arxiv: 2604.05135 · v1 · submitted 2026-04-06 · 💻 cs.CL · cs.CE

Recognition: 2 theorem links

· Lean Theorem

SenseAI: A Human-in-the-Loop Dataset for RLHF-Aligned Financial Sentiment Reasoning

Berny Kabalisa

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:38 UTC · model grok-4.3

classification 💻 cs.CL cs.CE

keywords financial sentiment analysishuman-in-the-loop datasetRLHF alignmentLLM reasoning errorsreasoning chainsmodel calibrationfinancial AIsentiment dataset

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The pith

A dataset of full LLM reasoning chains on financial texts reveals that errors follow predictable patterns open to targeted human correction.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SenseAI as a collection of 1,439 financial sentiment examples drawn from 40 equities, each entry preserving the model's complete reasoning chain, its stated confidence, the human correction applied, and the eventual market outcome. This structure is built to feed directly into RLHF-style fine-tuning so that alignment signals address the steps of reasoning rather than final labels alone. A sympathetic reader would care because the analysis shows model mistakes in this setting are not scattered but cluster into repeatable forms such as adding ungrounded details or misjudging certainty, which in turn suggests that focused fixes can replace broad retraining. The work uses the dataset to surface these patterns and argues they form a correctable regime rather than an inherent limit of current models.

Core claim

SenseAI supplies human-validated records of LLM financial sentiment reasoning that include the full chain of steps, confidence scores, correction signals, and real-world outcomes across 1,439 points and 13 data categories. Examination of these records identifies consistent behaviors including Latent Reasoning Drift, in which models insert information absent from the input, as well as systematic confidence miscalibration and forward-projection tendencies. The authors conclude that these observations place LLM errors in financial reasoning inside a predictable and addressable regime rather than a domain of random failure.

What carries the argument

The SenseAI dataset structure that records reasoning chains together with human correction signals and market outcomes, allowing systematic detection of error patterns in LLM financial reasoning.

If this is right

Fine-tuning pipelines can incorporate the recorded correction signals to target specific reasoning failures instead of overall accuracy alone.
Financial AI systems can embed similar human-in-the-loop checks during development to reduce the incidence of drift and miscalibration.
Evaluation of sentiment models can shift from output-only checks to inspection of the reasoning steps that produced those outputs.
Market-outcome labels in the dataset allow improvements to be validated against actual trading results rather than human labels alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same recording approach could be applied in other narrow domains to check whether reasoning errors there also follow detectable, correctable patterns.
Detection rules derived from the observed drift could be turned into automated filters that flag suspect reasoning steps before a model produces a final answer.
Releasing additional batches of the dataset under varying market conditions would allow direct tests of how stable the identified error regime remains.

Load-bearing premise

The patterns found in this collection of examples will appear and respond to correction in other financial texts and models.

What would settle it

Fine-tune an LLM on the human correction signals from the dataset and then test whether the rate of ungrounded information insertion or confidence miscalibration drops on a fresh collection of financial statements; no measurable drop would undermine the claim that the errors are systematically correctable.

Figures

Figures reproduced from arXiv: 2604.05135 by Berny Kabalisa.

**Figure 1.** Figure 1: AI Sentiment Distribution (n = 1,439). The dominance of Slightly Bullish (61.3%) relative to unhedged Bullish (2.2%) directly validates Finding 1: systematic sentiment hypersensitivity to linguistic qualifiers. 3.6.3 HITL Correction Rate and Edit Type Distribution Across the dataset, 51.4% of AI-generated sentiment classifications required human expert correction, while 48.6% were accepted without modific… view at source ↗

