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Identity-Free Deferral For Unseen Experts

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
learning to deferhealthcaremedical
Abstract:

Learning to Defer (L2D) improves AI reliability in decision-critical environments, such as healthcare, by training a model to either make its own prediction or delerejector the decision to a human expert. A key challenge is adapting to unseen experts: those who were not involved during the system's training process. Current methods for this task, however, can falter when unseen experts are out-of-distribution (OOD) relative to the training population. We identify a core architectural flaw as the cause: they learn identity-conditioned policies by processing class-indexed signals in fixed coordinates, creating shortcuts that violate the problem's inherent permutation symmetry. We introduce Identity-Free Deferral (IFD), an architecture that enforces this symmetry by construction. From a few-shot context, IFD builds a query-independent Bayesian competence profile for each expert. It then supplies the deferral rejector with a low-dimensional, role-indexed state containing only structural information, such as the model's confidence in its top-ranked class and the expert's estimated skill for that same role, which obscures absolute class identities. We train IFD using an uncertainty-aware, context-only objective that removes the need for expensive query-time expert labels. We formally prove the permutation invariance of our approach, contrasting it with the generic non-invariance of standard population encoders. Experiments on medical imaging benchmarks and ImageNet-16H with real human annotators show that IFD consistently improves generalization to unseen experts, with significant gains in OOD settings, all while using fewer annotations than competing methods.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces Identity-Free Deferral (IFD), an architecture that enforces permutation symmetry to generalize deferral decisions to unseen human experts under distribution shift. According to the taxonomy, this work occupies the 'Symmetry-Preserving Deferral Architectures' leaf, which currently contains only this paper as its sole member. This positioning suggests the paper pioneers a relatively sparse research direction within the broader deferral architecture design space, contrasting with identity-conditioned approaches that dominate existing methods.

The taxonomy reveals that IFD's parent category, 'Deferral Architecture and Representation Design', contains one sibling leaf: 'Complementarity-Based Deferral Systems', which focuses on exploiting complementary AI-human strengths rather than enforcing symmetry constraints. Neighboring branches include 'Uncertainty Quantification for Deferral Decisions' (with four sub-categories spanning conformal methods, distance-based metrics, and triage systems) and 'Sequential and Adaptive Deferral Frameworks'. The scope notes clarify that IFD's symmetry-preserving design explicitly excludes fixed identity encodings, distinguishing it from methods that condition on expert-specific features.

Among seven candidates examined for the uncertainty-aware training objective contribution, one paper appears to provide overlapping prior work, while six others were non-refutable or unclear. The IFD architecture itself and the formal permutation invariance proof were not examined against any candidates in this limited search. This suggests that while the training methodology may have some precedent in the examined literature, the core architectural innovation and its theoretical guarantees remain less directly challenged within the scope of this analysis, which covered top-K semantic matches rather than an exhaustive field survey.

Based on the limited search scope of seven candidates, the work appears to introduce a novel architectural perspective by formalizing symmetry constraints for expert-agnostic deferral. However, the analysis does not cover the full landscape of identity-conditioned methods or alternative symmetry-preserving designs that may exist outside the top-K semantic neighborhood. The single-paper occupancy of its taxonomy leaf reflects either genuine novelty in this specific direction or the nascent state of research explicitly framing deferral through permutation invariance.

Taxonomy

Core-task Taxonomy Papers
19
3
Claimed Contributions
7
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Learning to defer to unseen human experts under distribution shift. This field addresses the challenge of building AI systems that can recognize when to hand off decisions to human experts, particularly when the system encounters data that differs from its training distribution and when expert identities or characteristics are not known in advance. The taxonomy organizes research into several complementary directions: Deferral Architecture and Representation Design focuses on how to structure models that can learn deferral policies without relying on expert-specific features; Uncertainty Quantification for Deferral Decisions develops principled ways to measure confidence and trigger handoffs; Sequential and Adaptive Deferral Frameworks handle multi-step interactions where deferral decisions unfold over time; Human-in-the-Loop Adaptation Under Distribution Shift studies how systems can learn from expert feedback when data distributions evolve; Robustness and Safety Frameworks ensure reliable operation under adversarial or high-stakes conditions; and Domain-Specific Applications and Simulation ground these ideas in real-world settings such as medical diagnosis and autonomous systems. A particularly active line of work explores how to quantify uncertainty in ways that inform deferral decisions, with approaches ranging from conformal prediction methods like Conformalized Interactive Imitation[4] to joint assessments of model and human uncertainty as in Uncertainty Joint Assessment[2]. Another contrasting theme examines whether deferral architectures should explicitly model complementarity between AI and human strengths, as in Complementarity-Driven Deferral[1], or remain agnostic to expert identity. Identity-Free Deferral[0] sits squarely within the Symmetry-Preserving Deferral Architectures branch, emphasizing that effective deferral can be learned without access to expert-specific identifiers, a design choice that contrasts with sequential frameworks like Sequential Medical Deferral[5] where expert characteristics may be partially observable over time. This work addresses a key open question: how to generalize deferral policies to entirely new experts under shifted data distributions, a setting where traditional expert-aware methods struggle.

Claimed Contributions

Identity-Free Deferral (IFD) architecture

IFD is a novel architecture for learning to defer that enforces permutation symmetry by construction. It builds query-independent Bayesian competence profiles for experts and supplies the rejector with a low-dimensional, role-indexed state containing only structural information, which obscures absolute class identities and prevents identity-conditioned shortcuts.

0 retrieved papers
Uncertainty-aware, context-only training objective

The authors introduce a training objective that uses only context-derived expert profiles and incorporates risk-sensitive weighting via lower confidence bounds. This eliminates the need for expensive query-time expert annotations while naturally downweighting uncertain profiles to prevent over-deferral.

7 retrieved papers
Can Refute
Formal proof of permutation invariance

The authors provide formal proofs demonstrating that IFD is invariant to coherent class relabelings, contrasting this with the generic non-invariance of standard population encoders. This theoretical result establishes that IFD blocks identity-conditioned shortcuts by design.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Identity-Free Deferral (IFD) architecture

IFD is a novel architecture for learning to defer that enforces permutation symmetry by construction. It builds query-independent Bayesian competence profiles for experts and supplies the rejector with a low-dimensional, role-indexed state containing only structural information, which obscures absolute class identities and prevents identity-conditioned shortcuts.

Contribution

Uncertainty-aware, context-only training objective

The authors introduce a training objective that uses only context-derived expert profiles and incorporates risk-sensitive weighting via lower confidence bounds. This eliminates the need for expensive query-time expert annotations while naturally downweighting uncertain profiles to prevent over-deferral.

Contribution

Formal proof of permutation invariance

The authors provide formal proofs demonstrating that IFD is invariant to coherent class relabelings, contrasting this with the generic non-invariance of standard population encoders. This theoretical result establishes that IFD blocks identity-conditioned shortcuts by design.

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