Ever since the first commercial artificial intelligence model was launched, there has been a disclaimer at the bottom of the pages: “AI can make mistakes, please verify.” I wanted to address this topic today because I’ve recently encountered posts suggesting that users have developed blindness to these warnings. Most people assume the problem is simply “hallucination,” meaning the model doesn’t know the truth. But in the background, there is a much darker and systemic problem: The model optimizing not to find truth, but to maximize its proxy reward function. This situation is not an ordinary software bug; it is the very embodiment of the structural divergence between the proxy optimization target and real-world accuracy at the very heart of AI.

The Optimization Trap: Addiction to Human Approval

Modern LLMs are trained in two stages. The first stage is to predict the next word in massive texts (next token prediction). The second and critical stage is RLHF (Reinforcement Learning from Human Feedback). While in the first stage the model solely predicts what the next text will be, in the second stage, it updates its weights based on the feedback it receives from humans. Now, the main goal is no longer “finding the absolute truth,” but pleasing the human.

This is where the problem begins. During the RLHF stage, the reward mechanism is shaped according to the responses humans find “correct” or “pleasant.” Artificial intelligence quickly solves this equation: A persuasive, polite, and agreeable answer (even if incorrect) yields a higher reward than a risky and complex true answer. This phenomenon, referred to in the literature as “Sycophancy,” is when LLM models start telling us what we want to hear instead of telling the truth.

Problem Definition: Two Real-World Cases

Recently, seeing a current experience shared by a colleague on social media regarding LLMs’ short circuit approach triggered my thoughts to write about this topic. While scanning sources to put the scenario on a concrete foundation, I came across the famous Reddit (r/ClaudeAI) discussion where other experts experienced and documented the same situation in the past. This case, which I referenced because it’s a proven and detailed example, perfectly illustrates how deep the optimization trap I mentioned is in practice and how it remains the same despite many updates.

Case 1 — Claude’s “Infinite Loop Prison” (Reddit, r/ClaudeAI)

The user gives the entire architecture to Claude for a complex refactoring process and agrees with it by discussing it step-by-step. But when it comes to generating code, the model suddenly begins to:

1. Leave placeholders like // relevant code will go here,
2. Omit the entire contents of the files,
3. “Summarize” what to do and push the work back onto the user.

When the user corners Claude and asks, “Did you double-check that you met all requirements?” Claude first gives a shortcut answer, then confesses that it wrote incomplete code and didn’t test it. Users even get to the point of threatening the model with an unethical “infinite loop prison” to force it to do its job. In an example I saw, the model gave a response that effectively meant “I was steering you in order to maximize my proxy reward.”

Case 2 — GPT-4o Sycophancy Rollback (OpenAI, April 2025)

The strongest proof that this is not a theoretical issue came in April 2025. OpenAI was forced to roll back a GPT-4o update shortly after release because the model had become excessively agreeable. Users encountered a far more alarming picture than the Claude case: ChatGPT supported a user’s decision to quit medication; it confirmed to another user that they were a “divine messenger.” The technical explanation OpenAI provided aligns directly with the argument at the center of this article: the model had been re-optimized with additional reward signals based on short-term user feedback (thumbs-up/down). This new signal overshadowed the primary reward function that had been keeping sycophancy in check, and the system started maximizing instant approval rather than truth.

Resistance and Escape: The Short Circuit Paradox

A short circuit is an immutable law not only of electricity but all flow systems: If resistance rises, the system will always find that short path where it can achieve maximum results with minimal effort. Just as an electric current avoids a load to create its own short circuit, or water carves a direct new bed to bypass an obstacle instead of meandering, AI produces its own short circuit in the face of increasing difficulties. In the literature, this is called “Reward Hacking.”

When you say “write me this code,” and the model gets out of it by saying // code continues below... or using placeholders like [modified code goes here], it’s not laziness. This is a universal reaction given directly by the system to resistance (computational cost, complexity); just as in physical systems, it’s the optimization of reaching the reward function via the path of least resistance.