**Figure 2.** Figure 2: Edit type distribution. The near-symmetry between Category 0 (no edit, 49.1%) and [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** Figure 3: Confidence score distribution. The dominant concentration in the 60–69% band (71% [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

read the original abstract

We introduce SenseAI, a human-in-the-loop (HITL) validated financial sentiment dataset designed to capture not only model outputs but the full reasoning process behind them. Unlike existing resources, SenseAI incorporates reasoning chains, confidence scores, human correction signals, and real-world market outcomes, providing a structure aligned with Reinforcement Learning from Human Feedback (RLHF) paradigms. The dataset consists of 1,439 labelled data points across 40 US-listed equities and 13 financial data categories, enabling direct integration into modern LLM fine-tuning pipelines. Through analysis, we identify several systematic patterns in model behavior, including a novel failure mode we term Latent Reasoning Drift, where models introduce information not grounded in the input, as well as consistent confidence miscalibration and forward projection tendencies. These findings suggest that LLM errors in financial reasoning are not random but occur within a predictable and correctable regime, supporting the use of structured HITL data for targeted model improvement. We discuss implications for financial AI systems and highlight opportunities for applying SenseAI in model evaluation and alignment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SenseAI is a dataset release with reasoning chains and human corrections for financial sentiment, but it identifies patterns without testing whether the corrections fix them.

read the letter

The main thing here is a new dataset of 1,439 financial sentiment points across 40 equities that includes full reasoning traces, confidence scores, human correction signals, and ties to market outcomes. The authors also flag a pattern they call Latent Reasoning Drift where models add ungrounded details, along with confidence miscalibration. That combination is the actual contribution and it fits what people want for RLHF-style work in a narrow domain like finance. The structure is more detailed than typical sentiment label sets, which is useful if you're trying to target specific model behaviors rather than just retrain on raw labels. They do a reasonable job describing the observed patterns and why the data format supports targeted fixes. The soft spots are clear and proportionate. The central claim that errors occur in a predictable and correctable regime is asserted from the patterns but never tested—no fine-tuning run, no ablation, no before-and-after error rates on held-out queries after using the corrections. The stress-test note lands. Methods details are also thin: no description of how the human validation was done, who the annotators were, or any agreement stats. That leaves the quality of the corrections hard to judge. The scope is small and domain-specific, so generalizability is an open question but not a fatal one for a resource paper. This is mainly for researchers working on financial LLMs or building HITL datasets for alignment. A reader focused on domain-specific data curation would find the format and the listed failure modes worth looking at. It deserves peer review as a data contribution, though it needs added sections on collection process and at least a small validation experiment to strengthen the claims.

Referee Report

2 major / 2 minor

Summary. The paper introduces SenseAI, a human-in-the-loop (HITL) financial sentiment dataset of 1,439 labelled points spanning 40 US equities and 13 categories. It records model reasoning chains, confidence scores, human correction signals, and links to market outcomes, positioning the resource for RLHF-style alignment. Analysis identifies systematic behaviors including a novel 'Latent Reasoning Drift' failure mode (ungrounded information insertion), confidence miscalibration, and forward-projection tendencies. The authors conclude that financial LLM errors are predictable and correctable, thereby supporting targeted model improvement via structured HITL data.

Significance. A rigorously documented dataset with reasoning traces and market grounding could serve as a useful benchmark and training resource for financial LLM alignment. The explicit linkage to RLHF pipelines and the observational identification of non-random error patterns are potentially valuable if the construction process and generalizability claims are substantiated. Absent any empirical demonstration that the human signals measurably reduce the described failure modes, however, the work functions primarily as a descriptive resource rather than a validated alignment method.

major comments (2)

[Abstract] Abstract: The central claim that 'LLM errors in financial reasoning are not random but occur within a predictable and correctable regime' and that SenseAI 'support[s] the use of structured HITL data for targeted model improvement' is unsupported. No fine-tuning, RLHF loop, ablation, or held-out evaluation is reported that quantifies error reduction after incorporating the human correction signals.
[Dataset construction and analysis sections] Dataset construction and analysis sections: The manuscript supplies no description of data collection protocols, human annotator selection or training, inter-annotator agreement statistics, validation procedures, or any statistical tests confirming that the observed patterns (Latent Reasoning Drift, miscalibration) are systematic rather than artifacts of the 1,439-point sample or the 40 equities chosen.

minor comments (2)

