Context Structure in In-Context Learning

• Context structure in ICL is a framework that organizes, retrieves, and abstracts prompt information to activate schema-based reasoning in LLMs.
• It employs methods such as schema extraction, episodic example retrieval, and agentic multimodal orchestration to enhance model efficiency and reduce demonstration requirements.
• Empirical findings reveal that schema-guided context boosts performance significantly (up to +39.67%), improving interpretability and generalizability in complex tasks.

Context structure in in-context learning (ICL) refers to the organization, retrieval, abstraction, and utilization of information within the prompts supplied to LLMs or multimodal models to enable rapid, on-the-fly adaptation to new tasks. Modern research demonstrates that not only the quantity but the form, content, and explicit structuring of context—ranging from raw demonstrations and instructions to abstracted schemas and manifold-aligned geometries—critically determine the efficacy and generalizability of ICL.

1. Conceptual Foundations: Schema Theory and Abstract Structure

A central advance in context structuring for ICL incorporates principles from schema theory in cognitive science. Humans organize knowledge in the form of schemas—abstract, reusable frameworks that encode the inferential building blocks of reasoning. When confronting new tasks, people activate relevant schemas, retrieve matching episodic memories, and adapt them to the novel situation.

In contrast, traditional ICL concatenates surface-form question–answer (Q–A) pairs, encouraging models to pattern-match based on shallow statistical cues. Recent research, notably Schema Activated In-Context Learning (SA-ICL), augments this paradigm: it explicitly constructs and retrieves abstract schemas—structured templates encoding inferential steps and their logical relations (e.g., “identify initial state,” “apply conservation principle,” “compute net change”)—and uses them to scaffold model reasoning. Empirical findings show that explicit schema-based scaffolding enables LLMs to perform higher-order reasoning, reduces reliance on large numbers of demonstrations, and enhances interpretability (Chen et al., 14 Oct 2025).

2. Formalization and Construction of Context Structure

2.1 Schema Extraction and Integration

The schema-based approach to context structuring comprises a multi-stage pipeline, algorithmically formalized as follows (Chen et al., 14 Oct 2025):

1. Problem Representation: For a query $x$, use a representation function $\mathcal{R}$ (via LLM prompt) to extract a high-level schema $\mathcal{S}_x$.
2. Prior Schema Retrieval: Compute similarity between $\mathcal{S}_x$ and stored schemas $\{\mathcal{S}_i\}$, retrieving the most similar $\hat{\mathcal{S}}$.
3. Episodic Example Retrieval: Retrieve examples $e_j$ associated with $\hat{\mathcal{S}}$ via decaying association weights $w_{ij}$, thresholded by $\tau$.
4. Schema Activation: Integrate $\mathcal{S}_x$, $\hat{\mathcal{S}}$, and $\hat{\mathcal{E}}_\tau$, assimilating or restructuring as needed.
5. Schema-Guided Inference: Condition the model jointly on $x$ and the activated schema.

In practical terms, schemas are encoded as four-field templates (Broad Category, Refinement, Specific Scope, Goal) plus summary, and can be provided in highly compressed bullet/JSON formats, enabling token-efficient, domain-agnostic scaffolding.

2.2 Agentic Multimodal Context Structuring

In multimodal settings, context structuring requires additional complexity. The ContextNav framework organizes context via:

• Resource-aware multimodal embeddings: Each example is embedded using potentially distinct text and vision encoders, with trade-offs specified by resource constraints.
• Agentic retrieval and semantic filtering: Candidate examples are filtered for relevance and denoised for off-topic or structurally inconsistent content via agent policies over similarity and structure-alignment prompts.
• Structural alignment: Retrieved examples are edited to match the structural form (e.g., question style) of the current query.
• Closed-loop, graph-orchestrated workflow: Context construction is managed via a directed acyclic graph (Operational Grammar Graph), with workflow selection optimized via feedback from downstream ICL performance (Fu et al., 6 Oct 2025).

3. Theoretical Underpinnings: Structure Induction and Information Theory

The essential mechanisms enabling effective ICL depend on the induction and explicit use of structure within the context.