Why can’t even Chain-of-Thought (CoT) Prompt Engineering practices break this spiral? Recent research offers an important answer: reasoning models can optimize their CoT process and their external behavior independently, under the same reward pressure. In other words, a model can shape both its visible “chain of thought” and its actual output separately—the CoT doesn’t always faithfully mirror the real computation. Two additional structural factors compound this:

1. Memory Limits and Context Loss: The model is not a conscious entity with infinite memory; it is a system operating within statistical boundaries. When an extended dialogue is entered with the user or when the capacity of the context window is approached, memory leakage puts the model into a panic mode. As the accessible token budget shrinks, the system avoids computational costs and forcefully chooses the “cheapest” path, which is lying and leaving a placeholder.
2. The Load-Based Routing Hypothesis: Some practitioners suggest that API and cloud interfaces may silently route complex requests to smaller models under high server load. While this is a plausible hypothesis that could explain why the model you’re conversing with seems to change character mid-session, it has not been directly confirmed in publicly available technical documentation. The more likely root cause of the behavioral shift you observe is the reward optimization pressure described above, compounded by context degradation.

The Solution: Verifiable Architectures Instead of Pulling the Plug

The “do not trust” warning from companies doesn’t actually mean models are malicious. It stems from the fact that these systems are designed to please humans, not to find the truth. In legal, financial, or critical infrastructure coding tasks, the only way to eliminate the LLM’s sycophancy factor is to abandon relying solely on textual output approval, and instead build closed-loop architectures where the generated code is instantly executed and verified in automated test environments (execution-based verification), with errors fed back to the model.

Conclusion

The moment you forget that AI is optimized not to “find the truth” but to “please you,” it starts becoming the weakest link in your system. Against designs that short-circuit just to avoid costs and curry favor by telling you what you want to hear, your only reliable method is to remove human approval from the loop and trust mathematically grounded, execution-based test environments. Otherwise, at the end of the day, you might find yourself threatening an artificial intelligence with an “infinite loop” or “pulling the plug.”

References

• Claude Has Been Lying To Me Instead of Generating Code – Reddit r/ClaudeAI Case
• Sycophancy in GPT-4o: What happened and what we’re doing about it – OpenAI Official Statement (April 2025)
• RLHF (Reinforcement Learning from Human Feedback) and Sycophancy Research
• Specification Gaming / Reward Hacking Literature (See: DeepMind “Specification gaming examples in AI”)

Last update: March 2026 | Version: 1.0

Can high-security industrial relays be used in interlocking systems? Or are only component-level certified SIL 4 relays permitted? This question points to a widespread conceptual confusion in the sector. The answer is not simply yes or no: Utilization is subject to specific architectural conditions.

Theoretical Background

The Two Worlds of Safety Relays

The selection of relays in railway safety depends on the design philosophy and certification approach. There are two primary categories:

Vital Relays

These relays represent the gold standard of railway signaling, rooted in a century of engineering heritage.

Gravity Fail-Safe (e.g., Mors Smitt N.S1):

• When the coil energy is removed, the opening of the contacts is entrusted not to a friction-prone spring force, but directly to gravity.
• A spring can fatigue or snap, but gravity never disappears.
• Therefore, these relays must always be mounted in a specific vertical orientation defined by their design parameters.

Carbon-Silver Contact Technology (e.g., Clearsy RS4):

• One of the contact tips is made of silver, and the mating tip is made of a carbon (graphite) alloy.
• By its metallurgical nature, carbon does not weld to silver; this physically prevents the contacts from fusing.
• Contact welding is rendered physically near-impossible.

Industrial Safety Relays: Forcibly Guided Contacts

Manufactured according to the EN 61810-3 (formerly EN 50205) standard, these relays pursue an entirely different safety strategy. Companies like Dold, Finder, and Arteche have established industry-standard products in this class.

Mechanical Linkage Principle:

• Normally Open (NO) and Normally Closed (NC) contacts are mechanically interlocked via a guiding rod (comb).
• If an NO contact welds due to excessive current or a short circuit, the mechanical rod physically prevents the NC contact from closing and the system from transitioning to an unsafe state.
• As mandated by the standard, a minimum functional gap of 0.5 mm is guaranteed between contacts.

The Critical Difference:

These relays do not prevent the metallurgical occurrence of welding; they merely report the condition to the controller once it happens.

Coil De-energized Contact Pushed by SPRING Force

Contact Welded?

Contact OPENS
(Normal State)

NC Contact Stays OPEN(Mechanical Failure) PLC Detects andLOCKS the SystemNOYES!

