AuditFlow combines a graph-grounded symbolic environment with a multi-agent LLM setup to reach 82.09% joint audit accuracy on structured financial reports, 14.93 points above the strongest baseline.
Fino1: On the transferability of reasoning-enhanced llms and reinforcement learning to finance
11 Pith papers cite this work. Polarity classification is still indexing.
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StakeBench is a new benchmark using market-derived supervision from resolved prediction markets to test LLMs on commitment detection, side identification, action anticipation, and odds projection, revealing partial success on sides but structural failures on higher tasks.
Conv-FinRe is a new benchmark built from real market data and human trajectories that tests LLMs on generating utility-grounded stock rankings over fixed horizons while distinguishing rational analysis from behavioral mimicry or momentum.
FinAuditing is a taxonomy-structured multi-document benchmark with 1,102 instances averaging over 33k tokens from XBRL filings, defining three tasks to evaluate LLMs on financial auditing capabilities.
FinTagging decomposes XBRL tagging into FinNI extraction and FinCL full-taxonomy linking, showing LLMs handle extraction but struggle with fine-grained concept alignment in zero-shot settings.
CLExEval introduces a human-annotated evaluation framework on 40 rare cases that identifies verbosity bias, hidden knowledge paradox, and 68.6% reasoning-to-output mismatch in LLMs while showing LLM-as-a-Judge overestimates reliability.
JUDO enhances large multimodal models for industrial anomaly QA by juxtaposing query images with normal ones for visual comparison and using SFT plus GRPO with tailored rewards to inject domain knowledge, outperforming Qwen2.5-VL-7B and GPT-4o on the MMAD benchmark.
MFMDQwen is the first open-source LLM for multilingual financial misinformation detection, backed by a new instruction dataset and benchmark on which it outperforms other open-source models.
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.
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.
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
citing papers explorer
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AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification
AuditFlow combines a graph-grounded symbolic environment with a multi-agent LLM setup to reach 82.09% joint audit accuracy on structured financial reports, 14.93 points above the strongest baseline.
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StakeBench: Evaluating Language Understanding Grounded in Market Commitment
StakeBench is a new benchmark using market-derived supervision from resolved prediction markets to test LLMs on commitment detection, side identification, action anticipation, and odds projection, revealing partial success on sides but structural failures on higher tasks.
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Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation
Conv-FinRe is a new benchmark built from real market data and human trajectories that tests LLMs on generating utility-grounded stock rankings over fixed horizons while distinguishing rational analysis from behavioral mimicry or momentum.
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FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
FinAuditing is a taxonomy-structured multi-document benchmark with 1,102 instances averaging over 33k tokens from XBRL filings, defining three tasks to evaluate LLMs on financial auditing capabilities.
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FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information
FinTagging decomposes XBRL tagging into FinNI extraction and FinCL full-taxonomy linking, showing LLMs handle extraction but struggle with fine-grained concept alignment in zero-shot settings.
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CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning
CLExEval introduces a human-annotated evaluation framework on 40 rare cases that identifies verbosity bias, hidden knowledge paradox, and 68.6% reasoning-to-output mismatch in LLMs while showing LLM-as-a-Judge overestimates reliability.
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JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA
JUDO enhances large multimodal models for industrial anomaly QA by juxtaposing query images with normal ones for visual comparison and using SFT plus GRPO with tailored rewards to inject domain knowledge, outperforming Qwen2.5-VL-7B and GPT-4o on the MMAD benchmark.
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MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model
MFMDQwen is the first open-source LLM for multilingual financial misinformation detection, backed by a new instruction dataset and benchmark on which it outperforms other open-source models.
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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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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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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.