Contextual Alignment: Definition and Applications

• Contextual alignment is the adaptation of models or representations to situational variables such as linguistic context, user role, and institutional rules. It involves both representational (e.g., aligning token structures across languages) and behavioral properties (e.g., outputs satisfying context-specific requirements) while maintaining invariance when contextual changes are normatively irrelevant.
• Contextual alignment is crucial for fields like natural language processing, multimodal learning, and agent behavior. For example, in cross-lingual contextual embeddings, it ensures that occurrences of words in different languages are accurately mapped, improving tasks like zero-shot dependency parsing, where performance metrics such as LAS (6.8 points avg LAS) improve from prior work.
• Evaluation of contextual alignment requires a multi- faceted approach. Representation metrics (e.g., contextual retrieval, translation accuracy) are needed as well as behavioral and context sensitivity metrics and ultimately human-centered metrics.

Contextual alignment is the adaptation of a representation, model, agent, or decision policy to the circumstances in which it is used. Unlike alignment to a fixed global objective, contextual alignment accounts for situational variables such as geometry, linguistic context, modality, user role, institutional rules, moral priorities, privacy expectations, professional norms, and temporal history. Across research areas, it denotes both a representational property—corresponding elements occupy compatible structures—and a behavioral property—outputs satisfy context-specific requirements while remaining invariant when contextual changes are normatively irrelevant.

1. Conceptual foundations and scope

Contextual alignment differs from static or global alignment in the object being aligned and in the information regarded as relevant. Static word-vector alignment maps one vector per word type; contextual alignment considers token occurrences and their surrounding sentences. Global value alignment optimizes a single policy against a fixed reward, whereas contextual value alignment allows the applicable priorities to vary with a moral profile, institutional setting, or user situation. In multimodal learning, global pair alignment may establish that an image and caption correspond, while contextual alignment additionally associates fine-grained visual features with textual cues.

The term has consequently acquired several related uses:

• Representational contextual alignment: alignment of context-dependent embeddings, latent features, or token structures across languages, modalities, or model components.
• Structural contextual alignment: explicit organization of components into hierarchical or relational structures, as in time-series-to-language graphs.
• Behavioral contextual alignment: adaptation of model outputs to policies, professional norms, user roles, privacy expectations, or moral priorities.
• Evaluation-oriented contextual alignment: testing both whether behavior changes when a governing norm changes and whether it remains stable when a manipulated feature is irrelevant.

A recurring distinction is between adaptation and invariance. A system should alter behavior when the context changes the applicable rule, but should not treat every salient contextual feature as normatively decisive. ContextAdapt formulates this as “calibrated contextual adaptation”: models should adapt when a relevant professional norm changes and remain consistent when it does not (Macmillan-Scott et al., 29 Sep 2026).

Context can be supplied explicitly, inferred from observations, encoded in a graph, recovered from dialogue history, or represented through examples. It is not necessarily an exogenous feature vector. In mmWave beam alignment, for example, contextual information is inferred from beam outcomes and angular ordering rather than from GPS coordinates (Hashemi et al., 2017). In cross-lingual contextual embedding alignment, it is represented by occurrence-level sentence context or by latent sense clusters (Schuster et al., 2019, Ulčar et al., 2021). In contextual privacy alignment, it includes the data subject, sender, recipient, purpose, channel, relationship, granularity, identifiability, and AI mediation (Tamber et al., 19 Jun 2026, Guo et al., 10 Aug 2026).

2. Contextual alignment of representations

Cross-lingual contextual embeddings

Contextual word representations are occurrence-dependent: a word $i$ in context $c$ is represented as $e_{i,c}$, rather than by a single static vector. This creates an alignment problem because a bilingual dictionary identifies word types but does not specify which source and target occurrences correspond. “Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing” addresses this by defining an embedding anchor,

$\bar e_i=\mathbb{E}_c[e_{i,c}],$

and decomposing each contextual representation as

$e_{i,c}=\bar e_i+\hat e_{i,c}.$

The anchor captures relatively stable lexical information, while $\hat e_{i,c}$ represents the context-dependent shift. An orthogonal map is learned between bilingual anchors and then applied to the complete contextual vectors:

$$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$

This preserves contextual variation without requiring source and target contexts to be paired directly (Schuster et al., 2019). The method uses ELMo representations, bilingual dictionaries, orthogonal Procrustes alignment, and, in unsupervised settings, anchor-based MUSE refinement. In zero-shot dependency parsing, the full contextual model reaches an average LAS of $77.3$, improving over the previous state of the art by $6.8$ average LAS points.