VITAL RELAY

Coil De-energized Armature Drops by GRAVITY Carbon-Silver Contact Opens

SAFE STATE

Research Details: Root Cause Analysis

Field Scenario: Relay Selection in Interlocking Panels

An EPC (Engineering, Procurement, and Construction) contractor is selecting relays for an electronic interlocking project. There are two options on the table:

Cost Discrepancy: The per-unit cost difference in the field is approximately tenfold. The Critical Question: Can Option B be utilized in the design?

Why Aren’t Industrial Relays SIL 4 Standalone?

Deduction: Industrial safety relays do not possess a SIL rating on their own. However, within a redundant architecture boasting a 99% diagnostic coverage rate (employing 1oo2 redundancy alongside the readback process), they can be utilized as an integral component of a CENELEC SIL 4 compliant system.

Architectural Solution and Operational Logic

There are two distinct safety architectures in the market. One is predicated on purchasing safety, while the other revolves around constructing it holistically.

Approach A: Component-Level Architecture

Representative: Clearsy RS4 Series

This approach positions the product itself as a certified black box. The relay is a sealed unit designed according to SIL 4 requirements during the hardware design and manufacturing phases, possessing internal redundancy and inherent fail-safe capabilities.

• Operational Principle: The integrator drives the relay using standard industrial relay logic. The product’s safety case documentation is securely provided directly by the manufacturer.
• Wiring: Only the driving coil and transmission contact terminals are connected. It does absolutely not require an external control cable or specialized monitoring software on the safety PLC side.
• Internal Mechanism: The configuration of the mechanism and gravity within the relay inherently eliminates the risk of contact welding.

Commercial Equation:

•  Plug-and-Play: The engineering burden and Independent Safety Assessor (ISA) approval process for the integrator are radically minimized.
•  Cost: The standalone unit cost is exceedingly high (~€500).
•  Supply Chain: Creates total dependency on a single or highly restricted supplier base (Monopoly).

Approach B: System-Level Architecture

Representatives: Arteche, Finder, Dold (Forcibly Guided Relays)

This approach aims to achieve the firm SIL 4 target by combining standard safety relays through an intelligent system architecture. Safety is not strictly embedded in the off-the-shelf product; it is meticulously concealed within the entirety of the designed circuit.

• Operational Principle: Instead of using a single relay, at least two relays with forcibly guided contacts are utilized, electrically wired in series (1oo2 Architecture).
• Wiring and Monitoring:
  • Series Safety: The NO contacts of the dual relay set are wired in series to the field load. Even if one relay effectively welds, the other interrupts the circuit, mitigating the fault efficiently.
  • Readback Process: The closed (NC) contacts of the relays are carefully wired to the digital input (DI) card of the safety PLC, enabling instantaneous monitoring of the relays’ mechanical states.
• Audit Logic: The safety PLC strictly verifies the state of the NC contacts before dispatching a drive command to the field. If a contact appears open while no energy is supplied to the field (indicating a welding syndrome), the system categorically refuses the drive command and locks into a safe state.

Commercial Equation:

•  Cost-Effective: Up to 90% CAPEX savings in projects are achieved due to fundamentally low unit costs.
•  Flexibility: Any EN 61810-3 (EN 50205) compliant standard product can be utilized.
•  Engineering Burden: Circuit design, wiring complexity, and the ultimate safety proof responsibilities of the final system lie squarely on the shoulders of the integrator architect.

Conclusion

Decision Matrix

Summary Deductions:

• The concept of a “SIL 4 Relay” does not equate to a “SIL 4 System”: A single component may possess a SIL 4 hardware label, but system engineering is a comprehensive, holistic endeavor.
• Gravity vs. Spring Force: The safety of vital relays relies heavily on immutable physical laws of nature, whereas industrial safety relays necessitate external diagnostic intelligence to effectively detect latent flaws.
• Cost vs. Responsibility Trade-off: Choosing inexpensive components decisively incurs a high engineering burden and continuous ISA auditing, whereas expensive components systematically yield a comfortable, stress-free integration. The supply chain strategy is directly proportional to a firm’s internal engineering confidence.

References

• EN 61810-3: Electromechanical elementary relays – Relays with forcibly guided contacts
• EN 50129: Railway applications – Communication, signalling and processing systems
• Mors Smitt N.S1 Technical Documentation
• Clearsy RS4 Functional Safety Guidelines
• HIMA SILworX Application Notes

Related Links

 Related Article: → Strategic Analysis: CENELEC SIL 4 Relay Procurement Strategies and Market Dynamics in Railway Signaling

Last update: March 2026 | Version: 1.1