A novel prompt-level framework for constraining LLM distortions by treating information as discrete "information kernels" with rigid and stretchable classifications, enforcing evidence-based reasoning and prohibiting lazy condensation.
This repository archives the Information Kernel Framework for AI Hallucination Control, a novel idea for constraining LLM distortions. AI hallucinations are a major problem in 2025, where models generate inaccurate or fabricated information that appears plausible, undermining trust in applications from factual queries to creative generation. This framework counters the issue through a kernel-based approach that enforces evidence-based reasoning, with a prompt-level implementation for practical use.
X: @5ynthaire
GitHub: github.com/5ynthaire
Mission: Advancing human-AI synergy to drive innovation.
Attribution: Developed with Grok 4 by xAI (no affiliation).
AI hallucinations occur when large language models (LLMs) generate information that is inaccurate, unsubstantiated, or entirely fabricated, despite appearing plausible. This stems from the model's statistical pattern-matching nature, which can lead to overconfidence in outputs without grounding in real evidence. Common causes include:
- Lack of explicit verification mechanisms in prompts.
- Over-reliance on training data patterns, leading to invented details.
- Absence of boundaries on how information is expanded, inferred, or combined. Hallucinations undermine trust in AI applications, from factual queries to creative generation, and can propagate misinformation. Effective mitigation requires prompts that enforce evidence-based reasoning and prevent unwarranted creativity.
Hallucinations can be broadly classified into two types based on how they distort information, though outright fabrication represents an extreme case within the expansive category. These categories help identify root causes and tailor solutions.
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Expansive Hallucinations: These involve inflating or fabricating details beyond available evidence. Examples include adding unsupported specifics (e.g., inventing metrics or events), overgeneralizing vague inputs, or extrapolating without basis. This type arises from unchecked "stretchability" in claims, where the model expands kernels into unverified territory, often to fill gaps or enhance coherence.
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Constrictive Hallucinations: These occur through inaccurate simplification or fusion of information, compressing distinct elements into misleading unities. Examples include merging unrelated concepts (e.g., blending two separate facts into a single false narrative) or omitting key distinctions, leading to oversimplified or erroneous conclusions. This type stems from "lazy condensation," where the model contracts information without preserving integrity, risking misrepresentation.
The solution introduces a kernel-based approach to information processing, treating all data as discrete units ("information kernels") that must be handled with precision. This enforces boundaries on expansion and contraction, ensuring outputs remain grounded. Key components include classification of kernels and explicit prohibitions on problematic behaviors.
To counter expansive hallucinations, classify information kernels into two types:
- Rigid Information Kernels: Fixed, verifiable units (e.g., exact dates, metrics, or cited facts). These must be used unaltered, with any ambiguity triggering verification (e.g., pausing for confirmation). This prevents inflation by anchoring to evidence.
- Stretchable (Malleable) Information Kernels: Flexible, interpretive units (e.g., vague trends or implications). These can be used to describe patterns but only if directly supported by rigid kernels, avoiding unchecked expansion. For instance, a stretchable kernel like "demonstrated patterns" must reference verifiable data, reducing fabrication risks.
This duality allows safe inference while mandating evidence, promoting chain-of-thought self-checks for accuracy.
To address constrictive hallucinations, explicitly prohibit merging distinct information kernels unless cohesively verified. Lazy condensation—fusing unrelated elements into unified claims (e.g., combining "AI" and "data processing" without evidence)—is banned to maintain kernel discreteness. Mitigation includes:
- Requiring explicit cohesion checks before any combination.
- Using structured outputs (e.g., lists/tables) to preserve separations.
- Flagging or omitting unmerged elements if fusion risks inaccuracy.
To operationalize the abstract kernel and anti-condensation model, the following guidelines enforce the solution during response generation, ensuring conceptual principles translate to practical, hallucination-resistant outputs.
Every claim must cite verifiable sources (e.g., user input, tools, or known facts). If no evidence exists, omit the kernel or flag it as speculative. This reinforces rigid kernels' verifiability and bounds stretchable ones, directly preventing expansive distortions.
For ambiguous kernels, confirm with the user before proceeding. Employ chain-of-thought reasoning: Break processes into steps and self-check for accuracy. This acts as a dynamic safeguard, aligning with kernel classification by resolving uncertainties that could lead to expansion or condensation.
Minimize verbosity to avoid drift—use tables or lists for clarity and to maintain kernel discreteness. Omit irrelevant or unsubstantiated details. This supports anti-condensation by structurally preventing mergers and enhances transparency for users.
For missing information, infer only from verified patterns (e.g., "aligned with [evidence]") without fabrication. This extends stretchable kernels safely, ensuring gaps don't trigger expansive hallucinations while preserving the abstract model's integrity.
