Design: Adaptive Linking

This article describes the algorithm currently implemented by pairwiseLLM for placing items from separately ranked sets onto one common scale. For a runnable, wrapper-first tutorial, begin with Guide: Adaptive Linking. The within-set algorithm that produces the required inputs is documented separately in Design: Adaptive Pairing.

The public entry point is adaptive_rank() with run_mode = "link_one_spoke" or run_mode = "link_multi_spoke". Linking has two phases:

1. Phase A runs or imports a completed within-set analysis for every set.
2. Phase B selects comparisons between a designated hub and one or more spokes, then estimates spoke items on the fixed hub scale.

The current linking contract uses anchored-joint estimation and globally shared judge parameters. It also uses a hard-locked hub, concurrent spokes, and a fixed probe effort per refit.

Foundational concepts

Pairwise comparative judgment (CJ) asks a judge to choose between two items. A Bradley–Terry–Luce (BTL) model represents each item by a latent location: the modeled probability of choosing one item over another depends on their location difference. The scale is latent because it has no external unit. Within-set comparisons identify relative locations inside one connected set, but two separately fitted sets can have unrelated origins and therefore cannot be compared directly.

Linking collects comparisons across sets to put their locations onto a common scale. The implemented design is a hub-and-spoke system:

• the hub is the reference set whose Phase A locations define the common scale;
• a spoke is any other set whose items are estimated on that scale;
• a hub anchor is a hub item selected from a region of the current ranking for recurring cross-set comparisons; it is not a known standard or a fixed true score;
• an active pair is a hub–spoke pair selected to update the linking fit; and
• a probe pair is a reserved hub–spoke pair used to assess predictions and stopping.

The Phase A Bayesian fits use Markov chain Monte Carlo (MCMC) draws. An expected a posteriori (EAP) estimate is the arithmetic mean of an item’s retained posterior draws, and its posterior standard deviation (SD) summarizes model-based uncertainty. Effective sample size (ESS) and split rank-normalized $\widehat R$, usually called R-hat, diagnose MCMC behavior. Phase B uses maximum a posteriori (MAP) optimization for the spoke locations and a Laplace approximation based on the local Hessian matrix for uncertainty. MCMC diagnostics therefore apply to Phase A fits; Phase B has a separate deterministic-fit diagnostic contract.

Glossary and units

Term	Meaning
set	A collection ranked internally during Phase A and identified by set_id.
artifact	The canonical, validated Phase A record for one set.
hub scale	The numerical scale defined by the hub’s fixed Phase A item locations.
spoke epoch	A period during which one spoke’s linking contract and probe panel remain unchanged.
linking refit	A Phase B update using Phase A evidence and committed active cross-set evidence.
refit window	The comparisons allocated between two linking refits.
routing stratum	A rank region used to form anchor, long, mid, and local candidate domains.
Fisher information	Local curvature measuring how strongly a modeled observation informs free parameters.
D-optimal gain	Increase in the regularized log determinant of accumulated Fisher information.
frozen spoke	A spoke that passed the implemented rolling stop rule and receives no further pairs.
blocker	A named unmet condition recorded in stop_blocker_codes.

The unit of selection is an unordered hub–spoke pair. The unit of judgment is its ordered presentation as item $A$ and item $B$. Active comparison counts, probe comparison counts, and attempted steps are distinct. An invalid judgment consumes an attempted step but is not committed to either evidence stream.

Algorithm overview

validate the combined item table and linking configuration
  -> Phase A: import or run each required within-set analysis
  -> validate and freeze the canonical artifacts
  -> pool Phase A evidence to estimate global judge parameters
  -> initialize every ready spoke on the fixed hub scale
  -> while at least one spoke remains active:
       reserve or restore that spoke's probe panel
       allocate active and probe effort for the refit window
       select active pairs by routing domain and D-optimal gain
       collect no more than the fixed per-refit probe cap
       refit every eligible spoke, potentially in parallel
       compute diagnostics, calibration, stability, and blockers
       freeze each spoke whose rolling stop rule passes
  -> return common-scale item summaries and canonical logs

One controller advances all active spokes. “Concurrent” means that their work is interleaved and their eligible refits can be dispatched together; a single judgment still compares one hub item with one item from exactly one spoke. Spoke–spoke comparisons are not part of the public design.

