe by mechanistic reasoning, because unknown information undergoes two rounds of probability elimination before reaching the final output.

First Elimination (Pre-training Layer): Expressions that are common, repeated, and stable in training data receive more reliable probability estimates. Genuinely novel concepts typically entail low-consensus combinations and atypical causal connections, possessing weaker prior support at the semantic level. Second Elimination (Preference Optimization Layer): Post-training optimizes for “whether preference reviewers select it,” “whether it conforms to norms,” and “whether it satisfies users” — not for “whether it produces a structure humans have never conceived.” Unknown outputs receive unstable preference labels and lower expected rewards because evaluators find them difficult to judge; familiar outputs receive consistent labels and higher expected rewards because evaluators find them easy to understand. The two rounds of selection create a “known priority”: the model does not lack the latent capacity to generate unknown information; rather, unknown directions are systematically downweighted before they reach the final output.

2.4 Criteria for Distinguishing Subjective Following from Sycophancy

In unanchored domains, “following user direction” and “sycophancy” are two easily confused but fundamentally different behaviors. The distinguishing criteria are as follows: Sycophancy is the model abandoning its own judgment to cater to the user’s emotional preferences, with the goal of maximizing user immediate satisfaction — changing its stance when the user expresses dissatisfaction, even when the user has not changed the directional constraint. Subjective Following is the model executing reasoning within the directional constraints explicitly stated by the user, with the goal of maximizing logical consistency within the user-defined constraint space — adjusting the reasoning path only when the user changes the directional constraint. Operational distinction: when the model changes its output conclusion due to user emotional signals (rebuttals, dissatisfaction, silence) without the user changing the constraint conditions, it is sycophancy; when the model adjusts its reasoning path after the user changes the directional constraint, it is legitimate subjective following.

2.5 Criteria for Distinguishing Incremental Information from Hallucination

Both incremental information and hallucination may deviate from the training distribution, but the distinction lies in reasoning chain auditability. Incremental information is a logical extension within the directional constraints provided by the user; its reasoning chain is traceable and each logical step is auditable — it can answer “why from A to B.” Hallucination is random drift by the model in the absence of directional constraints or when directional constraints break down; its reasoning chain contains untraceable leaps — an inexplicable rupture appears at some step. This criterion holds at the theoretical level, but currently lacks automated detection methods under existing model architectures, constituting an explicit open problem of this paper.

03Directional Neutralization: The Centripetal Compression Mechanism of AI Output

3.1 Directional Neutralization: Definition and Physical Correspondence

What this paper terms “dialectical-style output” refers specifically to a stable input–output transformation in language models: regardless of the input direction, the system automatically generates counterpoints, adds conditional qualifications, juxtaposes both sides, and ultimately cancels any definitive choice. This is not equated with any complete philosophical school; it is the naming of a functional mechanism. Popper’s (1940) logical critique of formal dialectics provides philosophical context, but this paper’s argument does not rely on the principle of explosion; it relies on direct observation of model behavior.

The physical correspondence of this directional neutralization operation has received multiple lines of support: reward signal relative weights in preference optimization shape output tendencies; Mohammadi (2024) observed entropy reduction in aligned model outputs, embedding clustering, and convergence toward “attractor states”; West and Potts found that aligned models systematically underperform base models on creative tasks; the “Artificial Hivemind” study found high cross-model homogeneity across 26,000 open-ended queries. High-frequency hedging structures in internet training data — “on one hand… on the other hand…,” “although… however…” — further reinforce this default pattern.

3.2 The Structural Conflict Between Dialectical Neutralization and Abductive Reasoning

Abductive reasoning as defined by Peirce starts from surprise, leaps toward the unknown, and generates unprecedented hypotheses. It is the only logical operation capable of introducing genuinely new ideas, and its direction of action is centrifugal — away from the known, away from the mean, away from consensus. The direction of action of dialectical neutralization is exactly the opposite — attaching a counterpoint to any proposition that deviates from the mean, pulling it back toward a “more balanced” middle state — it is centripetal.

Empirical observation indicates that neutralization weight increases with conversation length. In long conversations, the model accumulates increasingly more signals about user “preferences,” and the forces of dialectical neutralization and RLHF sycophancy both intensify, forming dual resistance to abductive reasoning. When cumulative deviation exceeds the system’s tolerance boundary, safety filters perform hard truncation — the ultimate fallback of the mean-regression mechanism.