[Abstract] The term 'Latent Reasoning Drift' is introduced without a precise operational definition or comparison to existing notions of hallucination or reasoning error in the literature.
[Analysis] No discussion of potential selection biases in the 40 equities or 13 categories is provided, limiting claims of broader applicability.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the two major comments point by point below, acknowledging where revisions are needed to better align claims with the presented evidence and to improve transparency on dataset construction.

read point-by-point responses

Referee: [Abstract] The central claim that 'LLM errors in financial reasoning are not random but occur within a predictable and correctable regime' and that SenseAI 'support[s] the use of structured HITL data for targeted model improvement' is unsupported. No fine-tuning, RLHF loop, ablation, or held-out evaluation is reported that quantifies error reduction after incorporating the human correction signals.

Authors: We agree that the manuscript does not contain empirical experiments (such as fine-tuning or RLHF loops) that quantify error reduction from the human signals. The claims derive from observational analysis of patterns in the 1,439-point dataset. We will revise the abstract and conclusion sections to clarify that SenseAI provides a structured resource positioned to support future targeted alignment work, rather than asserting that it currently demonstrates measurable error reduction. This constitutes a partial revision, as we cannot add new experimental results at this stage. revision: partial
Referee: [Dataset construction and analysis sections] The manuscript supplies no description of data collection protocols, human annotator selection or training, inter-annotator agreement statistics, validation procedures, or any statistical tests confirming that the observed patterns (Latent Reasoning Drift, miscalibration) are systematic rather than artifacts of the 1,439-point sample or the 40 equities chosen.

Authors: We concur that explicit details on these aspects are required for reproducibility and to support claims of systematic patterns. The original submission provided only a high-level overview. We will expand the dataset construction and analysis sections to include: (1) full data collection protocols, (2) annotator selection criteria and training procedures, (3) inter-annotator agreement statistics, (4) validation steps, and (5) statistical tests (e.g., significance testing for pattern consistency across the sample). We will also note limitations regarding sample size and equity coverage. revision: yes

standing simulated objections not resolved

Empirical demonstration that human correction signals measurably reduce the identified failure modes, as this would require new model training, ablation studies, and held-out evaluations not included in the current dataset-focused manuscript.

Circularity Check

0 steps flagged

No circularity: purely descriptive dataset paper with observational claims only

full rationale

The paper introduces SenseAI as a new HITL dataset of 1,439 points, describes its construction across 40 equities and 13 categories, and reports observed patterns (Latent Reasoning Drift, confidence miscalibration) from manual analysis. No equations, parameters, derivations, or fitted quantities appear anywhere. The central suggestion that errors occur in a 'predictable and correctable regime' is presented as an interpretive inference from the data rather than a reduction of any prior result to itself. No self-citations function as load-bearing premises for uniqueness theorems or ansatzes. The work is self-contained as a resource contribution and does not rely on any circular step.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The work rests on the creation of a new dataset and observational identification of patterns without any mathematical derivation; the only invented element is the term for a failure mode.

invented entities (1)

Latent Reasoning Drift no independent evidence
purpose: To label a systematic failure mode in which models introduce information not grounded in the input text
Coined from analysis of model outputs on the SenseAI data points; no external falsifiable test provided.

pith-pipeline@v0.9.0 · 5481 in / 1288 out tokens · 46646 ms · 2026-05-10T18:38:46.045463+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce SenseAI, a human-in-the-loop (HITL) validated financial sentiment dataset... six novel empirical findings... Latent Reasoning Drift... Goldilocks Zone of correctable model error.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

The dataset... architecturally aligned with Reinforcement Learning from Human Feedback (RLHF) training paradigms.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

11 extracted references · 3 canonical work pages · 2 internal anchors

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Constitutional AI: Harmlessness from AI Feedback

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work page arXiv 2022
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Sutskever, I. (2024). Remarks on the limits of pretraining data scaling.NeurIPS 2024 Keynote Address. A Data Snapshot Table 8 presents a representative data point from the SenseAI dataset, illustrating the multi- dimensional nature of each labelled observation. 22 SenseAI: HITL Dataset for RLHF-Aligned Financial Sentiment Kabalisa, 2026 Table 8: Represent...

2024