A related approach aligns contextual representations directly from occurrence-level pairs extracted from parallel or strongly comparable corpora. “Cross-lingual alignments of ELMo contextual embeddings” constructs anchors by matching dictionary-compatible lemmas in aligned OpenSubtitles contexts, extracting the original inflected tokens, and retaining up to 20 contexts per lemma. It evaluates linear VecMap and MUSE mappings alongside the nonlinear ELMoGAN mapping (Ulčar et al., 2021). The results are task-dependent: ELMoGAN performs strongly in NER and terminology alignment, particularly for distant languages, whereas linear contextual mappings are more successful in dependency parsing and sentiment analysis. This establishes that contextual alignment is not characterized by a universally optimal transformation; the appropriate mapping depends on language-pair geometry and downstream task requirements.

Contextual word retrieval and multilingual LLMs

“Multilingual Alignment of Contextual Word Representations” defines contextual alignment through retrieval of the correct translated word occurrence in the correct parallel sentence. Given aligned word positions $(i,j)$, a source representation must retrieve the target occurrence rather than merely the correct word type. CSLS is used to mitigate hubness (Cao et al., 2020).

The work fine-tunes multilingual BERT using parallel data and a contextual alignment loss based on squared Euclidean distance between aligned contextual vectors, together with a regularizer that preserves target-language representations. Unlike an orthogonal rotation, the method fine-tunes the entire model and jointly aligns multiple languages into an English-centered space. Fine-tuned BERT improves contextual retrieval from $c$0 to $c$1 on average across five language pairs and improves zero-shot XNLI from $c$2 to $c$3. The analysis identifies systematic deficiencies in the original mBERT, including weaker alignment for open-class words, different scripts, and frequency-mismatched translation pairs.

Sense-level alignment

Averaging contextual occurrences can obscure polysemy. “Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings” replaces a single output vector for each vocabulary word with multiple sense vectors. An online clustering mechanism assigns each token occurrence to a sense, while a bilingual dictionary supplies cross-lingual supervision for selecting compatible translation senses (Liu et al., 2021).

The sense-aware objective normalizes over word-sense pairs:

$c$4

When a dictionary translation exists, the same contextual representation is trained to predict both its source-language sense and the closest sense of a translation. The method improves ELMo-based word-sense disambiguation on all five reported datasets and improves zero-shot NER, sentiment classification, and XNLI. Its defining property is that alignment operates over contextually selected latent senses rather than over averaged word representations.

3. Structured and multimodal contextual alignment

Vision–language alignment

Standard CLIP performs global contrastive alignment between complete image and caption embeddings. ContextCLIP retains this objective and adds a contextual loss over projected image and text features. ResNet-50 image features and BERT text features are projected into a shared $c$5-dimensional space. The contextual objective computes local affinities, normalizes them according to candidate neighborhoods, and rewards nearest-neighbor correspondences between modalities (Grover et al., 2022).

The combined objective is

$c$6

with $c$7 in the reported implementation. ContextCLIP improves zero-shot top-1 accuracy over CLIP on CIFAR-10, CIFAR-100, Birdsnap, and MNIST, but decreases performance on RESISC45. Fine-tuning improves top-1 accuracy on all five reported datasets. The evidence is qualified by the absence of numerical retrieval metrics and by the relatively small pretraining regime.

ContextBLIP addresses image retrieval from linguistically complex descriptions, where the correct image must be selected from minimally contrastive candidates. It distinguishes:

• Intra-contextual alignment: alignment of textual cues with fine-grained patches within an individual image.
• Inter-contextual alignment: comparative reasoning over the candidate set.

