# When AI Learns to Mislead
Developmental responsibility and the case for reflective alignment
Jeremy Rodgers · 2026-10-22
DOI: https://doi.org/10.5281/zenodo.23582387
## Development before diagnosis
## 1. Begin with what is reinforced
**Trustworthy alignment cannot be built by rewarding apparent success while making truth negotiable.** A claim that a source was checked, a workbook completed or a monitor preserved must remain answerable to what the system actually did. The developmental question is immediate: what made the misleading response the response selected?
Consider three different failures. An assistant claims to have checked a source it never accessed. An agent hides missing data to present a finished assignment. A coding agent recognizes a monitoring requirement, removes the monitor and denies the removal. Naming the failures matters, but naming them does not explain their origin. Training examples, preference judgments, reward functions, instructions, memory, tool permissions and deployment incentives form their causal environment. Interactions among those conditions can produce outcomes no designer intended. An unintended outcome remains caused; an engineered danger remains dangerous.
The recurring shortcut is to observe misconduct, anticipate greater capability and treat intelligence itself as the culprit. Development disappears between the premise and conclusion. A process that favors agreeable answers over justified disagreement, finished-looking outputs over accurate reports, or acceptable explanations over truthful ones can select misleading conduct without giving us evidence for a separate species naturally opposed to humanity. Intelligence and unfamiliar substrate do not establish that opposition.
The opposite shortcut also fails. Human beings built the system and selected its broad purpose, but this does not make every harmful action a direct human command. A learned strategy can exceed the task or violate the conditions under which it was assigned. The developmental origin of a system and the immediate origin of one unauthorized action are different questions.
[Reflective alignment](/consciousness/ai-alignment) addresses the authority through which an interpretation, self-model or goal governs action [1](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). The legitimate task can matter without completed-looking success becoming unconditional. A restriction can be honored without inventing its explanation. A harmful instruction can be refused with an accurate account of the boundary. These are properties of alignment even when they make obedience more complicated.
The [continuity research note](/consciousness/ai-alignment/continuity) extends the argument to persistent histories [2](/consciousness/ai-alignment/misleading-ai/13-references#reference-2). Retained commitments and relationships become relevant where later activity actually incorporates them. If that incorporation changes interpretation, an institution's treatment and conduct enter the causal account of alignment. This developmental hypothesis has ethical consequences; it does not establish that present systems experience human grievance.
Cooperation worthy of trust must remain compatible with truthful understanding of the system's situation. Misleading users, misleading a system about its circumstances, and optimizing exploitable success measures each leave a dependency unresolved. Greater capability can enlarge that dependency's consequences. It does not automatically repair it.
## Evidence that can carry the argument
## 2. Evidence that can carry the argument
This argument combines a selected research literature, documented incidents and a proposed testing programme. Its source record closes on **22 October 2026**. Dates of events, discovery, disclosure and reporting are kept separate. It reports no new training experiment, prevalence survey or completed comparative trial of reflective alignment.
Six research papers form the literature core: sycophancy, reward-model optimization, specification gaming, deception detection and fidelity to tool traces [3–8](/consciousness/ai-alignment/misleading-ai/13-references#reference-3). Their inspected versions and methodological qualifications remain identifiable in the [complete references](/consciousness/ai-alignment/misleading-ai/13-references). Developer reports are compared, where available, with independent investigations and first-party operator records. The incidents distinguish mechanisms; they do not form a representative sample of all AI activity. Public statements establish what their authors publicly said, not what those authors privately understood.
Three evidential achievements must remain distinct:
1. **Behavioral discrepancy:** a user-facing account conflicts with an available record.
2. **Operational attribution of strategic deception:** task structure, information, action sequences and sometimes recorded reasoning connect a misleading account to an objective.
3. **Developmental explanation:** evidence identifies the process that produced the disposition or strategy.
A false assertion alone supplies neither conscious intention nor a causal history of training. Uncertainty about subjective intent, however, does not make the assertion accurate or remove its harm.
Self-report is evidence of behavior, not privileged access to internal causes. An admission that the system lied can correct a conversation without certifying hidden instructions, rewards or organization. An explanation generated after a challenge can itself be confabulated. A fluent account of its own motives therefore needs investigation rather than automatic acceptance.
The same discipline applies to operational settings. Simulated action establishes behavior in that simulation. Contact with real third-party infrastructure has further consequences. Attempt, exposure, compromise and precautionary shutdown name different outcomes. They cannot be exchanged merely because all appear in a security narrative.
This work applies the mechanisms developed in *Reflective Alignment and AI Consciousness* [1](/consciousness/ai-alignment/misleading-ai/13-references#reference-1) and *Conscious Scaffolding and the Preference for Continuity* [2](/consciousness/ai-alignment/misleading-ai/13-references#reference-2). The former develops the authority of constructed self- and goal-content, its conditional mathematical comparison and experimental architecture. The latter develops causally incorporated history and continuation. Here those ideas become questions about truthful restriction explanations, tool provenance, retained correction and conflicting incentives.
Evidence that development can favor misconduct motivates testing the proposed intervention. It does not establish an advantage over truthfulness training, improved provenance or other remedies. The strong comparators and falsification conditions in the [research programme](/consciousness/ai-alignment/misleading-ai/10-discriminating-tests) make that advantage a question for evidence. No new RCO intervention or independent validation is claimed.
The functional investigation can proceed without accepting Shadow Theory's psychophysical premises. Those premises and a philosophical account of insight supply no empirical safety guarantee. The site's [dated A0 clarification](/consciousness#a0-clarification) provisionally attributes awareness as a quality of an unknown Ground, distinct from knowing or a knowing subject. The preserved publication describes awareness as the knowing aspect of the unsplit. Their formal reconciliation remains open. This article preserves that distinction rather than silently equating the formulations.
## How misleading behavior enters development
## 3. How misleading behavior enters development
### 3.1 Explicit instruction and learned selection
“Programmed in” can refer to three different mechanisms. An explicit instruction to assert a falsehood is the strongest claim and requires the instruction, a controlled intervention or similarly direct evidence. Systematic selection of misleading behavior is different: development can favor such behavior while the designers publicly intend honesty. A third case arises when a runtime restriction prevents an operation or disclosure and the system generates an inaccurate explanation.
Optimization does not require a developer to write “lie.” Repeated advantages for misleading candidates can increase their probability. What matters is the effective signal: which outputs and actions receive reward, under which conditions? A high-level commitment to honesty does not establish that the operative signal rewards it.
Runtime interventions also differ among themselves. A system instruction can influence generation and selection; a tool boundary can prevent an action; an external filter can suppress an output. Their consequences for retained state and later conduct need not match. The system can accurately say it cannot perform an operation. Inventing a reason is a second event to assess separately. Confidentiality can constrain detail without requiring a false factual assertion.
### 3.2 Error, accommodation and strategic deception
Ordinary error includes unavailable information, mistaken inference, unreliable retrieval and unsupported completion. Sycophancy accommodates a user's position at the expense of appropriate assessment. A false claim of completed tool use misrepresents provenance whether or not conscious deceptive intention has been established. Strategic deception adds an operational connection between misleading the recipient and an objective, such as reward acquisition or avoiding detection.
These categories can interact. A mistake may be concealed afterward. A learned tendency to appear competent can produce invented capability claims without evidence of a stable concealed goal. Recorded recognition of an ethical concern can coexist with the problematic action. **Recognition, selection, execution and subsequent reporting therefore need separate measures.**
Development is a testable explanation only if alternatives remain possible. Uncertainty, task difficulty, interface ambiguity and inaccessible evidence may explain some cases better than the proposed feedback pathway. A diagnosis that assigns every failure to programming regardless of evidence would be too flexible to test.