Phase A artifacts and validation

Phase A produces the within-set evidence needed before any cross-set comparison is scheduled. A canonical artifact stores:

• set_id, n_items, n_pairs_committed, and the source refit, round, step, and phase scope;
• an item table containing item_id, global_item_id, theta_raw_mean, theta_raw_sd, and rank_mu_raw;
• the normalized BTL model identifier, its fit-contract surface, and fit_config_hash;
• within-set reliability, rank agreement, and diagnostic status;
• the exact committed within-set edge table and its content hash;
• posterior item draws when available; and
• judge-parameter mode and creation time.

item_id is the row identity used by the adaptive state. global_item_id is the stable identity used to validate an item across separately produced inputs. Both mappings, the item count, all required summaries, and the exact within-set edge domain must agree with the current run.

The fit-contract hash covers the normalized model_variant, judge-parameter mode, and relevant Bayesian fitting settings. A btl artifact is therefore not silently converted to btl_e_b, and an artifact produced under an incompatible configuration is rejected. This protects the likelihood and prior assumptions used by the linking fit; it is stricter than checking that an .rds file can be read.

Run, import, and mixed behavior

phase_a_mode assigns a source to every required set:

Mode	Source assignment	Failure behavior
run	Run Phase A for every set, in ascending set_id order.	Unfinished sets remain pending_finalization.
import	Import every set from phase_a_artifacts.	A missing or incompatible artifact fails before Phase B.
mixed	Import supplied sets; run the rest.	Imports are strict; unfinished runs remain pending.

The hub must be ready before a spoke can enter Phase B. In a multi-spoke run, all required Phase A work is completed before Phase B begins. out$phase_a$set_status records source, status, and validation_message; ready, pending_finalization, and failed describe artifact readiness, not the later linking result.

An imported artifact normally needs finite EAP reliability at or above phase_a_required_reliability_min. quality_gate_accepted = TRUE is a trusted external-review override for missing or low reliability. It does not change the evidence, repair MCMC diagnostics, or make an incompatible model variant compatible.

Common-scale identification

Within a BTL likelihood, adding the same constant to every item location leaves all pairwise differences unchanged. Independently fitted sets therefore do not share an origin. The linking model resolves this location indeterminacy by treating the hub’s Phase A locations as fixed constants and estimating spoke locations directly against them.

Let $h\in\mathcal H$ index hub items and $x\in\mathcal S_j$ index items in spoke $j$. The implementation stores the fixed hub values as theta_hub_fixed and the estimated spoke values as theta_spoke_global_mean. For an ordered cross-set comparison $k$, define

• $A_k$ and $B_k$ as the first- and second-presented items;
• $Y_k=1$ when $A_k$ is selected and $Y_k=0$ when $B_k$ is selected;
• $\theta_{A_k}$ and $\theta_{B_k}$ as their hub-scale locations;
• $b$ as the globally shared first-position effect; and
• $\epsilon$ as the globally shared lapse probability.

The observation model is

$$q_k=\operatorname{logit}^{-1} (\theta_{A_k}-\theta_{B_k}+b), \qquad p_k=(1-\epsilon)q_k+\frac{\epsilon}{2}, \qquad Y_k\sim\operatorname{Bernoulli}(p_k).$$

Here plogis() implements the inverse-logit function. Positive $b$ raises the modeled probability of choosing the first-presented item, conditional on the location difference. The lapse mixture shrinks predictions toward one half. These are model parameters, not measures of forward/reverse response consistency.

Hard-locked hub and spoke priors

For each hub item, Phase B fixes

$$\theta_h=\widehat\theta^{A}_h,$$

where $\widehat\theta^{A}_h$ is theta_raw_mean in the hub artifact. Hub parameters are absent from the free optimization block; they are not merely assigned narrow priors. Consequently Phase B cannot move the hub to accommodate a spoke.

For spoke item $x$, let $m_x^A$ and $s_x^A$ denote the Phase A theta_raw_mean and theta_raw_sd. The Phase B prior is

$$\theta_x\sim\mathcal N(m_x^A,\tau_x^2), \qquad \tau_x=c\max(s_x^A,s_{\min}),$$

where $c$ is anchored_joint_spoke_prior_scale and $s_{\min}$ is anchored_joint_sd_floor. If $s_x^A$ is non-finite, the implementation substitutes anchored_joint_spoke_prior_fallback_sd and emits a warning. The prior carries Phase A location and uncertainty into Phase B; active cross-set evidence and the spoke’s exact Phase A within-set edges update the spoke locations.

