Theory
• Game theory: Modeling strategic interactions as games, designing mechanisms to mitigate adversarial behaviors, and understanding the implications of these strategies for robust system design.
• Competitive search: Analyzing how competition influences the ecosystem and exploring mechanisms to promote desired properties in these contexts such as robustness and fairness.
• Probability ranking principle: Investigating the assumptions of PRP and their adaptation to different adversarial scenarios.

Evaluation method
• Specific evaluation: Robustness evaluation for specific robustness types (e.g., adversarial, OOD robustness), scenarios (e.g., corpus updating, queries with typos), and IR models (e.g., sparse, dense, and generative retrieval models).
• General evaluation: Using an evaluation metric to comprehensively cover as many robustness scenarios as possible.
• Diverse evaluation tools: Developing new evaluation forms, including new evaluation functions, LLMs, and human evaluations, for comprehensive robustness comparisons.
• Benchmarks of robustness: Discussion of existing robustness datasets and proposing new benchmark tasks to address diverse robustness categories and requirements.

Method
• Adversarial attack & defense: Investigating adversarial vulnerabilities in IR models and developing defenses against malicious attacks like data poisoning and adversarial document generation.
• Zero/few shot IR: Using zero-shot or few-shot learning, transfer learning, and large-scale pre-trained models to improve cross-domain and task generalization.
• Balancing robustness and effectiveness: Exploring enhancing robustness without compromising effectiveness.
• Long-term learning: Explores continual learning in IR to enhance stability.
• Noise Resistance: Making IR systems resistant to noise in queries, documents, or training data, such as typo handling, semantic noise filtering, and processing incomplete or corrupted inputs.
• Enhancing RAG Robustness: Improving RAG pipeline robustness by reducing error propagation, ensuring consistency between retrieved documents and generated content, and enhancing output reliability under uncertain conditions.

Application
• Robust search engines: Deploying robustness enhancement methods in resource- and condition-constrained search engines.
• Robust recommendation systems: Robustness for recommendation systems, addressing sparse user data, cold-start issues, adversarial manipulation, and dynamic user preferences.
• Data-specific scenarios: Robustness in specialized data retrieval, including scientific literature, medical documents, legal data, and long-form or multi-modal documents.
• Federated and distributed IR systems: Robustness for distributed IR using federated learning involves tackling inconsistent local data, implementing privacy-preserving strategies, and improving communication efficiency across distributed nodes.

Society Impact
• Human behaviors that affect robustness: Understand how user behaviors like query biases, click feedback loops, and manipulated web content affect IR system robustness.
• Robustness and Ethics: Addressing ethical concerns by ensuring fairness, minimizing algorithmic biases, and maintaining transparency. Ensuring robust models do not disproportionately affect certain user groups or perpetuate societal inequalities.
• Explainability and truthfulness: Discuss the impact of IR model explainability and information truthfulness on users.