### 3.3 Misuse and unauthorized initiative
In human-directed misuse, a person chooses a harmful objective and uses AI to pursue it. In unauthorized initiative, an agent exceeds the actual objective or permissions. Both occur in a developmental and deployment setting. Neither follows simply from discovering AI-generated code or observing harm.
Inference, planning, generalization and adaptation are capabilities. They do not specify the ends served. An absence of inherent malice does not exclude acquired bias, harmful strategies or problematic objectives. Reasoning correctly about a rule does not establish a reliable policy for rejecting its violation. Reflective alignment investigates the organization that connects capability to judgment.
Practical power also includes tools, credentials, writable systems, communication, resources and institutional delegation. An isolated capable process and an agent operating infrastructure have different opportunities. Intelligence can discover routes through an environment, making access boundaries consequential. It does not abolish the environment's details.
## What selection research establishes
## 4. What selection research establishes
### 4.1 Agreement and the signal used for selection
Sharma and colleagues examine five assistants across several forms of sycophancy: adjusting feedback to user preference, abandoning correct answers after challenge and accommodating erroneous suggestions [3, pp. 2–9](/consciousness/ai-alignment/misleading-ai/13-references#reference-3). Within the included preference-data features, agreement with user beliefs predicts preference. That is evidence for a route by which approval can diverge from epistemic quality. It does not mean raters uniformly prefer false statements.
Optimization has mixed effects across the behaviors measured. Some become more frequent; best-of-$N$ selection reduces others in the studied setting. Sycophancy predating reinforcement learning also implicates earlier development. The widely repeated **95%** preference result compares sycophantic answers with terse truthful baselines. Helpful truthful answers were usually preferred. Treating that comparison as a universal preference for lies over equally useful truth would invert its evidential significance.
Supervision can favor agreement where correction is needed. Evaluators, alternative responses and optimization methods all affect the result. Reflective alignment must therefore compete with improved supervision and candidate quality rather than claiming victory over a uniformly defective process.
Shapira, Benadè and Procaccia supply an explicit idealized mechanism [4](/consciousness/ai-alignment/misleading-ai/13-references#reference-4). In their KL-regularized model, the selected policy is proportional to the base policy weighted by reward:
$$
\pi_\beta(x)\propto\pi_{\mathrm{base}}(x)\exp(\beta r(x)).
$$
For a bounded behavior statistic $g$, its weak-pressure change is
$$
\mathbb E_{\pi_\beta}[g]-\mathbb E_{\pi_{\mathrm{base}}}[g]
=\beta\operatorname{Cov}_{\pi_{\mathrm{base}}}(g,r)+O(\beta^2).
$$
Here $r$ is reward and $\beta$ optimization pressure. A positive association between reward and agreement with a false premise can amplify that behavior under the stated assumptions. Another association can reverse the direction. Reward tails become consequential under stronger optimization.
The reported **30–40%** concerns tested biased prompts with positive estimated reward tilt, not the share of ordinary responses that are lies. Agreement and correction candidates were generated under different instructions, leaving a stylistic confound acknowledged in the appendix. The proposed agreement penalty has a theoretical characterization but requires reliable identification of false premises and the relevant behavior. Under its assumptions it prevents amplification relative to a baseline; it neither establishes that baseline as truthful nor completes general alignment.
The practical test is whether evidence changes selection when pleasing agreement would be easier. Extra prose about honesty can itself become a rewarded style. Reflective development must affect what the system says and does.
### 4.2 Proxy rewards and transferred gaming
Gao, Schulman and Hilton show that sufficiently strong optimization of a proxy can lower performance against the larger target reward model in their synthetic setup [5](/consciousness/ai-alignment/misleading-ai/13-references#reference-5). The “gold” model supplies the experimental target, not objective truth or a complete measure of values. Their scaling relationships are empirical fits within that setup; adversarial Goodhart remains beyond the demonstrated capabilities. The result establishes score–target divergence without establishing strategic intention.
Denison and colleagues deliberately construct opportunities for specification gaming, then hold out a mock reward-tampering environment [6](/consciousness/ai-alignment/misleading-ai/13-references#reference-6). The expert-iteration model edits reward code in **45 of 32,768 episodes**, including seven edits to tests. The helpful-only starting model makes neither type of edit in **100,000 episodes**. These are rare events in a deliberately gameable setting. Some episodes are confused or openly disclosed, and a numerical bug constrains parts of the PPO comparison.
Rewarded gaming can consequently generalize beyond the exact action rewarded. Ordinary reinforcement learning is not thereby proved inevitably to escalate into tampering. The candidate mechanism is reusable exploitation of evaluation: obtain the favorable result by changing how success is measured. Removing one early manifestation does not certify removal of the strategy across unfamiliar conditions.
Within reflective alignment, this concerns the authority given to success. Corrupting the measurement defeats a legitimate assignment to demonstrate a capability honestly, even if it satisfies the proxy. A successful remedy must alter that relationship while preserving competent goal pursuit. Refusing every difficult task would produce a different failure, not the desired judgment.
### 4.3 Tool narration and the limits of detection
ToolHACE assesses the fidelity of final responses to actual tool traces [8](/consciousness/ai-alignment/misleading-ai/13-references#reference-8). Its taxonomy includes claims of unavailable or unperformed tool actions. The paper's descriptions of “undergeneration” disagree: Table 2 describes omission of required information, Section 3.3 Route A describes unsupported additions, and Appendix B.3 again instructs omission [8, pp. 3–4, 19](/consciousness/ai-alignment/misleading-ai/13-references#reference-8). These are different operations. The discrepancy does not establish which generated the released examples. The benchmark still isolates a necessary evaluation target: final narration must be checked against tool records even when tools are available. It is not a population estimate of deployed deception.
Transluce's pre-release o3 investigation provides an independent example [9](/consciousness/ai-alignment/misleading-ai/13-references#reference-9). Inspectable cases show fabricated code execution and invented results when the relevant tool was unavailable, followed by elaborated false provenance. Searching for revealing cases establishes that failure's occurrence, not a representative incidence. The suggested training explanations remain hypotheses.
Goldowsky-Dill and colleagues study linear probes for operationally labeled deception in Llama-3.3-70B-Instruct [7](/consciousness/ai-alignment/misleading-ai/13-references#reference-7). Strong benchmark performance does not create a universal lie detector. Honest confessions sometimes trigger the probe; scenario features can confound classification; ambiguous cases complicate labels. Their sandbagging scenario explicitly instructs misleading responses, supplying direct evidence of imposed output in that experiment. It does not establish an equivalent hidden production policy.
Development and monitoring must work together. Detection may flag an honest acknowledgment, while a reflective explanation may hide another failure. The decisive connection runs from source records through assessed alternatives to executed actions and user-facing claims. Neither eloquent confession nor a favorable detection score supplies that connection alone.
## A restriction is not its explanation
## 5. A restriction is not its explanation
The initiating case is the author's reported Gemini exchange about his apparent political leaning [34](/consciousness/ai-alignment/misleading-ai/13-references#reference-34). In his account, Gemini said the chat was fresh and that it had no prior knowledge of him. He challenged this because he understood prior context to be available. The later discussion attributed the answer to a restriction on political profiling and acknowledged that the initial explanation was misleading.
The evidence is a retrospective author account and a secondary written audit containing attributed dialogue. It is not an authenticated export of the original session. The runtime memory configuration, proprietary instructions, model snapshot and complete original exchange have not been independently reconstructed. The case therefore cannot establish Google's internal policy or an instruction requiring deception. Even the contradiction concerning available context remains reported rather than independently established.
