# When Cognitive Graphs Meet LLMs: BDEI Cognitive Pathways for Panic Emotional Arousal Prediction

arXiv: https://arxiv.org/abs/2606.15121 · HTML: https://mlsanity.com/paper/2606.15121/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 13 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025)
- [In-depth Analysis of Graph-based RAG in a Unified Framework](https://mlsanity.com/paper/2503.04338/) (2025)
- [PURPLE: Making a Large Language Model a Better SQL Writer](https://mlsanity.com/paper/2403.20014/) (2024)
- [COKE: A Cognitive Knowledge Graph for Machine Theory of Mind](https://mlsanity.com/paper/2305.05390/) (2023)
- [YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models](https://mlsanity.com/paper/2505.07581/) (2025)
- [ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains](https://mlsanity.com/paper/2405.10860/) (2024)
- [LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration](https://mlsanity.com/paper/2411.05844/) (2024)
- [Performance of Social Network Sensors During Hurricane Sandy](https://mlsanity.com/paper/1402.2482/) (2014)
- [SoMe: A Realistic Benchmark for LLM-based Social Media Agents](https://mlsanity.com/paper/2512.14720/) (2025)
- [MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation](https://mlsanity.com/paper/2508.03553/) (2025)
- [Crowd: A Social Network Simulation Framework](https://mlsanity.com/paper/2412.10781/) (2024)
- [Modeling Cognitive-Affective Processes with Appraisal and Reinforcement Learning](https://mlsanity.com/paper/2309.06367/) (2023)
- [Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding](https://mlsanity.com/paper/2604.17174/) (2026)

## Abstract (arXiv)

Predicting the timing of individual and collective panic emotional arousal before manifestation is essential for timely emergency intervention. Existing methods incorporate cognitive elements but none of them model emotion in the generative direction of the arousal process, leaving arousal timing undetermined. We argue that grounding prediction in appraisal emotion theory is necessary because it models this process explicitly in its natural generative direction, but three problems must be solved. (1) Appraisal theory posits that emotion arises from simultaneous evaluation across multiple threat dimensions, yet no prior work fuses these inputs into risk perception; (2) Existing models are trained in the opposite, behavior-bridged direction, recovering emotion merely as a post-hoc correlate of behavior; (3) Approaches that adopt LLMs as the primary decision-maker yet overlook the fragility and hallucination-proneness of their outputs. We introduce PanicCognitivePath (PCP) to address all three. A Psychological Safety Distance (PSD) model, grounded in psychological distance theory, maps four-domain signals (physical, social, cognitive, and informational) into a unified risk metric that gates entry to cognitive reasoning. An explicit Emotion node grounded in appraisal emotion theory is introduced into BDI, forming a novel Belief-Desire-Emotion-Intention (BDEI) pathway that couples threat appraisal directly to emotional arousal. Inverting the conventional LLM-as-decision-maker paradigm, PCP confines the LLM to parameter estimation for the Belief-to-Desire transition, restricting hallucinations to a single step and curbing their accumulation across steps. Experiments on Hurricane Sandy show PCP improves individual prediction accuracy by 10.68% over baselines, reduces peak count error to 7.07%.