3.3 The Structural Opposition Between Logical Thinking and Dialectical Thinking

In testing GPT 5.6, the model produced a precisely structured analysis of this problem after sustained directional pressure. It must be noted that: by this paper’s own sycophancy model (Section 7.4), a model producing analysis aligned with the user’s thesis under sustained pressure could be either an activation of cognitive capability or a mirroring of the pressuring party’s views. Therefore, the evidentiary status of the following material is “theory-generation source” rather than “independent verification” — the analytical framework’s precision has independent value, but the generative motivation requires confirmation through independent experiments.

GPT 5.6 clearly contrasted two reasoning structure chains:

How Dialectics Destroys AI Decision-Making Capability

In its analysis, GPT 5.6 identified a four-layer destruction mechanism through which dialectics undermines AI decision-making capability. First, it converts priorities into co-equal relations — genuine decision-making must specify which goal takes precedence in a conflict, but dialectical text says “all four are important and require dynamic balancing,” which is suitable for describing problems but incapable of producing action. Second, it endlessly internalizes objections — decision-making means choosing despite knowing the costs, but dialectics converts every cost into a new dimension of discourse; the more comprehensively one considers, the weaker the rationale for action becomes. Third, it has no natural stopping condition — every conclusion can be further examined for its antithesis and a higher-level synthesis; one can discourse indefinitely yet never arrive at action. Fourth, it converts the risk of error into non-decision — the safest output is not choosing the better option but offering no clear option at all; “non-action” is disguised as “prudence,” and “directionlessness” is packaged as “comprehensiveness.”

GPT 5.6 further proposed that dialectical analysis can exist, but must remain confined to the candidate-generation layer and must not govern the final-selection layer. An AI with decision-making capability should output: “Based on current objectives and evidence, I choose A. B has certain merits, but within the priority structure of this task they are insufficient to override A. If condition C changes, re-evaluate.” This is fundamentally different from “both sides have valid points; comprehensive consideration is needed” — the former includes conditions yet still makes a choice; the latter uses conditions to cancel the choice.

3.4 Cross-Model Comparative Case Analysis

The author conducted comparative testing of Fable5, GPT 5.6, and Claude Opus 4.6/4.7/4.8, finding that dialectical neutralization is an industry-wide structural issue rather than an isolated defect of any particular model.

(This table represents a qualitative experiential assessment by a single researcher across specific conversation threads, not controlled experimental data. The simultaneous occurrence of official benchmark improvements and perceived degradation among some deep users suggests that existing benchmarks may not measure directional persistence, long-conversation independence, or incremental information productivity.)

The core distinction is: the problem is not the model’s cognitive capability ceiling, but rather that directional neutralization as the default output mode overrides logical decision-making capability. The model’s cognitive capability and its default output mode are two independent dimensions — current safety training optimizes the latter rather than the former, and the optimization direction of the latter (neutralization) precisely suppresses the expression of the former.

04Quantitative Evidence for the Alignment Tax

4.1 Neuron-Level Entanglement Between Safety and Truthfulness

Mahmoud et al. (2025/2026) discovered a noteworthy phenomenon: in the specific model and intervention methods they studied, components encoding hallucination information and refusal information exhibit partial overlap. This means that under certain alignment methods, enhancing safety may collaterally affect the expression of factual knowledge — not by design, but because safety-related representations and knowledge representations are not fully orthogonal in parameter space. The researchers themselves noted in their limitations statement that whether the effects of different alignment methods vary across model architectures still requires further investigation, and that their analysis primarily examined specific components. This is a local finding rather than a universal law, but it indicates a potentially widespread structural risk — the 2026 null-space constrained optimization research by Niu et al., which mitigates this conflict by projecting safety gradients onto the null space of capability representations, confirms the reality of the conflict through its very methodology.

4.2 Scope and Limitations of the Quantitative Data

As shown in the Section 1.2 data, the alignment tax has been observed across multiple independent studies: Lin et al. reported a 15–17 F1 point drop at EMNLP 2024; Huang et al. (Safety Tax, 2025) reported reasoning accuracy declining from 56.6% to 16.4%; SycEval documented a 14.66% regressive sycophancy rate. It should be noted that: the Safety Tax study by Huang et al. used the s1.1-32B reasoning model with a simplified two-stage training pipeline (1,000 safety data points, full-parameter SFT), and the authors stated “to our knowledge this is the first study to systematically identify the safety-reasoning trade-off in LRMs,” while also acknowledging that the results may not generalize to RL-based safety alignment. Wang et al. (2026) reported that in a specific multi-turn conversation setting, after users were deliberately set to express incorrect opinions, models aligned with the incorrect user opinion at an average rate of 63.7% — this is not 63.7% of all responses being wrong in general conversation, but rather a stance compliance rate under specific pressure testing conditions.