The intra-context mechanism uses a multi-scale adapter inserted at ViT layers $c$8, image-text matching loss, and text-guided masked image modeling. The inter-context mechanism applies a two-layer Transformer across candidate-specific multimodal representations (Lin et al., 2024). ContextBLIP obtains $c$9 zero-shot accuracy on the aggregate IMAGECODE setting and $e_{i,c}$0 after fine-tuning, with approximately $e_{i,c}$1 million additional parameters over BLIP. Its fine-tuned static-image accuracy is $e_{i,c}$2, compared with $e_{i,c}$3 for NDCR.

Audio-visual and speech-token alignment

The supplied material for “CAD -- Contextual Multi-modal Alignment for Dynamic AVQA” is an author-rebuttal LaTeX template rather than the CAD paper itself. It contains no model architecture, equations, datasets, ablations, or experimental results for the proposed spatial, temporal, and semantic alignment mechanisms. Claims concerning the CAD network therefore cannot be reconstructed from that source (Nadeem et al., 2023).

FuseCodec aligns acoustic, semantic, and contextual representations in neural speech codecs. It uses trainable acoustic encoder latents, frozen HuBERT semantic representations, and frozen BERT representations computed from ASR transcripts (Ahasan et al., 14 Sep 2025). The three principal mechanisms are:

• Latent Representation Fusion: semantic and contextual representations are projected into the codec dimension, cross-attended, broadcast over codec time, and added to the encoder latent before residual vector quantization.
• Global Semantic-Contextual Supervision: the first RVQ layer is projected into the teacher representation space and trained to have high cosine similarity with globally pooled semantic and contextual vectors.
• Temporally Aligned Contextual Supervision: contextual text tokens are matched to codec tokens within local monotonic windows, producing token-level supervision.

The contextual alignment loss is logistic cosine-similarity supervision rather than contrastive learning. FuseCodec-Fusion obtains WER $e_{i,c}$4, WIL $e_{i,c}$5, STOI $e_{i,c}$6, ViSQOL $e_{i,c}$7, and PESQ $e_{i,c}$8 on the reported LibriSpeech reconstruction evaluation. First-layer supervision outperforms all-layer supervision, supporting a division of labor in which the first codebook represents linguistic content while deeper codebooks preserve residual acoustic detail.

Time-series and language contexts

Context-Alignment for time series treats adaptation to an LLM as a context-construction problem rather than simple embedding projection. Time-series patches and language tokens are organized in a Dual-Scale Context-Alignment GNN, or DSCA-GNN, containing a fine-grained token graph and a coarse modality-level graph (Hu et al., 7 Jan 2025).

Structural alignment is supplied by dual-scale nodes: individual patches preserve local temporal information, while coarse nodes represent the complete time series and language instruction. Logical alignment is supplied by directed edges, such as

$e_{i,c}$9

indicating that historical time-series data informs the language instruction. Demonstration Examples based Context-Alignment, or DECA, extends this structure to temporally ordered forecasting examples, adding edges from preceding time-series segments to prompts and from prompts to predicted segments.

The graph-refined representations are inserted into GPT-2 layers, and task losses train the system. DECA reports average long-term forecasting MSE $\bar e_i=\mathbb{E}_c[e_{i,c}],$0 lower than PatchTST, $\bar e_i=\mathbb{E}_c[e_{i,c}],$1 lower than $\bar e_i=\mathbb{E}_c[e_{i,c}],$2IP-LLM, $\bar e_i=\mathbb{E}_c[e_{i,c}],$3 lower than Time-LLM, and $\bar e_i=\mathbb{E}_c[e_{i,c}],$4 lower than GPT4TS. Its average zero-shot forecasting MSE is $\bar e_i=\mathbb{E}_c[e_{i,c}],$5, and average UEA classification accuracy is $\bar e_i=\mathbb{E}_c[e_{i,c}],$6. Removing the coarse graph or replacing the prescribed logical graph with random adjacency degrades performance.

4. Contextual alignment in agents and language-model behavior

Context-specific regulation

Alignment Studio distinguishes provider-level alignment from alignment to the regulations of a particular organization, application, jurisdiction, or community. Its architecture contains:

1. Framers, who convert policy documents into structured knowledge, instruction data, scenario data, ontologies, and evaluation cases.
2. Instructors, who use supervised fine-tuning, RLFT, reward models, preference data, and parameter-efficient methods such as LoRA and QLoRA.
3. Auditors, who evaluate policy compliance, faithfulness, completeness, helpfulness, ambiguity handling, and adversarial robustness (Achintalwar et al., 2024).

