The Vision
Research Agenda: Building the Next Generation of Adaptive, Cognitively-Inspired, and Knowledge-Integrated Information Systems
At the IRIS Lab, we address a fundamental question that sits at the heart of modern information technology: How can we design intelligent information access systems that go beyond simple retrieval—systems that can reason, adapt, integrate diverse knowledge sources, and interact meaningfully with human users across domains?
We are witnessing a transformative convergence where traditional Information Retrieval (IR), dense neural models, retrieval-augmented generation (RAG), and large language models (LLMs) are creating unprecedented opportunities for human-machine information interaction. This confluence promises to revolutionize how we discover, interpret, and leverage knowledge across every discipline and industry.
The Challenge Landscape
Despite remarkable advances in each of these areas, today’s information systems face critical limitations that hinder their real-world effectiveness. Current systems excel at retrieving information but often struggle with deeper reasoning—focusing on surface-level matches without incorporating symbolic knowledge or providing explainable, multi-step reasoning processes that humans can trust and understand. This limitation creates a significant gap between retrieval capability and actual problem-solving utility.
Another pressing challenge is the generalization barrier. While neural models demonstrate impressive performance on familiar domains and tasks represented in their training data, their effectiveness degrades significantly under domain shifts or when faced with novel tasks. This brittleness limits their practical application in dynamic real-world environments where information needs constantly evolve and cross domain boundaries.
Contemporary systems also lack the adaptability that characterizes human information processing. Unlike humans, who naturally adjust their information-seeking strategies based on task complexity, context, and prior knowledge, current systems rarely calibrate their retrieval approaches, reasoning depth, or interaction patterns in response to user intent or situational demands. This one-size-fits-all approach undermines both efficiency and effectiveness.
Perhaps most critically, today’s systems struggle with deeper knowledge integration—the challenge of coherently combining neural, symbolic, and multi-modal signals in ways that are both principled and scalable remains largely unsolved. This integration gap prevents systems from fully leveraging the complementary strengths of different knowledge representation paradigms.
Our Research Direction
The IRIS Lab seeks to address these intertwined challenges through a comprehensive research agenda that draws from multiple disciplines. We integrate principles from information retrieval, neuro-symbolic modeling, cognitive science, reinforcement learning, and adaptive language model training to create systems that transform how humans interact with information. See below for details of the current research directions we are pursuing.
Broader Impacts and Future Vision
Our work in the IRIS group aims to address critical gaps in current information systems through several complementary impact pathways:
Scientific Advancement: By investigating the theoretical foundations of neural-symbolic integration and adaptive retrieval mechanisms, our work could establish new formal frameworks that explain why certain approaches succeed or fail. This understanding is essential for moving beyond the current empirical trial-and-error paradigm toward more principled system design.
Transforming Domain-Specific Applications: The techniques we propose to develop could dramatically improve information access in high-stakes domains where current solutions fall short:
- In healthcare, systems that reliably integrate medical knowledge graphs with contextual reasoning could help clinicians navigate complex treatment decisions with greater confidence
- For scientific discovery, tools that perform multi-hop reasoning across research literature could accelerate hypothesis generation and testing
- In education, adaptive systems that adjust their reasoning depth to student needs could provide more personalized learning experiences
Enhancing Trust and Transparency: Our research on explainable reasoning processes and cognitive alignment aims to address one of the most significant barriers to AI adoption in critical settings—the “black box” problem. By designing systems where explanation is inherent to the reasoning process rather than retrofitted, we hope to create information tools that experts can confidently incorporate into their workflows.
Central to this vision is our work on developing rigorous mathematical frameworks that illuminate the inner workings of modern retrieval systems. By investigating the geometric and topological properties of embedding spaces using tools from differential geometry and topological data analysis, we aim to establish theoretical foundations that could lead to more transparent and principled system design.
Democratizing Advanced IR Capabilities: Current state-of-the-art information systems often require massive proprietary resources inaccessible to most researchers and developers. Our focus on parameter-efficient adaptation techniques and open infrastructure components seeks to lower these barriers, enabling broader participation in advanced IR research and application development. This could help prevent further concentration of these capabilities within a small number of large organizations.
Cross-Domain Impact and Applications: Our research carries implications across numerous domains where intelligent information access is crucial. In scientific research, our systems can help connect insights across disciplinary boundaries, accelerating discovery by surfacing non-obvious connections and reducing knowledge fragmentation. In healthcare, our adaptive retrieval frameworks can enable clinicians to access relevant medical knowledge tailored to specific patient contexts, improving decision quality while respecting the complexity of medical reasoning. In education, our work can support personalized learning by dynamically adjusting information presentation based on learner knowledge and learning objectives. For enterprise knowledge management, our systems can integrate diverse organizational knowledge sources while preserving provenance and supporting collaborative sense-making.
Why This Matters Now
The confluence of large language models with traditional IR creates an unprecedented opportunity to fundamentally rethink information access paradigms. However, without dedicated research on the challenges we’ve identified, we risk creating systems that appear intelligent but lack the reasoning depth, adaptability, and trustworthiness needed for transformative impact.
Through this multifaceted research agenda, the IRIS Lab aims to fundamentally transform the relationship between humans and information systems—creating technology that not only retrieves facts but enhances understanding, supports complex reasoning, and adapts seamlessly to evolving information needs across domains and contexts.
Our Research Directions
The IRIS Lab seeks to address these intertwined challenges through a comprehensive research agenda that draws from multiple disciplines. We integrate principles from information retrieval, neuro-symbolic modeling, cognitive science, reinforcement learning, and adaptive language model training to create systems that transform how humans interact with information. See below for details of the current research directions we are pursuing.
Adaptive Representation & Retrieval Architectures
We’re reimagining information retrieval by creating adaptive systems that seamlessly blend neural, sparse, and symbolic approaches. Our architectures dynamically balance precision, diversity, and efficiency—bringing together the best aspects of traditional IR and cutting-edge neural methods to create retrieval systems that work better across more diverse information needs.
Neuro-Symbolic Knowledge Integration
We’re solving the challenge of making AI systems experts in specialized fields while maintaining their general capabilities. Our research seamlessly integrates structured knowledge (like medical ontologies or legal frameworks) with the reasoning capabilities of large language models—creating systems that combine the precision of domain expertise with the flexibility of general intelligence.
Cognitively-Inspired Interaction & Reasoning
We’re building AI systems that think more like humans—balancing fast intuitive responses with careful deliberation, managing limited attention efficiently, and explaining their reasoning naturally. Our research creates intelligent agents that adapt their thinking processes to match task complexity and user needs, leading to more natural and effective human-AI collaboration.
Retrieval-Augmented Language Models & Systems
We’re developing specialized training techniques that teach language models to work seamlessly with external knowledge sources. Our research goes beyond simple retrieval augmentation to create models that actively guide the retrieval process, reason effectively over multiple sources, and maintain coherent understanding across extended interactions.
