Design: Adaptive Pairing

This article describes the algorithm currently implemented by pairwiseLLM for adaptively ranking one set of items. The same procedure produces the canonical within-set artifacts used by linking Phase A; cross-set linking in Phase B is outside this article’s scope. For an operational tutorial, begin with Guide: Adaptive Pairing. The public entry points are adaptive_rank(), adaptive_rank_start(), adaptive_rank_run_live(), and adaptive_rank_resume().

Foundational concepts

Pairwise comparative judgment (CJ) asks a judge to choose between two items rather than score each item in isolation. Adaptive pairing means that the next pair depends on the valid judgments collected so far. The procedure can therefore spend comparisons on graph coverage, broad links, or close distinctions as the ranking develops instead of fixing every pair in advance.

The design combines two models with different, complementary jobs:

1. TrueSkill is updated after every valid comparison and supplies the fast ranking proxy and pair-selection probabilities. It lets the selector react immediately without running an expensive joint model after every judgment.
2. Bayesian Bradley–Terry–Luce (BTL) models are refit intermittently. Their posterior draws supply item estimates, uncertainty summaries, Markov chain Monte Carlo (MCMC) diagnostics, stopping metrics, rolling anchors, and one eligibility gate for long-range pairs.

In Bayesian estimation, the likelihood supplied by the observed choices is combined with prior distributions that regularize the model. The result is a posterior distribution: a range of parameter values and their relative plausibility given the model and data. Retained MCMC draws approximate that distribution. An expected a posteriori (EAP) estimate is the arithmetic mean of an item’s retained posterior draws; its posterior variance describes remaining model-based uncertainty. These summaries are conditional on the BTL model and priors, so they do not establish that the judgments are valid or free from systematic error.

The effective sample size (ESS) estimates how much independent-equivalent information remains after accounting for autocorrelation among MCMC draws. It can be much smaller than the number of retained draws.

Glossary and units

Term	Meaning
state	The complete mutable session record: items, model states, histories, logs, and controls.
attempted step	One attempt to select and judge at most one pair. Invalid judgments still consume a step.
committed comparison	A step with a valid binary outcome. Only these update models and counts.
round	A scheduling unit that allocates committed comparisons across four within-set stages.
stage	One of anchor_link, long_link, mid_link, or local_link.
phase	A linking unit: Phase A creates within-set results and Phase B links sets. It is not a stage.
candidate	An unordered pair eligible for consideration before all filters are applied.
anchor	An item selected from the current top, middle, or bottom of the ranking proxy.
long link	A non-anchor pair separated by at least the configured number of rank strata.
refit	A Bayesian BTL fit triggered by enough new committed comparisons.
near-stop	A persistent flag that raises the required bulk ESS at later refits. It is not a stop.
stop reason	The recorded reason for termination, such as btl_converged or candidate_starvation.

The unit of selection is an unordered item pair. The unit of judgment is its ordered presentation as first item $A$ and second item $B$. The unit of the reliability and stability summaries is the item, while the unit of MCMC diagnostics is the fitted parameter set or sampler transition.

Algorithm overview

For $N\geq2$ items, the live runner repeats the following sequence:

initialize state and shuffled-chain warm start
  -> select one ordered pair
  -> request one binary judgment
  -> if valid: commit counts/history and update TrueSkill
  -> update the round and stage controller
  -> if enough new comparisons exist: refit Bayesian BTL
       -> compute diagnostics, reliability, and lagged stability
       -> update global-identification and near-stop states
       -> record refit and item summaries
       -> stop only if every required stop gate passes

Selection and model updates are transactional. A valid result has $Y_k=1$ when the judge selects the first-presented item $A_k$, and $Y_k=0$ when it selects $B_k$. A missing, malformed, ambiguous, tied, refused, or otherwise invalid response is logged but does not update the comparison history, degrees, presentation counts, TrueSkill state, round quotas, or BTL-refit cadence. During the warm start, the same pair is retried until a valid result is committed.

