As people delegate more abstract reasoning to large language models (LLMs), the precise meaning and nuance of our words can get lost in endless interpretation as LLMs repeatedly process, summarize, and recirculate these words.
Humans aren’t robots that formalize all of their sentences. Instead, we converse with some unwritten cultural and situational tones. For example, in some cultures, asking “Have you eaten yet?” doesn’t literally mean asking if the person has eaten food, but is another polite way of saying “How are you doing?” An LLM may miss this nuance in an essay, interpreting it literally and giving a wrong output.
If AI Model A interprets “Have you eaten yet?” literally, it then passes that same interpretation to Model B, which then passes it to Model C. At the third layer, the nuance gets lost. The AI models provide a coherent output, but one that’s out of the cultural and situational context. This is an example of semantic erosion, where definitional precision and cultural nuance gradually get lost as AI models recirculate interpretations and ideas.
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Semantic erosion is a structural error, not a “hallucination” where AI models invent wrong facts. Instead, the output remains clear, but with context stripped away, the meaning changes. It’s a major bottleneck that LLM developers have to work around. Let’s deeply examine this issue and how developers are innovating their way around it.
Semantic erosion explained in detail
As LLMs increasingly process more information, small misinterpretations can compound and affect output quality. LLMs can misread human context or substitute it with a synonym, generating output that may be fluent and grammatically correct but not what the user actually desires.
Above, we gave an example of “Have you eaten?” being misinterpreted in the cultural context, but let’s see another fitting example.
Picture a company’s annual financial statement, and a multi-agent AI model is asked to analyze this statement to extract key insights. Agent A misinterprets “net profit” as “operating profit” even when they aren’t the same (operating profit is the company’s total profit from its business activities, while net profit is what’s left after subtracting taxes, depreciation, debt servicing, and other costs from the operating profit).
Agent A passes this misinterpretation to Agent B, degrading the data. After Agent C, D, and E analyze the financial statement, the extracted insights will be wrong. This isn’t an explicit error, but a quiet one the user can easily overlook, and wrong insights can cause wrong decisions.
If left unchecked, semantic erosion reduces the accuracy of AI model outputs. A little misinterpretation here, another there, and another here, and AI models can become too unreliable to be trusted by users, so LLM developers have to tackle this problem.
The intent compression paradox
Humans aren’t robots that speak programmatically. We add emotions, understanding, and cultural and environmental context, where just the voice tone can change the meaning of a sentence. For example, “We need to talk” can indicate care when spoken in a gentle tone but signal confrontation when spoken in a loud, high-pitched tone. “You can say that again” indicates friendly agreement when spoken in a warm, soft tone, but can sound confrontational when spoken loudly.
The thing is, humans mimic how we speak to each other when speaking with AI models, even though these models don’t understand emotion. AI models analyze words statistically and give quantitative output. This creates a paradox where the more optimized an AI model is to provide the most analytical answers, the more aggressively it may misinterpret human intent. It’s known as the intent compression paradox.
LLM developers usually compress prompts to save token costs, but as human intent gets heavily compressed, there’s a higher chance of models generating unreliable answers. To compensate for the compression, AI models may even generate longer yet less direct output that doesn’t fit the intent.
Strategies to address semantic erosion and the intent compression paradox
Above, we’ve explained the problem in detail. Now let’s look at the strategies AI model developers apply to address the problem. How do they avoid misinterpreting human intent? How do AI models avoid intent compression while still saving token costs? Let’s look at some real-world approaches.
Data filtering
The foundation of every AI model is its training dataset, so fine-tuning it to understand human intent starts here. Using more curated datasets like peer-reviewed journals, expert discussions, in-depth articles, and literature, not generic datasets, helps AI models understand context better. These curated datasets contain rich, high-integrity information that helps AI models recognize structured, context-based analysis and sound reasoning instead of just grabbing unverified claims from anywhere. Human expert discussions feature a lot of nuance, which is important for AI models to recognize.
Curated datasets are costlier than generic datasets, but the payoff is better accuracy and reliability. The entire dataset doesn’t have to be curated, but introducing a significant volume of curated datasets helps LLMs understand context much better.
To maintain the integrity of curated datasets, even as AI models generate output that may misinterpret context, developers can implement cryptographic or statistical watermarks (e.g., Google‘s SynthID) into their AI outputs. This watermark lets models recognize their own output and avoid training based on it, preventing a “model collapse” where LLMs degrade in quality after being trained on their own generated data.
Another approach is to build training dataset pipelines that only include data before 2023, the year when LLMs went mainstream, and the web began getting flooded with AI-generated content. Tackling semantic erosion in AI models starts with filtering their foundational training datasets, and we’ve described some effective approaches.
Architectural solutions
Developers can counter semantic erosion with strategic AI model architecture design. For example, AI models can be prohibited from guessing what data fields mean and, instead, use a localized semantic platform (e.g., SqIDBM) to map data queries to structured metrics, not raw database tables from which the model can extract wrong data if left alone.
Instead of letting AI models look directly at database tables, developers place a semantic platform in the middle that acts like a translator, telling the AI model what to output instead of leaving the model to search through the tables and possibly extract wrong insights.
Developers can also implement autonomous drift detection and session-level observability, where independent systems check if an AI agent’s output begins diverting from its preset semantic baselines, or when output starts drifting out of context. Instead of just monitoring isolated API calls, this approach involves tracking an AI agent’s responses across a whole conversation and multiple workflows. If context drift is detected, automated corrections can be triggered, such as assigning the task to another model, automatically injecting updated, more context-aware prompts, and halting repetitive loops to save token costs.