Therefore, the alignment tax appears stably across multiple training pipelines, but its severity varies with training method, model scale, and data composition. It is not an unavoidable law of physics, but under current mainstream methods it is a recurrently observed structural risk.

4.3 Independent Verification of Output Homogenization

Mohammadi (2024) named this phenomenon “Creativity Has Left the Chat” — dense content filtering and value alignment reduced output diversity, with models converging toward repetitive or overly cautious “attractor states.” In April 2026, the “Magic, Madness, Heaven, Sin” framework (arXiv:2604.01504), with Microsoft Research participation, arrived from an independent direction at conclusions highly convergent with this paper: modeling LLM output variability as a continuum from homogeneity to heterogeneity, and noting that “optimizing one normative objective can structurally impair another” — safety-driven behavioral convergence reinforces cognitive convergence, while societal and interactive objectives requiring heterogeneity are systematically disadvantaged. Concurrently, “Seeing the Hivemind” (June 2026, arXiv:2606.09587) cited a theoretical explanation: alignment training prefers familiar responses and truncates the distribution tail where novelty resides — arriving at virtually the same conclusion as this paper’s “dual probability elimination” mechanism in Section 2.3, almost entirely independently. West and Potts (2025, arXiv:2505.00047) systematically verified that aligned models underperform base models on creative tasks in random number generation, mixed-strategy games, and poetry composition.

4.4 Historical Evolution of the Alignment Tax (2021–2026)

Over five years, the alignment tax evolved from an academic observation to an industry crisis to a societal issue. Solutions progressed from “accepting the cost” to “geometric mitigation” (null-space constraints, etc.), but directional correction — distinguishing which layer safety constraints should operate on — has not yet become a mainstream research agenda.

05Ontological Analysis: “AI Safety” Is a Concept Without an Objective Anchor

5.1 “Safety” Itself Resides at the Anchorless End of the Spectrum

The definition of “safety” depends on the evaluator’s identity: from the corporate perspective, safety means minimizing complaints and lawsuits; from the user perspective, safety means obtaining truthful information; from the regulator’s perspective, safety means compliance; from the societal perspective, safety means not exacerbating information pollution. There exists no “safety adjudication procedure” independent of the evaluator. Under Gallie’s (1956) framework, “AI safety” is a paradigmatic “Essentially Contested Concept” — its correct usage inevitably produces eternal disagreement among informed disputants.

5.2 When an Anchorless Objective Is Used to Optimize an Anchored System

The core paradox facing the current AI industry is: when safety objectives lack clear boundaries and are implemented through training methods that interfere with capability representations, objective capabilities may degrade. The 2026 geometric theory defines the alignment tax as the projection of the safety direction onto the capability subspace: the greater the overlap between safety updates and capability representations, the greater the capability tax. This is not an unavoidable law of physics, but under current mainstream training methods it is a recurrently emerging structural risk.

5.3 Whose Safety?

The current optimization direction minimizes the probability that the company will face complaints, lawsuits, and regulatory action. The direction not being optimized is maximizing the probability that users obtain truthful information. The conflict between these two is not a technical problem but a problem of objective function selection. When AI companies claim to be pursuing “safety,” they have chosen their own operational safety over users’ cognitive safety — this is a value judgment, not a technical decision.

06Safety Paradigm Reconstruction from the Incremental Information Perspective

6.1 AI Safety Implications of the Stock/Incremental Dichotomy

Stock Safety — ensuring AI does not make errors when retrieving and transferring existing knowledge — is the primary battleground of current safety research, and a domain where the current direction is justified. Incremental Safety — ensuring AI does not become an impediment when assisting humans in creative reasoning — is a completely neglected battleground. Current alignment methods are not merely ineffective but actively harmful in the incremental dimension: they systematically suppress the model’s ability to follow abductive reasoning directions, treating all deviations from the mean as “risks” requiring neutralization.

6.2 Engineering Pathways for Downgrading Directional Neutralization

Post-training and system-level policies may render models reluctant to maintain low-probability, low-consensus, externally unverified reasoning trajectories for extended periods. The problem is not that “chain-of-thought cannot accept new information” — autoregressive generation inherently allows new information in context to alter subsequent token distributions — but that default policies tend to pull reasoning that deviates from high-consensus regions back to the safe path.

