AutoRedTrader generates synthetic financial misinformation via behavioral bias manipulation and agent feedback to red-team LLM trading agents, reaching 69% exposure and 26.67% attack success on Bitcoin data simulations.
Finrobot: An open- source ai agent platform for financial applications using large language models
10 Pith papers cite this work. Polarity classification is still indexing.
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Moira parameterizes hierarchical RL policies for pair trading with LLMs and adapts them via prompt updates based on trajectory and episode feedback, outperforming baselines on real market data.
CSTrader is a multi-agent LLM trading system for CS2 skins that outperforms a -15.62% market index and single-prompt baselines with up to 7.58% returns by using specialized agents for liquidity, sentiment reversal, and risk control.
KTD-Fin benchmark with data masking and return attribution shows frontier LLM agents on CSI300 generate returns mainly from market and style exposure rather than persistent stock-selection alpha.
DTap is a new red-teaming platform for AI agents that uses autonomous exploration across realistic simulations to discover vulnerabilities and creates a verifiable benchmark dataset.
CogGen uses a cognitively inspired recursive architecture with AVR for multimodal content to generate deep research reports that achieve SOTA among open-source systems and surpass Gemini Deep Research on a new OWID benchmark.
ProfiliTable is a multi-agent system with profiler, generator, and evaluator components that outperforms baselines on 18 tabular task types via dynamic profiling and closed-loop refinement.
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
FinSec is a multi-stage detection system for financial LLM dialogues that reaches 90.13% F1 score, cuts attack success rate to 9.09%, and raises AUPRC to 0.9189.
citing papers explorer
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AutoRedTrader: Autonomous Red Teaming of Trading Agents through Synthetic Misinformation Injection
AutoRedTrader generates synthetic financial misinformation via behavioral bias manipulation and agent feedback to red-team LLM trading agents, reaching 69% exposure and 26.67% attack success on Bitcoin data simulations.
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Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading
Moira parameterizes hierarchical RL policies for pair trading with LLMs and adapts them via prompt updates based on trajectory and episode feedback, outperforming baselines on real market data.
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CSTrader: A Testbed for Language-Grounded Trading in a Community-Driven Virtual Asset Market
CSTrader is a multi-agent LLM trading system for CS2 skins that outperforms a -15.62% market index and single-prompt baselines with up to 7.58% returns by using specialized agents for liquidity, sentiment reversal, and risk control.
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From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets
KTD-Fin benchmark with data masking and return attribution shows frontier LLM agents on CSI300 generate returns mainly from market and style exposure rather than persistent stock-selection alpha.
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DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents
DTap is a new red-teaming platform for AI agents that uses autonomous exploration across realistic simulations to discover vulnerabilities and creates a verifiable benchmark dataset.
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CogGen: A Cognitively Inspired Recursive Framework for Deep Research Report Generation
CogGen uses a cognitively inspired recursive architecture with AVR for multimodal content to generate deep research reports that achieve SOTA among open-source systems and surpass Gemini Deep Research on a new OWID benchmark.
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ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
ProfiliTable is a multi-agent system with profiler, generator, and evaluator components that outperforms baselines on 18 tabular task types via dynamic profiling and closed-loop refinement.
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MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
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Conversations Risk Detection LLMs in Financial Agents via Multi-Stage Generative Rollout
FinSec is a multi-stage detection system for financial LLM dialogues that reaches 90.13% F1 score, cuts attack success rate to 9.09%, and raises AUPRC to 0.9189.
- MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)