We invite contributions, but not limited to, the following key topics: 

1. Handling ambiguity and vagueness: real user queries are rarely precise, they formulate vague queries like “cheap hotels near the center”,  “high-rated restaurants”; this introduces concepts from Fuzzy Logic, where terms like cheap or near don’t have fixed and precise boundaries. The Challenge is designing systems that interpret vague language consistently while still giving useful, ranked results, not arbitrary ones. 
2. Natural language understanding at depth:flexible query systems increasingly rely on advances in Natural Language Processing byLarge Language Models and prompt engineering. Thus challenges include mapping natural language to formal queries (e.g., SQL or graph queries);  understanding user’s intent, not just keywords;  handling multi-turn or conversational queries.    
3. Foundation Large Language Models fine tuning:creating foundation LLMs requires both huge computational and infrastructural resources; thus their fine tuning for specific tasks is a challenging research issue, that requires building reliable, controllable, and explainable systems on top of Foundation LLMs, which are powerful but largely opaque and only partially customizable.
4. Query relaxation and approximation:when exact answersdon’t exist, systems must relax constraints for example by dropping conditions, by broadening ranges, by suggesting near-matches. This connects to approximate query processing, whose core difficulty is to decide how to relax queries optimally without drifting too far from the user’s intent, how explaining why a result was returned despite not matching exactly. 
5. Ranking and relevance under uncertainty:flexible answers require ranking possible results rather than returning exact matches. Challenges include defining relevance when constraints are soft, combining multiple weak signals (ex. distance, price, ratings, preferences); avoiding bias or misleading rankings. This often blends machine learning with traditional IR (information retrieval) methods. 
6. Integration of heterogeneous data sources:modern systems pull from structured databases, Knowledge graphs, unstructured texts, multimodal information. Challengeare aligning schemas and meanings across sources and media, resolving conflicts and inconsistencies, querying across formats, media and genre. 
7. Explainability and trust:users increasingly expect to know why a result was returned, why it ranked higher than others. Flexible systems are especially tricky because the results may not strictly satisfy the query. The logic may involve heuristics or learned models. Open problems include generating clear, human-understandable explanations for approximate answers.
8. Personalization vs. generalization:flexible query answering often depends on user context, preferences, location, history. There is a tension between personalization to improves relevance and the risks of overfitting, of violating privacy issues, and the lack of transparency.
9. Efficiency and scalability:flexible queries are computationally expensive: relaxation creates large search spaces; ranking requires scoring many candidates.Thus challenges are maintaining real-time performance on large-scale systems. 
10. Neuro-symbolic integration:there is a growing interest in combining neural models (for language reproduction and interpretation) and symbolic systems (for structured querying and constraints, and query understanding). This challenges neuro-symbolic AI. The key difficulty is making these systems work togethercoherently, and preserving both flexibility and correctness. 
11. Evaluation and benchmarks:it’shard to measure the success of a FQAS: what counts as a “good” approximate answer?, How do you evaluate user satisfaction? Challenge include designing benchmarks that reflect real-world flexible querying, not just exact-match accuracy.  
12. Green and Sustainable FQAS:one main drawbacks of current Large LanguageModels  is their huge demand and consumption of both energy and water; thus a challenge is designing Small Language Models and solutions that are energy-efficient and resource-aware.  
13. Predictive and Prescriptive Answers:current systems have been designed to describe the past and the present but not to anticipate the future by answering predictive queries  (e.g., “what is likely to happen if…?” ) or prescriptive queries ( e.g., “what should I do if …?”). One challenge is designing intelligent advisorsand  decision engines able to yield reliable forecasts and optimization strategies.