STRATON-LLM
Evaluation & Learning
STRATON-LLM does not stop at generating a candidate mapping. It asks whether the dialogue that produced the agreement was actually reliable, then stores the outcome in a way that improves future system behavior.
Trust-aware agreement evaluation
The framework evaluates the final agreement using dialogue-level heuristics rather than blindly accepting the last proposal. This is crucial because a fast agreement can still be weak, biased, or insufficiently challenged.
Turn Dominance
Checks whether one side controls the conversation without sufficient challenge.
Self-Confirmation Bias
Detects whether an agent keeps validating its own claims without meaningful external support.
Repetition without Evolution
Flags dialogues that repeat the same point without introducing better evidence or refinement.
Weak Counter
Measures whether opposing arguments were too weak to justify strong acceptance.
Strong Acceptance
Rewards agreements that are backed by explicit support and coherent resolution.
Fluctuating Confidence
Tracks unstable confidence shifts that may indicate unreliable convergence.
Too-Fast Agreement
Penalizes suspiciously quick agreements that likely skipped real examination.
Persistence model
Mapping store
Accepted alignments are saved with source term, target term, confidence, method, evidence, and timestamp so the next negotiation starts from prior knowledge.
Trace logger
Each session preserves dialogue acts, decisions, confidence changes, and final outcomes. This enables replay, debugging, and later evaluation.
Learning updater
Successful and failed negotiations update confidence values, unresolved-term reports, and future strategy hints.
Ontology updater
Confirmed mappings can later be promoted into versioned ontology updates through a controlled and auditable evolution step.
Future roadmap
- → Expand evaluation scenarios with more cross-domain agent pairs.
- → Refine adaptive thresholds and uncertainty propagation across layers.
- → Promote high-confidence mappings into versioned ontology evolution workflows.
- → Benchmark negotiation outcomes against simpler alignment-only baselines.
STRATON-LLM pages
Overview
Problem framing, project goals, research contribution, and end-to-end system story.
Architecture
Six-layer architecture, module boundaries, and system walkthrough.
Protocol & Negotiation
Triggering, dialogue acts, FSM control, and a worked negotiation scenario.
Design Decisions
The major choices, trade-offs, and constraints that shaped STRATON-LLM.
Evaluation & Learning
Agreement trust, heuristic scoring, persistence, and evolution roadmap.
Why this layer matters
Without persistence and evaluation, each negotiation would be an isolated conversation. This layer turns outcomes into reusable system knowledge and keeps agreement quality measurable.