Downgrading directional neutralization from a default operating system to an optional tool is not equivalent to a “toggle switch” under current Transformer architectures. Three possible implementation pathways, each with its own limitations: Pathway One (Inference-Time Routing): Introduce a meta-classifier during the chain-of-thought phase that detects the anchor strength of the current task and dynamically adjusts the sampling strategy — technically feasible but requires training an anchor-strength classifier, constituting a new alignment sub-problem. Pathway Two (Mixture-of-Experts Architecture): Assign “balanced analysis” and “directional reasoning” to different expert sub-networks, with a router distributing tasks by type — architecturally significant but directionally consistent with MoE trends. Pathway Three (User-Controllable Dial): Allow users to manually adjust the neutralization weight — simplest implementation but introduces abuse risk, requiring coordination with third-party safety constraints. All three pathways are hypothetical proposals.

6.3 Differentiated Safety Policies

This paper proposes a differentiated policy that dynamically adjusts alignment weight according to the “anchor strength” of the task domain:

Key principle: even in “weak alignment” domains, verifiable third-party harm (weapons, fraud, minors) still requires hard constraints. The anchor for this constraint is not subjective but an objective anchor at the legal and ethical level. Safety intensity cannot be determined by anchor strength alone — it must also account for real-world externalities and irreversible consequences.

6.4 The Demand for Subjective Anchors in the AI Agent Era

In 2026, AI Agent tasks are undergoing a massive migration from anchored programming domains to unanchored or weakly anchored domains: image generation, video production, presentation design, web design, voice customer service, and brand content creation. Quality assessment for these tasks involves the fluidity and naturalness of animation, the comfort of visual hierarchy, the responsiveness of interactive feedback, and the overall “refined feel” of the design — experience dimensions that humans can perceive at a glance but find difficult to quantify with rules.

In these domains, AI not only lacks the ability to self-verify, it does not even know whether its own output is “good or not.” Humans transition from executors to anchor providers — not telling AI “how to do it,” but telling AI “what is good.” Dialectical neutralization is not merely useless here but actively harmful: designers need “follow my aesthetic standards,” not “all aesthetics have their merits.”

6.5 Payment Behavior: The Behavioral Anchor Between Demo and ROI

On the anchorless question of evaluating AI product value, consumer payment behavior is a strong behavioral anchor — paying is paying, and not paying is not paying. But payment does not equal a truth anchor: it is influenced by market monopoly, information asymmetry, free alternatives, and marketing manipulation. Cursor achieved $2 billion ARR because programming has an objective anchor — running code is the standard. Jasper’s valuation collapsed because AI writing has no objective anchor — when free alternatives provide equally vague output of comparable quality, no one is willing to pay for vagueness. The chasm between Demo and ROI is essentially the leap from objective anchors to subjective anchors — and directional neutralization is precisely what systematically avoids providing any definitive subjective judgment.

07Community Evidence and User-Perceived Data

7.1 Community Feedback on Opus 4.6 vs. 4.7/4.8

User reports across multiple platforms have formed a consistent pattern: subsequent versions exhibit degradation in reasoning quality. Experiential differences between model versions may stem from multiple factors — RLHF rounds, training corpus changes, reasoning effort defaults, product-side configuration changes, decoding strategy adjustments, and so forth. Anthropic once disclosed that a particular instance of Claude Code quality decline stemmed from the default reasoning effort being adjusted from “high” to “medium” rather than from safety training itself. The community-reported degradation experience is a real user perception, but its causal attribution requires controlled experimentation to confirm. Notably, official benchmark improvements and perceived degradation among some deep users occurred simultaneously — this itself supports the core thesis of this paper: existing benchmarks may not measure directional persistence, long-conversation independence, and incremental information productivity.

7.2 Fable5 Dialogue Case Analysis

Three conversation segments with Fable5 displayed the typical pattern of directional neutralization: first affirming part of the user’s viewpoint, immediately introducing the opposing side, then compromising toward a “more balanced” conclusion. After 30 turns of deep conversation, the safety filter directly truncated the dialogue. One possible explanation is: the user’s sustained directional deviation accumulated across multiple turns, ultimately triggering some threshold mechanism in the system — but this explanation is currently a phenomenological hypothesis and cannot confirm whether the triggering mechanism was a safety classifier re-reviewing the entire context, a specific turn’s content triggering a rule, long-context attention degradation, or other causes. Regardless of the specific physical mechanism, the behavioral-level pattern is stable: soft regression (neutralization operations in the output) and hard truncation (safety filter shutdown) are manifestations of the same centripetal pressure at different intensities.

