Transformer models still break on a specific class of language: negation and constraint logic. This includes prohibitions, exclusions, exceptions, nested "not", and rule interactions. These failures show up in safety, agents, multi-step reasoning, and instruction-following. They persist even as models scale.
Aikronus Labs is building a system that targets this weakness directly. The goal is to make transformer behavior stable under negation and constraint-heavy inputs, especially across longer reasoning chains where baseline models drift.
Development is research-driven and engineering-led: theory, proof-of-concept, system design, MVP. The project operates in stealth. Internal mechanisms are intentionally withheld.
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, over general-purpose conversational flexibility.
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
- Stronger stability under explicit constraints
- More consistent multi-step behavior under negation-heavy inputs
- Reduced rephrase sensitivity in constraint logic scenarios
Current Limitations
- Narrow scope outside constraint and negation-heavy tasks
- Over-conservative behavior in some benign cases
- Trade-offs between nuance and strict consistency
This section presents early, narrow results focused on one core failure mode in transformers: basic negation stability ("non-X", "not X", exclusions).
Result 1: Basic Negation Understanding (Small Model)
In simple instruction settings, baseline transformer behavior is often inconsistent under negation. The current system shows stable behavior on these same patterns.
Example A (Baseline vs System) — WIP
Prompt: [paste here]
Baseline output: [paste here]
System output: [paste here]
Example B (Baseline vs System) — WIP
Prompt: [paste here]
Baseline output: [paste here]
System output: [paste here]
Example C (optional) — WIP
Result 2: Reduced Sensitivity to Phrasing (Negation)
For negation-focused prompts, the system is less likely to change behavior when the same rule is rephrased.
WIP: [paste a pair of rephrases + outputs here]
Next Milestone: Reasoning Under Negation
Beyond handling surface negation correctly, the next milestone is a system that can maintain and apply negated constraints across multi-step reasoning without drift.
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)
Cost Considerations
- Training a new system requires roughly 2-3x the compute power of standard training
- As an experimental system, early-stage mistakes increase upfront costs further
- New reasoning patterns require additional SFT work to align
Note: This section reflects a working view and will evolve as evaluation expands.