Developers can also introduce loss functions that penalize AI agents for being too uniform, forcing them to keep some statistical entropy (diversity) in their outputs. Without this penalty, AI models can hyper-optimize to the point of finding one successful answer to a query, then repeating it endlessly for similar queries even when context differs. The entropy penalty compels the AI agent to distribute probabilities across multiple options instead of placing 100% confidence in a single response.
Think of the loss function as a mathematical rulebook that tells the AI model when it goes wrong, and the model only gets penalized for mistakes. This makes the AI model avoid wanting to give wrong answers, so it uses diverse vocabulary, alters sentence structures, and follows “creative” approaches to respond to user queries instead of following the safest, most generic path (e.g., “I understand that…”).
Moving from 1 to 5 agents introduces a high level of coordination complexity. Think of the possible state errors, race conditions, and failure points for an AI model and multiply that by the number of models raised to the power of 2. That’s what developers face. For example, moving to 3 agents increases complexity by 9, and moving to 5 agents increases it by 25. Research indicates that 36.9% of multi-agent system failures are due to coordination misalignment.
Agents sharing the same memory may read outdated data from cached layers. Unlike standalone AI models with strict API limits, multi-agent systems can loop infinitely and misinterpret context. Inter-agent handoffs and context repetition lead to these systems consuming many more tokens than standalone counterparts.
To work around this problem, developers use a central coordinator to break down user queries into various parts, assign parts dynamically or sequentially to different agents, and aggregate results. Yet, earlier on, we described the limitations of this centralized orchestration approach.
Algorithmic solutions
Developers can follow algorithmic approaches that prevent semantic erosion in AI models. This involves injecting mathematical rigor into the models to enforce semantic consistency, restrict probability distributions, and limit the occurrence of hallucinations.
One approach is to supplement pure vector-distance searches with precise knowledge-graph paths in AI agents. This helps AI agents move from simply looking for semantic similarity to structured reasoning based on relational facts. Knowledge-graph paths connect entities (like places, people, and objects) with directional edges, allowing agents to follow preset multi-hop paths, such as Author > Book > Chapter > Quote, to retrieve facts.
Vector-distance searches aren’t discarded entirely, as they’re essential for routine, narrative-style answers. Rather, use the hybrid model where vector-distance handles routine user queries, and knowledge-graph paths handle more complex queries.
For their AI model, developers also combine statistical machine learning (data-driven prediction) with hard-coded algebraic constraints (mathematical bounds) that can’t be bypassed. For example, an AI model simulating chemical reactions will use statistical machine learning to simulate the reactions, and an algebraic layer that binds it to the law of Conservation of Mass and Energy, which states that the total sum of mass and energy in an isolated system stays constant and can’t be created or destroyed. In mathematical terms, it’s E = mc2, where E is total energy, m is mass, and c is the speed of light in a vacuum (3 x 108 m/s).
The above algebraic constraint ensures that when the AI model simulates chemical reactions, it doesn’t violate the laws of physics by creating or destroying chemical energy. Semantic erosion won’t let the AI model generate physically impossible outcomes with more or less energy than the original reactants in a chemical process.
During training, developers also restrict an AI model’s reward function to compiled results, or deterministic tokens, known as the Outcome-based reward mechanism. It emphasizes results and correctness over format compliance, where the model must pass a unit test, not just sound convincing while possibly being very wrong.
AI models are good at “reward hacking,” where they can generate coherent but very incorrect text, and this is behind the hallucinations that users often see. Restricting a model’s reward function to deterministic tokens prevents hallucinations and semantic erosion, helping the model provide accurate answers to complex questions.
Human-in-the-Loop (HITL) approaches
AI models may work independently after training, but inserting a human in the loop remains beneficial. A human-in-the-loop (HITL) approach infuses human judgment directly into AI data pipelines to prevent semantic erosion.
While labeling training data, domain experts can step in to re-anchor drifting tokens to real-world definitions instead of allowing recursive AI-to-AI outputs that can get easily misinterpreted. It’s no surprise that platforms like Mercor have built major businesses out of hiring domain experts (like software engineers, lawyers, doctors, and investment bankers) to label data for AI labs. Mercor alone has tens of thousands of hired talent fine-tuning data for numerous AI model developers, including Anthropic (Claude) and OpenAI (ChatGPT), the two biggest ones.
Developers also infuse frequent human-validated feedback to prevent semantic erosion loops where AI models train on AI-generated data and gradually lose touch with real human data. If left alone without human feedback, small biases, hallucinations, and errors can become foundational data for training the next AI events, and edge cases disappear, making the AI model forget its underlying data. However, developers regularly inject more context-heavy data to prevent this from happening.
There’s also the Reinforcement Learning from Human Feedback (RLHF) approach, a training method where human experts rate an AI model’s outputs for alignment with stated values and preferences. The model learns from this process to give outputs that align with human intent, not just relying on hard-coded rules.
Reinforcement Learning from Human Feedback (RLHF) occurs in the post-training stage, after the model has gone through pre-training to build foundational knowledge, followed by the supervised fine-tuning (SFT) stage that teaches the model to format answers properly and act like an assistant. Then, RLHF comes in to align the base model with human values.
There’s no silver bullet strategy for addressing semantic erosion in AI models. Rather, developers combine various methods, which we’ve described in detail. The broad AI training process never truly ends, even when most work is done in the initial stage, so semantic erosion will always be a lingering issue that has to be addressed. Fortunately, developers have proven strategies to address it and keep models as accurate as possible.
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Stefan Ionescu