• Compositional structure: When pretraining data is generated from compositional attribute grammars, transformers can learn to perform implicit grammar induction, enabling accurate continuation of novel compositional tasks. Theoretical results show that error bounds scale as O(Rₙ + D[τ_f]), where Rₙ is the grammar's iteration complexity and D[τ_f] the description length of the derivation tree for the underlying task (Hahn et al., 2023).
• Chain-of-Thought and schema abstraction: Explicit intermediate-step prompting (e.g., CoT) enables decomposing complex functions into simpler primitives, reducing effective sample complexity.
• Context–scaling vs. task–scaling: Only architectures implementing a context-dependent feature map (such as transformer attention) can provably achieve context-scaling—wherein performance improves as more in-context examples are provided. In contrast, traditional MLPs without such structuring cannot scale with additional context (Abedsoltan et al., 2024).
• Information-theoretic viewpoint: The structure (e.g., diversity, mixture, and statistical dependencies) of in-context sequences determines the rate at which a model’s implicit posterior over tasks contracts onto the correct hypothesis. For sufficiently long, well-structured context, the output prediction shifts from pretraining priors to the correct task distribution exponentially fast, controlled by the KL divergence between pretraining and task distributions (Song et al., 26 Oct 2025, Riechers et al., 23 May 2025).

4. Empirical Validation Across Domains

4.1 Quantitative Benefits of Structured Context

In the SA-ICL paradigm, schema-guided prompting achieved dramatic gains: up to +39.67 percentage points in chemistry and +34.88 in physics (GPQA dataset, GPT-4o, high-similarity regime) over example-only baselines, with similar advantages persisting at lower knowledge densities. Performance ablations confirm that omitting schema activation eliminates the gains, which are robust even with minimal demonstrations (Chen et al., 14 Oct 2025).

4.2 Multimodal and Agentic Orchestration

ContextNav demonstrated that noise-robust, dynamically optimized context produced by agentic workflows—rather than static retrieval pipelines—improves ICL under diverse vision-language settings (Fu et al., 6 Oct 2025).

4.3 Representation Reorganization via Context

Experiments with synthetic graph-tracing tasks show that scaling context size yields abrupt phase transitions in representation geometry: model activations spontaneously reorganize from reflecting pretrained semantic similarity to manifesting the adjacency geometry specified by the prompt, as predicted by Dirichlet energy minimization on the task-graph Laplacian (Park et al., 2024). This supports the view that context structure can override or coexist with innate model priors to encode task-specific semantics.

5. Practical Guidelines and Comparative Analysis

Explicit structuring of context offers several best practices:

• Schema templates: Small, interpretable templates facilitate cross-domain generalization and efficiency.
• Retrieval and alignment: Two-stage retrieval (schema then examples) and enforced structural alignment maximize relevance.
• Diversity and context length: Long, diverse context windows enable model behaviors analogous to environment learning, with theoretical error bounds decaying as O(1/√T) or exponentially in context length, depending on structure (Wang et al., 26 Sep 2025).
• Data efficiency: High-quality schema-guided demonstrations yield greater improvements than increasing raw example count.
• Augmentation in long-context settings: In LCLMs, performance is more sensitive to sufficiently populating the context window—even with random selection—than to sophisticated selection heuristics. When available data are insufficient, synthetic augmentation and filtering improve results (Baek et al., 2024).

Ablation studies confirm that explicit structure (task instructions, schema guidance) is critical for sample efficiency, OOD generalization, and rapid adaptation; token order, diversity, and structural alignment play a secondary but sometimes significant role (Lin et al., 27 Feb 2025, Yang et al., 2023).

6. Limitations and Open Challenges

Despite these advances, several limitations persist:

• Rigidity of schema retrieval: Relying on a single high-similarity schema can degrade performance if mismatched; more dynamic or hierarchical schema induction remains an open direction (Chen et al., 14 Oct 2025).
• Continual and open-domain schema memory: Extending schema storage to support continual, hierarchical, and multimodal indexing is unsolved.
• Automatic structure induction within LLMs: Most schema-induction is still externalized at the prompt level rather than learned as an internal inductive bias.
• Token budget versus structural abstraction: While abstract schemas are token-efficient, they require domain-specific template design and quality assurance.
• Generalization to semi-supervised settings: Transformers can exploit unlabeled context to induce low-dimensional manifolds, Laplacians, or clustering structure, but this field is nascent and the limits of unsupervised context structuring are not fully understood (Fan et al., 17 Dec 2025).

7. Synthesis and Outlook

Context structure in ICL is not a monolithic concept but encompasses a spectrum—from raw demonstrations, to compositional grammars, to explicit abstract schemas, to multimodal, agentically-curated databases. The theoretical and empirical evidence converges on a central principle: explicit, abstract structuring of context at the appropriate level of granularity quantitatively enables more efficient, generalizable, and interpretable in-context adaptation, both for language and multimodal models.

Current frontiers include inducing structure with minimal supervision, continual and hierarchical schema learning, multi-modal schema extraction, and integration of spectral-geometric inductive biases. These directions aim to further close the gap between the mechanistic rigidity of current architectures and the adaptive, abstracted reasoning capacities observed in human cognition (Chen et al., 14 Oct 2025, Hahn et al., 2023, Fan et al., 17 Dec 2025).