Self-Questioning in Autonomous Systems

Updated 28 November 2025

Self-questioning is a structured, iterative methodology that decomposes tasks into sub-questions and answers to boost deep reasoning, internal introspection, and error analysis.
It leverages modular roles and architectures in vision-language systems and language models to improve task performance and mitigate hallucinations.
Applications span diverse domains—from multimodal QA to robotics and education—with measurable gains in accuracy and self-assessment capabilities.

Self-questioning is a structured, iterative methodology in which an autonomous agent—whether a LLM, a vision-language system, or a hybrid—generates its own intermediate questions and attempts to answer them prior to (or in the course of) addressing a main task. This paradigm has emerged as a critical tool for enhancing deep reasoning, internal model introspection, error analysis, self-knowledge evaluation, and domain adaptation across language, vision, and multimodal systems. Self-questioning is instantiated in diverse ways, from “sub-question chains” in multimodal VQA, to reinforcement learning-driven self-play in LLMs, to pedagogical comprehension checks in programming education, and fine-grained claim decomposition in self-evaluation frameworks.

1. Core Principles and Formalization

Self-questioning formalizes the hypothesis that agents improve reasoning and alignment by decomposing tasks into a sequence of internally generated sub-questions, addressing each with targeted answers, and leveraging these as scaffolds for final inference or judgment. Notation varies by context, but a typical sequence for vision-language reasoning is:

Given input $I$ (e.g., an image), and main question $q$ ,
Generate sub-questions $q_1$ , …, $q_T$ iteratively ( $Q$ -generation),
Obtain answers $a_1$ , …, $a_T$ for each $q_t$ ( $A$ -generation),
Aggregate $\{(q_t, a_t)\}_{t=1}^T$ to answer the main $q$ via a Reasoner.

This process directly operationalizes $P(q_t \mid I, q_{<t}, a_{<t})$ , $P(a_t \mid I, q_{\le t}, a_{<t})$ , and $P(y \mid I, q, \{q_t,a_t\})$ , with specific workflows and architectures varying by application domain (Jang et al., 25 Sep 2025, Sun et al., 17 Mar 2024).

Self-questioning is also utilized for internal self-assessment and fine-grained claim verification, as in answer-based claim decomposition (ABCD), where a complex query is partitioned into atomic claims, each verified separately for binary satisfaction, exposing the model’s reasoning and gaps (Balepur et al., 2023).

2. Model Architectures and Algorithms

The self-questioning framework is realized via a modular division of labor among roles: Questioner, Answerer, and Reasoner—often instantiated as parameter-shared variants over the same backbone, with specialized tuning or prompting schemes. Key architectural variants include:

SQ-InstructBLIP: Utilizes an InstructBLIP-Vicuna7B backbone with a frozen vision encoder and LLM, and a trainable Q-Former. Iteratively generates diverse, image-aware sub-questions, answers them, and reasons over the resulting sub-QA pairs for final VQA output (Jang et al., 25 Sep 2025).
SQ-LLaVA: Augments vision-language alignment by incorporating self-supervised question-generation alongside classic answer-prediction, employing prototype-enhanced vision embeddings and a dual loss function $L_{total} = L_Q + L_A$ ; this directly leverages intra-image context to enhance modulation and reasoning in the LLM (Sun et al., 17 Mar 2024).
Self-Questioning LLMs (SQLM): Casts a LLM as both “proposer” (problem generator) and “solver” (problem answerer) in an asymmetric self-play RL setting. The proposer is rewarded for generating challenging, but solvable, problems; the solver optimizes for high accuracy given only internally generated data, with no external supervision (Chen et al., 5 Aug 2025).

Self-questioning can also be paired with uncertainty-aware filtering, as in BoViLA for video-language alignment, which uses evidential deep learning heads to soft-filter low-quality self-generated questions via uncertainty scores $u$ , weighting their contribution to training (Chen et al., 17 Sep 2024).

Pseudocode for the core self-questioning loop in SQ-InstructBLIP is:

Input: image I, main question q, max turns T
Initialize context C ← []
for t in 1…T:
    q_t ← Questioner.generate(I, q, [q_1…q_{t-1}])
    C.append("SubQ: " + q_t)
    a_t ← Answerer.generate(I, q_t)
    C.append("SubA: " + a_t)
y ← Reasoner.generate(I, q, C)
return y

(Jang et al., 25 Sep 2025)

3. Domains of Application

Self-questioning has demonstrated broad impact across the following domains:

Vision-Language Reasoning and Multimodal QA

Iterative self-questioning frameworks (SQ-InstructBLIP, Socratic Questioning, SQ-LLaVA) significantly improve performance on VQA-Introspect, A-OKVQA, ScienceQA-IMG, and hallucination benchmarks by explicitly decomposing reasoning tasks and enforcing multi-step retrieval of fine-grained visual evidence, leading to 2–11% absolute performance gains and robust mitigation of hallucinations (Jang et al., 25 Sep 2025, Sun et al., 17 Mar 2024, Hu et al., 6 Jan 2025).

LLM Self-Improvement and Curriculum Learning

In SQLM, self-questioning is operationalized via asymmetric self-play, leveraging internally generated problems for RL-based learning. This paradigm yields 8–16% absolute test accuracy improvements for arithmetic, algebra, and code generation without curated datasets (Chen et al., 5 Aug 2025).

