XConf (eXperiential Confidence) estimates how likely a model is to be right by combining two signals: the historical success rate of similar, graded episodes and a revised confidence judgment informed by those episodes. Instead of judging only the answer in front of it, the model consults a record of how it has performed before.
How XConf turns past outcomes into a confidence estimate
XConf treats confidence as something that can be informed by experience. An episode in its experience bank records a task, the model’s reflection and stated confidence, the task outcome, and a lesson added after grading. When a new task arrives, the method uses that bank in two ways: Recall looks at relevant past outcomes, while Reflect asks the model to reconsider its confidence in light of those experiences.
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Recall: estimate from similar graded episodes
The repository describes Recall as retrieving 50 prior episodes using task embeddings and stated confidence. It calculates the hit rate—the proportion of retrieved episodes that succeeded—as one reading of confidence. In the paper’s description, the retrieved episodes are similar both in task and in the confidence the model expressed at the time.
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Reflect: revisit confidence with experience in view
Reflect presents the model with short cards summarizing relevant episodes and asks it to identify a recurring failure mode before giving an updated confidence. The repository says XConf takes the mean of this reflection-based reading and the Recall hit rate as its final estimate. The result therefore combines an empirical signal from past outcomes with the model’s informed reassessment; it is not simply the model repeating its initial confidence.
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What goes into the experience bank
Because Recall depends on outcomes, the bank needs more than task descriptions and model-generated confidence scores: episodes must be graded. The project page reports that an independent LLM judge agreed with gold labels 0.91 of the time and retained most of XConf’s value. It also reports that a bank labeled by the model itself performed worse than a bank without outcome labels. That finding makes grading quality and independence central to any implementation, rather than a detail that can safely be replaced with the model’s own guesses.
How XConf differs from other confidence approaches
Confidence methods can use different evidence: the model’s own verbal judgment, token probabilities, repeated answers to the current task, or performance on a calibration set. XConf’s distinctive move is to bring accumulated, graded episodes from other tasks into the inference-time estimate. The comparison below describes the approaches at a high level; capabilities and requirements can vary among methods within each category.
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| Approach | Primary confidence signal | Logits or token probabilities | Uses accumulated graded experience? |
|---|---|---|---|
| Verbalized confidence | The model’s stated confidence in its current answer | Not required by the basic approach | No |
| Likelihood or P(True) methods | Token probabilities or likelihood-related signals | Yes | No, not as the defining signal |
| Self-consistency | Agreement among repeated samples for the current task | Not required | No; it resamples the current task |
| Post-hoc or conformal calibration | A calibration procedure applied to model outputs | Varies by method | Uses calibration evidence, but not necessarily a retrieved bank of similar episodes at inference time |
| XConf | Hit rate among similar graded episodes, averaged with a reflection-informed confidence reading | No | Yes; this is its central distinction |
The authors describe XConf as needing neither logits nor weight updates, and as applicable to outputs ranging from multiple-choice answers to programs and agent rollouts. That breadth matters because confidence is not limited to cases where a model selects one token from a small answer set. It does not, by itself, establish that retrieval or calibration will work equally well in every domain.
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In an arXiv preprint submitted September 15, 2026, Caiqi Zhang, Xiaochen Zhu, Chengzu Li, Yulong Chen, Dharshan Kumaran, and Nigel Collier report evaluations across nine benchmarks covering reasoning, coding, multimodal question answering, and interactive agents. They use four models from three model families. These are the authors’ reported experimental results, not independent replication.
Discrimination, calibration, and generation cost
The authors report that XConf beats or matches ten-sample self-consistency on AUROC in 23 of 24 comparisons. AUROC measures how well a confidence score ranks more reliable cases above less reliable ones; it does not mean that every individual confidence value is accurate. The paper also reports much lower expected calibration error (ECE), a measure of the gap between stated confidence and observed accuracy, and generation cost one-tenth that of ten-sample self-consistency. The source material does not give a numerical ECE reduction or enough protocol detail to generalize that cost ratio to every deployment.
Abstaining on low-confidence cases
Confidence is useful not only for ranking answers but also for deciding when not to deliver one. The paper reports that withholding the 10% least-confident agent episodes raised delivered success by up to 8.7 percentage points on agent tasks. Separately, the authors’ project page reports a 4.8-point average increase in delivered accuracy across 36 model-dataset cells when the least-confident 10% were withheld, with every cell gaining. The first figure is a maximum for agent-task success; the second is an average across the stated set of cells, so they describe different results.
These figures describe selective prediction: the system answers fewer cases, but the answers it does deliver are more often successful or accurate in the reported evaluations. They do not show that abstention is cost-free. A real system must decide what happens to withheld cases—for example, whether to route them for review, ask for more information, or return no answer—and assess the effect of that policy on its users.
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What a deployment would need to get right
Collect relevant episodes and trustworthy outcomes
A useful bank needs examples that resemble the tasks on which confidence will be used, plus outcomes that have been graded reliably. If the bank is sparse or drawn from a different task mix, retrieved hit rates may not represent current performance. The project page’s result on self-labeling is a specific warning against treating model-generated outcomes as a substitute for dependable grading.
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Check retrieval and calibration in the target domain
Retrieval quality is part of the estimate: the hit rate is meaningful only insofar as the selected episodes are relevant to the new task and comparable in the model’s stated confidence. Before relying on a threshold to abstain or escalate, evaluate calibration and selective performance on the actual model, task distribution, and grading process. The reported benchmark results are encouraging evidence for XConf, not a guarantee of gains for every live system.
Why this changes the way confidence is measured
Most familiar confidence signals ask what can be inferred from the current answer, its token probabilities, or repeated attempts at the same task. XConf adds a different question: when the model has faced comparable tasks and expressed similar confidence, how often was it right? Its final estimate also asks the model to reflect on those precedents. This makes confidence partly a record of observed performance, rather than solely a self-assessment made in the moment.
The method’s promise and its dependency are the same: accumulated experience can make confidence more informative, but only when the retrieved episodes and their outcome labels are relevant and reliable. The preprint and author-maintained project materials report favorable results; the available evidence does not establish independent validation or universal performance.
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