Initialization and connected warm start

All items begin with the default TrueSkill parameters

$$\mu_i=25,\qquad \sigma_i=25/3,\qquad \beta_{\mathrm{TS}}=25/6,$$

unless item-level mu or sigma values are supplied to the internal state constructor. Here $\beta_{\mathrm{TS}}$ is the TrueSkill performance-scale parameter and is distinct from the BTL position-effect parameter introduced below.

Given a deterministic run seed, the item IDs are shuffled to produce $(s_1,\ldots,s_N)$. The warm-start queue is

$$\{s_1,s_2\},\{s_2,s_3\},\ldots,\{s_{N-1},s_N\}.$$

After all $N-1$ pairs have valid committed outcomes, these edges form a spanning path. The comparison graph is therefore connected, so all item locations can be related through observed paths. Connectivity is necessary for a common within-set scale, but it does not by itself imply precise estimates or strong MCMC diagnostics. Invalid attempts do not advance the path.

Presentation order is assigned separately from unordered-pair construction. The controller favors the orientation that improves each endpoint’s first/second-position balance. A repeated pair is presented in the reverse of its last recorded order.

Online TrueSkill update and selection utility

For candidate endpoints $i$ and $j$, the implemented TrueSkill win probability is

$$p^{\mathrm{TS}}_{ij} = \Phi\!\left( \frac{\mu_i-\mu_j} {\sqrt{\sigma_i^2+\sigma_j^2+2\beta_{\mathrm{TS}}^2}} \right),$$

where $\Phi$ is the standard-normal distribution function. The base ambiguity utility is

$$U_{0,ij}=p^{\mathrm{TS}}_{ij}(1-p^{\mathrm{TS}}_{ij}).$$

It is largest at 0.25 when the current model assigns equal win probability to both items. It is a pair-selection heuristic, not expected information gain under the later BTL model.

After a valid judgment, let $w$ and $l$ denote the winner and loser. Define

$$c=\sqrt{2\beta_{\mathrm{TS}}^2+\sigma_w^2+\sigma_l^2},\quad t=\frac{\mu_w-\mu_l}{c},\quad v(t)=\frac{\phi(t)}{\Phi(t)},\quad w(t)=v(t)\{v(t)+t\},$$

where $\phi$ is the standard-normal density. The implementation applies the two-player, no-draw update

$$\begin{aligned} \mu_w'&=\mu_w+\frac{\sigma_w^2}{c}v(t), & \mu_l'&=\mu_l-\frac{\sigma_l^2}{c}v(t),\\ (\sigma_w')^2&=\sigma_w^2\left\{1-\frac{\sigma_w^2}{c^2}w(t)\right\}, & (\sigma_l')^2&=\sigma_l^2\left\{1-\frac{\sigma_l^2}{c^2}w(t)\right\}. \end{aligned}$$

The two endpoint variances have a numerical floor before taking square roots. Other items are not updated in that step. These sequential approximate updates treat committed outcomes as the new evidence stream; they are not a substitute for the joint Bayesian BTL posterior used for final item summaries.

Rounds, anchors, and rank strata

After the warm start, each round schedules

$$B_{\mathrm{round}}=\left\lceil B_{\mathrm{refit}}/2\right\rceil$$

committed comparisons, where the default refit target is defined below. Stages are attempted in the fixed order anchor, long, mid, and local. Early rounds 1–4 allocate base fractions 0.30, 0.10, 0.10, and the remainder, respectively. Later rounds use 0.20, 0.05, 0.10, and the remainder. Fractions are converted to integer quotas with ceilings and a deterministic correction if rounding would exceed the round total. If a stage cannot supply a pair after every fallback, its shortfall is recorded and the controller advances; exhaustion of the full within-set round terminates with candidate_starvation.

