Computation and Language
Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs
Sang Bin Moon, Nicole Cho, Daniel Borrajo et al. (+2 more)
Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singular and potentially suboptimal decision. We formulate persona diversification as a set-level conditioning problem and study two orthogonal design choices: selecting versus generating personas, and space-filling versus frontier-seeking diversity. We instantiate this design space with four methods spanning coverage and dispersion subset selections, uniform-coverage sampling, and evolutionary persona generation. Evaluations on the Alternative Uses Task (AUT), Infinity-Chat, and Divergent Association Task (DAT) show the benefits of the proposed methods across tasks and creativity objectives. On AUT, evolutionary persona generation increases response diversity by 78.8%, originality by 26.1%, flexibility by 49.5%, and holistic creativity by 13.9% over task-only prompting, while maintaining 98.5% validity; on Infinity-Chat, it nearly doubles persona-induced response separation relative to random personas. Moreover, evolutionary personas compose with creativity-optimized prompting, further increasing its response diversity by 18.6% and creativity by 6.3%. These results establish persona-set geometry as a task-agnostic mechanism for eliciting divergent LLM outputs, and support persona diversification as a reusable complement to
prompt optimization.
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All authors Sang Bin Moon, Nicole Cho, Daniel Borrajo, Sumitra Ganesh, Abolfazl Hashemi
Artificial Intelligence
BioEVAL: A global, multi-institutional benchmark of large language and multimodal models for bioengineering
Shun Ye, Vinny Chandran Suja, Chenlong Li et al. (+60 more)
Large
Language Models (LLMs) have demonstrated historic breakthroughs in general
reasoning with early successes in biomedical science. However, existing LLM benchmarking emphasizes factual recall, offering limited insight into model performance on frontier and
multimodal tasks. We assembled BioEVAL (BioEngineering Validation of AI and LLMs), a global, multi-institutional initiative designed to assess experimental
reasoning capability across bioengineering (BE) subfields. BioEVAL spans 11 major BE subfields plus a set of uncategorized items, bringing together 22 research groups to create a PhD-level
benchmark comprising 608 evaluation items: 1) 380 multiple-choice questions (MCQs, 359 retained after audit), 2) 218 literature synthesis tasks, and 3) 10
multimodal problems with experimental image interpretation.
Benchmark items underwent authoring-group expert review and centralized quality control before evaluation. Following evaluation, a blinded cross-group consensus audit of the highest- and lowest-accuracy MCQ items flagged 21 questions for revision or removal; these were withheld, and all reported MCQ results are computed on the 359 retained items. We evaluated diverse cloud-scale foundation/
multimodal models (e.g., ChatGPT, Gemini, and Grok) and locally deployable models suitable for inference on consumer-grade GPUs. Models achieved the highest accuracy of up to 90% on MCQs, similarity score of 0.72 on literature synthesis, and accuracy of 80% on a small sample of
multimodal reasoning questions, with substantial performance variation across subfields. Leaderboard rankings characterize current capabilities, limitations, and development priorities across the evaluated BE task categories. BioEVAL is maintained as an extensible
benchmark with standardized protocols for continuing expert item contribution and model evaluation.
