In-Context Conditioning in Machine Learning

Updated 1 November 2025

In-context conditioning is a machine learning paradigm where models use auxiliary input sequences to guide predictions without parameter updates.
Transformer architectures implement this via specialized attention mechanisms, pseudo-token methods, and modular control to enhance context processing.
Empirical evidence highlights that context quality, selection, and calibration significantly influence model performance and robust prompt design.

In-context conditioning is a paradigm in modern machine learning—especially prominent in LLMs, multimodal generative models, and meta-learning frameworks—where the predictions or outputs of a model are controlled or adapted at inference solely by auxiliary input sequences (“context”), rather than through parameter updates. This mechanism allows a model to flexibly respond to or adapt for downstream tasks, user prompts, or multimodal control signals by interpreting and acting in context.

1. Foundations and Definitions

In-context conditioning extends the standard notion of conditional modeling ( $p(y|x)$ ) to settings where a context $C$ is supplied in addition to the query $x$ . In context-aware sequence-to-sequence architectures, $C$ is typically a long-form document, dialogue history, demonstration examples, or arbitrary control signals that co-determine the output alongside a focused query input or prompt (Wang et al., 2019). In LLMs, this context is usually a series of labeled examples or instruction strings preceding the test query, resulting in in-context learning (ICL), a particular form of in-context conditioning (Wies et al., 2023).

The general modeling objective is to construct the predictive distribution as $p_\theta(y | C, x)$ , with $C$ potentially much larger, more variable, or noisier than $x$ , and where $\theta$ remains frozen at inference.

2. Architectural and Algorithmic Implementations

a. Transformer-based Models and Attention Schemes

Transformers implement in-context conditioning by encoding the query and context separately (or together), and modulating decoder attention according to specialized architectures:

Independent encoding, intertwined decoding: Source and context are encoded independently, then decoder cross-attention is explicitly routed either to the query, to context, or in specific combinations (“concatenate”, “alternate”, “interleave” patterns) (Wang et al., 2019).
Feature-map aggregation: Self-attention acts as a feature map, aggregating contextual information to enable context-scaling—where adding more in-context examples monotonically improves performance (Abedsoltan et al., 16 Oct 2024). In this regime, the model can generalize as context grows, which is not possible in vanilla MLPs without explicit aggregation (Abedsoltan et al., 16 Oct 2024).

b. Induction of Modular Control

Distinct handling of query and context enables focused modifications—such as sharpening or localizing attention over $C$ with temperature or windowed mechanisms—to better leverage noisy or long contexts (Wang et al., 2019). Notably, this is unattainable when context and query are serialized as a single input.

c. Pseudo-token and Efficient Processing

For context types that might be large collections of sets (e.g., sets of datasets in neural processes), pseudo-token transformers enable efficient in-context in-context learning, conditioning not only on sets of points but also on sets of sets, preserving permutation invariance and scalability (Ashman et al., 19 Jun 2024).

d. Weight-conditioned Manifolds

An alternative class of approaches eschews context as only an input signal, instead parameterizing weights as functions of context variables—thus modulating the entire network for each context value. This “weight-manifold” perspective enables topological inductive bias, explicit alignment of model capacity to structured context spaces (e.g., lines, ellipses), and superior OOD generalization compared to input concatenation (Benjamin et al., 29 May 2025).

3. Theoretical Properties and Formal Guarantees

a. Identifiability and Task Inference

PAC learning analyses formalize in-context conditioning as a Bayesian task-inference mechanism: given a frozen model trained on a distribution of tasks, concatenating enough demonstrations in context enables identification of the correct task component (latent function) (Wies et al., 2023). The probability of misidentification decays exponentially in the number of demonstrations and in the Kullback-Leibler divergence between task distributions; polynomial sample complexity suffices for efficient in-context learning under realistic assumptions (Wies et al., 2023).

b. Context-vs-Pretraining Tradeoff

A properly constructed context can shift a pretrained model’s output distribution toward that of an unseen task, even when pretraining and query tasks are substantially different. The convergence rate to the correct behavior is explicitly governed by the KL divergence between pretraining and query distributions, and by context length (Song et al., 26 Oct 2025).

c. Bayesian and Kernel Analyses

Scaling laws based on Bayesian models clarify that in-context conditioning approximates Bayesian updating; the posterior over tasks, after prompt $C$ , determines future predictions. Empirical scaling curves of ICL can be modeled by explicit Bayesian laws in terms of prior, likelihood, and per-example learning efficiency (Arora et al., 21 Oct 2024). In simplified transformer models, context-aggregation via self-attention recovers kernel regression estimators in the limit, linking context conditioning to nonparametric smoothing (Abedsoltan et al., 16 Oct 2024).