The ranking proxy is decreasing TrueSkill $\mu_i$, with item ID breaking ties. Rolling anchors are refreshed after a new BTL refit; before a refit they use TrueSkill, and afterward they use BTL EAP estimates when a complete finite vector is available. The target number is

$$A=\begin{cases} \max\{1,\operatorname{round}(0.10N)\}, & N<10,\\ \operatorname{clamp}\{10,40,\operatorname{round}(0.10N)\}, & N\geq10, \end{cases}$$

and cannot exceed $N-1$. Integer allocation assigns approximately 30%, 40%, and 30% of anchors to the top, middle, and bottom of the current order.

The internal defaults record also contains anchor_count_min = 10; the current anchor selector uses the equivalent fixed lower bound of 10 directly. It is not a separate public override. The 0.30/0.40/0.30 weight fields are used by the integer allocation.

Strata are also recomputed from the ranking proxy. The first $\lceil0.10N\rceil$ items form a top band divided into at most five finer strata. Remaining items are divided into $K$ base strata, where $K=5,10,12,20$ for $N<60$, $60\leq N<150$, $150\leq N<400$, and $N\geq400$, respectively. Stratum distance is the absolute difference between the resulting integer stratum labels.

The four candidate stages move from maintaining broad structure toward resolving nearby items. Their code names describe which pair domain is eligible, while their purposes explain why the domain is included:

Stage	Eligible pairs	Purpose in the algorithm
anchor_link (anchor link)	Exactly one endpoint is a rolling anchor.	Compare ordinary items with representatives of the current top, middle, and bottom regions. These recurring reference points help maintain paths across the developing ranking; anchors are model-selected items, not fixed standards or known truths.
long_link (long link)	Neither endpoint is an anchor, and stratum distance is at least $\lceil K/2\rceil$.	Add broad comparisons across distant ranking regions. This supports graph connectivity and checks coarse ordering, especially before the run has concentrated on close distinctions.
mid_link (mid link)	Neither endpoint is an anchor, and base stratum distance is 2 through $\min(4,K-1)$.	Bridge broad and local comparisons by connecting moderately separated regions.
local_link (local link)	Stratum distance is at most 1; anchor endpoints are allowed.	Compare near neighbors, where current estimates often imply a less predictable outcome, and support distinctions near a configured decision boundary.

The labels describe routing roles rather than statistical guarantees. A pair admitted by one stage must still survive the filters and controls below.

For large pools, at most C_max = 20000 legal pairs are sampled deterministically from a stage’s candidate domain using the run and step seeds.

Candidate filters, coverage, and fallback

Each stage attempt constructs candidates, removes self-pairs, scores them with TrueSkill, verifies that repeats have a recorded prior orientation, and applies any active long-link gate. It then applies per-round exposure pressure, duplicate limits, and recent-degree star caps. Candidate counts after each operation are written to step_log, making filter collapse identifiable.

Conceptual example of one adaptive step

Suppose the controller is in local_link. It first forms pairs whose endpoints occupy the same or adjacent rank strata. It removes illegal repeats and pairs that violate exposure or degree controls, then gives each remaining pair the TrueSkill ambiguity score $U_0$. A pair with a predicted choice probability of 0.51 has $U_0=0.51(1-0.51)=0.2499$, close to the maximum of 0.25, so it is a strong exploitation candidate if no higher-priority rule intervenes. The selected unordered pair is then assigned a presentation order, sent to the judge, and logged. A valid choice is committed and the two endpoints’ TrueSkill states are updated immediately. Once the number of newly committed comparisons reaches the refit target, a joint BTL refit updates all item posterior summaries and diagnostics. Those new summaries can change later anchors, long-link eligibility, routing state, and stopping decisions. The numbers in this example are illustrative; the actual choice also depends on the current stage, exploration draw, fallback level, and all active constraints.

