138 papers
This paper proposes a novel approach to image-based shape retrieval that leverages pre-aligned multi-modal encoders and a hard contrastive learning loss to achieve state-of-the-art performance in both zero-shot and supervised settings, eliminating the need for explicit view-based supervision or view synthesis.
This paper introduces a scalable semantic search system for a corpus of 9.2 million mathematical theorems, demonstrating that representing theorems with natural-language descriptions significantly improves retrieval accuracy for both specific theorems and entire papers compared to existing baselines.
KrishokBondhu is a voice-enabled, call-center-integrated advisory system for Bengali farmers in Bangladesh that leverages a Retrieval-Augmented Generation (RAG) framework to deliver high-quality, real-time agricultural guidance, achieving a 44.7% performance improvement over existing benchmarks.
The paper introduces OfficeQA Pro, a challenging enterprise benchmark using a massive corpus of U.S. Treasury Bulletins to demonstrate that current frontier AI agents struggle significantly with grounded, multi-document reasoning, achieving low accuracy even with direct document access and benefiting notably from structured document representations.
The paper introduces SynPlanResearch-R1, a framework that synthesizes tool-use trajectories to encourage deeper exploration during supervised fine-tuning, thereby overcoming the limitations of reinforcement learning with verifiable rewards and significantly improving research agent performance across multiple benchmarks.
This Systematization of Knowledge (SoK) paper establishes the first unified framework for Agentic Retrieval-Augmented Generation (RAG) by formalizing autonomous loops as decision-making processes, proposing a comprehensive taxonomy and architectural decomposition, critiquing current evaluation limitations and systemic risks, and outlining critical research directions for building reliable and scalable agentic systems.
This paper introduces DCTR, a table retrieval mechanism that leverages fine-grained typed query decomposition and global connectivity awareness to effectively handle complex, open-domain question answering over relational databases, demonstrating robustness on industry-aligned benchmarks.
This paper proposes a method to equip LLM agents with native retrieval capabilities by projecting their hidden states directly into the embedding space via a lightweight head, thereby eliminating the need for a separate embedding model while retaining 97% of baseline retrieval quality.
SPD-RAG is a hierarchical multi-agent framework that improves scalability and answer quality for complex cross-document queries by assigning dedicated agents to process individual documents and synthesizing their outputs through a token-bounded coordinator, achieving superior performance on the LOONG benchmark with significantly reduced API costs compared to standard RAG and full-context baselines.
This paper addresses the scarcity of expert textual relevance labels in large-scale app store search by leveraging a specialized, fine-tuned LLM to generate millions of high-quality labels, which, when used to augment the production ranker, significantly improves both offline metrics and real-world conversion rates, particularly for tail queries lacking reliable behavioral data.
This paper identifies and formalizes "Retrieval Pivot Attacks" in Hybrid RAG systems, demonstrating how vector-retrieved seeds can inadvertently pivot through knowledge graph links to cause cross-tenant data leakage, and proves that enforcing authorization specifically at the graph expansion boundary effectively mitigates this risk with minimal overhead.
The paper proposes PESO, a continual learning method for LLM-based recommender systems that utilizes a proximal regularizer to anchor LoRA adapters to their most recent frozen states, thereby effectively balancing adaptation to evolving user preferences with the preservation of recent behavioral patterns.
This paper presents a machine learning-focused survey of Computerized Adaptive Testing (CAT), exploring how ML techniques can optimize measurement models, question selection, bank construction, and test control to create more robust, fair, and efficient adaptive assessment systems across various domains.
This paper unifies online A/B testing and off-policy evaluation by proving that standard variance reduction methods in both domains are mathematically equivalent, specifically showing that Difference-in-Means estimators correspond to optimal Inverse Propensity Scoring and that regression adjustment techniques align with Doubly Robust estimation.
This paper introduces Dial, a knowledge-grounded framework that addresses the challenges of generating executable SQL across heterogeneous database systems by employing dialect-aware logical planning, a hierarchical intent-aware knowledge base, and an execution-driven debugging loop, achieving significant improvements in translation accuracy and dialect feature coverage on the newly constructed DS-NL2SQL benchmark.
This paper introduces Projection-Augmented Graph (PAG), a novel Approximate Nearest Neighbor Search framework that integrates projection techniques into graph indexing to simultaneously achieve high query efficiency, fast indexing, low memory usage, and robust scalability across modern AI workloads, outperforming existing methods like HNSW by up to 5x in speed while supporting online insertions.
The paper proposes T-REX, a novel transformer-based architecture that addresses the unique challenges of online grocery shopping by generating personalized category-level basket recommendations through dynamic sequence splitting, adaptive positional encoding, and causal masking to effectively capture both short-term dependencies and long-term user preferences.
This paper introduces Exploration Space Theory (EST), a formal lattice-theoretic framework that adapts Knowledge Space Theory to location-based recommendation by modeling prerequisite dependencies among points of interest, thereby providing structural guarantees for validity, optimality, and explainability in the Exploration Space Recommender System (ESRS).
This paper introduces the Isotonic Layer, a novel differentiable framework that integrates piecewise linear fitting and learnable embeddings into neural architectures to enforce global monotonicity, thereby enabling granular, context-aware debiasing and improved calibration for large-scale recommendation systems.
The paper proposes WeDas, a novel framework that enhances Deep Search Agents by integrating a few-shot probing mechanism to dynamically align reasoning-driven queries with web content distribution structures, thereby improving retrieval precision and sub-goal completion across multiple benchmarks.