arxiv: 2604.04565 · v1 · submitted 2026-04-06 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning

Madhav S Baidya

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:45 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords PassiveQAepistemic calibrationquestion answeringsupervised finetuningRAGabstentionhallucination reductioninformation sufficiency

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The pith

Supervised finetuning on structured information states trains models to choose answer, ask, or abstain based on query sufficiency.

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

Large language models and retrieval-augmented systems tend to generate answers even when queries lack key details, leading to hallucinations. The paper demonstrates that standard approaches fail to detect insufficient information reliably. It proposes a framework that uses supervised finetuning to teach a planner explicit decision-making among three actions using structured state representations and knowledge graph context. Experiments across QA datasets report gains in macro F1 and abstention recall alongside fewer hallucinations. The work concludes that epistemic calibration must be acquired through training rather than added as post-hoc rules.

Core claim

A finetuned planner that integrates structured information-state representations, knowledge graph-grounded context, and explicit modeling of missing variables produces decision behavior aligned with information sufficiency, yielding higher macro F1, improved abstention recall, and lower hallucination rates than baseline or enhanced RAG systems under constrained training.

What carries the argument

The finetuned planner that explicitly models missing variables and decision reasoning inside a three-action framework (Answer, Ask, Abstain) built on structured information-state representations.

Load-bearing premise

That supervised finetuning on structured information-state representations will yield generalizable decision rules for recognizing when information is insufficient.

What would settle it

Evaluation on a held-out QA dataset with deliberately incomplete queries where the finetuned planner shows abstention recall and hallucination rates no better than standard RAG baselines.

Figures

Figures reproduced from arXiv: 2604.04565 by Madhav S Baidya.

**Figure 1.** Figure 1: Full PassiveQA pipeline. Left to right: the four source datasets are merged into a unified 61K-sample schema with explicit variable-state fields (§4); a knowledge base of 105,420 chunks is constructed and indexed (§5); three progressive RAG architectures are evaluated on the KB alone (§5); the KB is simultaneously processed through a three-phase KG construction pipeline producing the decision-weighted grap… view at source ↗

**Figure 2.** Figure 2: Overview of the PassiveQA pipeline. After hybrid retrieval and evidence scoring, either [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗

**Figure 3.** Figure 3: Three-phase construction of the PassiveQA knowledge graph. [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗

**Figure 4.** Figure 4: Three-agent architecture. The finetuned planner parses the [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗

read the original abstract

Large Language Models (LLMs) have achieved strong performance in question answering and retrieval-augmented generation (RAG), yet they implicitly assume that user queries are fully specified and answerable. In real-world settings, queries are often incomplete, ambiguous, or missing critical variables, leading models to produce overconfident or hallucinated responses. In this work, we study decision-aware query resolution under incomplete information, where a model must determine whether to Answer, Ask for clarification, or Abstain. We show that standard and enhanced RAG systems do not reliably exhibit such epistemic awareness, defaulting to answer generation even when information is insufficient. To address this, we propose PassiveQA, a three-action framework that aligns model behaviour with information sufficiency through supervised finetuning. Our approach integrates structured information-state representations, knowledge graph-grounded context, and a finetuned planner that explicitly models missing variables and decision reasoning. Experiments across multiple QA datasets show that the finetuned planner achieves significant improvements in macro F1 and abstention recall while reducing hallucination rates, under a compute-constrained training regime. These results provide strong empirical evidence that epistemic decision-making must be learned during training rather than imposed at inference time.

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

PassiveQA proposes a finetuned three-action planner for epistemic calibration in QA, but the evidence for its effectiveness remains thin without detailed results.

read the letter

The punchline is that PassiveQA uses supervised finetuning to create a planner that decides whether to answer, ask for clarification, or abstain based on information sufficiency, but the paper provides no quantitative details to support its claims of improvement. The approach is new in its specific combination of structured information-state representations and knowledge-graph grounding for the decision process. It builds on RAG by adding an explicit learned policy for the three actions rather than relying on the base model to handle uncertainty. The paper does well at identifying the problem with standard systems that default to generating answers even on incomplete queries. This leads to overconfident or hallucinated outputs, and the framework aims to fix that through training. Soft spots include the absence of any numbers, baselines, or error analysis in the description. The abstract asserts better macro F1, abstention recall, and fewer hallucinations, yet without the actual figures or dataset information, it's impossible to assess if the gains are substantial or if they hold up. The assumption that finetuning on these representations will produce generalizable behavior is reasonable but untested in the provided text. The stress-test concern about label quality holds: without knowing how the Answer/Ask/Abstain labels were created independently, the planner might simply replicate whatever the label source did. The knowledge graph grounding adds another layer where incompleteness could cause failures at inference. This paper is for researchers and practitioners in QA and RAG who deal with real-world ambiguous questions and want more reliable epistemic behavior. It offers a concrete framework that could be built upon. I would bring it to the next reading group to discuss the planner architecture. I would not cite it in the next year without seeing stronger evidence. It deserves serious peer review to examine the experimental details and validate the results.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes PassiveQA, a three-action (Answer/Ask/Abstain) framework for question answering under incomplete or ambiguous information. It argues that standard and enhanced RAG systems lack epistemic awareness and default to answer generation. The approach uses supervised finetuning on structured information-state representations combined with knowledge-graph-grounded context to train a planner that explicitly models missing variables and selects among the three actions. Experiments on multiple QA datasets are reported to yield improvements in macro F1 and abstention recall while reducing hallucination rates under a compute-constrained regime. The central thesis is that epistemic decision-making must be learned via finetuning rather than imposed at inference time.