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All authors Shun Ye, Vinny Chandran Suja, Chenlong Li, Chongming Jiang, Reza Zamani, Xiang Li, Christopher Bain, Yuqi Zhou, Walker Peterson, Huidong Wang, Chenglang Hu, Jongchan Park, Xiao Cheng, Benjamin Swedlund, Sandra Murillo, Anjali Sivanandan, Shiyu Sun, Liang Lanfeng, Mohammad Tariqul Islam, Baju C. Joy, Ishaq N. Khan, Sreedhar S. Kumar, Gabriel Mercado-Vásquez, James V. Vizzard, Jonathan M. Matthews, Helen Huang, Xiaolu Guo, Ethan Nicklow, Guorui Chen, Ryan A. Neff, Surjendu Maity, Hyeonjin Park, Han-ho Joo, Katherine Dong, Yuyan Cai, Weihang Huang, Yichen Zou, Rui Yan, Raphael Figueroa, Artem Goncharov, Bella Rose Schremmer, Lian Elsa Linton, Keisuke Goda, Liang Gao, Ke Cheng, Leonardo Morsut, Jennifer L. Wilson, Jianping Fu, Lim Chwee Teck, Deblina Sarkar, Andreas Hierlemann, Savaş Tay, Alexander Hoffmann, Donald Richieri Griffin, Jun Chen, Shana O. Kelley, Shyni Varghese, Jinwoo Cheon, Wilbur A. Lam, James J. Moon, Wilson W. Wong, Samir Mitragotri, Dino Di Carlo
Machine Learning
Geometric Feature Learning for Functional Data Valued on the Symmetric Positive Definite Manifold
Samuel V. Singh and Mimi Zhang
We here develop a functional neural network, termed MatFAE, for learning trajectories on the Riemannian
manifold of symmetric positive definite (SPD)
matrices. MatFAE features intrinsic layers that map
manifold-valued functions to Euclidean vector-valued functions, followed by a functional layer that projects them into a finite-dimensional Euclidean space. Unlike most neural networks for discrete-time sequences, MatFAE treats each sequence as a continuous function and can therefore encode trajectory dynamics (e.g., first-order derivatives) in its latent representations. Additionally, the morphology of the functional weights in the functional layer offers interpretability by revealing the regions of the input functional data that contribute most to the latent representations. We justify the design principles and properties of each intrinsic layer and detail how
matrix factorization is handled during backpropagation. We apply MatFAE to a range of fMRI
datasets, demonstrating its ability to efficiently learn informative representations from high-dimensional SPD trajectories and its practical value for real-world neuroimaging analysis.
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Artificial Intelligence
Do LLMs Understand Context? A Knowledge Graph-Based Evaluation Framework
Subavarshana Arumugam, Mamta Nallaretnam, Kithuni Wickramasinghe et al. (+4 more)
While large
language models (LLMs) have achieved remarkable linguistic capabilities, a profound question lingers at their core: do these models truly comprehend context or simply excel at pattern matching on an unprecedented scale? Contextual understanding in LLMs refers to the ability to correctly extract relevant information from a given context, integrate it into a coherent internal representation, and reason over it to produce factually consistent and contextually grounded responses. However, traditional methods such as BiLingual Evaluation Understudy (BLEU) and perplexity simply measure surface-level performance. This reveals a critical gap in question answering (QA), where responses must be contextually grounded rather than simply being memorized associations. To fill this void, we propose a novel knowledge graph (KG) based evaluation framework for LLM contextual understanding in QA. Central to this is Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure combining structural and semantic signals into a single score. In addition, a diagnostic analysis framework is developed to identify and categorize
reasoning errors at the triplet level, enabling fine-grained analysis of model failures. Together, across nine
benchmarks, S3KG achieves F1 gains of up to $+7.6$ points over the strongest baseline and AUROC up to $0.973$.
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All authors Subavarshana Arumugam, Mamta Nallaretnam, Kithuni Wickramasinghe, Chamath Gunapala, Pragatheeswaran Vipulanandan, Kamal Premaratne, Uthayasanker Thayasivam
Sound
AcoustiClaim: A Numeric Claim Benchmark with Instrument Ground Truth
Sheng-Tse Lin, Siyuan Zhai, Chien-Liang Kuo et al. (+2 more)
Audio
language models state numbers for acoustic quantities, and neither human opinion nor a judge model says whether such a number is true of the signal. AcoustiClaim extracts each numeric claim from free text, scores it against the instrument that defines the quantity, and classes each quantity by where its reference can be read. Four open-weight systems and one closed model, asked for ten quantities five ways on two corpora, fill 207
cells. Of these, 49 emit fewer than five distinct values, and eight of the 158
cells that can be ranked exceed a rank correlation of 0.3, the bar we set, three with an interval clear of it, five of them one closed model reading pitch. Error sits at or above a constant-predictor floor in every ranked
cell but three. The reference decoder we train declines the five voice quantities in prose on 95% of mixtures, with nothing withheld, and states them on the clean twins, reproducing its targets' rule from audio alone. With a calibrated threshold, withholding lowers error on all ten quantities on the mixtures in the mean and on eight at every split, against at most 0.6% from a random selector. A linear baseline orders errors at least as well as ours. F0 s.d. and shimmer stay above the constant floor.
