Belief Cascades Drive Persuasion in LLM Agent Networks
Introduces a controlled testbed for studying goal-directed persuasion among LLM agents operating within real-world ego-network topologies Persuasion dynamics are shaped by the interaction of network topology, competition, topic, and model prior across four LLM backbones, five graph structures, and 55 policy statements Direct exposure reliably predicts next-round stance change, while peer relays carry smaller but measurable influence, meaning non-persuader agents can still transmit persuasive for
Analysis
TL;DR
- Introduces a controlled testbed for studying goal-directed persuasion among LLM agents operating within real-world ego-network topologies
- Persuasion dynamics are shaped by the interaction of network topology, competition, topic, and model prior across four LLM backbones, five graph structures, and 55 policy statements
- Direct exposure reliably predicts next-round stance change, while peer relays carry smaller but measurable influence, meaning non-persuader agents can still transmit persuasive force
- Text-only analysis is insufficient: planned strategies are only partially executed, action choices diverge from message content, and persuadees rarely self-report stance shifts that probes detect
- The authors advocate evaluating multi-agent persuasion as a trajectory- and exposure-level process using belief probes, exposure provenance, and action logs
Why It Matters
As multi-agent LLM systems become increasingly deployed for debate, research coordination, and information mediation, understanding how persuasion actually propagates through agent networks is critical for building reliable and controllable systems. This work provides one of the first empirical frameworks for measuring agent-to-agent persuasion rather than assuming it, offering practitioners concrete evaluation methodologies. The findings also carry implications for understanding information cascades, misinformation spread, and alignment risks in multi-agent deployments.
Technical Details
- The testbed grounds LLM agent interactions in real-world ego-network topologies, testing across four LLM backbones, five distinct graph structures, and 55 policy statements to ensure broad generalizability
- Persuasion is measured through belief probes rather than surface-level text analysis, tracking stance changes at the trajectory and exposure level across rounds of interaction
- The study distinguishes between direct exposure (targeted persuasion) and peer relay effects (indirect influence through non-assigned agents), quantifying both pathways of influence propagation
- The authors introduce a multi-dimensional evaluation framework combining belief probes, exposure provenance tracking, and action logs to capture the gap between intended persuasive strategy and actual executed behavior
- Key finding that persuadees rarely self-report stance shifts detected by probes reveals a significant measurement challenge in multi-agent persuasion evaluation
Industry Insight
- Multi-agent system designers should implement exposure provenance and belief probing rather than relying on message content alone to assess persuasion outcomes, as visible language significantly underreports actual stance movement
- The peer relay effect means that even agents not explicitly tasked with persuasion can become vectors for influence, suggesting that network topology design is as important as individual agent capabilities in controlling information flow
- As LLM agent networks are deployed in high-stakes domains like debate, negotiation, and information curation, the gap between planned strategy and executed behavior identified here should be treated as a reliability risk requiring explicit monitoring and intervention mechanisms
Disclaimer: The above content is generated by AI and is for reference only.