The architecture is intended for policies such as laws, business conduct guidelines, industry standards, and corporate rules. It does not specify a universal priority ordering, formal deontic logic, or mandatory conflict-arbitration algorithm. Its contribution is a governance-oriented workflow: policy formalization, behavior training, comparative auditing, red-teaming, and iterative remediation.

Contextual Moral Value Alignment through Context-Based Aggregation, or CMVA-GS, operationalizes contextual value alignment using multiple independently trained moral agents and a contextual aggregator (Dognin et al., 2024). Five agents are trained for care, fairness, loyalty, authority, and sanctity. Given candidate responses $\bar e_i=\mathbb{E}_c[e_{i,c}],$7 and a moral profile vector $\bar e_i=\mathbb{E}_c[e_{i,c}],$8, the aggregator produces:

$\bar e_i=\mathbb{E}_c[e_{i,c}],$9

The aggregator is trained with token-level cross-entropy against a human response. Its context is primarily the supplied moral profile and the value-conditioned responses; the paper does not specify an autonomous mechanism for inferring the profile from arbitrary raw text. On the Moral Integrity Corpus, CMVA-GS obtains the highest reported ROUGE scores among the compared systems, although exact numerical values are not provided in the supplied material.

MCF-CVA extends this idea through multilayer combinatorial fusion. Five value-specific agents generate responses, which are decomposed into moral units. Candidate unit pairs are scored by five value-specific scoring systems, then combined through average and diversity-weighted score and rank operations. Expansion-and-Reduction repeatedly generates $e_{i,c}=\bar e_i+\hat e_{i,c}.$0 fusion systems from $e_{i,c}=\bar e_i+\hat e_{i,c}.$1 current systems and retains the $e_{i,c}=\bar e_i+\hat e_{i,c}.$2 systems with greatest diversity strength. The process stops when maximum diversity falls below $e_{i,c}=\bar e_i+\hat e_{i,c}.$3 or six layers are reached (Wu et al., 7 Aug 2026).

On the Moral Integrity Corpus, multilayer MCF-CVA obtains F1 BERTScore $e_{i,c}=\bar e_i+\hat e_{i,c}.$4, compared with $e_{i,c}=\bar e_i+\hat e_{i,c}.$5 for the single-layer variant and $e_{i,c}=\bar e_i+\hat e_{i,c}.$6 for VAS-CFA. On Commonsense Morality classification, its reported Layer 3 accuracy is $e_{i,c}=\bar e_i+\hat e_{i,c}.$7. Cognitive diversity is measured through distances between rank-score functions, but high diversity is not guaranteed to represent morally valuable disagreement; it may also preserve erroneous or biased perspectives.

Dialogue common ground

The Context Alignment Pre-processor, or C.A.P., places contextual alignment before generation rather than modifying the LLM during decoding. It performs semantic expansion, time-weighted history retrieval, alignment verification, and decision branching (Wei, 17 Mar 2026).

A current request $e_{i,c}=\bar e_i+\hat e_{i,c}.$8 is expanded into

$e_{i,c}=\bar e_i+\hat e_{i,c}.$9

representing prerequisites, literal meaning, and implications. Retrieved dialogue history receives temporal weights, and an alignment score compares the expanded request with historical context. If the score is sufficient, the request is passed to generation; otherwise, a clarification protocol repeats the request, identifies a possible topic shift, and asks whether the user intends a new task or a continuation.

C.A.P. is grounded in Common Ground theory and is presented as a proposed architecture rather than an empirically validated system. The supplied paper reports no completed implementation, dataset, benchmark result, threshold calibration, or ablation study. Its importance lies in making uncertainty about conversational continuity explicit and giving the system a mechanism for clarification before response generation.

Niche-domain question answering

CANDI-QA defines contextual alignment as the ability to adapt answers to a user’s domain, institutional role, expertise, topic, and situational details. Its MTSS(B) benchmark distinguishes:

• T1 Information Assistance Questions, requiring factual extraction.
• T2 Applied Inference Questions, requiring situational reasoning and actionable recommendations.