Self-Knowledge and Consistency Evaluation

Feynman-inspired frameworks test whether a model can solve or verify its own generated questions, quantifying the self-knowledge score $S_{self} = \frac{1}{n}\sum_{i=1}^n I(a_i = \hat a_i)$ . Empirical findings indicate significant self-knowledge gaps ( $S_{self}$ typically 0.26–0.47), revealing limitations in internal model consistency and highlighting the importance of attention alignment for self-verification (Tan et al., 10 Jun 2024).

Explanation and Decision Support/Critical Reflection

Taxonomies of Socratic or XAI-derived self-questioning codify ten classes of critical-reflection questions aligned with system inputs, data quality, causal inferences, alternatives, and stakeholder perspectives. This systematic schema supports deliberate oversight in machine-assisted decision-making (Fischer et al., 17 Apr 2025).

Automated Self-Assessment in Robotics and Education

For robotic agents, self-questioning is formalized as introspection and self-assessment: robots estimate $P(\text{success of T}\mid C,E)$ based on planned actions and context, answer meta-queries before/during/after execution, and update plans and abilities in real time (Frasca et al., 2020).
In programming assessment, self-questioning frameworks automatically generate code comprehension questions from static and dynamic analyses, probing execution traces, variable roles, and control flow, reinforcing student understanding beyond unit-test correctness (Lehtinen et al., 2021).

4. Evaluation Protocols and Quantitative Results

Evaluation of self-questioning frameworks leverages both task metrics (accuracy, F1, ROUGE, Date-F1 for timeline retrieval) and process-oriented metrics (self-consistency, self-knowledge, hallucination rates, uncertainty scores, and information gain). Examples include:

System / Context	Metric	Baseline	Self-Questioning	Gain (%)
VQA-Introspect	Multi-choice Acc	85.53	86.84	+1.31
A-OKVQA	Multi-choice Acc	72.75	73.28	+0.53
CapQA Hallucination	HalS	69.3	90.9 – 93.0	+31.2
SQLM, Arithmetic	Test Accuracy	0.79	0.948	+15.7
Self-Knowledge	S_self (mean)	0.26-0.47	–	–

Empirical studies demonstrate that multi-turn or multi-step self-questioning yields diminishing returns after a critical threshold (typically T=3 for VQA-Introspect) (Jang et al., 25 Sep 2025). Fine-tuning LLMs on self-generated problems or answers shows measurable self-improvement in both math and code generation (Tan et al., 10 Jun 2024, Chen et al., 5 Aug 2025).

5. Limitations and Current Challenges

Despite improvements, self-questioning frameworks face notable limitations:

Answerer Reliability: Imperfect sub-answers or self-answers can propagate errors, misleading final inferences or amplifying noise (Jang et al., 25 Sep 2025, Chen et al., 17 Sep 2024).
Quality Control: Automatic filtering, e.g., via evidential uncertainty, is necessary to prevent self-questioning loops from reinforcing spurious questions or trivial patterns in early training (Chen et al., 17 Sep 2024).
Curriculum Stability: In self-play RL settings, proposer–solver feedback requires careful tuning (e.g., update frequency $k$ ) for stable curriculum progression (Chen et al., 5 Aug 2025).
Diversity and Redundancy: Ensuring that generated sub-questions (or claims in ABCD) are non-redundant and maximally informative remains a challenge, motivating objectives for question diversity and relevance (Jang et al., 25 Sep 2025, Tan et al., 10 Jun 2024).
Latency and Complexity: Multi-step self-questioning introduces additional computational cost and inference time, requiring trade-offs between accuracy and latency in deployment scenarios (Jang et al., 25 Sep 2025).

6. Theoretical Insights and Extensions

Self-questioning mechanisms provide a formal proxy for information-theoretic objectives: maximizing conditional entropy over possible answers reveals otherwise latent internal knowledge, and external retrieval-augmented QA demonstrates that model parameters compress only a subset of relevant information (Wu et al., 18 May 2025).

The approach is fundamentally aligned with cognitive science results: explicit self-explanation prompts or Socratic questioning improve analytic reasoning and reduce overreliance on model recommendations, as empirically established in medicine and education (Fischer et al., 17 Apr 2025, Lehtinen et al., 2021).

Extensions include multi-modal generalizations (audio, video, time-series), recursive chaining for deeper self-dialogue, cross-model collaborative self-questioning (where small models generate fundamental questions for large models), and interfacing with XAI for critical reflection (Wu et al., 18 May 2025, Fischer et al., 17 Apr 2025).

7. Practical Guidelines and Future Trajectories

Best practices for deploying self-questioning include:

Tuning the balance between question and answer objectives during training
Filtering or soft-selecting self-generated questions based on uncertainty or relevance
Leveraging few-shot or example-based prompts for initial question generation
Iteratively fine-tuning models on self-generated curriculum data, optionally verified against human-ground truth for maximal learning gains
Integrating self-questioning as both a training-phase curriculum and a deployment-phase diagnostic tool (Tan et al., 10 Jun 2024, Jang et al., 25 Sep 2025, Chen et al., 5 Aug 2025).

Long-term directions emphasize joint training of Questioner and Reasoner, adaptive stopping strategies, human-in-the-loop refinement, and extending beyond single QA tasks to tasks demanding sustained, multi-hop, or creative deliberation—such as timeline construction, code synthesis, and high-stakes decision support (Wu et al., 1 Jan 2025, Fischer et al., 17 Apr 2025, Balepur et al., 2023).

Self-questioning thus constitutes a general-purpose, model-agnostic principle for fostering deep, transparent, and self-improving reasoning in autonomous systems.