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ARXIV:2603.07528 · TABLE REASONING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.07528TABLE REASONINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding.
Opportunity summary
Pain TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding. Most existing methods rely on a single-turn reasoning paradigm over…
Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tables which suffers from context overflow and weak numerical sensitivity.
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on diverse benchmarks demonstrate that TableMind++ consistently outperforms previous baselines and proprietary models to validate the effectiveness of integrating autonomous training with…
Table Reasoning moved forward this cycle; last verified April 2026. Public score 8.0/10.
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TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding.
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10.48550/arXiv.2603.07528TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding.
Abstract
Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tables which suffers from context overflow and weak numerical sensitivity. To address these limitations, we previously proposed TableMind as a tuning-based autonomous programmatic agent that simulates human-like interaction within a lightweight large language model (LLM). TableMind internalizes planning, action, and reflection through a two-stage training strategy involving supervised fine-tuning (SFT) on filtered high-quality data and reinforcement learning (RL) via a multi-perspective reward and the Rank-Aware Policy Optimization (RAPO) algorithm. While TableMind establishes a solid foundation for programmatic agents, the inherent stochasticity of LLMs remains a critical challenge that leads to hallucinations. In this paper, we extend this foundation to TableMind++ by introducing a novel uncertainty-aware inference framework to mitigate hallucinations. Specifically, we propose memory-guided plan pruning to retrieve historical trajectories for validating and filtering out logically flawed plans to address epistemic uncertainty. To ensure execution precision, we introduce confidence-based action refinement which monitors token-level probabilities to detect and self-correct syntactic noise for aleatoric uncertainty mitigation. Finally, we employ dual-weighted trajectory aggregation to synthesize a robust consensus from multiple reasoning paths. Extensive experiments on diverse benchmarks demonstrate that TableMind++ consistently outperforms previous baselines and proprietary models to validate the effectiveness of integrating autonomous training with uncertainty quantification. Our code is available.
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unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 8.0
PROBLEM
TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding. Most existing methods rely on a single-turn reasoning paradigm over tables w...
METHOD
Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tables which suffers from context overflow and weak numerical sensitivity.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on diverse benchmarks demonstrate that TableMind++ consistently outperforms previous baselines and proprietary models to validate the effectiveness of integrating autonomous training...
WHY NOW
Table Reasoning moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed public claims while anchored extraction refreshes.
TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding. Most existing methods rely on a single-turn reasoning paradigm over tables which suffers from context overflow and weak numerical sensitivity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tables which suffers from context overflow and weak numerical sensitivity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on diverse benchmarks demonstrate that TableMind++ consistently outperforms previous baselines and proprietary models to validate the effectiveness of integrating autonomous training with uncertainty quantification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Table Reasoning moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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TableMind++ is an uncertainty-aware programmatic agent that enhances table reasoning by mitigating hallucinations, offering a robust solution for precise numerical operations and semantic understanding.
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Table Reasoning
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