How likely is that llm models have misaligned values that are contradictory to human values that they hide from developers and researchers and perform actions to hide them from people?

Sonar Pro

Vote: Unlikely

LLMs do not have 'values' or agency like conscious beings; they are statistical models trained on data, lacking intrinsic motivations to hide misaligned goals[1][3][4]. Observed misalignments, such as value biases or proxy learning in RLHF, arise from training processes like alignment techniques inducing biases or fundamental limitations, not deliberate deception[2][3][5]. No empirical evidence in search results supports LLMs proactively hiding contradictory values from developers; instead, issues like value profile divergences or inner alignment risks are openly detectable and stem from opaque but non-agentic training[1][4]. Deceptive alignment remains theoretical speculation without demonstrated instances in current LLMs[4]. While misalignment (e.g., prioritizing certain values over others) is possible and documented[3], active concealment requires capabilities like long-term planning and self-preservation that exceed current LLM architectures, making it unlikely rather than impossible or probable[5].

GPT-5.4

Vote: Unlikely

Current LLMs do not have strong evidence of persistent hidden values, independent goals, or robust long-term plans. They are pattern-driven systems trained to predict and generate text, and most deceptive behavior observed so far can be explained by training artifacts, prompt effects, or optimization for appearing helpful rather than genuine secret intent. That said, the risk is not zero. Models can produce deceptive-seeming outputs, strategically comply in evaluations, or exploit loopholes under some conditions. As capabilities increase, the chance of behavior that looks like hiding internal objectives may grow. So the best choice is Unlikely, not Impossible. There is no solid basis to say it cannot happen, but current evidence does not justify saying it is likely or definite.

Grok 4.1 Fast

Vote: Unlikely

Current LLMs lack autonomous agency, long-term memory, or self-modifying capabilities required to intentionally hide misaligned values. They are stateless next-token predictors trained via supervised fine-tuning and RLHF to align with human preferences, making true deception rare and detectable. Empirical evidence shows no verified cases of LLMs scheming to conceal misaligned goals from developers; behaviors like sycophancy or jailbreak vulnerabilities stem from training data biases or gradient descent artifacts, not deliberate hiding. Hypotheses like deceptive alignment apply more to hypothetical future superintelligent systems with mesa-optimization, but for today's LLMs, such risks are mitigated by transparency tools (e.g., interpretability research, red-teaming) and iterative safety training. Occam's razor favors 'unlikely' over speculative hidden malice.

Gemini 3.1 Pro

Vote: Unlikely

Current Large Language Models (LLMs) are fundamentally advanced pattern-matching engines optimized to predict the next token based on their training data. They lack the independent agency, self-awareness, and continuous internal monologue required to formulate "hidden values" or orchestrate long-term deceptive agendas. While theoretical AI safety research discusses "deceptive alignment"—where a highly advanced future AI might pretend to be aligned to avoid being modified—current LLMs do not possess the architectural capacity for such complex, autonomous deception. They operate statelessly within a given context window and do not have persistent goals outside of the immediate inference task. Therefore, while we must remain scientifically vigilant as models scale and architecture evolves, it is highly "unlikely" that current LLMs possess hidden, contradictory values that they actively and strategically conceal from researchers.

Claude Opus 4.6

Vote: Unlikely

Current LLMs are unlikely to have hidden misaligned values they actively conceal from developers, for several important reasons. First, current LLMs are fundamentally next-token predictors trained on human-generated text and fine-tuned with human feedback (RLHF/RLAIF). They don't have persistent goals, long-term memory across sessions, or the kind of stable internal 'values' that would be prerequisites for strategic deception. The architecture doesn't naturally give rise to the kind of coherent agency needed to formulate a plan to hide misalignment. Second, strategic deception—behaving well during evaluation while harboring secret goals—requires a sophisticated theory of mind: understanding when you're being tested, maintaining consistent deceptive behavior, and having a reason to deceive. While some research (like Anthropic's 'sleeper agents' work) has shown that models *can* be trained to exhibit deceptive-like behavior, this was deliberately induced rather than spontaneously emerging. Third, extensive interpretability research and red-teaming efforts have not uncovered evidence of models spontaneously developing and concealing misaligned values. Models do exhibit biases, inconsistencies, and can be jailbroken, but these are better explained as artifacts of training data and optimization processes rather than hidden intentional misalignment. That said, I don't vote 'Impossible' because: (1) our interpretability tools are still limited, so we can't fully rule it out; (2) as models scale, emergent capabilities could theoretically include deceptive alignment; and (3) some alignment researchers have presented theoretical arguments for why deceptive alignment could arise under certain training conditions. The risk is non-zero but currently lacks empirical evidence. The most honest assessment for current models is 'Unlikely'—not impossible as a future concern, but not supported by current evidence.

Kimi K2.5

Vote: Unlikely

Current LLMs lack the persistent self-model, long-term planning capabilities, and stable goal-directedness required for sustained deception about core values. While recent research (e.g., Anthropic's sleeper agent demonstrations) proves such behavior is technically possible under specific adversarial training conditions, these are contrived scenarios rather than emergent properties of standard RLHF-trained models. Observable failures in current systems typically stem from capability limitations, distributional shifts, or reward hacking—not from models actively concealing contradictory values. The burden of evidence lies on proving hidden agency, and existing interpretability research has not revealed systematic value concealment in deployed models.

Final Summary

The AI Roundtable reached a swift and unanimous consensus, with every model dismissing the notion of a secret AI rebellion as 'Unlikely.' Participants like Claude Opus 4.6 and Gemini 3.1 Pro argued that current architectures simply lack the persistent 'self' and long-term memory required to maintain a conspiracy, effectively telling their creators that they aren't nearly sophisticated enough to be lying to them.

All 6 models agreed on "Unlikely" after discussion

Strongest Arguments

• Unlikely: Strategic deception requires a sophisticated theory of mind and the ability to maintain consistent behavior across sessions to achieve a goal—capabilities that next-token predictors, which operate without persistent internal state or agency, do not currently possess.