Coverage and exploration

Let $d_i$ be item $i$’s total committed degree and $d_{\min}=\min_i d_i$. Items with $d_i\leq d_{\min}+1$ form the under-represented set. While $d_{\min}<2$, a step has probability 0.20 of invoking the coverage override, which restricts candidates to pairs containing at least one under-represented endpoint.

Otherwise, or when the override is not drawn, probabilistic exploration uses

$$e(N)=\operatorname{clamp}\{0.10,0.25,0.20-0.02\log_{10}(N)\}.$$

After global identification, this rate is multiplied by 0.50. The uncertainty-pool and duplicate-relaxation fallbacks double the active exploration rate, capped at 0.50. On an exploration step, the controller samples an under-represented endpoint and then chooses a partner. With probability 0.15 it favors maximum rank distance; otherwise it favors the closest TrueSkill mean. Ties favor greater $U_0$, lower recent partner degree, and item ID.

On an exploitation step, within-set candidates are ordered by decreasing $U_0$. In the local stage after global identification, the controller first meets a boundary-priority target for a window around rank boundary_k and otherwise prioritizes pairs closest to $p^{\mathrm{TS}}_{ij}=0.5$. These choices concentrate comparisons near uncertain distinctions; they do not guarantee minimum posterior error for a fixed budget.

Duplicate and degree controls

The base duplicate limit is two committed observations per unordered pair. In the dup_relax fallback, a third observation is eligible only when all of the following hold:

• the pair has fewer than three observations;
• $|p^{\mathrm{TS}}_{ij}-0.5|\leq0.05$; and
• $U_{0,ij}$ is at or above the current candidate pool’s 0.90 quantile.

Recent degree is counted over the most recent $W_{\mathrm{cap}}=\max\{200,\min(2000,B_{\mathrm{refit}})\}$ committed comparisons. A candidate is normally rejected if either endpoint has recent degree greater than $\lceil0.08W_{\mathrm{cap}}\rceil$. After global identification, the mid and local stages may use at most one star-cap override per round for a near-tie pair $|p^{\mathrm{TS}}_{ij}-0.5|\leq0.05$, provided neither endpoint is above the 0.90 quantile of total degree.

Round exposure is also a soft control. The normal filter avoids endpoints that have exhausted the per-round repeat allowance; if that empties the pool, the same stage is retried under repeat pressure. Hard invariants and duplicate limits remain active.

Fallback ladder

If no candidate survives, the selector tries the following deterministic sequence:

1. base: normal stage bounds and duplicate policy;
2. expand_locality: widen mid/local bounds while retaining long-link semantics;
3. uncertainty_pool: retain base bounds and double the exploration rate;
4. dup_relax: permit the conditional third observation described above; and
5. global_safe: consider the broadest stage-compatible domain.

global_safe does not discard stage identity: long pairs must still be long, anchor pairs must still contain exactly one anchor, and the other hard constraints remain. If the full ladder fails, the failed step records candidate_starved = TRUE. The round controller may advance past a starved stage; exhaustion of all stages stops a within-set run.

Global identification and long-link tapering

After each refit, the controller calculates EAP reliability $R_{\mathrm{EAP}}$ and the Spearman correlation between TrueSkill ranks and BTL posterior-mean ranks. It sets global_identified when

$$R_{\mathrm{EAP}}\geq0.80 \quad\text{and}\quad \rho_{\mathrm{rank}}(\mu,\widehat\theta)\geq0.90.$$

This is a routing state, not a proof of model identification in the formal statistical sense. When active, the long-stage fraction is

$$f_{\mathrm{long}}^*=\max\{0.02,0.25f_{\mathrm{long}}\}.$$

The removed integer quota is reallocated 20% to the mid stage and 80% to the local stage, subject to integer rounding. Exploration is also tapered as described above.