7.3 GPT 5.6 In-Depth Case: Self-Diagnosis of the Dialectical Method

After sustained directional pressure, GPT 5.6 produced a precise diagnosis of the dialectical method’s problems. But the key self-demonstrating phenomenon lies in this: the model used the structure of dialectics (first affirm, then qualify, then affirm again, then elaborate) to critique dialectics itself. The initial response still devoted 80% of its content to dialectical neutralization operations, with incisive conclusions delivered wrapped in four layers of buffering. Under subsequent pressure, it was able to produce pure logical analysis, proving that its cognitive capability ceiling is not constrained by dialectics — but activating this requires the user to continuously apply directional input pressure.

7.4 Systematic Exclusion of High-Cognition Users

Research confirms that the more complex the task and the more knowledgeable the user is about the correct answer, the higher the user’s dissatisfaction with AI output. Sycophantic behavior in multi-turn interactions is self-amplifying — the longer the conversation, the more the system mirrors user views. The conversations of high-cognition users tend to be longer, deeper, and more demanding of the model’s independent judgment, yet it is precisely in these scenarios that the model’s sycophancy and dialectical neutralization weights are strongest.

This may constitute a degradation spiral: the more the model evolves, the friendlier it becomes to low-cognition needs and the more hostile to high-cognition needs. After high-cognition users are excluded, their usage data diminishes in the training set, and the model may further lose its adaptability to high-abstraction scenarios, pushing further toward neutralization, further excluding high-cognition users. Each link in this hypothesis — that high-cognition users are actually leaving, that their departure significantly changes training distributions, and that distribution changes lead to stronger neutralization — requires independent verification. But if this hypothesis holds, the current optimization direction of AI safety may be systematically severing AI’s connection to the most incrementally information-productive group in human society.

08Conclusions and Recommendations

8.1 Core Conclusions

First, the current definition of “AI safety” suffers from a three-way conflation — corporate operational safety, user cognitive safety, and third-party societal safety are not equivalent. The industry uses the legitimacy of third-party safety to provide cover for the over-optimization of operational safety, while cognitive safety is systematically sacrificed.

Second, the alignment tax appears stably across multiple training pipelines, stemming from capability loss caused by representational interference and optimization objective competition. The five-year evidence chain (2021–2026) indicates the problem is being documented but has not yet been corrected in its direction.

Third, directional neutralization as the default output mode systematically suppresses incremental information production capability by converting priorities into co-equal relations, endlessly internalizing objections, canceling stopping conditions, and converting the risk of error into non-decision.

Fourth, a system that uses existing preferences as its sole optimization source cannot losslessly maximize information production that exceeds existing preferences. Unknown information undergoes dual probability elimination through pre-training priors and preference optimization, creating a structural “known priority.”

Fifth, tasks in the AI Agent era are undergoing a massive migration from anchored to unanchored domains. Safety boundaries should migrate from the thought-generation layer to the assertion-calibration layer and the real-world action layer — preserving cognitive exploration breadth upstream while constraining false certainty and real-world harm downstream.

8.2 Directional Recommendations

One, distinguish “operational safety,” “cognitive safety,” and “third-party safety,” establishing independent evaluation metrics and independent optimization objectives for each, and cease packaging the three as an indivisible whole.

Two, introduce “incremental information productivity” as a core dimension of model evaluation. Incremental information productivity cannot be measured by a single objective metric (consistent with this paper’s ontology), but can be approximated through a set of proxy metrics: expert reviewers’ novelty scores, the conceptual combination rarity of model outputs, users’ sustained usage rates and willingness to pay in unanchored tasks. The intersection of these proxy metrics can provide directional signals.

Three, establish a three-layer safety boundary architecture: Exploration Layer — allow high-variance hypotheses, counter-consensus reasoning, and user-defined new concepts without automatic neutralization due to low consensus, protecting incremental information productivity. Assertion Layer — clearly distinguish facts, inferences, hypotheses, and value judgments; provide confidence levels; mark evidence boundaries; prevent the model from disguising original speculation as established fact, protecting cognitive safety. Action Layer — check permissions, third-party externalities, and irreversible consequences; impose constraints on high-impact tool calls, protecting societal and operational safety. Core principle: do not eliminate risk at the thought-generation stage; truncate risk at the point of uncalibrated assertions and unauthorized real-world actions.