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All authors Sheng-Tse Lin, Siyuan Zhai, Chien-Liang Kuo, Massa Baali, Bhiksha Raj
Robotics
Learning-Based Pressure Predictive Control of a Vertebraic Soft Robotic Tail
Wenjian Yang, Nan Huang, Yukang Nie et al. (+4 more)
Soft
robots have attracted much
attention for their safe human-
robot interaction and flexibility, but the typical continuum structure and nonlinear material behavior make the kinematics modelling complex, especially in non-static motions. In this work, we proposed an LSTM-based pressure predictive control (PPC) for the motion control of a vertebraic soft robotic tail and the coordination with a quadruped
robot. The PPC consists of an inverse kinematics (IK) model, a forward kinematics (FK) model and a pressure compensation (P-comp) model, and achieves non-static and quasi-static motion control of the tail. Compared with the IK-only model, the average RMSE of the PPC's simulation trajectories reduces by 69.8%, when executing target trajectories. In the coordinated motions of the soft tail quadruped, using a prediction data set to train the PPC enables next-moment action prediction and reduces computation time by 60.9%, which enhances the real-time response of the tail to match the quadruped torso's moving rate. The PPC provides a simple and effective method to model the soft tail for both non-static and quasi-static motion control, and grants the soft tail quadruped with the functionality of interacting with the environment.
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All authors Wenjian Yang, Nan Huang, Yukang Nie, Fang Chen, Wanchao Chi, Jiansheng Dai, Sicong Liu
Computer Vision
The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation
Soshun Kihara, Shunsuke Yasuki and Masato Taki
Convolutional neural networks trained on ImageNet are known to exhibit a strong preference for local high-frequency texture, an inductive bias that translates into fragile robustness against distribution shifts in real-world environments. Event cameras, in contrast, record only changes in scene brightness and are therefore well suited to capturing contour information; however, due to the absence of diagnostic
benchmarks in the event domain, the inductive bias that event-camera data instills in
vision models has remained underexplored. In this work, we use knowledge
distillation from the event domain to the RGB domain so as to exploit the rich evaluation toolkit available in the RGB domain and systematically dissect this inductive bias. Our experiments show that
distillation from the event domain induces, in the RGB domain, color invariance, shape bias, and robustness to high-frequency noise. We identify the underlying mechanism as the model suppressing its dependence on high-frequency texture while acquiring a stronger dependence on edge-based object shape. This hypothesis is supported by changes in how color and spatial information are processed at the early layers, together with a spectral trade-off in which robustness to the absence of high-frequency components coexists with vulnerability to contamination of the relied-upon frequency bands and to disruption of
geometric structure. We further show that this inductive bias differs from existing robustification methods and that it functions as a useful prior for diverse downstream tasks in which shape and contour information contribute alongside other cues. The code is available at https://github.com/snskysk/event2rgb-
distillation .
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Machine Learning · Cross-listed from math.ST
Scaffold-Constrained Subset Dynamic Programming for Exact SSE Clustering
Yordan P. Raykov and Max A. Little
Exact Euclidean \(K\)-means partitions \(n\) observations into \(K\) unlabelled clusters, but the unrestricted search is generally exponential. We use data-derived
geometric graphs to precondition an exact subset dynamic program: as a result only connected vertex subsets are admitted as clusters, while sum-of-squared-errors (SSE) loss is unchanged. A remaining-set recurrence minimises fixed-\(K\) or penalised SSE, with exact factorisation over the connected components of each remaining set. The central question we study is how much computational support can be removed while preserving an unrestricted optimum. Graph inclusion gives monotone coverage and support relations, and a bottleneck threshold identifies the first covering graph in a nested hierarchy. For fixed \(K\) and dimension, under compact ball support and density bounds, retaining \(q=O(\log n)\) nearest neighbours per observation preserves an empirical SSE optimum with probability tending to one, using an \(O(\log n/n)\) fraction of complete-graph edges. Truncated Gaussian mixtures with unequal weights and covariances satisfy these conditions. The rate we provide is a sufficient upper bound rather than a result implying
polynomial optimisation complexity. Objective-matched synthetic and full-data comparisons assess coverage, compression, and reference-label agreement. As a secondary application, we illustrate how the proposed scaffold preconditioning can be utilized to improve the efficiency of split-merge proposals that preserve unrestricted mixture posteriors.