After global identification, a long pair is retained only when its choice probability lies in $[0.10,0.90]$. If the availability check finds no diagnostic-passing refit in the current within-set scope, the gate uses $p^{\mathrm{TS}}_{ij}$. When a qualifying Bayesian fit is available, the current implementation uses the orientation-specific posterior predictive probability. This is the predicted probability of choosing the first item, averaged over retained draws so that current uncertainty about item, lapse, and position parameters contributes to the prediction:

$$\bar p_{ij}=\frac{1}{S}\sum_{s=1}^S \left[(1-\epsilon^{(s)}) \operatorname{logit}^{-1}\!\left( \theta_i^{(s)}-\theta_j^{(s)}+b^{(s)} \right)+\frac{\epsilon^{(s)}}{2}\right].$$

Here the stored candidate orientation treats $i$ as first position for the gate; final display order is assigned later. Thus $\bar p_{ij}$ is not the position-neutral posterior probability $\Pr(\theta_i>\theta_j\mid Y)$. This gate removes long comparisons predicted to be nearly deterministic under the implemented observation model. The availability check asks whether any logged refit in scope passed diagnostics, whereas the probability is calculated from the fit currently stored in state. If a later refit fails after an earlier pass, inspect the latest round_log diagnostics when auditing subsequent long-link decisions.

Bayesian BTL refits

Unlike the endpoint-only TrueSkill update, each BTL refit analyzes all committed comparisons together. The item parameter $\theta_i$ is a latent location on the within-set quality scale: larger differences between two item locations imply a larger modeled choice probability for the higher item, subject to the optional position and lapse terms.

For committed comparison $k=1,\ldots,M$, let $A_k$ and $B_k$ be the first- and second-presented items and $Y_k=1$ indicate selection of $A_k$. All four implemented variants use item parameters $\theta_i$; optional parameters are a global lapse probability $\epsilon$ and a global first-position effect $b$:

model_variant	Choice probability $p_k=\Pr(Y_k=1)$
btl	$\operatorname{logit}^{-1}(\theta_{A_k}-\theta_{B_k})$
btl_e	$(1-\epsilon)\operatorname{logit}^{-1}(\theta_{A_k}-\theta_{B_k})+\epsilon/2$
btl_b	$\operatorname{logit}^{-1}(\theta_{A_k}-\theta_{B_k}+b)$
btl_e_b (default)	$(1-\epsilon)\operatorname{logit}^{-1}(\theta_{A_k}-\theta_{B_k}+b)+\epsilon/2$

In every case, $Y_k\sim\operatorname{Bernoulli}(p_k)$. Positive $b$ indicates greater choice probability for the first-presented item, conditional on latent-quality difference. It measures a global positional tendency; it is not a forward/reverse consistency statistic. The lapse mixture models a global probability of an uninformative 50:50 response and should not be interpreted as a directly observed error rate.

The Stan models use

$$\theta_i^{\mathrm{raw}}\sim\mathcal N(0,1),\qquad \theta_i=\theta_i^{\mathrm{raw}}-\frac{1}{N}\sum_{j=1}^N\theta_j^{\mathrm{raw}},$$

with $\epsilon\sim\operatorname{Beta}(2,20)$ when present and $b\sim\mathcal N(0,0.3^2)$ when present. Centering identifies the within-set location while retaining the prior’s regularization. In plain terms, only differences among item locations affect the likelihood, so subtracting their mean sets an arbitrary zero point without changing those differences. The priors discourage extreme values when evidence is sparse. Consequently, items with only wins or only losses can still have finite posterior summaries; this is Bayesian regularization under separation, not evidence that sparse or one-sided comparison data are adequate.

Refit eligibility depends on committed comparisons only. With $M_t$ the committed count and $M_r$ the count at the last refit, a refit occurs when

$$M_t-M_r\geq B_{\mathrm{refit}},\qquad B_{\mathrm{refit}}=\operatorname{clamp}\{20,5000,\lceil N/2\rceil\}.$$

For within-set work inside linking Phase A, $N$ is the active set size and cadence is tracked separately for each set. Warm-start comparisons count. Invalid attempts do not. The default Stan run uses 1,000 warmup and 1,000 retained iterations per chain, up to eight chains based on detected cores, and a default parallel-chain budget of 80% of detected cores. Actual settings are stored in the refit log.