4. Empirical Effects and Data Properties

a. Example Quality and Prompt Construction

In-context conditioning's effect size is highly dependent on the quality and selection of contextual examples. Influence-based selection methods can identify positive and negative examples, leading to promptings that differ in effectiveness by as much as $16.3\%$ in accuracy, compared to random or similarity-based selection (Nguyen et al., 2023).

b. Pretraining Data and Repetitions

Emergence and stability of in-context conditioning hinges on properties of pretraining data. The presence of exact (conceptual) repetitions in training corpora is essential for robust ICL: it supports the emergence of induction heads—attention circuits that enable look-up and match-to-context behaviors (Bratulić et al., 9 Jan 2025). High task difficulty and distributions with many rare tokens further increase in-context ability (Han et al., 2023, Bratulić et al., 9 Jan 2025).

c. Calibration and Marginal Shift

Output variability in in-context conditioning often arises from marginal label shift—context can bias the output label distribution $p(y)$ away from the true marginal $q(y)$ . Calibrating the in-context model by correcting for estimated label marginal (using, e.g., Monte Carlo over model generations) can dramatically and robustly improve performance (Jiang et al., 2023).

5. Generalizations Beyond Language Modeling

In-context conditioning extends to multimodal generation, reinforcement learning, and meta-learning:

Reinforcement learning: Agents condition on histories or prompts to synthesize new behaviors at test time, with policy $\pi_\theta(a|s, C_t)$ (Moeini et al., 11 Feb 2025).
Video and image diffusion models: Arbitrary, fine-grained controllability (spatial, temporal, attribute) is achieved by concatenating multimodal context tokens (images, frames, poses) with latent variables, jointly processed by full attention; efficiency bottlenecks are mitigated by dynamic token selection and context caching (Cai et al., 9 Oct 2025, He et al., 4 Jun 2025).
Meta-learning and neural processes: In-context in-context learning enables group-level adaptation by conditioning on sets of datasets, not just on sets of points (Ashman et al., 19 Jun 2024).

6. Practical Impact, Limitations, and Open Problems

In-context conditioning, as realized in modern transformer-based models, provides the foundation for adaptable, promptable architectures and has revolutionized deployment in NLP, multimodal generation, and interactive agents. Its main advantages are parameter efficiency, control flexibility, and the ability to incorporate contextual and domain-specific information at inference.

However, key challenges include:

Sensitivity to context quality and construction: Model behavior is highly volatile with respect to example choice, order, and demonstration properties (Nguyen et al., 2023, Jiang et al., 2023).
Calibration and alignment brittleness: Safety alignment via post-training is brittle, as many-shot in-context conditioning can reintroduce suppressed behaviors, highlighting the fundamental limits of inference-time control (Arora et al., 21 Oct 2024).
Scaling and data pathologies: The capacity for context-scaling is intrinsic to self-attention, but absent in architectures lacking contextual aggregation (Abedsoltan et al., 16 Oct 2024).
Emergent but fragile generalization: Strong context conditioning may appear only under precise pretraining regimes (repetitions, burstiness, long-tail tokens) and may be fragile outside these conditions (Bratulić et al., 9 Jan 2025).

7. Summary Table: Key Mechanisms and Properties

Mechanism / Aspect	Archetypal Approach	Critical Properties/Outcomes
Encoder/Decoder structure	Separate C and S encodings, intertwined attention	Enables modular focus and more efficient context usage
Pretraining data	High repetition, high long-tail token mass	Predicts emergence of robust in-context conditioning
Context scaling	Self-attention (transformers), feature maps	Necessary for utilizing more examples effectively
Example selection	Influence-based ranking, model-specific analysis	Substantial accuracy/robustness gains in ICL
Calibration	Generative/Monte Carlo estimation of label marginals	Makes predictions robust to prompt bias
Weight-level modulation	Weight manifolds parameterized by context	Aligned OOD generalization, topology-exploiting bias

In sum, in-context conditioning is a foundational paradigm that unifies much of modern context-aware, prompt-driven inference, enabling models to fluidly adapt to new tasks, domains, and user requirements with only "in-context" information, and without parametric updates. Theoretical models, data analysis, engineering techniques, and empirical validations collectively converge to a rigorous understanding of its mechanisms, benefits, and limitations.