The paper introduces the Data-centric Reasoning Compiler (DCRC), a novel data-driven framework that enhances financial QA systems by compiling user queries and retrieved documents into verifiable, exe…

The paper introduces ForesightFlow, an Information Leakage Score (ILS) framework, to quantify pre-event information leakage in prediction markets, and proposes a necessary extension to analyze empiric…

The paper introduces KTD-Fin, a novel benchmark that evaluates LLM trading agents by masking historical market data and decomposing returns, finding that LLM agents' profits are largely due to passive…

The paper proposes the Interaction-Native Knowledge Harness (InKH), an architecture that absorbs complex context into financial LLM agents, significantly improving performance, reducing latency, and e…

This study systematically evaluates a wide range of chunking methods for Retrieval-Augmented Generation (RAG) to assess their effectiveness and highlight the overlooked challenges associated with chun…

The paper proposes InSemRAG, an enhanced RAG framework that improves retrieval accuracy and knowledge integrity by incorporating intent-aware retrieval and semantics-preserving chunking, achieving sta…

GS-Fuse is a novel multimodal framework that improves financial forecasting by adaptively fusing event text and price data, achieving state-of-the-art performance by explicitly modeling the directiona…

DynaTree introduces a two-stage framework that pre-constructs a reusable retrieval tree offline using coordinated agents, allowing for efficient, structure-aware, and highly effective time-sensitive n…

The paper systematically compares multiple content representations for RAG pipelines and finds that answer retention—the ability of the representation to preserve the original answer-bearing content—i…

The paper introduces NumLeak, a framework demonstrating that top-tier LLMs often exhibit high fidelity recall of specific public numeric benchmarks (like financial factors) due to memorization, which…

The paper introduces NumLeak, a framework demonstrating that top-tier LLMs often exhibit high fidelity recall of specific public numeric benchmarks, suggesting that their apparent skill may be due to…

The paper introduces a Retrieval-Augmented Generation (RAG) system that uses targeted query filtering and LLM semantic reasoning to accurately and cost-effectively analyze complex cybersecurity incide…

This paper introduces CFMME, a comprehensive Chinese financial multimodal benchmark, and evaluates current Large Vision-Language Models (LVLMs), finding that while state-of-the-art models perform mode…

The paper proposes ReCAP, a novel continual learning framework for portfolio management, which adaptively combines policies from a library based on detected market regimes to achieve superior long-ter…

The paper systematically evaluates advanced retrieval-augmented generation (RAG) architectures for Cyber Threat Intelligence (CTI), demonstrating that a hybrid graph-text approach significantly improv…

This paper proposes Tail-Aware Adaptive-k (TAA-k), a training-free framework for adaptive context selection in retrieval-augmented generation systems using Extreme Value Theory.

The paper introduces a novel paradigm where a fine-tuned LLM acts as an ancillary predictor to forecast likely advertisers, significantly improving ad recommendation systems by augmenting candidate ge…

This paper proposes a multi-turn retrieval-augmented generation pipeline for conversational systems across four domains.

This paper introduces cost-aware Retrieval-Augmented Generation (RAG), demonstrating that fixed evidence selection is brittle and that adaptive, agentic controllers are necessary for effective knowled…

SkillPager is a novel two-stage framework that efficiently selects minimal, execution-sufficient context from large procedural skill documents by leveraging typed semantic nodes, significantly reducin…