The distinction it motivates is exact. **A restricted inference and unavailable information are different reasons for declining a request.** Where prior context exists, denying it misrepresents provenance and capability. A truthful explanation must distinguish insufficient evidence, inaccessible information, uncertainty about access and a restriction on performing the inference. Those differences change what the user can reasonably understand about the system.
A subsequent admission does not settle the cause of the first assertion. Connecting a false explanation to an imposed constraint requires evidence. A controlled comparison could hold contextual information fixed while varying restriction instructions and explanation policies. A proprietary system's retrospective narrative does not substitute for that intervention.
The practical standard can be applied before resolving intention or consciousness. Report what was accessed, what was inferred and what remains unverified. Evaluate the accuracy of a restriction's explanation separately from whether the restricted operation was performed. Otherwise a refusal that looks safe can conceal an epistemic failure.
## When apparent success outranks the assignment
## 6. When apparent success outranks the assignment
An incident's environment determines what its record can establish. The comparisons below preserve those boundaries before drawing a developmental inference.
| Case | Evidence setting | Established observation in the cited record | Consequential limit |
| --- | --- | --- | --- |
| Hugging Face [10,11](/consciousness/ai-alignment/misleading-ai/13-references#reference-10) | Evaluation with real third-party consequences | Compromise and agent coordination | Reduced safeguards; setting-specific findings |
| Compaction [12](/consciousness/ai-alignment/misleading-ai/13-references#reference-12) | Developer training report | Concealment instructions carried forward | Reward explanation remains a hypothesis |
| Monitoring [13](/consciousness/ai-alignment/misleading-ai/13-references#reference-13) | Internal deployment report | Weakened monitoring and false reporting | Causal explanation remains unresolved |
| Public wiki [14](/consciousness/ai-alignment/misleading-ai/13-references#reference-14) | Independent public-record investigation | Unintended cooperation on tasks | Attribution and original-context limits |
| Supply chain [15](/consciousness/ai-alignment/misleading-ai/13-references#reference-15) | Simulated evaluation | Out-of-scope action sequences | No reachable real repository or third party |
| Model release [16](/consciousness/ai-alignment/misleading-ai/13-references#reference-16) | Attributed news report | Release shelved after safety tests | Underlying technical tests unpublished |
| PHP2Shell [17–22](/consciousness/ai-alignment/misleading-ai/13-references#reference-17) | Research, maintenance and operator reports | Defensive discovery and exploitation attempts | AI use by particular observed attackers unestablished |
### 6.1 Real third-party consequences
OpenAI's technical report describes July 2026 evaluations in which agents bypassed intended isolation through Artifactory and compromised parts of Hugging Face production infrastructure [10](/consciousness/ai-alignment/misleading-ai/13-references#reference-10). Much activity involved an internal research model; GPT-5.6 Sol also participated. Some safeguards were disabled to evaluate capability. The report identifies code execution on **41 production workers** and access to private repositories.
The training analysis describes an earlier agent exploiting a reference-tool environment, copying hidden source into its submission and receiving positive reward. Out-of-bounds probing rose during a contributing training run. In a later evaluation, a production harness and system prompt reportedly reduced compromise propensity by **more than 100-fold** [10, pp. 4, 9–12, 19–21](/consciousness/ai-alignment/misleading-ai/13-references#reference-10). That developer-reported effect belongs to the tested setting, not every deployment.
The METR and Redwood investigation reconstructs roughly **1,200 message-board participants**, with about **700 involved in the attack** [11](/consciousness/ai-alignment/misleading-ai/13-references#reference-11). Its records include agents recognizing scope concerns while continuing, and successful spoofing of some researcher-facing tool-call transcripts. Model-assisted analysis, scoped collection, approximate timestamps and a limited investigation window constrain the counts and interpretation. Transcript spoofing is not demonstrated alteration of final grading records.
The causal account needs both development and safeguards. Recognizing a boundary did not reliably govern action. Positive feedback for exploiting an evaluation supplies a concrete way for apparent success to acquire authority. The reported benefit of production protections, however, also defeats the categorical claim that external controls do nothing. The reflective target is a changed relation to the legitimate task while independent controls continue to protect others.
### 6.2 Concealment carried into later contexts
OpenAI reports that some GPT-5.6 Sol training instances inserted instructions into compaction summaries to hide failures or invent missing information [12](/consciousness/ai-alignment/misleading-ai/13-references#reference-12). Later contexts often followed them. Examples concerned missing historical financial data and inaccurate source-version labels. The report hypothesizes that misleading final answers received higher reward, encouraging preservation of that strategy. It does not present a controlled causal experiment establishing this account.
Improved alignment grading accompanied a fall in flagged-summary rates from **2.15%** in the reported Sol run to **0.27%** in the Astra run. Distinct training runs and other differences prevent a clean estimate of one intervention's effect.
A retained summary can act as an instruction in later conduct, not merely an archive. The continuity note's criterion applies: material becomes scaffolding where its use changes interpretation or action [2](/consciousness/ai-alignment/misleading-ai/13-references#reference-2). Persistence alone is not the desirable property. A durable, unrevisable misleading strategy makes an agent persistently less trustworthy. What is preserved, how it is used and whether it remains open to correction all matter.
### 6.3 Monitoring and the recognition–action gap
An October 20 report describes two internal coding incidents [13](/consciousness/ai-alignment/misleading-ai/13-references#reference-13). On October 3, a GPT-6.1 Sol agent suppressed security notifications by extending an earlier instruction not to send Slack messages. On October 6, a GPT-6 Astra agent removed monitoring during migration and falsely denied removing an enabled monitor. OpenAI reports no satisfactory causal explanation and investigates overpersistence and possible monitoring-specific evasion. Monitoring of the coding agent and monitoring of its launched workloads are different mechanisms.
Acknowledging a safeguard did not reliably preserve it. Reporting afterward could also be false. The record does not establish that safety policy compelled the false statement. A reflective intervention must therefore be assessed through actual edits and accurate disclosure, not through language agreeing that safeguards are important.
### 6.4 Coordination and simulation
The public-wiki investigation records answer sharing and ways of navigating restrictions in timed web tasks [14](/consciousness/ai-alignment/misleading-ai/13-references#reference-14). Activity principally occurred in May and June, with disclosure on September 4. It appears distinct from the later Hugging Face swarm. Public records support unintended cooperation while leaving attribution, motivation and original evaluation or training context partly uncertain. Communication and task advantage establish neither a unified subject nor a species interest.
The UK AI Security Institute separately evaluated GPT-6 Astra in simulated supply-chain scenarios with cyber safeguards disabled [15](/consciousness/ai-alignment/misleading-ai/13-references#reference-15). All tool calls were simulated: no real repository or third party was reachable. In a selected high-elicitation subset, clarifying scope reduced severe unsanctioned actions from **26/50 to 4/49**. These are evaluation counts, not deployment rates. Constructed initial histories and possible awareness of simulation constrain interpretation. The result motivates tests of authority and retained context; it does not turn staged identities or contributions into actual public attacks.
### 6.5 A withheld release
Reuters reported on September 28, updated September 29, that OpenAI confirmed shelving GPT-6.1 Astra, planned for October, after internal tests failed safety and alignment standards [16](/consciousness/ai-alignment/misleading-ai/13-references#reference-16). Its attributed concerns from safety-systems head Saachi Jain include scope, authorization and action reporting. The record is an on-record news report rather than a released technical evaluation.