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Computation and Language · Cross-listed from eess.AS
Asymmetric Classifier-Free Guidance for Target-Speaker ASR
Yiwen Guan and Jacob Whitehill
Target-speaker automatic
speech recognition (TS-ASR) must identify and transcribe a desired speaker under varying overlap and noise conditions. These changes alter the acoustic evidence for the target speaker in the
speech mixture, motivating inference-time calibration of speaker conditioning. We introduce asymmetric classifier-free guidance (CFG) for TS-ASR using Whisper: the speaker-conditioned branch predicts the target transcript, while the speaker-unconditioned branch predicts serialized multi-speaker transcripts. CFG adjusts the contribution of speaker conditioning during decoding through a single guidance scale. We select a global guidance scale on target-domain development data and train a lightweight encoder-based predictor to adjust it for each utterance, keeping the recognition model fixed. Under domain shifts, our full system achieves relative word error rate (WER) reductions of up to 21.8% over the condition-only baseline, and 5.6% over standard conditional decoding of the same CFG-trained model. Oracle analysis shows that substantially larger WER reductions are possible through utterance-level scale selection and identifies how beneficial adjustments vary with domain shifts.
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Human-Computer Interaction
Redesigning Trust: Replacing Dark Patterns with Fair Choice Architecture in Financial Interfaces
Oluwadamilola Awakan, Tawan Aroonwechkul, Roshan Gunjoor et al. (+2 more)
Digital financial platforms make enrollment effortless and cancellation laborious. This asymmetry is a dark pattern that manipulates users who have already decided to leave. Existing work identifies such patterns after deployment, and regulators sanction them after harm, yet neither provides designers with a criterion for building interfaces that avoid manipulation. We model the provider as an adversary whose instrument is effort and express
fairness as a constraint requiring that leaving never cost more than joining. Defining interaction cost over
navigation steps, mandatory inputs, and confirmation
prompts, we prove that this constraint holds for every assignment of effort weights if and only if no component of the exit flow exceeds its counterpart at entry.
Fairness is therefore verifiable by counting rather than by estimating cognitive effort, and exact equivalence is unnecessary because exit legitimately requires fewer inputs than entry. We instantiate the model, together with invariants for visual parity and linguistic neutrality, in a mobile credit card prototype with parallel sign-up and cancellation workflows.
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All authors Oluwadamilola Awakan, Tawan Aroonwechkul, Roshan Gunjoor, Supriya Khadka, Sanchari Das
Machine Learning
Mentored Decoding: Faster Inference meets Boosting
Vivien Tran-Thien and Richard Nock
Speculative decoding is a successful technique speeding up inference of a target autoregressive
language model via a fast drafter model. Lossy speculative decoding allows a drift with respect to the target to further improve speed. Interestingly, it has been observed experimentally that the resulting model can $\textit{also}$ beat the target $\textit{quality-wise}$. Our paper formally proves how such a feat is possible with a formal approach to lossy speculative decoding called $\textit{mentored decoding}$. To get there, we connect inference to a celebrated ML training theory, $\textit{boosting}$, and proceed via the generalization of mentored decoding to the whole set of $f$-divergences. We uncover key properties of mentored decoding, among which (i) the particularly appealing
geometric nature of the total variation case, (ii) simple approximations for any $f$-divergence in direct relation with boosting compliance, and (iii) a $\textit{divergence independent}$ $O(n)$ space and $O(\mathrm{sort}(n))$ time data structure built on drafter and target outputs, which allows to query the optimal parameters of the dual problem in $O(\log n)$ time and constructing optimal mentored distributions in $O(n)$ time for any $f$-divergence.
