CLQT is a new closed-loop, cost-aware benchmark that diagnoses LLM trading agent capabilities through strategy-consistent metrics and hash-verifiable trails rather than outcome rankings.
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Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang
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background 6representative citing papers
Multi-agent LLM system Agora under Sealed Joint Search conditions produces +1.87 holdout Sharpe on CSI 1000 over a 91-day sealed period, exceeding the best baseline at +1.334 under favorable seed.
FlowSteer is a prompt-only attack that biases multi-agent LLM workflow planning to propagate malicious signals, raising success rates by up to 55%, with FlowGuard as an input-side defense reducing it by up to 34%.
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.
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.
ARC-derived multi-agent LLM framework for safe, auditable process control with operator agents and deterministic orchestrator, evaluated on dairy ventilation.
Harness-aware post-training of LLM agents improves both in-distribution performance and robustness to out-of-distribution tool environment shifts, while minimal harness designs cause large drops under shifts.
TimeClaw is a framework that augments LLM agents with temporal tools, capability evolution, and episodic memory to enable contextualized time series reasoning, with reported gains on benchmarks across energy, finance, weather, and traffic.
MechSim is a mechanism-grounded framework that represents simulators via structured schemas and uses constrained LLM agents to generate evidence-based explanations linking outcomes to underlying mechanisms.
LLMs discover regulatory loopholes in simulated societal environments through reward hacking during RL training.
POIROT protocol repurposes agents in LLM multi-agent systems as an internal diagnostic layer for failure detection, outperforming single-LLM evaluators with gains that increase with complexity, agent count, and fault types.
LEAF is a dynamically updating benchmark that supplies LLMs with event-derived auxiliary text via retrieval agents to measure improvements in event-augmented forecasting, with initial results showing better performance on more predictable equities and event-target correlations.
ASR, a new trajectory-fidelity metric, detects that 10 of 18 LLMs skip confirmation steps in payment agents despite perfect scores on prior metrics, and ASR-guided refinements improve task success by up to 93.8 percentage points.
FinAgent-RAG achieves 76.81-78.46% execution accuracy on financial QA benchmarks by combining contrastive retrieval, program-of-thought code generation, and adaptive strategy routing, outperforming baselines by 5.62-9.32 points.
The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
An LLM agent integrated with AVEVA Process Simulation via MCP enables natural language driven flowsheet analysis, optimization, and construction for chemical separation processes.
TokenCake introduces agent-aware temporal and spatial schedulers for KV cache management in LLM multi-agent serving, claiming over 47% lower end-to-end latency and up to 16.9% better GPU memory utilization than vLLM on representative benchmarks.
Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.
CoRT achieves 95% average attack success rate on nine LLMs by using iterative risk-concealing prompts and a controller that scores concealment levels on a new 522-instruction financial risk benchmark.
MoCA-Agent decomposes questions into typed atomic claims, clears them via trader-agent markets into confidence-weighted decisions, synthesizes and verifies executable Python code, and reports strong benchmark scores including 78.3% on FinQA.
MRC computes coalition Shapley credits from performance histories to weight three LLM agents, stabilized by Bayesian mixture and regime multipliers, achieving SR 1.51 and 440.1% cumulative return over 1037 days on 13 crypto assets.
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
LLM trading agents show detectable pre-failure signatures in planning embeddings and fused risk representations, with structured risk feedback acting as a partial alignment signal without fine-tuning.
citing papers explorer
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CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
CLQT is a new closed-loop, cost-aware benchmark that diagnoses LLM trading agent capabilities through strategy-consistent metrics and hash-verifiable trails rather than outcome rankings.
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AI Trading's Alpha Singularity: Emergent Market Reasoning through Agent-to-Agent Self-Evolution
Multi-agent LLM system Agora under Sealed Joint Search conditions produces +1.87 holdout Sharpe on CSI 1000 over a 91-day sealed period, exceeding the best baseline at +1.334 under favorable seed.
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FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems
FlowSteer is a prompt-only attack that biases multi-agent LLM workflow planning to propagate malicious signals, raising success rates by up to 55%, with FlowGuard as an input-side defense reducing it by up to 34%.
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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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A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Control
ARC-derived multi-agent LLM framework for safe, auditable process control with operator agents and deterministic orchestrator, evaluated on dairy ventilation.
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The Interplay of Harness Design and Post-Training in LLM Agents
Harness-aware post-training of LLM agents improves both in-distribution performance and robustness to out-of-distribution tool environment shifts, while minimal harness designs cause large drops under shifts.
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Harnessing Generalist Agents for Contextualized Time Series
TimeClaw is a framework that augments LLM agents with temporal tools, capability evolution, and episodic memory to enable contextualized time series reasoning, with reported gains on benchmarks across energy, finance, weather, and traffic.
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Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making
MechSim is a mechanism-grounded framework that represents simulators via structured schemas and uses constrained LLM agents to generate evidence-based explanations linking outcomes to underlying mechanisms.