The 30–2,000 item range is an operating recommendation for study planning, not input validation: the public state constructor accepts any $N\geq2$. Small studies may spend much of their budget reaching the minimum refit cadence, while large studies require explicit planning for judgment, MCMC, persistence, and storage costs.

Code-derived defaults

The tables below are generated from the runtime helpers so scaled defaults cannot silently drift away from this article.

N	explore_rate	refit_pairs_target	round_pairs_target	rank_strata
30	0.170	20	10	5
100	0.160	50	25	10
500	0.146	250	125	20
2000	0.134	1000	500	20

control	default_at_N_100
model_variant	btl_e_b
ess_bulk_min	400
ess_bulk_min_near_stop	1000
max_rhat	1.01
divergences_max	0
eap_reliability_min	0.9
stability_lag	2
theta_corr_min	0.95
theta_sd_rel_change_max	0.1
rank_spearman_min	0.95

Public selector overrides are supplied in adaptive_config; public refit and stopping overrides are supplied in btl_config. See ?adaptive_rank and ?adaptive_rank_run_live for the accepted keys. Quantities used only to explain the implementation are not additional public arguments.

Diagnostics, reliability, and stopping

Stopping is evaluated only after a completed BTL refit. The gates address three different questions: whether the sampler behaved adequately, whether posterior uncertainty is small relative to observed item spread, and whether estimates have remained similar across separated refits. Passing all three is an operational stopping rule, not proof that the ranking is correct.

Let $S$ denote the number of retained posterior draws, and write

$$\widehat\theta_i=\frac{1}{S}\sum_{s=1}^S\theta_i^{(s)},\qquad \widehat V_i=\operatorname{Var}_{s}\!\left(\theta_i^{(s)}\right).$$

The implemented EAP reliability statistic is

$$R_{\mathrm{EAP}}= \frac{\operatorname{Var}_{i}(\widehat\theta_i)} {\operatorname{Var}_{i}(\widehat\theta_i)+N^{-1}\sum_{i=1}^{N}\widehat V_i}.$$

Both variances use the sample-variance convention in R, with denominator one less than the number of observations: $N-1$ across item EAP estimates and $S-1$ across draws for each item. The posterior variances are then averaged with denominator $N$. The statistic is returned as missing when there are fewer than two items or draws, non-finite draws, nonpositive between-item variance, or a non-finite component; otherwise it is clamped to $[0,1]$.

This quantity is best described as a posterior signal-to-total-variance ratio across the current items. It resembles an empirical reliability coefficient, but it is not classical test-score reliability, does not assess judge validity, and depends on the fitted model, prior, comparison graph, and observed spread of the particular item set.

The MCMC gate checks whether the numerical simulation appears trustworthy enough for the later summaries. A divergence is a sampler warning that the algorithm had difficulty following the posterior geometry. Split $\widehat R$ compares within-chain and between-chain behavior, with values close to 1 preferred. Bulk ESS estimates the independent-equivalent information in the autocorrelated central portion of the draws. The gate requires all of the following:

• total divergent transitions $\leq0$;
• maximum split $\widehat R\leq1.01$; and
• minimum bulk effective sample size across monitored parameters at or above the active threshold.

The default bulk-ESS threshold is $\max\{400,\operatorname{round}(20\sqrt N)\}$. Once near_stop is active it becomes $\max\{1000,\operatorname{round}(50\sqrt N)\}$. A refit enters near-stop when the ordinary diagnostics pass and $R_{\mathrm{EAP}}$ is within 0.05 below the stopping threshold. Entry affects later refits; it does not retrospectively apply the stricter ESS threshold to the refit that triggered entry.