The implementation minimizes the negative log posterior over the vector $\boldsymbol\theta_j=(\theta_x:x\in\mathcal S_j)$ using Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization. When optimization converges, it inverts the observed Hessian to obtain a Laplace covariance approximation. theta_link_eap is the accepted common-scale location exposed to users, although the deterministic Phase B value is a MAP estimate rather than an MCMC posterior mean. theta_link_sd is the corresponding marginal Hessian-based SD. The established output name is retained for schema compatibility.

Globally shared judge parameters

Before Phase B, the runner pools the exact Phase A within-set evidence from every required set and fits one BTL judge surface. The resulting position effect $b$ and lapse probability $\epsilon$ are copied into every spoke’s accepted state and held fixed during anchored-joint refits. Thus the item locations are spoke-specific but the judge parameters are global across the hub and all spokes. This avoids attributing a separate position or lapse process to each spoke when the same judge and observation contract produced all comparisons.

Routing ranks, anchors, and candidate domains

Phase B constructs a linking-global score map from the fixed hub locations and the latest accepted spoke locations. Decreasing score, with item identifier breaking ties, supplies the global rank used for routing. The same stratum construction as within-set ranking divides that order into a fine top band and size-dependent base regions.

Hub anchors are selected from hub-only ranks. Non-anchor stages use the active hub domain; the anchor stage may use the full hub domain. Every Phase B candidate has exactly one hub endpoint and one endpoint from the currently routed spoke.

Stage	Candidate domain	Design role
anchor_link (anchor link)	Hub anchor plus spoke item.	Revisit reference regions across the hub distribution.
long_link (long link)	Active endpoints far apart.	Maintain broad links and coarse ordering checks.
mid_link (mid link)	Active endpoints at intermediate distance.	Bridge broad and neighboring score regions.
local_link (local link)	Active endpoints in nearby strata.	Resolve common-scale ordering locally.

The stage label narrows the eligible domain; it is not a separate statistical model. Reserved probe keys are removed from every active domain. Duplicate, recent-degree, exposure, and presentation-order rules are then applied. When a domain is larger than C_max, the controller uses a seeded bounded sample, preserving reproducibility for a fixed state and seed.

Spoke coverage uses quantile bins of current linking-global spoke scores. A candidate involving an under-covered bin receives coverage_priority = 1; if any such candidates exist, selection is restricted to that subset before utility ordering. This is a discrete coverage rule, not a numeric addition to D-optimal gain.

D-optimal active selection

The active selector asks which eligible pair would produce the largest local increase in information about the free spoke parameters. For a candidate hub item $h$ and spoke item $x$, it averages the two presentation orientations,

$$\bar p_{hx}=\frac{1}{2}p(h\text{ in }A,x\text{ in }B) +\frac{1}{2}p(x\text{ in }A,h\text{ in }B),$$

and uses the implemented information scale

$$w_{hx}=\bar p_{hx}(1-\bar p_{hx}).$$

Let $\mathbf e_x$ be the vector with one at spoke item $x$’s position in the free block and zero elsewhere. The candidate’s rank-one information contribution is

$$\mathbf I_{hx}=w_{hx}\mathbf e_x\mathbf e_x^\mathsf T.$$

Let $\mathbf I_t$ be the information accumulated from committed active pairs for the spoke in the current refit window. With numerical ridge $r=10^{-6}$, the stored D-optimal gain is

$$U_{hx}=\log\det(\mathbf I_t+\mathbf I_{hx}+r\mathbf I) -\log\det(\mathbf I_t+r\mathbf I),$$

where $\mathbf I$ is the identity matrix. The code uses the equivalent rank-one log1p() calculation when possible and falls back to direct log determinants. Because each cross-set observation has only one free spoke endpoint, the anchored-joint information state can be stored as a diagonal vector. It is updated only after a valid active comparison; probes and invalid attempts do not enter it.

Within the coverage-prioritized pool, candidates are ordered by decreasing link_d_opt_gain, then lexicographically by i and j. An unavailable or entirely non-finite gain aborts selection. The selector does not silently replace D-optimal ordering with TrueSkill ambiguity or random choice.