Significance. If the empirical results are substantiated with clear baselines, label-generation details, and robustness checks, the work would provide useful evidence that supervised finetuning on explicit decision representations can improve reliability in underspecified QA settings. The structured three-action formulation and focus on information sufficiency are practical contributions that could inform more calibrated RAG pipelines, especially in resource-limited training scenarios.

major comments (3)

[Abstract / §4] The abstract and introduction assert that the finetuned planner achieves 'significant improvements in macro F1 and abstention recall while reducing hallucination rates,' yet no quantitative results, specific dataset names, baseline comparisons, or statistical details are supplied in the provided text. Without these, the central empirical claim cannot be evaluated. (This is load-bearing for the paper's contribution.)
[§3 (Method) / §4 (Experiments)] The claim that supervised finetuning produces generalizable epistemic decision behavior aligned with true information sufficiency rests on the assumption that the training labels for Answer/Ask/Abstain were generated independently of the model family and reflect genuine sufficiency. The manuscript does not describe the label creation process, any human validation, or inter-annotator agreement. If labels were produced by a similar LLM without external grounding, the learned policy may reproduce labeler biases rather than discover sufficiency. This directly affects the interpretation of the reported F1 and abstention gains.
[§3.2 / §4] The framework relies on knowledge-graph-grounded context during both training and inference. The paper should include an ablation or robustness analysis showing performance when the KG context is incomplete or noisy at test time, as any mismatch would break the alignment the planner was trained to exploit. No such analysis is described.

minor comments (2)

[Abstract] The term 'PassiveQA' is introduced without an explicit expansion or motivation for the name in the abstract or introduction.
[§3] Notation for the three actions and information-state representation should be defined consistently with an equation or diagram early in §3 to aid readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their valuable comments on the manuscript. We address each of the major concerns below and commit to making the suggested revisions to enhance the paper's clarity and completeness.

read point-by-point responses

Referee: [Abstract / §4] The abstract and introduction assert that the finetuned planner achieves 'significant improvements in macro F1 and abstention recall while reducing hallucination rates,' yet no quantitative results, specific dataset names, baseline comparisons, or statistical details are supplied in the provided text. Without these, the central empirical claim cannot be evaluated. (This is load-bearing for the paper's contribution.)

Authors: We agree that the abstract would benefit from including concrete quantitative results to substantiate the claims. In the revised version, we will incorporate specific performance metrics (e.g., macro F1, abstention recall, hallucination rates), dataset names, and baseline comparisons into the abstract and introduction, drawing from the detailed results in Section 4. revision: yes
Referee: [§3 (Method) / §4 (Experiments)] The claim that supervised finetuning produces generalizable epistemic decision behavior aligned with true information sufficiency rests on the assumption that the training labels for Answer/Ask/Abstain were generated independently of the model family and reflect genuine sufficiency. The manuscript does not describe the label creation process, any human validation, or inter-annotator agreement. If labels were produced by a similar LLM without external grounding, the learned policy may reproduce labeler biases rather than discover sufficiency. This directly affects the interpretation of the reported F1 and abstention gains.

Authors: We acknowledge the need for transparency in label generation. We will revise Section 3 to include a comprehensive description of the process used to create the training labels for the three actions, detailing how information sufficiency was assessed via the structured representations and KG context. This addition will clarify the grounding of the labels and allow readers to assess potential biases. revision: yes
Referee: [§3.2 / §4] The framework relies on knowledge-graph-grounded context during both training and inference. The paper should include an ablation or robustness analysis showing performance when the KG context is incomplete or noisy at test time, as any mismatch would break the alignment the planner was trained to exploit. No such analysis is described.

Authors: We concur that robustness to variations in KG quality is an important consideration. We will add an ablation experiment in the revised Section 4, where we evaluate the planner under conditions of incomplete or noisy KG context at inference time, to assess the impact on decision-making performance and provide insights into the framework's reliability. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes PassiveQA as a supervised finetuning framework on external QA datasets to train a planner for Answer/Ask/Abstain decisions using structured information-state representations. No equations, derivations, or self-referential definitions appear in the provided text. Results are presented as empirical improvements in macro F1, abstention recall, and hallucination reduction measured on multiple datasets under a compute-constrained regime. The central claims rest on standard finetuning practices and external data rather than any reduction to fitted parameters renamed as predictions, self-citation chains, or ansatzes smuggled via prior work. The derivation chain is self-contained and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that supervised finetuning can instill reliable epistemic decision-making and that the three actions suffice to cover real-world incomplete queries. No free parameters or invented entities are described in the abstract.

axioms (1)

domain assumption Supervised finetuning on structured information-state representations will align model behavior with information sufficiency and reduce hallucinations.
The paper treats this alignment as achievable through the proposed training regime without providing a derivation or prior proof.

pith-pipeline@v0.9.0 · 5521 in / 1274 out tokens · 53033 ms · 2026-05-10T19:45:32.982536+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/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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

We define the information state of q as S(q) = (Vknown, Vmissing, C) ... a∗(q) ≈ Answer if I(q)≈0, Ask if 0<I(q)<1 ∧ Vmissing recoverable, Abstain otherwise.
IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat recovery theorem unclear

?

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

The finetuned planner achieves 55.6% macro F1 ... via LoRA on Mistral-7B with structured XML reasoning chains.

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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.
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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

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