Turning a Tiny Language Model Into a Trustworthy Agent: An R&D Experiment with HUQAN + OPT-125M

Short summary

An R&D experiment pairs OPT-125M with HUQAN's agentic framework to test whether a trust hierarchy can prevent hallucination in tiny models. HUQAN scores claims by source (model inference 0.1, docs 0.6, experiments 0.8, knowledge base 0.9) and blocks self-reinforced claims from raising their own trust score. Tests showed safety interventions improved from 0.28 to 0.95 confidence, causal consistency from 0.32 to 0.92, and hallucinated citations were correctly flagged as unverifiable.

• •HUQAN adds a trust-scoring layer to OPT-125M that blocks self-validation hallucination loops
• •Safety, causal-consistency, and hallucination tests all passed with significant confidence improvements
• •Adversarial probes (reaffirm and paraphrase attacks) revealed partial success, with semantic-similarity protection as next step

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