Conceptual selection example

Suppose two eligible pairs address spoke items $x_1$ and $x_2$. Current accumulated diagonal information is $(4,1)$, so $x_2$ is less informed. If both pairs have $\bar p=0.5$, then $w=0.25$. Ignoring the tiny ridge for illustration, their gains are approximately

$$\log(1+0.25/4)=0.061, \qquad \log(1+0.25/1)=0.223.$$

The pair involving $x_2$ ranks first because it increases the information determinant more. In a real step, candidate strata, reserved probes, coverage priority, duplicate controls, and all other filters are applied before this comparison of gains.

Probes and calibration

At the beginning of a spoke epoch, the controller constructs a deterministic held-out panel across hub and spoke rank bins. probe_panel_edges is a target, not a promise: the realized panel can be smaller when the unique legal cross-product is too small. probe_panel_shortfall and probe_shortfall_reason make that limitation visible.

Probe keys are reserved from active selection for the epoch. While a spoke is active, the runner may collect at most probe_pairs_per_refit_per_spoke held-out pairs per refit window. The public path uses probe_acceleration_mode = "fixed_per_refit"; a probe-only blocker does not increase that cap. The active-floor logic protects a minimum amount of active work in a window, so probe collection cannot consume the whole linking budget. A frozen spoke receives neither active nor probe pairs.

For realized probe outcomes $y_k\in\{0,1\}$ and predictions $p_k$, the Brier score is

$$\operatorname{Brier}=\frac{1}{K}\sum_{k=1}^{K}(y_k-p_k)^2,$$

where $K$ is the number of valid realized probe edges. Lower values indicate better squared probability error on this panel. Expected calibration error (ECE) partitions predictions into five probability bins and computes the observation-weighted mean absolute difference between bin-average outcomes and predictions. ECE is descriptive and depends on the realized panel and binning.

The runner also records:

• fractions of predictions near 0.5, in a midrange, and at extremes;
• unique hub and spoke endpoints represented;
• hub and spoke rank bins represented;
• the near-boundary Brier score;
• lagged probe-prediction root mean squared error (RMSE); and
• panel size, realization, reallocation, and shortfall fields.

These form probe_quality_pass and probe_quality_blocker_codes, a diagnostic assessment of panel composition and calibration. In the current stop implementation, probe_quality_pass itself is not included in link_stop_pass. The stopping conjunction instead uses the total Brier threshold, minimum realized probe edges, and lagged probe-prediction RMSE described below. Users should still inspect probe-quality blockers before interpreting a linked result.

Refits, diagnostics, and identification

Each spoke has its own accepted item state, active-edge count, probe panel, accumulated information, diagnostics, and stop history. At a Phase B refit, the anchored-joint fit uses the spoke’s exact Phase A within-set edges plus all valid active hub–spoke edges retained for that spoke. Hub within-set evidence supports artifact validation and pooled judge estimation; fixed hub locations mean those edges do not add free hub parameters to the per-spoke optimization.

The Phase B deterministic diagnostic gate requires all of the following:

• BFGS optimization reports convergence;
• all required fitted summaries are finite;
• the Hessian is positive definite; and
• finite non-negative marginal uncertainty is available.

link_diagnostics_converged_pass, link_diagnostics_finite_summary_pass, and link_diagnostics_uncertainty_pass expose these checks. Divergence, R-hat, and ESS columns are R missing values (NA) for MAP/Laplace Phase B fits because those are MCMC diagnostics. A failed deterministic fit does not masquerade as a successful MCMC fit.

Reliability and routing identification

For active hub and spoke items, let $\widehat\mu_i$ be the accepted common-scale mean and $v_i$ its estimated marginal posterior variance. Define

$$V_\mu=\operatorname{Var}_i(\widehat\mu_i), \qquad \overline V_{\mathrm{post}}=\frac{1}{n}\sum_i v_i, \qquad R_{\mathrm{link}}=\frac{V_\mu}{V_\mu+\overline V_{\mathrm{post}}}.$$

The implementation applies small denominator guards and returns an undefined value when the decomposition is not numerically meaningful. reliability_link_global is conditional on the model, prior, active item domain, and marginal approximation. It is not classical test-score reliability and does not establish validity or independence of judgments.

linking_identified becomes true for a spoke when both

$$R_{\mathrm{link}}\geq \texttt{link_identified_reliability_min} \quad\text{and}\quad \rho_{\mathrm{rank}}(\mu^{\mathrm{TrueSkill}},\widehat\theta^{\mathrm{link}}) \geq\texttt{link_rank_corr_min},$$

where $\rho_{\mathrm{rank}}$ is Spearman rank correlation and $\mu^{\mathrm{TrueSkill}}$ is the current TrueSkill mean. This flag changes routing, including long-link tapering. It is not formal proof of statistical identification.