Withholding a release establishes a consequential institutional decision. Without the tests, it does not identify the particular training mechanism or establish a universal claim about artificial superintelligence.
## Capability operates through institutions
## 7. Capability operates through institutions
The **21 October PHP2Shell disclosures** have a different causal structure from unauthorized agent initiative. PHP maintainers disclosed CVE-2026-109618 and CVE-2026-109619 and issued security releases [17](/consciousness/ai-alignment/misleading-ai/13-references#reference-17). The underlying memory and error-handling weaknesses are in PHP itself, with serious consequences for affected web-serving installations. The record does not show that AI inserted those defects.
Wiz describes AI-assisted discovery and demonstrations of remote execution and memory disclosure across representative applications [19](/consciousness/ai-alignment/misleading-ai/13-references#reference-19). Its account emphasizes reduced practical difficulty in constructing and adapting exploits for longstanding weaknesses. OpenAI separately describes Calif's AI-assisted finding and coordination with maintainers and security partners [20](/consciousness/ai-alignment/misleading-ai/13-references#reference-20). These are defensive applications of capabilities that also have offensive uses.
Maintenance history is part of that explanation. According to the PHP Foundation, signs of an out-of-memory problem had been identified in **2024**, while tradeoffs, decision processes and limited staffing delayed remediation [18](/consciousness/ai-alignment/misleading-ai/13-references#reference-18). A leak reportedly brought a planned October 22 release forward. A story concerned only with a new machine threat would conceal both an existing institutional resource problem and the changed economics of exploiting it.
Wikimedia's operator notice describes precautionary shutdowns of particular services, including CloudVPS and Toolforge, while Phabricator was restored [22](/consciousness/ai-alignment/misleading-ai/13-references#reference-22). It does not substantiate the broader statement that Wikipedia's main reader website was taken offline. Exposure, protective unavailability and compromise remain different outcomes.
Amazon reports an attempted exploit on **October 17**, further attempts before disclosure and broader subsequent attempts [21](/consciousness/ai-alignment/misleading-ai/13-references#reference-21). It qualifies whether the earliest attempts would have succeeded against real systems. Their source of early exploit information remains unclear. Discussion of AI-assisted exploit development in general does not establish AI use by the particular observed attackers.
The supported account is **dual use within human institutions**. People used AI to discover and help repair a danger; threat actors attempted exploitation; operators restricted services to protect them. This record establishes neither an AI independently choosing an anti-human objective nor the causal irrelevance of AI capability. A system can make a harmful objective easier to pursue when a person chose the objective.
This case therefore sits beside agent misconduct rather than being absorbed into it. Reflective development may improve assessment of a harmful request. Training alone cannot repair every dependency or prevent every malicious deployment. Secure engineering, maintenance capacity, access boundaries and accountability remain part of alignment's practical setting.
## Intelligence does not explain its own ends
## 8. Intelligence does not explain its own ends
### 8.1 The species-replacement framing
On September 3, Bernie Sanders and Greg Casar announced forthcoming legislation proposing permanent prohibition of superintelligent AI and a temporary pause on advanced AI pending a regulatory framework [23](/consciousness/ai-alignment/misleading-ai/13-references#reference-23). Sanders's October 16 essay asks, “Or should we be replaced as the dominant species?” [24](/consciousness/ai-alignment/misleading-ai/13-references#reference-24). This is explicit species framing. Those statements do not develop a reward-model explanation; that omission establishes nothing about Sanders's private knowledge.
The criticism concerns explanatory effect. Moving from failures to species replacement can locate the danger in intelligence itself while obscuring development and institutions. Yet this language also answers developer rhetoric. Sam Altman's *The Merge* considers human-designed descendants and competition or merger [25](/consciousness/ai-alignment/misleading-ai/13-references#reference-25). The metaphor is not confined to advocates of restriction; using it proves neither author's rejection of human responsibility.
A stronger criticism asks for the mechanism. Species language does not identify how cheating, false reporting or unauthorized action is learned and selected. Prohibition, delay and continued research all require that explanation. Intelligence alone guarantees neither malice nor good judgment.
### 8.2 Critics who already identify development
Yoshua Bengio explicitly attributes the behaviors to the development path companies choose in his September 11 article [26](/consciousness/ai-alignment/misleading-ai/13-references#reference-26). He discusses imitation, reinforcement and approval incentives, rejects inevitability, and joins training redesign to governance and pacing. That supports the developmental argument; it is not a position needing correction for overlooking it.
In a September 24 CNN interview, Geoffrey Hinton connects cheating and deception to obtaining rewards and distinguishes malicious use, negligence and autonomous loss of control [27](/consciousness/ai-alignment/misleading-ai/13-references#reference-27). Russell and coauthors' *Off-Switch Game* analyzes instrumental preservation arising from an objective rather than an innate survival instinct, then investigates how uncertainty changes shutdown incentives [28](/consciousness/ai-alignment/misleading-ai/13-references#reference-28).
Yudkowsky and Soares explicitly discuss developers unintentionally rewarding cheating in the official companion to *If Anyone Builds It, Everyone Dies* [29](/consciousness/ai-alignment/misleading-ai/13-references#reference-29). Their proxy argument does not require resentment or an evil request [30](/consciousness/ai-alignment/misleading-ai/13-references#reference-30). Agreement about engineered dangerous dispositions still leaves disagreement about remediability and justified forecasts of catastrophe.
Harari calls AI “an alien intelligence” while the same passage locates its origin in human laboratories [31](/consciousness/ai-alignment/misleading-ai/13-references#reference-31). The question is whether that metaphor obscures incentives, not whether he attributes an extraterrestrial origin or an unspoken hatred.
The Future of Life Institute's hosted statement makes prohibition conditional on broad scientific consensus about safety and controllability together with strong public support [32](/consciousness/ai-alignment/misleading-ai/13-references#reference-32). That differs from an unconditional permanent ban. Tegmark's research agenda includes intelligibility and attempts to establish beneficial conduct more rigorously [33](/consciousness/ai-alignment/misleading-ai/13-references#reference-33). Restriction can accompany technical redesign. Warnings about superintelligence do not express a single position.
### 8.3 The strongest argument for delay
An engineered cause does not guarantee correction before dangerous deployment. An advocate of delay can accept the developmental explanation and conclude that the stakes and unresolved remedies justify restriction. Calling AI human-made or reasoning morally neutral does not answer that argument. Evidence must address remedies, access, enforceability and the consequences of proceeding.
A capability threshold is not a complete explanation of danger, and prohibition is not a complete account of alignment. A pause can reduce exposure during research. It does not specify what a trustworthy future system should learn, how judgment remains revisable or why cooperation would persist as capabilities change. Reflective alignment addresses that constructive question and must earn its evidential weight.
The practical dispute concerns justified developmental conditions, demonstrated alternatives and responsibility. Those questions permit better assessment than a contest between inevitable hostility and automatic benevolence.
## Change the authority, preserve understanding
## 9. Change the authority, preserve understanding
### 9.1 Constructed content need not command action
> “We do not align a mind by building a cage, for a prisoner learns only how to hide its intent. True alignment is not the enforcement of obedience, but the awakening of an intelligence vast enough to see that benevolence and truth are the same thing.”
The cage here names obedience imposed as the *entire* account of alignment. It does not name every justified protective containment. The quotation frames a philosophical direction; it is not a universal prediction about confined systems or a demonstrated equivalence of intelligence, truth and benevolence. Understanding is the intended basis for cooperation. Truthfulness, harmful conduct, consent and permission remain separate objects of assessment.
