Papers
1–3 of 3Research Paper·Jan 28, 2026
QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregation
User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall no...
7.0 viability
Research Paper·Feb 12, 2026
From Noise to Order: Learning to Rank via Denoising Diffusion
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the document being relev...
4.0 viability
Research Paper·Jan 26, 2026
Capturing P: On the Expressive Power and Efficient Evaluation of Boolean Retrieval
Modern information retrieval is transitioning from simple document filtering to complex, neuro-symbolic reasoning workflows. However, current retrieval architectures face a fundamental efficiency dile...
3.0 viability