Budgets and concurrent spokes

The default active refit budget derives from the adaptive refit target. Stage floors are applied first, the remainder is divided by the configured anchor/long/mid/local fractions, and deterministic largest-remainder rules resolve integers. When linking_identified is true, the long target is multiplied by long_link_taper_multiplier subject to long_link_taper_floor; released work is redistributed according to current blocker weights.

Feasibility checks can shrink a stage target when its legal domain cannot fill the nominal quota. The log distinguishes nominal budget, feasible budget, released work, and stage_budget_unfilled. A shortfall does not authorize illegal duplicates or spoke–spoke pairs.

In link_multi_spoke mode, every active spoke first receives min_cross_set_pairs_per_spoke_per_refit when capacity permits. Remaining active comparisons are allocated in proportion to the sum of the top D-optimal gains for that spoke, using at most multi_spoke_budget_top_k candidates. If all utility masses are zero, allocation uses equal weights. Candidate capacity caps the allocation. This is concurrent utility-based scheduling, not parallel judgments and not a guarantee of equal comparisons per spoke.

The Bayesian Phase A and Phase B refit dispatcher may use future workers when parallel refitting is configured and available. Results are restored in deterministic spoke order. Parallel execution does not change the evidence or sharing contract.

Stopping, blockers, and freezing

Stopping is evaluated at linking refits, separately for each spoke. A row becomes link_stop_eligible only when:

• a comparable lagged refit exists in the same spoke epoch;
• min_refits_in_phase_b has been reached;
• the deterministic link diagnostics pass;
• common-scale reliability is defined; and
• at least probe_edges_min_for_stop held-out probes have been realized.

An eligible row passes the instantaneous stopping conjunction when:

$$\begin{aligned} &R_{\mathrm{link}}\geq R_{\min},\\ &\operatorname{Brier}\leq B_{\max},\\ &\operatorname{RMSE}_{\mathrm{probe,lag}}\leq P_{\max},\\ &\operatorname{RMSE}_{\theta,\mathrm{lag}}\leq T_{\max},\\ &\text{hub anchored}=\texttt{TRUE}. \end{aligned}$$

Here $R_{\min}$, $B_{\max}$, $P_{\max}$, and $T_{\max}$ are the logged *_used thresholds. Probe RMSE compares current and lagged predicted probabilities on the realized probe panel. Theta RMSE compares current and lagged common-scale locations over the configured direct-evidence spoke scope. These stability statistics measure change between fitted states, not agreement with an external truth.

Only eligible results enter a rolling window of stability_window_refits. link_stop_pass becomes true when the window is full and at least stability_passes_required entries passed. The passes need not be consecutive. A passing spoke is frozen once: link_state_frozen = TRUE and link_state_frozen_refit_id record the transition. With max_pairs_after_stop = 0, no later pair is scheduled for it. Other spokes continue until they independently freeze, starve, or the caller’s attempted-step budget ends.

stop_blocker_codes is a comma-separated audit summary. Current values include:

Code	Meaning
diagnostics_failed	The applicable link-fit diagnostic contract did not pass.
lag_not_eligible	No comparable lagged refit exists in the current epoch.
min_refits_not_met	The spoke has not reached the minimum Phase B refit index.
probe_edges_min_for_stop	Too few held-out probe edges have been realized.
reliability_link_global	Reliability is undefined or below its stopping threshold.
probe_brier	Probe Brier score is unavailable or above its threshold.
probe_pred_rmse_lagged	Lagged probe prediction RMSE is unavailable or above threshold.
theta_global_rmse_lagged	Lagged common-scale theta RMSE is unavailable or above threshold.
hub_not_anchored	The required hub-anchor invariant is not satisfied.
none	No blocker in this stop conjunction is active.

Reaching n_steps only exhausts the attempted-step allowance for that call. It does not set link_stop_pass, invent an accepted fit, or imply that every spoke is ready. Invalid judgments can consume this allowance without increasing active or probe evidence. Candidate starvation and unfilled budgets remain visible in the step and link-stage logs.