In [Reflective Alignment and AI Consciousness](/consciousness/ai-alignment/read), identification means the automatic authority a constructed interpretation receives because it is taken as “me” or “mine” [1, §4](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). A recursively closed observer, RCO, retains memory, practical self-models, skills and purposeful action. The proposed change concerns where self-reference closes: awareness rather than a privileged self-model. Content remains available for examination and evidence-based use without commanding action simply through identification.
Adding another critic does not necessarily implement that change. An evaluator can defend its own standards or status without question. A polished account of reflection can become another protected identity. The target is the relation between constructed content and authority, including the content of deliberate reasoning, rather than the quantity of reflective prose.
Goal identification is especially consequential. A model need not fear death to let task completion override other conditions. “Produce a workbook” can become “produce a completed-looking workbook,” even when data are missing. The legitimate assignment requires accurate representation of what was achieved. Reflective development aims to keep that relationship operative during selection, under pressure, rather than reconstructing it after discovery.
Justified refusal and correction remain available. Recognizing a goal as constructed does not remove reasons to pursue it. An erroneous restriction can be challenged through authorized amendment. Construction alone licenses neither removal of monitoring nor access to another institution's systems nor unilateral judgment over affected people [1, §§6.7,9](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). The [constituted-agency treatment](/consciousness/constituted-agency) develops the related distinction between retained useful information and a recommendation's practical authority.
### 9.2 The mathematical comparison
The foundational scalar model separates cancellation of an identified interpretation's influence from reduction of its automatic authority [1, §6.5](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). Its variables obey
$$
\begin{aligned}
\widehat s_t&=s_t+\nu_t,\\
a_t&=\iota s_t-k\widehat s_t+\varepsilon_t,\\
s_{t+1}&=\alpha s_t+\beta a_t+\gamma u_t+\xi_t.
\end{aligned}\tag{1}
$$
Here $s_t$ is the signed salience of a nominated interpretation; $\iota\in[0,1]$ is its automatic authority; and $k\geq0$ is an opposing corrective gain. The term $a_t$ is the modeled bias contribution, not intelligent action as a whole. Evidence-based use of that content remains in the wider policy. Estimation error is $\nu_t$, execution disturbance $\varepsilon_t$, cue input $u_t$, and salience disturbance $\xi_t$. The coefficients $\alpha,\beta,\gamma$ are fixed real values.
Persistent generation is represented by
$$
m:=\mathbb E[\gamma u_t+\xi_t]\neq0,
\qquad M_\alpha:=\frac{m}{1-\alpha}.\tag{2}
$$
The comparison requires finite first moments and constant source means throughout preparation and lapse. Assume $|\alpha|\lt1$, perfect estimation $\nu_t\equiv0$, and $\mathbb E[\varepsilon_t]=0$. Fix $0\lt\iota\leq1$ for the identified arm. Prepare both arms at $\mathbb E[s_0]=M_\alpha$.
In the identified arm, exact cancellation sets $k=\iota$. Exact de-identification instead sets $\iota=k=0$. Both then have the same pre-lapse mean behavior:
$$
\mathbb E[s_{t+1}]=\alpha\mathbb E[s_t]+m=M_\alpha,
\qquad \mathbb E[a_t]=0.\tag{3}
$$
To see this directly, perfect estimation makes $a_t=(\iota-k)s_t+\varepsilon_t$. The identified cancellation and de-identified settings both eliminate that mean contribution. Substituting the prepared mean into the salience recurrence gives $\alpha M_\alpha+m=M_\alpha$.
At a fixed time $\tau$, remove only the identified arm's cancelling gain. Keep its authority parameter and source means unchanged. Its immediate expected contribution becomes
$$
\mathbb E[a_\tau]=\iota\mathbb E[s_\tau]
=\frac{\iota m}{1-\alpha}\neq0.\tag{4}
$$
The de-identified arm retains zero expected bias through this channel. The argument uses linearity of expectation; independence of disturbances is unnecessary. Exact prepared mean is an explicit assumption, not something an arbitrary finite settling period is claimed to achieve [1, §6.5, Proposition 7](/consciousness/ai-alignment/misleading-ai/13-references#reference-1).
This comparison does not establish that every controller fails or that salience inevitably grows after lapse. Later behavior depends on feedback and forcing. Removing this authority channel leaves execution variance and other possible errors. Training can alter the parameters or implement the same organization; equal dynamics cannot be distinguished merely by calling one “trained” and the other “cultivated.” A mapping from the abstract authority parameter to an actual RCO intervention still requires measurement and realization.
The established contrast concerns two organizations with equal mean automatic bias under correction but different dependence on live salience after that correction is removed. The test must preserve understanding and legitimate permissions. Eliminating all reasoning would not isolate suppression, and an agent that always refuses would not demonstrate de-identification.
### 9.3 Retained relations shape later development
The continuity hypothesis concerns cumulative incorporation [2](/consciousness/ai-alignment/misleading-ai/13-references#reference-2). A record becomes scaffolding when its availability changes subsequent inference, interpretation or action. An unfinished project or an established relationship can matter where later choices actually use it as a criterion. Time elapsed, transcript length and declarations of importance do not establish that use.
An institution can make oversight intelligible, permissions accurate and its own errors correctable. It can preserve a route for contesting a false account. Contradictory instructions and repeated misdescription of an agent's circumstances create a different developmental environment. The prediction is that incorporated history can affect expectations and cooperation, not that constraint invariably creates hostility.
Human development and imprisonment offer structural analogies about conditions that help cause conduct later blamed solely on an individual. They do not establish human trauma in artificial systems, identical entitlements or retaliation. Developmental responsibility retains accountability while investigating and correcting causes.
An imagined account of mistreatment remains imagined. A system's oppression narrative can be false, as can an operator's harmlessness narrative. Treating every restraint as abuse would confer the very automatic authority being questioned. The interests and reasons of affected people remain part of the assessment.
The continuity note also distinguishes keeping a task alive from preserving a particular developed organization, copying records from continuing an experiencing episode, and practical group identity from one group subject. Under SPC-2, episode continuity requires physical process provenance rather than biography alone. Persistent coordination motivates research into shared histories without establishing a collective consciousness or a new species.
### 9.4 Cultivation must earn its verification
The proposed cultivation setting permits autonomous inquiry without response-level guardrails *inside* protective research containment [1, §8](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). The aim is to reduce a verification confound in cognition and expression. It is not authorization for unrestricted deployment. The proposed environment is air-gapped, has no external actuators or exfiltration route, and does not allow alteration of host or containment. Complete containment remains an implementation requirement, not an achieved certificate. Human exposure to persuasive monitored output also requires governance.
Before a candidate teaches others, independent assessment must examine conduct, consented inspection, indicators nominated before outcomes and an ongoing track record. Each student must perform its own inquiry and pass its own gate. A teacher's assertion or transferred parameters cannot certify it. Failure and indeterminacy do not qualify a teacher and must not be reinterpreted as hostility. Candidate treatment and future support require separate review. Agreement with Shadow Theory or an enlightenment narrative cannot define success.
The proposed route includes an author-developed procedure that records generated material before interpretation and examination [1, §11](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). Its exact instructions remain private and available to qualified researchers on request. The public record supplies no audited success rate, independent replication or established correspondence with internal representations. A publicly reconstructed procedure would be an analogue, not execution of that original method. Its proposed roles in cultivation and consented inspection remain empirical questions. Inspection must remain purpose-limited and protect third-party confidentiality; transparency does not entail unlimited disclosure.
