SMASH 2026

But a second reasoner reduces error only insofar as it fails differently from the first. We say that a clinician and a model have complementary vulnerabilities to the extent that they break down through different mechanisms and on different cases. We therefore propose a predeployment audit in which each site measures error overlap on its own cases.

We introduce gated Knowledge-Guided Prototype Transfer (KGPT), a pre-repulsion module that transfers supported-class visual residuals to missing classes using VLM similarity and an ontology-based clinical distance. A validation-free evidence gate scales transfer based on semantic relevance and residual agreement, thereby limiting unsupported extrapolation.

In privacy-sensitive applications, the server observes the query and controls which evidence is returned, creating both confidentiality and integrity risks. Our protocol reduces private and verifiable retrieval to multiplication of a committed matrix by an encrypted vector and uses low-bit quantization to make this computation practical.

We proposed a predictive pipeline that automates the extraction of clinical OCT biomarkers using fine-tuned DL models. The combination of these automated biomarkers with baseline best-corrected visual acuity serves as inputs for an interpretable classifier.

In a Brazilian cohort of 8,524 patients, retinal images acquired with the Nikon NF5050 were more than twice as likely to have inadequate image quality compared with those acquired with the Canon CR-2, despite refurbished Canon systems costing more than twice as much as Nikon systems. We find that camera-associated performance differences vary substantially across tasks, and that neither higher input resolution nor classifier choice consistently mitigates cross-camera variation.

Clinical prediction models are commonly compared across demographic subgroups, but a raw performance gap is not a fixed property of a model. Observed gaps should prompt investigation of metric choice, prevalence, threshold, sample composition, measured case mix, and uncertainty rather than being treated alone as proof of discrimination, fairness, or equivalence.

Among 17 mid-pilot survey respondents, while 52% reported the AI scribe was easy to use, 88% reported that AI generated notes required substantial editing … These interim findings suggest that the implementation of an AI scribe is feasible in hospital-based ambulatory care but may not independently reduce documentation burden …

MED3pa first discovers interpretable low-confidence patient profiles, and Detectron is then applied within each profile to test whether its degradation is associated with a detectable harmful covariate shift. An injected age shift, undetectable at the population level, was nonetheless localized to specific degraded profiles …

TRACE learns a codebook of recurring activation patterns and assigns each token activation to its nearest codebook vector, defining a latent concept. During steering, activations assigned to harmful concepts are steered along directions defined by nearby benign targets.

We introduce the multi-agent system questionnaire (MASQ), a three-stage screening framework that connects system design choices to plausible miscoordination risks and their observable manifestations. Stage 3 guides researchers in examining MAS execution traces for evidence of anticipated and unanticipated miscoordination.

We develop a framework that conceptualizes sustainable deployment of AI as an emergent organizational property produced through expertise coordination, governance infrastructures, and knowledge infrastructures. Four propositions are advanced linking expertise coordination and organizational infrastructure to accountability and sustainability.

We design a privacy-preserving text inference framework based on CKKS homomorphic encryption for health-misinformation classification and semantic retrieval. The hybrid classifier achieved an accuracy of 0.971 and a macro-F1 score of 0.950, with identical reported metrics under plaintext and ciphertext-based inference.

Using automated insulin dosing as a testbed, we show that conventionally trained policies occupy one such undisclosed point on a tradeoff surface, producing clinically consequential failure modes invisible to standard metrics. We construct an approximate Pareto frontier through preference-conditioned RL and show that it recovers policies with similar aggregate performance but different risk profiles across three simulated patients.

We show that the impact of false-negative errors on recipient client–subgroup pairs can be predicted from the global model's curvature together with source and recipient-group feature summaries, without retraining a model for each possible error. Notably, a non-target subgroup can experience the largest degradation.

We present NeuroBuilder, an agentic adaptation workflow that combines semantic reasoning with deterministic extraction, validation, provenance, and execution controls while reserving consequential decisions for researchers. TUEG received a full pass with zero fabricated events.

A fact can be extracted correctly and still mislead if it is presented without temporal context, detached from its source, merged with conflicting information, or rewritten in a way that changes perceived urgency, certainty, or relevance. The framework combines deterministic checks, review of clinical meaning, and task-based evaluation of how users interpret and verify summaries …

In crossover STEAM education (n = 49), interest favored gamified instruction (dz = 0.64), whereas perceived expression acquisition favored conventional instruction (dz = -0.55). In disaster education (n = 37), engagement was high and perceived knowledge and competence increased, but intention changed little.

Our evasion is an argmax-preserving logit transform that makes signals on contaminated items resemble those on unseen items, while leaving every benchmark answer unchanged. We then introduce a detector based on Item Response Theory (IRT) residuals, which calibrates item difficulty on a population of 102 LLMs and flags items where a model outperforms its estimated ability. Because it reads only binary responses, its AUROC is exactly unchanged under the evasion.

We thus propose a privileged learning framework in which a teacher model has access to bilateral CFP images during training and learns representations informed by information from both eyes, while the student is trained to transfer this knowledge from the bilateral teacher using only a single-eye image as input. At inference, the student operates exclusively on this single-eye input and does not require the contralateral image.

We advance a concept of spoiled expertise which takes distinct forms based on two dimensions: knowledge workers' stance of adoption of epistemic technology, and locus of evaluation of expertise. Together these two dimensions culminate into four forms of spoiled expertise: (1) slopped expertise, (2) lagging expertise, (3) shunned expertise, and (4) outdated expertise.