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Attend to the Active: Structure-Aware Dynamic Attention in LLMs for Compositional Instruction Following

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Instruction Following; Dynamic Attention; Large Language Models
Abstract:

Large language models (LLMs) have exhibited strong instruction-following capabilities; however, they often struggle with compositional instructions involving multiple interleaved yet logically independent sub-tasks. These sub-tasks are typically organized in mutually exclusive structures, such as branching, chaining, or paralleling, where only one sub-task should be active at each generation step, while the others remain dormant. Despite their inactivity, dormant sub-tasks can inadvertently attract the model's attention due to structural entanglement within the input context or intermediate representations, leading to interference that compromises output fidelity. To address this challenge, we propose ATA, a structure-aware dynamic attention mechanism grounded in compositional structures, which dynamically identifies the active sub-task during generation while suppressing attention to inactive ones. By precisely steering the model’s focus, ATA mitigates interference and explicitly enhances model adherence to the active sub-task. Importantly, ATA operates within a single forward pass without requiring parameter updates. Extensive experiments show that ATA consistently enhances LLMs' instruction-following ability across various compositional structures, effectively mitigating attention distraction and demonstrating a strong generalization ability.

Disclaimer
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 ATA, a structure-aware dynamic attention mechanism that adaptively identifies active sub-tasks in compositional instructions while suppressing attention to dormant ones. Within the taxonomy, it resides in the 'Dynamic and Structure-Aware Attention' leaf under 'Attention Mechanisms and Model Architecture'. Notably, this leaf contains only the original paper itself—no sibling papers are present—indicating a relatively sparse research direction. The broader parent branch includes one other leaf on hyperbolic representations, suggesting that dynamic attention conditioned on compositional structure represents an emerging rather than crowded area.

The taxonomy reveals that neighboring branches address related but distinct concerns. 'Multi-Task and Auxiliary Learning Frameworks' explores training paradigms with multiple objectives, while 'Task Decomposition and Planning' focuses on breaking complex tasks into executable sub-tasks. ATA diverges by operating within a single forward pass at the attention level, rather than through multi-objective training or explicit task decomposition. The scope note for the original leaf emphasizes 'adaptively modulating attention based on task structure during inference', distinguishing it from static architectural designs and general multi-task methods found in adjacent branches.

Across three identified contributions, the literature search examined 30 candidates total, with 10 candidates per contribution. None of the contributions were clearly refuted by prior work among these candidates. Contribution A (the ATA mechanism itself) examined 10 papers with zero refutable matches, as did Contribution B (identifying three prototypical composition structures) and Contribution C (mutual attention masking). This suggests that within the limited search scope—top-K semantic matches plus citation expansion—no directly overlapping prior work was identified, though the search was not exhaustive.

Given the sparse taxonomy position and absence of refutable prior work among 30 examined candidates, the paper appears to occupy a relatively novel niche. However, the limited search scope means that related work in broader attention mechanism literature or compositional reasoning may exist beyond the candidates examined. The analysis captures novelty within the surveyed subset but does not constitute a comprehensive field-wide assessment.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: The paper addresses an unspecified core task, yet the taxonomy reveals a rich landscape spanning diverse methodological and application domains. At the highest level, the field divides into twelve major branches that range from technical machine learning concerns—such as Attention Mechanisms and Model Architecture, Multi-Task and Auxiliary Learning Frameworks, and Training Objectives and Optimization—to more applied and interdisciplinary areas including Task-Specific Applications, Benchmark Datasets and Evaluation, and even Organizational and Policy Studies. Within the technical branches, researchers explore how models can be designed with flexible attention schemes, how multiple objectives can be balanced during training (as seen in works like Multi-Objective Reward Modeling[8]), and how tasks can be decomposed or planned (Interactive Planning LLMs[12]). Meanwhile, branches devoted to benchmarks (MATH Dataset[6]) and task-specific applications (Precision Agriculture Datasets[43]) provide the empirical testbeds and real-world contexts that ground these methodological advances. The taxonomy also includes branches on Research Methodology and Problem Formulation, as well as studies in medical, biological, educational, and policy domains, reflecting the broad interdisciplinary reach of the field. Within this landscape, a particularly active line of work focuses on dynamic and structure-aware attention mechanisms, where models adapt their focus based on input structure or task demands. Active Structure Attention[0] sits squarely in this branch, emphasizing how attention can be conditioned on structural properties rather than relying solely on static or content-based weighting. This contrasts with more general architectural innovations and with multi-task frameworks that prioritize shared representations across objectives. Nearby efforts in modular system design (Object Oriented Modularization[28]) and task decomposition (Interactive Planning LLMs[12]) share a concern for flexible, interpretable computation, yet they typically address modularity at the level of entire subsystems rather than within the attention mechanism itself. The open questions in this area revolve around how to balance expressiveness and efficiency when attention must respect complex structural constraints, and how such mechanisms generalize across diverse tasks and data modalities.

Claimed Contributions

ATA: Structure-aware dynamic attention mechanism for compositional instructions

The authors introduce ATA, a novel attention mechanism that analyzes compositional instruction structures (chain, branch, parallel) to dynamically identify which sub-task is active at each generation step and suppresses attention to structurally exclusive inactive sub-tasks. This mechanism operates within a single forward pass without parameter updates.

10 retrieved papers
Systematic identification of three prototypical composition structures

The authors systematically identify and formalize three fundamental composition structures in compositional instructions: chaining (sequential execution), branching (conditional selection), and paralleling (parallel independent tasks). They are the first to introduce the parallel structure in this research area.

10 retrieved papers
Mutual attention masking between exclusive sub-tasks during encoding

The authors propose a mutual attention masking technique that prevents attention flow between structurally exclusive sub-task pairs during the encoding phase. This prevents blending comprehension of multiple mutually exclusive sub-tasks and ensures their representations remain independent.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ATA: Structure-aware dynamic attention mechanism for compositional instructions

The authors introduce ATA, a novel attention mechanism that analyzes compositional instruction structures (chain, branch, parallel) to dynamically identify which sub-task is active at each generation step and suppresses attention to structurally exclusive inactive sub-tasks. This mechanism operates within a single forward pass without parameter updates.

Contribution

Systematic identification of three prototypical composition structures

The authors systematically identify and formalize three fundamental composition structures in compositional instructions: chaining (sequential execution), branching (conditional selection), and paralleling (parallel independent tasks). They are the first to introduce the parallel structure in this research area.

Contribution

Mutual attention masking between exclusive sub-tasks during encoding

The authors propose a mutual attention masking technique that prevents attention flow between structurally exclusive sub-task pairs during the encoding phase. This prevents blending comprehension of multiple mutually exclusive sub-tasks and ensures their representations remain independent.

Table of Contents