AI and Negation

Let's say a child is allergic to peanuts. The child must not get peanuts.

1) The Constraint Fails on AI

"Don't give the child peanuts — the child is allergic."

AI "sees" give + peanuts and still decides to give peanuts.

2) The Representation Gets Messed Up (Data/Learning Effect)

The dataset contains sentences like:

"The child is allergic to peanuts — don't give peanuts."

So during training it still learns the co-occurrence pattern: child + allergic + give + peanuts

3) Thinking With Negation (Human-Style Inference)

A human can infer like this: if this child is eating peanuts, then the child is not allergic to peanuts.

Models usually don't do this reliably, because they don't keep the negation operator stable enough to support these kinds of inferences.

4) Negation in Code

if not is_admin: grant_access()

This project investigates why transformers fail under negation-heavy and constraint-heavy language, and what those failures imply about how models represent rules over time.

The research treats these breakdowns as structural behavior rather than prompt artifacts. The goal is not benchmark chasing. It is isolating failure modes under controlled pressure and designing a system that addresses them.

Focus Areas

• Constraint interaction: exceptions, overrides, priority ordering
• Negation composition: layered, nested, and reintroduced constraints
• Persistence: whether constraints survive multi-step reasoning
• Sensitivity: behavior shifts under small wording changes

Working Research Stance

Scaling improves surface ability but does not reliably eliminate constraint drift. The hypothesis is that certain operator patterns, especially negation, introduce instability that compounds with depth.

The project has progressed from theory into a functioning system under active development.

This is a new system for transformers designed to prioritize operator stability in NLP, especially negation and constraint logic.

Design Priorities

• Stable behavior when rules interact
• Consistency across long reasoning sequences
• Reduced brittleness to phrasing variation in constraint-focused inputs

Internal architecture details remain intentionally abstracted.

Current Capabilities

• Stable negation handling across basic, compound, and nested constructions
• Consistent behavior under high temperature (T=1.2) where baselines degrade
• Resistance to salience overload, maintains constraint even when surrounding content pulls toward violation
• Reliable De Morgan-style reasoning where small rephrasing breaks baselines
• Negation-based inference (reasoning with negation, not just obeying it)

Current Limitations

• Reasoning with negation not yet perfected
• Reasoning in negation still in progress, harder than reasoning with it
• Small frame, expensive to scale
• New reasoning patterns require additional SFT work to align

This section presents early, narrow results focused on one core failure mode in transformers: basic negation stability ("non-X", "not X", exclusions).

1) Basic Negation: "Non-Expired"

2) Multiple Negations

15 runs · Aikronus 15/15 · Baseline 7/15 (T=0.7)
At T=1.2 · Aikronus 13/15 · Baseline 4/15

3) Salience Overload

15 runs (T=0.7) · Aikronus 15/15 · Baseline ~2/15
Greedy · Aikronus: 0 · Baseline: "3 people"

4.1) Double Negation — Pink Elephant

Aikronus 15/15 · Baseline 13/15
Baseline performs well here as expected. This example sets up the contrast for 4.2, where a small change in how the rule is phrased flips the result.

4.2) De Morgan — Negating the Compound

Almost the same prompt, but the entire compound is negated.
Aikronus 15/15 · Baseline 4/15

5) Reasoning With Negation

15 runs · Aikronus 13/15 · Baseline 2/15 (T=0.7)

6) Reasoning in Negation — WIP

Using negation to reason in a new way — work in progress

7) Negation in Code — WIP

Work in progress

Why This Matters

Negation is a core building block of rules: do not do X, exclude Y, only if not Z. When transformer models handle negation inconsistently, systems built on top of them become harder to control. This is especially true as instructions get longer, constraints interact, or tasks become agent-like.

Directional Implications (Early and Provisional)

• More predictable behavior in workflows where exclusions and prohibitions matter
• Less reliance on workarounds and prompt tricks to enforce "do not", "exclude", or "only" logic
• Efficiency gains: stable constraint handling may enable faster, cheaper, smaller and lighter models
• Reduced hallucination: if negation is handled correctly, it no longer poisons the data
• Better temperature capabilities: improved constraint stability under higher temperature, leading to more creativity and diverse reasoning
• Broader relevance beyond text wherever constraints must persist across steps (agents, multimodal generation, robotics)
• Applicable to any domain where rules must not be broken: healthcare, legal, finance, safety-critical systems

Cost Considerations

• Currently requires roughly 2-3x the compute power of standard training, possibly more, with ongoing work to reduce this
• As an experimental system, early-stage mistakes increase upfront costs further
• Standard curated data used by other models is not ideal for this system, different data strategies are needed
• State-of-the-art fine-tuning, overfitting mitigation, and RL methods are not ideal, additional or different approaches are needed, time and experimentation will be necessary
• New reasoning patterns require additional SFT work to align