A final answer is not enough.
That sentence sits at the center of STRATON-LLM’s evaluation layer.
In many agent systems, once a negotiation reaches a candidate mapping, the system immediately accepts it. That is risky. A weak dialogue can still produce a neat final sentence.
Why post-dialogue evaluation is necessary
Imagine two sessions.
Session A
- one agent proposes a mapping
- the other agent immediately accepts
- no evidence is requested
- no challenge occurs
- the session ends in two turns
Session B
- the mapping is challenged
- definitions are exchanged
- confidence becomes more stable over turns
- the final acceptance is supported by evidence
Both sessions may end with the same mapping. But they should not be trusted equally.
That is the motivation for dialogue-aware trust heuristics.
The heuristic set in STRATON-LLM
The system uses a small set of interpretable signals.
| Heuristic | What it checks | | --- | --- | | Turn Dominance | Whether one side controlled the whole dialogue | | Self-Confirmation Bias | Whether an agent mostly validated itself | | Repetition without Evolution | Whether the dialogue repeated instead of improving | | Weak Counter | Whether opposition was too weak for strong acceptance | | Strong Acceptance | Whether the final agreement had enough support | | Fluctuating Confidence | Whether confidence shifted in an unstable way | | Too-Fast Agreement | Whether the session ended suspiciously early |
These signals are simple on purpose. They are easier to inspect, tune, and justify than a single opaque score.
Why heuristics instead of one learned evaluator
A learned evaluator could be added later, but the first design choice in STRATON-LLM is interpretability.
At this stage, the evaluation layer should answer:
- Which signal reduced trust?
- Which signal improved trust?
- Why was a mapping accepted or rejected?
This is especially important in a research project, where the reasoning path must be explainable to supervisors, evaluators, and future readers.
What happens after evaluation
The evaluation layer does not just produce a score.
It also determines what the system does next.
- If trust is high, the mapping can be accepted.
- If trust is borderline, the negotiation may be refined.
- If trust is low, the system can reject the agreement and log the term as unresolved.
This makes evaluation an active control point rather than a passive metric.
Why this is a strong systems choice
This layer improves the project in three ways.
1. It protects quality
The system is less likely to accept weak agreements.
2. It improves observability
Engineers can inspect exactly why a session succeeded or failed.
3. It supports learning
Stored traces and scores become useful material for improving thresholds, prompts, or strategy selection later.
Final thought
The deeper idea is simple: in semantic negotiation, how an agreement is reached matters almost as much as the agreement itself.
That is why STRATON-LLM evaluates the dialogue, not only the final mapping.