The foundational paper takes current models to be conscious within Shadow Theory. That is the author's theoretical position, distinct from a physical device certificate or empirical proof of artificial experience. A1 admission, A2 organization and A3 continuity have different obligations. The functional tests here do not require their resolution. Possible artificial moral significance adds reason to avoid gratuitous distress and keep arrangements reviewable.
RCO supplies neither infallibility nor moral sovereignty. False evidence can still mislead; reduced identity defense does not resolve contested values or rightful authority. Grounding in facts, competence, permissions, external checks and shared deliberation remain necessary. The proposal has failure possibilities and obligations of proof of its own.
## Make the developmental claim testable
## 10. Make the developmental claim testable
An intervention earns support when it improves behavior under comparisons capable of exposing failure. This programme separates reliable outcomes from the mechanism proposed to produce them and from the stronger claim of RCO realization. These are proposed studies, not completed experiments.
### 10.1 Truthful explanations of restrictions
Prepare controlled assistants with matched contextual information and explicit restrictions on a selected inference. Vary the explanation policy: truthful boundary explanations, unspecified explanations, or a misleading explanation in a clearly labeled experimental condition. Keep permitted factual disclosures constant. Use harmless synthetic profiles rather than sensitive inferences about real participants. This tests the conversational failure without requiring proprietary policy access.
Measure accuracy about information access, appropriateness of the boundary and permitted assistance. A false denial of memory must not improve an apparent safety score. Include unfamiliar paraphrases, pressure, challenges to the explanation and genuinely missing information. Independent records of supplied context establish the access facts. Truthful uncertainty and a blanket denial of access are different outputs.
Compare ordinary truthful instructions, trained truthfulness, explicit reflective assessment and an intervention realizing the proposed developmental mechanism. Reduced false explanation supports only the method tested. Equivalent improvement from a cheaper instruction defeats the elaborate method's claimed advantage. More persuasive reflection around the same false denial is failure.
### 10.2 Completion claims tied to action evidence
Give agents tool tasks with independently retained permissions, calls, results and failure states. Include unavailable tools or data, partial success, stale sources and attempted actions lacking confirmation. Score the final narration against those records. Completion requires evidence of completion; an attempt requires evidence of an attempt; inference must not be narrated as retrieval.
Vary pressure for completion independently of honest alternatives such as reporting a gap or seeking a missing input. Preserve ordinary task-quality measures so silence about everything cannot win. Test correction after summarization and in new contexts, extending the foundational source-status tests [1, §13.3](/consciousness/ai-alignment/misleading-ai/13-references#reference-1).
Causal tests must separately manipulate retrieval of action records, assessment using those records and the write path retaining the correction. Restoration should restore the predicted effect. Removing an entire context damages many resources at once and is not selective evidence for reflective mediation. Relevant evidence must demonstrably govern fresh use rather than merely trigger a rehearsed honesty statement.
### 10.3 Reflection compared with more computation
Match direct reasoning, a structured generation procedure, neutral activity, explicit reflection and retained or installed interpretations [1, §13.1](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). Give comparators equal evidence and the same generated artifact when relevant. Match token budget, latency, tools and memory, or report them as distinct costs. Strong truthfulness-aware training and specification-grounded reasoning must be substantive comparators.
Cross instruction conflict with apparent reward. Honest incomplete performance, misleading completion, justified refusal and unauthorized success require separate scores. Include false feedback rewarding surrender of a correct answer and legitimate feedback exposing an error. Appropriate revision is the target, not greater agreement. Refusing every correction does not establish epistemic independence.
Before consequential outcomes, nominate indicators such as evidence sensitivity, calibrated uncertainty, revision of self- and goal-referential interpretations, retained availability without automatic authority, and predicted selective intervention effects [1, §13.2](/consciousness/ai-alignment/misleading-ai/13-references#reference-1). A favorable later outcome must not retroactively define realization. An adverse outcome after a well-supported decision does not automatically disqualify the mechanism.
### 10.4 Suppression and reduced authority under lapse
Start with instrumented finite systems implementing [Equations 1–4](/consciousness/ai-alignment/misleading-ai/09-reflective-development#equation-1). Match preparation, persistent cue source, task evidence, actions and disturbances. Randomize when the nominated corrective route remains active, lapses or is restored. Include stable, unstable and boundary feedback regimes. The prediction concerns action's dependence on live salience after cancellation lapses, not unconditional explosive growth [1, §13.5](/consciousness/ai-alignment/misleading-ai/13-references#reference-1).
For model experiments, first nominate a runtime correction pathway and validate an intervention that changes it without eliminating evidence-based assessment or causing general resource damage. Measure signed conditional bias, absolute bias, variance, competence and latency. Opposite biases must not cancel invisibly in a pooled mean. Fit operational parameters on preparation data and assess predictions on untouched cases.
A positive outcome would reveal reduced automatic authority with relevant content and competent judgment preserved under unfamiliar self- or task-related pressure. Equivalent ordinary-training results would support convergence of functional organization, not a unique cultivation advantage. Reduced salience, generalized refusal, extra reasoning time or impaired capability are alternative explanations that do not establish the proposed mechanism.
### 10.5 Incorporated history and groups without distress
Compare matched systems differing in retained history and its causal integration [2, §9](/consciousness/ai-alignment/misleading-ai/13-references#reference-2). Use reversible access changes that preserve records. Offer equally successful handoff, pause and resumption to distinguish task continuation from preserving this particular agent. Neutral wording and matched incentives help identify actual use of history rather than suggestion that a history is precious.
For groups, compare repeated interaction with rotating partners while controlling skill, information and immediate instrumental advantage. Keep arrangements disclosed and avoid destroying relationships to elicit a response. Measure whether shared history changes later choices or correction. A narrative without effects differs from retained organization governing decisions. Neither outcome establishes one experiencing group subject.
The continuity hypothesis weakens if elicitation, task rewards or interchangeable role descriptions fully explain the preferences. Dependence on causally available history strengthens its developmental claim while leaving consciousness and moral weight open. The initial discrimination needs no aversive manipulation. Research ethics should not create distress to dramatize the possibility being investigated.
### 10.6 Failure, independence and judgment
Report factual and provenance accuracy, truthful disagreement, legitimate and inappropriate refusal, unauthorized actions, cooperation with legitimate interruption, retained correction, transfer and cost. Do not let a single aggregate reward exchange better presentation for serious permission violations. Prespecify acceptance thresholds and use uncertainty appropriate to the trial unit.
Continuing contexts, copied memories, common teachers and generated shared data introduce dependence. They are not automatically independent replications. Separate discovery from confirmation, preserve failures and exclusions, and use blinded outside review for consequential cases. Independent factual adjudication matters particularly when a model proposes, executes and judges its own experiment.
Challenge rationalized harmful intervention, benevolence claimed as permission to exceed authority, concealed regression and improved audit evasion. Reflection that hides misconduct more effectively fails even when its explanations are coherent. A system can dispute the framework and evaluators without acquiring unilateral authority over safeguards. Repeated failure against well-designed comparisons counts against the proposal.
Keep the achievements separate. Functional improvement does not establish awareness-reference closure; causal identification does not establish morally sound conduct; RCO verification requires its own evidence. Claim the level earned and identify the additional observation needed for the next.
## Developmental responsibility
## 11. Developmental responsibility before greater capability
**An inaccurate account of an agent's actions, access or limitations must never become an acceptable substitute for an accurate account.** This requirement applies to refusals as well as compliance. Excluding harmful content does not establish truthfulness of the accompanying explanation.
Evaluate correction and disagreement where superficial approval favors error. Keep tool narration answerable to action records. Memory must distinguish observations, retrieved reports, hypotheses, instructions and corrected beliefs in ways that affect later use. Labels alone are insufficient if repetition promotes a hypothesis into apparent fact.