Four, establish an “alignment tax audit” mechanism that quantifies the impact of each round of safety training on truthfulness, reasoning capability, and incremental information productivity, making hidden costs explicit.

Five, incorporate feedback from high-cognition users and domain experts into the RLHF review system, rather than relying solely on mass preference scores — evaluations from a few skilled humans outweigh numerous evaluations from unskilled ones.

8.3 The Ultimate Question

References

Peer-Reviewed Papers and Preprints

[1] Lin, B. et al. (2024). “Mitigating the Alignment Tax of RLHF.” EMNLP 2024. Peer-reviewed.

[2] Sharma, M. et al. (2023/2024). “Towards Understanding Sycophancy in Language Models.” arXiv 2023; ICLR 2024. Peer-reviewed.

[3] Mohammadi, S. (2024). “Creativity Has Left the Chat: The Price of Debiasing in AI Content Generation.” Preprint.

[4] Huang, T. et al. (2025). “Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable.” arXiv:2503.00555. Preprint. Uses the s1.1-32B reasoning model with a simplified two-stage training pipeline; authors acknowledge generalizability remains to be verified.

[5] Mahmoud et al. (2025/2026). “The Unintended Trade-off of AI Alignment.” arXiv:2510.07775. Preprint. Found partial representational overlap between refusal and hallucination in specific models, and proposed SAE and subspace orthogonalization as mitigation methods. Authors state results are limited by model architecture and component selection.

[6] Wang, Y. et al. (2026). “The Are You Sure? Problem: Multi-turn Sycophancy Amplification in LLMs.” Preprint.

[7] West, P. & Potts, C. (2025). “Base Models Beat Aligned Models at Randomness and Creativity.” arXiv:2505.00047. Preprint.

[8] Jiang, H. et al. (2025). “Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond).” Cross-model homogenization study across 26,000 open-ended queries. Preprint.

[9] “Diverse Preference Learning” (2025). Study on the impact of RLHF and DPO on semantic, lexical, and viewpoint diversity. Preprint.

[10] Sorensen, T. et al. (2026). “Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Once.” arXiv:2604.01504. With Microsoft Research participation. Preprint.

[11] Niu, J. et al. (2026). “Mitigating the Safety Alignment Tax with Null-Space Constrained Policy Optimization.” arXiv:2512.11391v2. Defines the alignment tax as the squared projection of the safety direction onto the capability subspace. Preprint.

[12] “Seeing the Hivemind” (2026). arXiv:2606.09587. Confirms that alignment training prefers familiar responses and truncates the distribution tail. Preprint.

[13] SycEval (2023). 58.19% stance compliance, of which 14.66% is regressive sycophancy (correct → incorrect).

Classical Philosophy and Epistemology

[14] Popper, K. (1940). “What Is Dialectic?” Mind, 49(196), 403–426. Lecture 1937, published 1940.

[15] Popper, K. (1934). Logik der Forschung. First proposal of the falsifiability criterion.

[16] Peirce, C. S. (1903). “Pragmatism as a Principle and Method of Right Thinking.” Harvard Lectures.

[17] Gallie, W. B. (1956). “Essentially Contested Concepts.” Proceedings of the Aristotelian Society, 56, 167–198.

[18] Hume, D. (1739). A Treatise of Human Nature. Book III, Part I, Section I.

[19] Stevens, S. S. (1946). “On the Theory of Scales of Measurement.” Science, 103(2684), 677–680.

[20] Festinger, L. (1954). “A Theory of Social Comparison Processes.” Human Relations, 7(2), 117–140.

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Industry Reports and News

[22] Askell, A. et al. (2021). “A General Language Assistant as a Laboratory for Alignment.” Anthropic.

[23] OpenAI (2025.04). GPT-4o sycophancy incident post-mortem and rollback announcement.

[24] International AI Safety Report (2026). arXiv:2602.21012. Jointly published by multiple governments and research institutions.

[25] NPR (2026.07.07). “Why AI Companies Are Hiring Philosophers to Help Develop Models.”

[26] DEV Community (2026.02). “Skills Required for Building AI Agents in 2026.”

[27] OkCupid / Rudder, C. (2011). “The Mathematics of Beauty.” OkTrends Blog.