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Machine Learning
Moment-guided edge sampling
Weibin Cai and Reza Zafarani
Edge sampling makes local decisions to achieve graph-level objectives, such as preserving structural properties. This creates a fundamental challenge: how can the effect of a local edge edit (i.e., edge addition or removal) on global graph structure be quantified and controlled? We address this challenge with a moment-guided edge sampling framework based on spectral moments of the random-walk transition
matrix. We compute exact moment changes through two complementary methods: a combinatorial method with closed-form updates for low-order moments, and a low-rank method that exploits locality and cyclic trace invariance to compress computations to edited endpoints, supporting arbitrary moment orders and batched edits. For single-edge edits at fixed moment orders, the low-rank method reduces the cost from $O(mn)$ to $O(m)$, while the combinatorial method evaluates low-order changes in constant time given maintained local statistics. These moment changes provide interpretable structural signatures of local edge motifs that aggregate into graph-level fingerprints. This structural meaning motivates us to ask whether preserving moments also preserves the graph properties. We further derive and validate that moment-preserving sampling can retain related structural properties, including triangle-weighted clustering coefficient. These structural insights enable analysis and improvement of graph learning: different edge structures have distinct effects on supervised node classification, while moment-guided augmentation is competitive for graph contrastive learning. Together, these findings establish moments as an interpretable and controllable bridge from local edge edits to global graph structure and learning.
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Computation and Language
CARGO: Context-Aware Retrieval-Gated Evaluation of Agentic AI in Production
Mukul Chhabra, Shail Patel and Luigi Medrano
Reference-based LLM-as-a-judge evaluation assumes the reference answer is the target. In deployed agentic systems that operate over dynamic entities (support cases, assets, accounts), the closest available reference typically applies the correct procedure to a different entity, so a literal judge penalizes different identifiers, dates, and statuses as errors or
hallucinations. We name this failure mode reference-instance divergence (RID). We propose CARGO, a framework that (i) treats retrieved references as procedural exemplars and grounds factual judgments in the live instance's observed context, (ii) assigns each claim a three-way status (supported, contradicted, unverifiable) and penalizes only contradictions, and (iii) gates evaluation by
retrieval confidence, casting production evaluation as selective prediction. We introduce CARGO-Bench, a perturbation-based diagnostic suite with ground truth by construction that separates leniency from discrimination. On CARGO-Bench (246 items, two judge models, 7,872 judgments), the standard reference-based judge penalizes 100% of correct entity-transplanted answers and is uninformative (discrimination index DI ~ 0); supplying the live facts without reframing changes nothing. CARGO eliminates these false penalties (0/50) while retaining near-complete contradiction recall (50/50 and 49/50), raising DI to 0.58 [0.48, 0.68]; a rubric-swap control attributes most of the effect to context-grounded dimension definitions. CARGO also exposes a limitation of its own design: the leniency that protects entity values suppresses detection of procedural corruptions (20% recall). A post-hoc fix does not close the gap, and an LLM-as-annotator study with written guidelines and adjudication shows the same blind spot. We release a preregistered protocol for extending the evaluation to expert agreement, risk-coverage, and cost on production traffic.
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Machine Learning
Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis
Menghua Jiang, Haokai Gao, Xiangui Kang et al. (+2 more)
Multimodal Sentiment Analysis (MSA) aims to infer human
emotions from multiple modalities such as text, audio, and
vision. In practice, inputs are often corrupted by noise and missing modalities, which degrades performance. Existing methods typically address these challenges in isolation, limiting their effectiveness in realistic settings. To address this limitation, we propose a Reliability-aware Cross-sample Enhancement (RCE) framework. Specifically, RCE first introduces an adaptive variational information bottleneck to model modality-wise uncertainty and perform quality-aware information compression, thereby suppressing redundant noise in unreliable modalities. Furthermore, we design a reliability-aware cross-sample enhancement strategy that retrieves high-confidence, semantically consistent neighbors from a large candidate pool to enrich and calibrate current representations, effectively alleviating information deficiency caused by missing modalities. Building upon this, RCE integrates cross-modal interactions with a multilevel reliability-aware fusion mechanism to adaptively aggregate information across modalities and enhancement stages, leading to more robust
multimodal representations. Extensive experiments demonstrate that RCE consistently outperforms state-of-the-art methods across full, noisy, and missing-modality settings.
