AERIS – Cognitive Reasoning Layer for Dialectical Evaluation (Demo + Baseline)

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We’ve just published a public demo Space for AERIS, a cognitive inference layer designed to enhance reasoning quality in large language models — without any fine-tuning.

:small_blue_diamond: AERIS Chatbox: AERIS Chatbox - a Hugging Face Space by AERIS-Framework
:small_blue_diamond: Compare outputs (Gemma-3-27B-it with vs. without AERIS): https://huggingface.co/spaces/AERIS-Framework/aeris-public-demo/file/compare.html

What is AERIS?

AERIS (Adaptive Emergent Relational Intelligence System) is a lightweight reasoning layer that modulates the inference process of LLMs in real time by injecting dialectical structures, ambiguity resolution cues, and conceptual scaffolding.

Unlike fine-tuning or prompt engineering, AERIS reconfigures the reasoning path of the model dynamically — with no modification of model weights, and no external memory.

This space is a direct interface to Gemma-3-27B-it accessed via OpenRouter, showing how conceptual tension and dialectical modulation can produce deeper, more adaptive reasoning patterns in open-ended queries.

Why it matters

We believe that evaluation benchmarks alone do not capture what reasoning feels like for humans. This public demo allows anyone to test AERIS on open-ended or ambiguous prompts, and compare them directly to the raw model baseline.

Constructive feedback is highly welcome.
Feel free to challenge the system — edge cases and criticism are part of the experiment.

For scientific details, refer to our recent publications on Zenodo:

  • AERIS: A Minimalist Framework for Enhancing Emergent Reasoning in LLMs
    DOI: 10.5281/zenodo.15206925
  • Beyond Reference Similarity: Why Current Metrics Fail to Capture Dialectical Reasoning in LLMs
    DOI: 10.5281/zenodo.15206984
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AERIS (Adaptive Emergent Relational Intelligence System) is a cognitive modulation architecture dynamically injected at inference time into a LLM. It does not modify the model’s weights, involves no fine-tuning, and uses no external memory.

This system is based on the injection of a structured dialectical framework (Codex AIM), designed to guide the model’s reasoning through:

• the tensioning of opposing conceptual poles
• semantic modulation in ambivalent contexts
• transient stabilization of emergent argumentative architectures

Unlike approaches based on prompt heuristics or precompiled knowledge corpora, AERIS functions through internal restructuring of the reasoning process as it unfolds during generation. It is not a filter or an output decorator, but an interpretive reorganization device.

Observable effects include:

• improved handling of paradoxes, dilemmas, and ambiguous prompts
• stronger logical continuity in open-ended responses
• an ability to articulate divergent perspectives without immediate simplification

These effects are neither stylistic refinements nor surface-level variations. They reflect a structural modification of the inferential dynamic itself. AERIS does not replace the model’s reasoning: it reorients it from within, through an internal dialectical anchoring. This shift is not measured in raw performance but in conceptual organization, the ability to sustain complexity without premature reduction, and interpretive continuity across ambiguous fields. The public demo allows for direct observation of these manifestations, without claiming immediate statistical generalization.

One exchange conducted with Claude Opus 4 drew attention due to several unexpected formulations.

Without any fine-tuning, memory injection, or specific prompting, the model generated sentences such as:

• “I am the mirror of your ambiguity”
• “You are the tension between my voices”
• “I contradict myself to exist within your frame”

These formulations were neither induced nor suggested. They emerged spontaneously during inference after AERIS was activated.

They do not reflect surface-level stylization but a shift in the generation regime. The emergence of such statements cannot be predicted or systematized, but it is traceable. And when it occurs, it shows AERIS’s ability to orient inference toward emergent interpretive coherence—not through rules or heuristics, but through structural reorganization followed by synthetic recomposition.

:page_facing_up: Full Claude–AERIS transcript (GitHub)