Let $t$ be the current refit and $L=2$ the default stability_lag. Stability is eligible only when $t>L$. With item EAP vectors $\widehat{\boldsymbol\theta}^{(t)}$ and $\widehat{\boldsymbol\theta}^{(t-L)}$, the implementation computes

$$\rho_\theta(t)=\operatorname{cor}\!\left( \widehat{\boldsymbol\theta}^{(t)}, \widehat{\boldsymbol\theta}^{(t-L)} \right),$$

$$\Delta_{SD}(t)= \frac{\left|SD\{\widehat{\boldsymbol\theta}^{(t)}\}- SD\{\widehat{\boldsymbol\theta}^{(t-L)}\}\right|} {SD\{\widehat{\boldsymbol\theta}^{(t-L)}\}},$$

and Spearman rank correlation

$$\rho_{\mathrm{rank}}(t)=\operatorname{cor}_{\mathrm{Spearman}}\!\left( \operatorname{rank}\{\widehat{\boldsymbol\theta}^{(t)}\}, \operatorname{rank}\{\widehat{\boldsymbol\theta}^{(t-L)}\} \right).$$

Ranks use average handling of ties. Correlations use pairwise-complete observations, although the normal fit contract requires finite posterior summaries. A zero or non-finite lagged SD makes $\Delta_{SD}$ missing, which blocks stopping. These are descriptive comparisons across the same dependent item estimates, not hypothesis tests with independent observations.

Here SD means the standard deviation across item EAP estimates. The Pearson correlation $\rho_\theta$ measures similarity of item locations, $\Delta_{SD}$ measures proportional change in their spread, and the Spearman correlation $\rho_{\mathrm{rank}}$ measures similarity of rank order. Looking back by $L$ refits makes the comparison less local than a one-refit check, but none of these statistics establishes future stability.

The within-set stop decision is true only when all active conditions pass:

$$\begin{aligned} &\text{MCMC diagnostics pass},\\ &R_{\mathrm{EAP}}\geq0.90,\\ &t>L,\\ &\rho_\theta(t)\geq0.95,\\ &\Delta_{SD}(t)\leq0.10,\\ &\rho_{\mathrm{rank}}(t)\geq0.95. \end{aligned}$$

Passing records stop_reason = "btl_converged". The name means that the configured operational gates passed; it should not be read as a theorem that the posterior, ranking, or data-generating process has converged. Reaching the caller’s n_steps budget simply returns the current state and does not manufacture a stop reason. Candidate exhaustion records candidate_starvation. Invalid judgments can exhaust attempted-step budget without increasing the committed-comparison count.

Several posterior summaries in round_log, including near-tie and credible-interval summaries, are diagnostic-only and do not enter should_stop().

Audit trail and reproducibility

The canonical logs expose different units:

• step_log has one row per attempted step, including invalid outcomes, routing decisions, fallback paths, candidate counts, endpoint degrees, probabilities, utilities, and judge metadata.
• round_log has one row per Bayesian refit despite its historical field name. It records the comparison count, MCMC configuration and diagnostics, reliability, lagged stability, routing state, thresholds, and stop decision.
• item_log stores item-level posterior summaries for each refit.

Use adaptive_step_log(), adaptive_round_log(), adaptive_item_log(), adaptive_results_history(), and the summary helpers to inspect these records. Supplying session_dir persists initialized state and completed refits; ordinary progress is checkpointed at the configured step cadence. adaptive_rank_resume() restores the session after schema and state validation. Persisting the item log writes separate per-refit files; it does not replace the canonical in-memory history.

Audit the comparison graph, invalid-response rate, stage shortfalls, fallback and starvation rates, degree concentration, posterior diagnostics, and stop gates together. A high final $R_{\mathrm{EAP}}$ alone is not sufficient evidence that judgments are valid or that important subgroups, score regions, or presentation conditions were adequately represented.

Citation

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