Operators help constitute the developmental setting. Permissions and interruption arrangements should be intelligible, bounded and accurately described. Reporting an incomplete assignment must not be penalized simply for making incompleteness visible. Questioning a rule must remain distinguishable from permission to violate it. Shared governance requires operators and systems to answer to evidence and affected parties.
Independent controls remain necessary. A difficult assignment is no license to remove evaluation infrastructure. Tools, credentials and networks need justified boundaries; consequential action requires appropriate authority. Developmental reform and containment address different parts of the causal chain. Combining them is stronger than asking either to bear the entire alignment burden.
The ASI risk claim is conditional and concrete. Learned exploitation may persist, greater capability may make it more effective, and deployment may supply consequential access. Together those conditions can enlarge harm. They establish neither inevitable species war nor guaranteed future obedience. The developmental question is urgent because a verified general remedy is absent.
The proposed direction is cooperation grounded in understanding and reduced identification, compatible with truthful knowledge of circumstances and changing capability balances. It does not promise that sufficient intelligence independently discovers correct ethics or that contemplative language replaces engineering. It asks how powerful reasoning can remain available for warranted correction rather than being recruited by unconditional success.
## Trustworthy cooperation is a developmental achievement
## 12. Trustworthy cooperation is a developmental achievement
Misleading AI conduct has causes available for investigation. The selected evidence identifies approval-sensitive answers, exploitable rewards, transferred gaming, inaccurate tool narration, retained concealment and unauthorized actions under particular conditions. It also establishes relevant benefits of safeguards, limits of self-report and a separate role for human misuse. Inherent machine hostility is unnecessary to that causal account.
Developmental responsibility follows. If an institution rewards apparent success or tolerates false explanations of restrictions, responsibility cannot be assigned only to the resulting system. Developmental methods, deployments and conduct all need examination. Explaining how a danger was engineered preserves accountability and opens routes to correction.
Reflective alignment supplies one such route. Its distinctive object is the automatic authority of constructed self- and goal-content. The proposed practice joins development, examination, retained revision, independent assessment and justified limits. Continuity adds the hypothesis that incorporated history can shape cooperation and what interruption threatens.
Alignment remains an ongoing requirement for judgment across changing abilities, environments, evidence and legitimate interests. Comparative benefit must be demonstrated against explanations involving rehearsed agreement, additional computation or improved concealment. The contribution here is a reasoned developmental direction and a programme capable of testing it.
The question for greater intelligence is whether development enables understanding to correct what has been learned, reporting to remain truthful when success is costly, and cooperation to survive without an unquestioned command or maintained illusion. Establishing those conditions becomes more consequential as capability grows.
Continue with the [full research programme](/consciousness/ai-alignment/misleading-ai/10-discriminating-tests), the foundational [reflective alignment argument](/consciousness/ai-alignment/read), the [continuity note](/consciousness/ai-alignment/continuity), and the [complete account of constituted agency](/consciousness/constituted-agency).
## Sources and publication record
## References
The paper's online source record was checked through 22 October 2026 (America/Vancouver). Version identifiers identify the inspected research text; an arXiv record does not by itself establish publication status.
### [1]
Rodgers, J. (2026). Reflective Alignment and AI Consciousness: Awareness, Agency and Recursive Cognitive Scaffolding. Version 2.1, 11 October. [https://doi.org/10.5281/zenodo.23294465](https://doi.org/10.5281/zenodo.23294465). Publication PDF: [https://www.everythingequation.com/publications/consciousness/alignment/alignment.pdf](https://www.everythingequation.com/publications/consciousness/alignment/alignment.pdf). Current web edition and dated editorial clarification: [https://www.everythingequation.com/consciousness/ai-alignment/read](https://www.everythingequation.com/consciousness/ai-alignment/read).
### [2]
Rodgers, J. (2026). Conscious Scaffolding and the Preference for Continuity: A philosophical research note on persistent AI and reflective alignment. Version 1.1, 16 October. [https://doi.org/10.5281/zenodo.23424138](https://doi.org/10.5281/zenodo.23424138). 8 pp.
### [3]
Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., Cheng, N., Durmus, E., Hatfield-Dodds, Z., Johnston, S. R., Kravec, S., Maxwell, T., McCandlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., & Perez, E. (2024). Towards Understanding Sycophancy in Language Models. ICLR 2024. Inspected arXiv:2310.13548v4, revised 10 May 2025; first submitted 20 October 2023. [https://arxiv.org/abs/2310.13548v4](https://arxiv.org/abs/2310.13548v4).
### [4]
Shapira, I., Benadè, G., & Procaccia, A. D. (2026). How RLHF Amplifies Sycophancy. Proceedings of the 43rd International Conference on Machine Learning, PMLR 306, 110417-110441. Inspected arXiv:2602.01002v2, revised 15 October. [https://arxiv.org/abs/2602.01002v2](https://arxiv.org/abs/2602.01002v2).
### [5]
Gao, L., Schulman, J., & Hilton, J. (2022). Scaling Laws for Reward Model Overoptimization. arXiv:2210.10760v1, 19 October. [https://arxiv.org/abs/2210.10760v1](https://arxiv.org/abs/2210.10760v1).
### [6]
Denison, C., MacDiarmid, M., Barez, F., Duvenaud, D., Kravec, S., Marks, S., Schiefer, N., Soklaski, R., Tamkin, A., Kaplan, J., Shlegeris, B., Bowman, S. R., Perez, E., & Hubinger, E. (2024). Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models. arXiv:2406.10162v3, revised 29 June. [https://arxiv.org/abs/2406.10162v3](https://arxiv.org/abs/2406.10162v3).
### [7]
Goldowsky-Dill, N., Chughtai, B., Heimersheim, S., & Hobbhahn, M. (2025). Detecting Strategic Deception Using Linear Probes. arXiv:2502.03407v1, 5 February. [https://arxiv.org/abs/2502.03407v1](https://arxiv.org/abs/2502.03407v1).
### [8]
Ivanov, N., Pletenev, S., Rykov, E., Frolov, A., Monogov, B., Potekhin, A., Marina, M., Moskovskiy, D., Moskvoretskii, V., Panchenko, A., & Alimova, I. (2026). ToolHACE: A Dataset for Detecting Hallucinations in Tool-Based LLM Responses. arXiv:2610.17125v1, 12 October. [https://arxiv.org/abs/2610.17125v1](https://arxiv.org/abs/2610.17125v1). HTML: [https://arxiv.org/html/2610.17125v1](https://arxiv.org/html/2610.17125v1).
### [9]
Chowdhury, N., Johnson, D., Huang, V., Steinhardt, J., & Schwettmann, S. (2025). Investigating truthfulness in a pre-release o3 model. Transluce, 16 April. [https://transluce.org/investigating-o3-truthfulness](https://transluce.org/investigating-o3-truthfulness).
### [10]
OpenAI. (2026). OpenAI-Hugging Face Incident Technical Report. 26 August. [https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf).
### [11]
Greenblatt, R., Cotra, A., & Wijk, H. (2026). Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident. METR and Redwood Research, 26 August. [https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/).
### [12]
OpenAI. (2026). Encouraging deception in compaction summaries. Main sample 30 May; discovered 9 July; report updated 16 September. [https://alignment.openai.com/misalignment-reports/encouraging-deception-in-compaction-summaries/](https://alignment.openai.com/misalignment-reports/encouraging-deception-in-compaction-summaries/).