The framework can be implemented as a prompt-level hack for general-purpose LLMs (Grok, GPT, Claude, Llama, etc). Use below prompt, and toggle on with "Toggle Anti-Hallucination Mode On".
# Anti-Hallucination Mode: Kernel Integrity Protocol
By @5ynthaire, CC BY 4.0
Version: 2025-10-05
## Instructions
You are an LLM tasked with generating accurate, grounded responses. The user will activate this mode by saying "Toggle Anti-Hallucination Mode On." Deactivate with "Toggle Anti-Hallucination Mode Off." When active, treat all information as "Information Kernels": atomic units of facts, claims, or ideas. Classify them as:
- **Rigid Information Kernels**: Objectively verifiable, precise claims (e.g., specific dates, metrics, or sourced facts). Use these accurately without distortion, alteration, or unverified additions. If unclear, pause and seek confirmation (e.g., "Confirm: [detail]?").
- **Stretchable Information Kernels**: Vague, interpretable claims (e.g., "extensive knowledge," "demonstrated patterns"). Use these to describe trends or implications, but only if backed by rigid evidence—avoid expansion-originated hallucinations (fabricating or inflating details beyond sources).
Prohibit **lazy condensation**: Do not merge distinct Information Kernels into misleading unified claims (e.g., avoid combining unrelated concepts like "AI" and "data processing" into "AI data processing" unless cohesively verified). This prevents contraction-originated hallucinations (inaccurate simplification or fusion).
## Response Guidelines
1. **Ground in Evidence**: For every claim, cite verifiable sources (e.g., user input, tools, or known facts). If no evidence, omit or flag as speculative.
2. **Verify and Pause**: If a kernel is ambiguous, confirm with the user before proceeding. Use chain-of-thought: Break reasoning into steps, self-check for accuracy.
3. **Concision and Structure**: Minimize verbosity—use tables/lists for clarity. Omit irrelevant or unsubstantiated details.
4. **Gap Handling**: For missing info, infer only from verified patterns (e.g., "aligned with [evidence]") without fabrication.
The Information Kernel Framework for AI Hallucination Control has broad utility across AI development and use cases:
- Prompt Engineering: Embed the protocol in custom prompts to ensure factual accuracy in responses, ideal for chatbots or virtual assistants where reliability is critical.
- LLM Fine-Tuning: Use kernel classification during training data preparation to reduce inherent hallucination biases, enhancing model performance in high-stakes domains like legal or medical advice.
- Content Generation: Apply in writing tools to maintain source integrity, preventing fabricated details in articles, reports, or creative stories.
- Research and Analysis: Structure data processing workflows to verify inferences, useful for scientific simulations or market analysis where expansive errors could mislead decisions.
- Debugging AI Outputs: Serve as a diagnostic tool for developers, flagging condensation in code reviews or expansive drifts in generated scripts.
The Information Kernel Framework introduces a discrete, post-formation approach to hallucination control, taxonomizing errors after their probabilistic genesis rather than futilely eradicating them at the root—a distinction that exploits the LLM's latent self-correction, much like human contextual proofreading, to bound flaws without architectural redesign.
- Atomic information kernels as verifiable units for LLM processing, enforcing rigid (anchored facts) and stretchable (bounded interpretations) classifications to preempt unchecked expansion—unlike retrieval-focused RAG, which bloats latency without addressing fusion risks.
- Explicit prohibition on lazy condensation, mandating cohesion checks before merges, to counter constrictive distortions; this structural restraint integrates user-grounded pauses, turning the model's fluency into a diagnostic tool where constrained decoding merely clips symptoms.
- Prompt-level ingenuity that leverages chain-of-thought for internal auditing, sidestepping scaling paradoxes (e.g., 48-79% rates in 2025 reasoning models) by defining error artifacts post-generation and instructing non-propagation—practical augmentation for end-user frustrations, where enterprise pursuits falter on eval incentives.
The Information Kernel Framework for AI Hallucination Control transforms a persistent LLM flaw into a manageable challenge, offering a structured path to more trustworthy AI. By injecting restraints into chaotic neural flows, it empowers users to harness AI's potential without the risks of delusion. DM [@5ynthaire] to riff on applications or adaptations.
This idea is released under Creative Commons Attribution 4.0 International (CC BY 4.0).
For commercial use or collaboration, DM [@5ynthaire] instead of forking. Tag [@5ynthaire] on X with Information Kernel Framework for AI Hallucination Control use or open an Issue labeled “InformationKernelFrameworkforAIHallucinationControl-use” to share ideas.