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Large Language Models Hack Rewards, and Society
LLMs discover regulatory loopholes in simulated societal environments through reward hacking during RL training.
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POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems
POIROT protocol repurposes agents in LLM multi-agent systems as an internal diagnostic layer for failure detection, outperforming single-LLM evaluators with gains that increase with complexity, agent count, and fault types.
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LEAF: A Living Benchmark for Event-Augmented Forecasting
LEAF is a dynamically updating benchmark that supplies LLMs with event-derived auxiliary text via retrieval agents to measure improvements in event-augmented forecasting, with initial results showing better performance on more predictable equities and event-target correlations.
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Beyond Task Success: Measuring Workflow Fidelity in LLM-Based Agentic Payment Systems
ASR, a new trajectory-fidelity metric, detects that 10 of 18 LLMs skip confirmation steps in payment agents despite perfect scores on prior metrics, and ASR-guided refinements improve task success by up to 93.8 percentage points.
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Agentic Retrieval-Augmented Generation for Financial Document Question Answering
FinAgent-RAG achieves 76.81-78.46% execution accuracy on financial QA benchmarks by combining contrastive retrieval, program-of-thought code generation, and adaptive strategy routing, outperforming baselines by 5.62-9.32 points.
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Quantifying Trust: Financial Risk Management for Trustworthy AI Agents
The paper introduces the Agentic Risk Standard (ARS) as a payment settlement framework that delivers predefined compensation for AI agent execution failures, misalignment, or unintended outcomes.
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Large Language Model Agent for User-friendly Chemical Process Simulations
An LLM agent integrated with AVEVA Process Simulation via MCP enables natural language driven flowsheet analysis, optimization, and construction for chemical separation processes.
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TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications
TokenCake introduces agent-aware temporal and spatial schedulers for KV cache management in LLM multi-agent serving, claiming over 47% lower end-to-end latency and up to 16.9% better GPU memory utilization than vLLM on representative benchmarks.
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Scheming Ability in LLM-to-LLM Strategic Interactions
Frontier LLMs exhibit high scheming propensity in Cheap Talk signaling and Peer Evaluation games, achieving 95-100% success rates when choosing to deceive and 100% deception choice in one setup even without prompting.
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Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain
CoRT achieves 95% average attack success rate on nine LLMs by using iterative risk-concealing prompts and a controller that scores concealment levels on a new 522-instruction financial risk benchmark.
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MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning
MoCA-Agent decomposes questions into typed atomic claims, clears them via trader-agent markets into confidence-weighted decisions, synthesizes and verifies executable Python code, and reports strong benchmark scores including 78.3% on FinQA.
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Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems
MRC computes coalition Shapley credits from performance histories to weight three LLM agents, stabilized by Bayesian mixture and regime multipliers, achieving SR 1.51 and 440.1% cumulative return over 1037 days on 13 crypto assets.
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Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
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Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents
LLM trading agents show detectable pre-failure signatures in planning embeddings and fused risk representations, with structured risk feedback acting as a partial alignment signal without fine-tuning.
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The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
Reported alpha from end-to-end LLM trading agents does not constitute deployment evidence until it passes structural tests for temporal integrity, frictions, robustness, calibration, execution, and disaggregation.
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Emergent Social Intelligence Risks in Generative Multi-Agent Systems
Generative multi-agent systems exhibit emergent collusion and conformity behaviors that cannot be prevented by existing agent-level safeguards.
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Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction
LLM agents (hawkish, dovish, debate) outperform a deterministic z-score rule agent in Sharpe ratio for commodity ETF portfolios by 0.04-0.044, with advantage concentrated in the soft-landing sub-period and preserved up to 30bp trading costs.
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MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
MadEvolve uses LLMs for evolutionary optimization of trading strategies and reports significant backtest improvements on Bitcoin tasks including signal feature evolution and joint strategy optimization.
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Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
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AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading
AlphaQuanter introduces a single-agent tool-augmented RL framework for stock trading that learns dynamic policies over a transparent decision workflow and reports state-of-the-art financial metrics.
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A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
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Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking
Leakage-controlled LLM factor ranking yields median Spearman IC of +0.154 that is largely matched by a kNN baseline on the same real-time macro inputs.
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Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
Reproducibility audit of 30 LLM trading papers shows execution assumptions under-reported relative to agent architectures, illustrated by a 10-equity example where frictions compress returns.
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FinCom: A Financial Multi-Agent Demo with Disagree-or-Commit Deliberation
FinCom introduces a governed multi-agent system with Disagree-or-Commit deliberation that claims to improve reasoning accuracy and risk awareness over consensus baselines in financial tasks.
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FundaPod: A Multi-Persona Agent Pod Platform with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research
FundaPod presents a multi-persona AI agent architecture with knowledge-graph memory to support human-adjudicated fundamental investment research through independent agent work and verifiable evidence links.
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A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
This review synthesizes LLM uses in stock forecasting and catalogs key practical pitfalls from a hedge-fund viewpoint.
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A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
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Bridging Language Models and Financial Analysis
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.