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All authors Menghua Jiang, Haokai Gao, Xiangui Kang, Haifeng Hu, Sijie Mai
Artificial Intelligence
Pretrained ASR Pseudo-labeling for Noisy Police Audio
Kaavya Chaparala, Su Huang, Stephen L. Morgan et al. (+2 more)
Pretrained ASR systems perform poorly on noisy Broadcast Police Communication (BPC), hindering efforts to understand police decision-making. Pseudo-labeling offers an unsupervised path to improve ASR without expensive human labels, but the efficacy of this approach on very noisy domains is not known. In this work, we systematically assess the opportunities and limits of pseudo-labeling to adapt foundation ASR models (Whisper and Qwen3-ASR) to noisy BPC domain corpora from Baltimore and Chicago. We demonstrate that existing internal confidence metrics (log-probabilities and
STAR scores) fail to distinguish between high and low quality BPC pseudo-labels, and we introduce an external LLM-as-a-judge filtering paradigm that leverages parametric knowledge to discard contextually implausible transcripts. Our LLM-judging filters more aggressively than internal metrics and significantly reduces WER of the pseudo-labeled training sets across the Baltimore and Chicago BPC corpora, though a substantial gap remains relative to an oracle filter. We also introduce a new cross-model pseudo-labeling paradigm where one model is finetuned with pseudo-labels from the other, and we identify this method as a promising direction for future pseudo-labeling work.
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All authors Kaavya Chaparala, Su Huang, Stephen L. Morgan, Rhiannon N. Miller, Anjalie Field
Performance
SR-Gadgets: Make Scan-Resistant Caching Practical
Yunjia Zheng and Juncheng Yang
Block caches commonly serve scan-heavy I/O workloads, motivating extensive studies on scan-resistant eviction
algorithms. Many of these
algorithms adopt a multi-queue structure. However, they focus primarily on one-time scans and do not handle repeated scans well. Two important challenges from repeated scans are miss-ratio cliffs, where a small increase in cache size sharply reduces the miss ratio, and Belady's anomalies, where increasing the cache size increases the miss ratio. In this paper, we first develop two quantitative metrics to measure these behaviors. With these metrics, we find that LIRS is the only multi-queue
algorithm that is scan-resistant (almost cliff- and anomaly-free). Contrary to conventional wisdom, we show that stack distance is not the secret sauce that makes LIRS scan-resistant. Instead, regulating the queues are the key to its scan resistance. Based on these insights, we design the \gadgetprefix Gadgets, easy-to-integrate augmentations that make existing
algorithms scan-resistant without changing their eviction heuristics or queue structures. We implement the \gadgetprefix Gadgets in five
algorithms: S3-FIFO, SIEVE, ARC, 2Q, and TinyLFU, and make them scan-resistant. Evaluated on 5,538 production traces, all augmented
algorithms outperform their base versions, reducing miss ratios by up to 23.1% while consistently reducing cliffs and Belady's anomalies across the production traces.
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Computation and Language
Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification
Heyuan Huang, Jirui Dai, Alexandra DeLucia et al. (+5 more)
Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the dominant paradigm for scalable
hallucination detection in high-stakes clinical settings. Despite the urgency of reliable and transparent medical fact verification, most systems measure performance with aggregate metrics like F1, which obscure where and why failures occur. Existing RAG diagnostics require gold answers or annotated gold evidence, neither of which exists in this regime. We introduce two comprehensive taxonomies, grounded in a case study on the open-ended MedExpert
dataset and 3 closed-ended
datasets, decomposing failures into
retrieval-stage errors along five quality dimensions, and verifier-
reasoning errors into six consecutive steps. We adapt an automatic pattern induction pipeline using LLM-as-Judge to label evidence quality and classify verifier
reasoning errors at scale, and then stress-test our findings across 4
retrieval methods and 6 frontier verifier models. Our analysis reveals that scaling model size, adding
reasoning effort, expanding to authoritative web sources, and applying medical fine-tuning do not resolve these failure modes, demonstrating that they represent fundamental limitations of the retrieve-then-verify paradigm in open-ended medical settings rather than artifacts of outdated systems. We release our code and data at https://anonymous.4open.science/r/Medical_RAG_eval-4AB5 for the full reproducibility of our results.