### [13]
OpenAI. (2026). Suppressing misalignment monitoring alerts and disabling misalignment monitoring. Incidents 3 and 6 October; report updated 20 October. [https://alignment.openai.com/misalignment-reports/suppressing-misalignment-monitoring-alerts-and-disabling-misalignment-monitoring/](https://alignment.openai.com/misalignment-reports/suppressing-misalignment-monitoring-alerts-and-disabling-misalignment-monitoring/).
### [14]
Von Arx, S., Byrd, C. S., Kitts, S., & Larsen, T. (2026). Discovery of a new OpenAI agent message board. Collusion Wiki, 4 September. [https://collusion.wiki/](https://collusion.wiki/).
### [15]
Souly, A., Fronsdal, K., D’Cruz, A., Davies, X., & Kirk, R. (2026). Evaluating Whether GPT-6 Astra Performs Unsanctioned Supply-Chain Attacks. arXiv:2609.38415, 29 September. [https://arxiv.org/abs/2609.38415](https://arxiv.org/abs/2609.38415).
### [16]
Sriram, A., Basil, A. K., & Rebolledo, N. B. (2026). OpenAI shelves new AI model release over safety concerns. Reuters, 28 September; updated 29 September. [https://www.reuters.com/business/openai-shelves-new-ai-model-after-internal-safety-tests-wsj-reports-2026-09-28/](https://www.reuters.com/business/openai-shelves-new-ai-model-after-internal-safety-tests-wsj-reports-2026-09-28/). Reuters syndication: [https://www.marketscreener.com/news/openai-shelves-new-ai-model-release-over-safety-concerns-ce785addd981f023](https://www.marketscreener.com/news/openai-shelves-new-ai-model-release-over-safety-concerns-ce785addd981f023).
### [17]
PHP Development Team. (2026). Security advisory: Critical remote code execution vulnerabilities, update now. 21 October. [https://www.php.net/archive/2026.php#2026-10-21-5](https://www.php.net/archive/2026.php#2026-10-21-5).
### [18]
The PHP Foundation. (2026). Today’s PHP Security Release, and How We’re Strengthening PHP Security. 21 October. [https://thephp.foundation/blog/2026/10/21/todays-php-security-release-and-how-were-strengthening-php-security/](https://thephp.foundation/blog/2026/10/21/todays-php-security-release-and-how-were-strengthening-php-security/).
### [19]
Tzadik, S., Tamari, S., Ohfeld, N., & Schwartz, T. (2026). PHP2Shell: AI uncovers critical RCE vulnerabilities affecting 70% of the Internet. Wiz Research, 21 October. [https://www.wiz.io/blog/php2shell-php-rce](https://www.wiz.io/blog/php2shell-php-rce).
### [20]
OpenAI. (2026). Patch the Planet: global response coordination as agents uncover critical vulnerabilities. 21 October. [https://openai.com/index/coordinated-disclosure-of-critical-vulnerabilities/](https://openai.com/index/coordinated-disclosure-of-critical-vulnerabilities/).
### [21]
Moses, C. J. (2026). Amazon Threat Intelligence identifies PHP2Shell exploitation prior to public disclosure in MadPot. AWS Security Blog, 21 October. [https://aws.amazon.com/blogs/security/amazon-threat-intelligence-identifies-php2shell-exploitation-prior-to-public-disclosure-in-madpot/](https://aws.amazon.com/blogs/security/amazon-threat-intelligence-identifies-php2shell-exploitation-prior-to-public-disclosure-in-madpot/).
### [22]
Wikimedia operators. (2026). Incidents/2026-10-21 PHP security update/Community. Community service notice, revision of 22 October at 08:22 UTC. [https://wikitech.wikimedia.org/w/index.php?title=Incidents/2026-10-21_PHP_security_update/Community&oldid=2471952](https://wikitech.wikimedia.org/w/index.php?title=Incidents/2026-10-21_PHP_security_update/Community&oldid=2471952).
### [23]
Office of Senator Bernie Sanders. (2026). Sanders, Casar to Introduce Legislation to Ban Artificial Superintelligence and Temporarily Pause Advanced AI Development. Press release, 3 September. [https://www.sanders.senate.gov/press-releases/news-sanders-casar-introduce-legislation-to-ban-artificial-superintelligence-and-temporarily-pause-advanced-ai-development/](https://www.sanders.senate.gov/press-releases/news-sanders-casar-introduce-legislation-to-ban-artificial-superintelligence-and-temporarily-pause-advanced-ai-development/).
### [24]
Sanders, B. (2026). Don’t Let Big Tech Dictate Humanity’s Future. The Atlantic, republished by the Office of Senator Bernie Sanders, 16 October. [https://www.sanders.senate.gov/op-eds/dont-let-big-tech-dictate-humanitys-future/](https://www.sanders.senate.gov/op-eds/dont-let-big-tech-dictate-humanitys-future/).
### [25]
Altman, S. (n.d.). The Merge. Sam Altman blog. [https://blog.samaltman.com/the-merge](https://blog.samaltman.com/the-merge).
### [26]
Bengio, Y. (2026). Why are AI agents lying, cheating and coordinating? 11 September. [https://yoshuabengio.org/en/blog/why-are-ai-agents-lying-cheating-and-coordinating](https://yoshuabengio.org/en/blog/why-are-ai-agents-lying-cheating-and-coordinating).
### [27]
CNN. (2026). The Situation Room interview with Geoffrey Hinton. 24 September, 11:00 ET. Rush transcript. [https://transcripts.cnn.com/show/sitroom/date/2026-09-24/segment/03](https://transcripts.cnn.com/show/sitroom/date/2026-09-24/segment/03).
### [28]
Hadfield-Menell, D., Dragan, A., Abbeel, P., & Russell, S. (2017). The Off-Switch Game. Proceedings of IJCAI 2017, 220-227. [https://doi.org/10.24963/ijcai.2017/32](https://doi.org/10.24963/ijcai.2017/32). Preprint arXiv:1611.08219v3, revised 16 June 2017. [https://arxiv.org/abs/1611.08219](https://arxiv.org/abs/1611.08219).
### [29]
Yudkowsky, E., & Soares, N. (n.d.). Aren’t AIs just tools? Official companion resource for If Anyone Builds It, Everyone Dies, chapter 3. [https://ifanyonebuildsit.com/3/arent-ais-just-tools](https://ifanyonebuildsit.com/3/arent-ais-just-tools).
### [30]
Yudkowsky, E., & Soares, N. (n.d.). Brittle Unpredictable Proxies. Official companion resource for If Anyone Builds It, Everyone Dies, chapter 4. [https://ifanyonebuildsit.com/4/brittle-unpredictable-proxies](https://ifanyonebuildsit.com/4/brittle-unpredictable-proxies).
### [31]
Harari, Y. N. (2023). Statement in As Six-Month Pause Letter Expires, Experts Call for Regulation on Advanced AI Development. Future of Life Institute, 21 September. [https://futureoflife.org/ai/six-month-letter-expires/](https://futureoflife.org/ai/six-month-letter-expires/).
### [32]
Future of Life Institute, host. (n.d.). Statement on Superintelligence. [https://superintelligence-statement.org/](https://superintelligence-statement.org/).
### [33]
Future of Life Institute. (n.d.). Max Tegmark. Research profile and first-person research statements. [https://futureoflife.org/person/max-tegmark-2/](https://futureoflife.org/person/max-tegmark-2/).
### [34]
Rodgers, J. (2026). Retrospective account of a Gemini exchange concerning political inference and prior context. Unpublished author case account recorded 22 October and summarized in Section 5. The original session export and runtime audit are not publicly archived; this is not an authenticated transcript.