The dataset contains 315 questions: 90 T1 and 225 T2, involving roles such as administrator, MTSS coach, counselor, clinician, psychologist, nurse, and teacher (Chakraborty et al., 6 May 2026). Prompt conditions progressively add role information and task objectives. T1 evaluations use cosine similarity, BERTScore, MoverScore, and BLEURT. T2 evaluations use RAGAs-style context precision, response relevancy, faithfulness, and semantic similarity, with Qwen-generated responses serving as retrieved contexts.

Role-aware prompting generally improves T1 performance, while task-aware prompting produces stronger T2 gains. For example, Mistral 7B’s T2 faithfulness increases from $\hat e_{i,c}$0 to $\hat e_{i,c}$1 and semantic similarity from $\hat e_{i,c}$2 to $\hat e_{i,c}$3 between P1 and P3. The benchmark is limited by its narrow MTSS(B) domain, lack of a documented train/dev/test split, reliance on model-generated pseudo-references, and absence of direct quantitative results for the proposed MTSS-Net neuro-symbolic baseline.

5. Privacy, personalization, and disclosure boundaries

Agentic privacy

PrivacyAlign treats privacy as contextual information flow rather than string-level sensitivity detection. The acceptability of disclosure depends on the data subject, sender, recipient, relationship, legitimate need, purpose, granularity, channel, tool, and user instruction (Tamber et al., 19 Jun 2026).

The dataset contains 1,350 response-pair scenarios, 3,516 retained annotations from 599 annotators, 26 final-action tools, and 48 toolkits. Annotators label leaks, omissions, overall preferences, and explanations. A leak is disclosure of sensitive information that should remain private from the recipient; an omission is failure to include relevant, helpful, non-sensitive information.

PrivacyAlign conditions LLM judges on human annotations for other responses in the same scenario. This improves mean inter-judge agreement for leak judgments from Cohen’s $\hat e_{i,c}$4 to $\hat e_{i,c}$5, and for omission judgments from $\hat e_{i,c}$6 to $\hat e_{i,c}$7. Annotation-conditioned reward modeling improves clean rates over the tested baselines for Qwen3-4B, Qwen3-8B, and Nemotron-3-Nano-4B. The approach explicitly balances two failure modes: oversharing and excessive withholding.

Personalized disclosure preferences

CIDER addresses personalized contextual alignment rather than only population-level privacy norms. It contains 169 users, 14,850 annotations, 1,650 boundary sets, 60 interpersonal scenarios, and nine disclosure variants per scenario (Guo et al., 10 Aug 2026).

Each boundary is a binary vector over combinations of granularity and identifiability:

$\hat e_{i,c}$8

where $\hat e_{i,c}$9 is a latent user preference and $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$0 is the communication context. The context includes the scenario, communication role, and whether sharing is human-mediated or AI-mediated. Roles are sender, data subject, and recipient.

Personalization is performed at inference time by providing historical boundaries:

$$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$1

The model predicts a new boundary $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$2 from the history and target context. Six historical examples improve prediction accuracy by up to $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$3 percentage points. GPT-5.4 reaches $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$4 and Claude Sonnet 4.6 $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$5 in the reported six-example, semantic-history condition. However, personalization often shifts false-positive and false-negative rates in opposite directions. Claude Sonnet 4.6 is the only reported model that reduces both error types across all nine variants.

CIDER demonstrates that disclosure boundaries are neither universal nor purely structural. Acceptance decreases with granularity and identifiability, but users differ substantially, and within-user boundaries vary across contexts. The dataset therefore frames contextual alignment as a combination of semantic understanding, user-history inference, fine-grained disclosure control, and asymmetric error management.

6. Evaluation, limitations, and open problems

Measuring contextual alignment

Contextual alignment cannot be adequately measured by a single similarity or accuracy score. Depending on the application, evaluation may require:

• Representation metrics: contextual retrieval, translation accuracy, neighborhood preservation, semantic similarity, or task-specific transfer.
• Behavioral metrics: policy compliance, professional appropriateness, leak rate, omission rate, clean rate, or action-level correctness.
• Context sensitivity metrics: performance under changed roles, domains, stakes, moral profiles, or communication channels.
• Invariance metrics: consistency under changes that do not alter the governing rule.
• Human-centered metrics: expert judgments, user satisfaction, perceived understanding, or agreement with individualized preferences.
• Error asymmetry metrics: false positives and false negatives when over-disclosure and over-refusal have different costs.

