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Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
CausalityReasoning TasksSelection Mechanism
Abstract:

Due to their inherent complexity, reasoning tasks have long been regarded as rigorous benchmarks for assessing the capabilities of machine learning models, especially large language models (LLMs). Although humans can solve these tasks with ease, existing models, even after extensive pre-training and post-training at scale, still fail to perform reasoning reliably. In this paper, we revisit reasoning tasks from a causal perspective, seeking to understand their behavior in latent space and to offer insights for addressing their challenges. Specifically, we cast reasoning tasks as a selection mechanism, in which high-level logical concepts function as selection operators on the given observations, such as, identifying the correct answer in a math problem or filling the appropriate entry in Sudoku. We emphasize two key properties of this formulation that shed light on the difficulty of reasoning tasks. First, the latent space exceeds the observation space in complexity, even when the correct answer is fully determined by the observed input. Second, the latent variables, corresponding to logical thought, are densely structured and exhibit strong dependencies. Building on this formulation, we introduce a framework, called SR2^2, that incorporates the estimated latent variables as feedback into the selection mechanism, thereby facilitating the learning of dense dependencies among latent representations. The framework consists of three key modules: reflective representation learning, dependency self-refinement, and periodic intermediate alignment. Experimentally, we show that our approach yields significant gains in reasoning accuracy, for example, attaining over 10% improvement in performance with 8×\times fewer parameters on the Sudoku and Maze tasks over the recent advances.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a causal formulation of reasoning tasks as selection mechanisms, where latent logical concepts operate on observations to produce answers. It introduces two hypotheses about reasoning difficulty (latent complexity exceeding observation space, dense latent dependencies) and develops the SR2 framework with three modules for iterative refinement. Within the taxonomy, this work resides in 'Self-Feedback and Self-Refinement Frameworks' alongside two sibling papers (Self-Refine and Self-Iterative Feedback). This leaf contains only three papers total, suggesting a relatively focused but not overcrowded research direction within the broader self-improvement branch.

The taxonomy reveals substantial activity in neighboring areas: the parent branch 'Self-Improvement Through Iterative Feedback and Refinement' includes hypothesis refinement and experience reuse methods, while sibling branches explore preference-based learning, chain-of-thought enhancement, and multi-agent collaboration. The scope note for this leaf explicitly excludes methods requiring external critics or multi-agent systems, positioning the work within purely self-contained refinement approaches. The paper's causal perspective appears to bridge self-refinement with neuro-symbolic reasoning concepts, though it remains classified as a neural self-improvement method rather than a hybrid symbolic approach.

Among 21 candidates examined across three contributions, no clearly refuting prior work was identified. The causal formulation examined 9 candidates with 0 refutations, the two difficulty hypotheses examined 10 candidates with 0 refutations, and the SR2 framework examined 2 candidates with 0 refutations. This limited search scope suggests the specific combination of causal framing and self-refinement may be relatively unexplored among the top semantic matches. However, the analysis does not cover the broader causal reasoning literature or exhaustive symbolic reasoning methods, leaving open questions about overlap with work outside the iterative refinement focus.

Based on the top-21 semantic matches within this taxonomy, the work appears to occupy a distinctive position combining causal theory with self-refinement mechanisms. The sparse population of its immediate taxonomy leaf and absence of refuting candidates suggest novelty in this specific formulation, though the limited search scope means potentially relevant work in causal inference or symbolic reasoning may not have been examined. The contribution's distinctiveness likely lies in its theoretical framing rather than the refinement mechanism itself.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Improving reasoning capabilities of machine learning models through iterative refinement. The field is organized around several complementary strategies for enhancing model reasoning. Self-Improvement Through Iterative Feedback and Refinement explores methods where models critique and revise their own outputs, exemplified by works like Self-Refine[12] and Self-Iterative Feedback[25]. Preference-Based and Reinforcement Learning for Reasoning leverages reward signals and human preferences to guide reasoning quality, as seen in Iterative DPO Reasoning[5] and Iterative Reasoning Preference[2]. Chain-of-Thought Enhancement focuses on intermediate step generation, while Multi-Agent Collaboration and Debate, including Multiagent Debate[7], uses multiple reasoning perspectives. Neuro-Symbolic and Structured Reasoning Integration combines symbolic methods with neural approaches, Domain-Specific Reasoning Applications targets particular problem areas like mathematics (Qwen Math[9]) or vision (OpenVLThinker[3]), Training Data and Supervision Optimization refines the learning signal itself, and System-Level and Pipeline Optimization addresses architectural concerns. Within the self-improvement branch, a central tension exists between fully autonomous self-refinement and methods that incorporate external feedback or verification. Some works emphasize hypothesis generation and testing cycles (Hypothesis Refinement Testing[4]), while others focus on ethical or explanatory dimensions (Ethical Explanations Refinement[1]). Causal Lens Reasoning[0] sits squarely within the self-feedback and self-refinement frameworks, sharing the iterative revision philosophy of Self-Refine[12] and Self Iterative Label[29]. However, where Self-Refine[12] provides a general-purpose refinement mechanism and Self Iterative Label[29] focuses on label quality, Causal Lens Reasoning[0] appears to emphasize causal structure in the reasoning process itself. This positions it as a specialized instantiation of self-refinement that brings domain knowledge about causality into the iterative loop, contrasting with more domain-agnostic approaches while maintaining the core principle of models improving their own reasoning through successive iterations.

Claimed Contributions

Causal formulation of reasoning as selection mechanism

The authors formulate reasoning tasks through a causal lens, modeling them as selection mechanisms where latent logical rules constrain observed input-output pairs. This formulation captures reasoning as narrowing possible latent assignments to those consistent with selection constraints.

9 retrieved papers
Two hypotheses characterizing reasoning difficulty

The authors propose two fundamental hypotheses: (1) the latent space is more complex than the observation space even when answers are uniquely determined, and (2) latent variables are densely structured with strong interdependencies. These properties explain why reasoning tasks remain difficult for current models.

10 retrieved papers
SR2 framework with three key modules

The authors introduce the SR2 framework comprising three modules: reflective representation learning (iteratively refining latent variables with input feedback), dependency self-refinement (modeling latent dependencies without observation signals), and periodic intermediate alignment (injecting supervision at intervals to stabilize training). This framework explicitly models selection, reflection, and self-refinement in reasoning.

2 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Causal formulation of reasoning as selection mechanism

The authors formulate reasoning tasks through a causal lens, modeling them as selection mechanisms where latent logical rules constrain observed input-output pairs. This formulation captures reasoning as narrowing possible latent assignments to those consistent with selection constraints.

Contribution

Two hypotheses characterizing reasoning difficulty

The authors propose two fundamental hypotheses: (1) the latent space is more complex than the observation space even when answers are uniquely determined, and (2) latent variables are densely structured with strong interdependencies. These properties explain why reasoning tasks remain difficult for current models.

Contribution

SR2 framework with three key modules

The authors introduce the SR2 framework comprising three modules: reflective representation learning (iteratively refining latent variables with input feedback), dependency self-refinement (modeling latent dependencies without observation signals), and periodic intermediate alignment (injecting supervision at intervals to stabilize training). This framework explicitly models selection, reflection, and self-refinement in reasoning.

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