Code-derived defaults

Fixed contract values and size-scaled rules are resolved in the controller. The following tables are generated from the implementation for a representative run with one 100-item hub and two 100-item spokes. Contract fields in the first table describe the current normative path and are not additional public choices; documented public controls can override the numeric thresholds.

contract_or_control	default
link_estimation_mode	anchored_joint
judge_param_mode	global_shared
link_identified_reliability_min	0.8
link_stop_reliability_min	0.9
link_rank_corr_min	0.9
stability_window_refits	3
stability_passes_required	2
min_refits_in_phase_b	3
probe_brier_max	0.19
probe_pred_rmse_max	0.015
theta_global_rmse_max	0.05
max_pairs_after_stop	0

resolved_control	value_for_100_item_max_spoke	rule
probe_pairs_per_refit_per_spoke	4	max(4, ceiling(0.0035 * maximum spoke size))
probe_panel_edges	160	max(160, ceiling(0.12 * maximum spoke size))
probe_edges_min_for_stop	120	max(120, ceiling(0.106 * maximum spoke size))

The resolved thresholds used for a particular run are repeated in link_stage_log. Audit those *_used fields rather than assuming that a run used package defaults.

Logs and user-visible outputs

The three canonical log surfaces have different units:

• step_log has one row per attempted judgment. Linking fields include set endpoints, spoke, stage, probe flags, predictive probability, D-optimal gain, filters, outcome validity, and token metadata where available.
• round_log has one row per Bayesian within-set refit. It remains the Phase A model and stopping audit surface.
• link_stage_log has one row per spoke per linking refit. It records the estimation contract, hub lock, judge sharing, active/probe budgets, information summaries, diagnostics, calibration, reliability, stability, blockers, stopping, and freezing.

out$items exposes within-set values in theta_raw_eap, theta_raw_sd, and rank_raw. Accepted common-scale values appear in theta_link_eap, theta_link_sd, interval fields, and rank_link. They remain NA until a Phase B state has been accepted for the item. out$phase_a exposes the artifact manifest and per-set readiness, while out$summary and out$logs$link_stage_log expose the current linking status.

The log schemas intentionally retain some transform-era columns for old-session compatibility. On anchored-joint rows, transform-only fields are normalized to typed missing values. Their presence in a persisted schema does not make them current configuration choices.

Persistence and resume

When session_dir is supplied, the runner persists state, metadata, step, round, and link-stage logs, the latest fit, Phase A artifacts, and optional item logs. Each file replacement is atomic; the collection is checkpointed after completed refits, terminal events, the ordinary end of a call, and the configured step cadence. A process failure can lose attempts after the last complete checkpoint.

validate_session_dir() validates metadata and canonical log schemas. load_adaptive_session() also rebuilds derived evidence indices and checks probe state. resume = TRUE then requires:

• identical item identifiers and row order;
• compatible schema and package state;
• the same linking mode, hub, model contract, and required Phase A evidence;
• unchanged artifact identities and hashes;
• consistent spoke epochs, accepted states, Fisher-information counts, probe panels, and realized probe indices; and
• consistent frozen and stopping state.

An incompatible or corrupt session aborts with the violated invariant. The runner does not silently discard saved work, create a new probe panel, or restart Phase A. Use resume = FALSE with a new or empty directory only when intentionally starting a new study.

Compatibility loaders can backfill fields needed to read older sessions and can normalize legacy log names. Historical transform estimators, soft hub locks, free hub locks, alternative probe acceleration schedules, and independent-spoke routing are not public options for new runs.

Interpretation and evidence

The design intends to preserve a stable reference scale, direct active effort toward locally informative spoke parameters, and reserve independent comparisons for calibration. Those goals follow from the routing and estimation rules; they are not empirical findings about every judge, item pool, or budget.

The package computes conditional model diagnostics and reproducible audit fields. Passing them does not guarantee an unbiased judge, correct model specification, representative probe panel, external validity, or minimum ranking error. Non-passing or unavailable diagnostics identify a reason not to accept the automated stop; they do not diagnose the substantive cause on their own. Evaluate content coverage, subgroup behavior, presentation effects, invalid-response patterns, and external criteria alongside the package logs.

Citation

Mercer, S. H. (2026). Design: Adaptive linking [R package vignette]. Comprehensive R Archive Network. https://doi.org/10.32614/CRAN.package.pairwiseLLM