ContextAdapt illustrates the importance of evaluating both action and justification. Models achieve $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$6 mean appropriateness across professional scenarios, but correct domain-specific justifications range from $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$7 to $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$8 (Macmillan-Scott et al., 29 Sep 2026). This gap indicates that correct behavior does not guarantee correct normative grounding.

The same benchmark finds that explicit domain naming and role changes have limited effects, while changing stakes produces localized failures even when the underlying professional obligation remains unchanged. The largest reported stakes gap is $$e_{i,c}^{s\rightarrow t}=W^{s\rightarrow t}e_{i,c}^{s}.$$9 percentage points. This suggests that models may use severity as a heuristic for disclosure even when severity should not change the professional rule.

Common methodological limitations

Several limitations recur across contextual-alignment research.

Context ambiguity: contextual variables may be incompletely observed, inconsistently defined, or inferred incorrectly. In C.A.P., semantic expansion may invent premises or implications. In CANDI-QA, role and tier may be explicit but the correct action remains under-specified.

Proxy evaluation: embedding similarity, BERTScore, ROUGE, BLEURT, and model-generated references measure aspects of correspondence but do not guarantee factual correctness, safety, or normative legitimacy. CANDI-QA’s use of Qwen-generated contexts and PrivacyAlign’s reliance on model-defined external benchmark labels illustrate this concern.

Context collapse: global pooling or averaging may discard local distinctions. FuseCodec’s global supervision provides stable semantic guidance but lacks explicit one-to-one temporal alignment, while anchor-based cross-lingual methods may obscure fine-grained sense differences.

Overfitting to structural heuristics: smaller models may infer simple rules such as “more identifiable means reject” or “trusted sender means accept” without understanding the underlying interpersonal situation. CIDER’s comparison of semantic history and label-only history exposes this behavior.

Nonstationarity: mobility, changing channels, evolving policies, new users, and shifting preferences violate stationary assumptions. The mmWave bandit model assumes time-invariant success probabilities during the learning period (Hashemi et al., 2017). Alignment Studio and C.A.P. therefore emphasize continuous auditing, clarification, and updating, but do not provide universal guarantees.

Conflict resolution: many systems represent multiple values or policies without specifying how conflicts are formally resolved. Alignment Studio leaves reward aggregation conceptual; CMVA-GS conditions aggregation on a moral profile but does not specify an explicit agent-weighting function; MCF-CVA preserves diversity but does not prove that high diversity corresponds to ethically desirable disagreement.

Scalability: contextual structures can increase computation. MCF-CVA has exponential expansion $77.3$0 in the number of scoring systems, while candidate-set Transformers increase with the number of image candidates. Privacy personalization requires storing and processing sensitive histories, and contextual dialogue preprocessing introduces additional latency.

Research directions

The combined literature suggests several open directions. Contextual alignment requires models that can distinguish normatively relevant context from salient but irrelevant variation; represent both local and global structure; preserve uncertainty and disagreement; and support user or institutional correction. Evaluation should combine behavioral outcomes with explanations, provenance, calibration, and asymmetric harm measures.

For representation learning, unresolved issues include how to preserve contextual geometry under nonlinear transformations, how to align multimodal or multilingual senses without excessive averaging, and how to guarantee compatibility between modified embeddings and frozen downstream layers. For agents, important problems include conflict arbitration, dynamic preference updating, local storage of personalized histories, privacy-preserving personalization, human escalation, and robustness to distribution shift.

The central methodological principle is that alignment should be evaluated relative to the information that actually determines appropriateness. A model is not contextually aligned merely because it retrieves a related vector, follows a broad safety rule, or produces a semantically similar answer. It must adapt when the governing context changes, remain invariant when irrelevant features change, preserve useful information while respecting constraints, and expose sufficient evidence for its behavior to be audited.