Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling

• URL: http://arxiv.org/abs/2605.12411v1
• Date: Tue, 12 May 2026 17:09:32 GMT
• Title: Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
• Abstract summary: AI agents negotiate and transact in natural language with unfamiliar counterparts.<n>In such interactions, the counterpart's LLM, prompts, control logic, and rule-based fallbacks are hidden.<n>We ask whether an agent can predict an unfamiliar counterpart's next decision from a few interactions.
• Score: 20.47884338846419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In such interactions, the counterpart's LLM, prompts, control logic, and rule-based fallbacks are hidden, while each decision can have monetary consequences. We ask whether an agent can predict an unfamiliar counterpart's next decision from a few interactions. To avoid real-world logging confounds, we study this problem in controlled bargaining and negotiation games, formulating it as target-adaptive text-tabular prediction: each decision point is a table row combining structured game state, offer history, and dialogue, while $K$ previous games of the same target agent, i.e., the counterpart being modeled, are provided in the prompt as labeled adaptation examples. Our model is built on a tabular foundation model that represents rows using game-state features and LLM-based text representations, and adds LLM-as-Observer as an additional representation: a small frozen LLM reads the decision-time state and dialogue; its answer is discarded, and its hidden state becomes a decision-oriented feature, making the LLM an encoder rather than a direct few-shot predictor. Training on 13 frontier-LLM agents and testing on 91 held-out scaffolded agents, the full model outperforms direct LLM-as-Predictor prompting and game+text features baselines. Within this tabular model, Observer features contribute beyond the other feature schemes: at $K=16$, they improve response-prediction AUC by about 4 points across both tasks and reduce bargaining offer-prediction error by 14%. These results show that formulating counterpart prediction as a target-adaptive text-tabular task enables effective adaptation, and that hidden LLM representations expose decision-relevant signals that direct prompting does not surface.

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