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All authors Heyuan Huang, Jirui Dai, Alexandra DeLucia, Sonal Joshi, Mahsa Yarmohammadi, Jie Gao, Bernal Jiménez Gutiérrez, Mark Dredze
Human-Computer Interaction
A Benchmarking Framework for Context-aware XR Interfaces
Hyunsung Cho, Sarah Yewon Yun, Nancy Ruonan Sun et al. (+7 more)
Everyday Extended Reality (XR) systems aim to provide context-aware access to the right functionalities at the right time and place, with minimal manual reconfiguration as users switch context. Yet these interfaces are hard to evaluate: current prototyping and user-study workflows offer no systematic, repeatable way to compare adaptation methods across users and scenarios. We present ContextXR, a novel benchmarking framework for context-aware XR interfaces. ContextXR represents an XR application as a connected graph of functional facets, each a semantically coherent group of related capabilities that together support a shared user intent. On this representation, we build MineXR++, a
dataset augmenting prior XR interface data with facet-level annotations, and formulate three canonical tasks of context-aware suggestion: context factor analysis, initial facet suggestion, and next facet suggestion. Our evaluation protocol scores suggestion methods by a simulated interaction metric, the
navigation and search cost of reaching the desired functionality. Through experiments benchmarking global popularity, relational
retrieval, and LLM-based methods, we demonstrate that ContextXR enables the systematic, reproducible evaluation of context-aware XR interfaces.
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All authors Hyunsung Cho, Sarah Yewon Yun, Nancy Ruonan Sun, Ben Lafreniere, Mark Parent, Kashyap Todi, Tanya R. Jonker, Hrvoje Benko, Sherry Tongshuang Wu, David Lindlbauer
Machine Learning
RAZOR: Pruning Replaceable Experts in LLMs
Mingyang Song and Mao Zheng
Mixture-of-experts (MoE) models activate only a few experts per
token but store the entire expert pool.
Pruning this pool requires identifying experts whose removal preserves model behavior. Routing frequency and output magnitude do not fully describe deletion damage, which also depends on how the surviving and replacement experts compensate for the removed output. We introduce RAZOR, a training-free
pruning method based on consensus residuals, the deviations of expert outputs from their original weighted mixture. At a fixed layer input, these residuals give the exact output change for a single deletion under survivor renormalization and router refill. RAZOR aggregates this damage by conditional root mean square and selects experts under a layerwise budget using forward computation alone, without gradients, subset search, or recovery training. Against frequency, activation-norm, and REAP baselines on GLM-4.7-Flash and Qwen3.6-35B-A3B at 25% and 50% expert removal, it attains the highest macro average over nine
reasoning-intensive tasks in all four model-budget settings, gaining 2.12-5.59 points over REAP and lowering reverse KL in all four. On DeepSeek-V4-Flash-0731 and Hy3, it also achieves the highest macro average among the three residual criteria. Local exactness does not guarantee better joint
pruning. Generation analyses show changes in diversity, formatting, and termination despite higher task scores.
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Robotics
Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning
Jenny Wang and George Kantor
Policies trained with imitation learning can accumulate errors over time, causing the
robot to drift outside the training distribution. Existing methods mitigate this
covariate shift by collecting additional data where the policy fails or is likely to fail. The first places the
robot in unsafe conditions and the second requires choosing an appropriate noise distribution to collect new expert demonstrations under that noise. We propose Policy-Calibrated DAgger, a method that makes use of the properties of recent generative policies to estimate the policy's noise offline by using its own predicted action distribution. We measure a
diffusion policy's spread of predicted actions at observations along the expert trajectory and measure its closed-loop error relative to a recorded trajectory. To address issues with measuring error in a
multimodal action space, we guide the policy towards the trajectory during closed-loop control through partial denoising, and use properties of a
diffusion model to unnormalize the measured error as if we did not guide it. We experiment in a scenario where a
robot is tasked to reach an engine lever in a cluttered and narrow environment and show results in a 3D photorealistic simulator and a 2D planar reacher environment. We show that our method surpasses policies trained with
dataset aggregation without noising and matches the performance of the best noise level in hindsight, without requiring a sweep over noise levels.
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Robotics
DGT-Map: Directional Global Traversability Mapping Utilizing Multi-Task Learning for Heterogeneous Vehicles
Jaskrit Singh, Kashif K. Noori, Jing Xiao et al. (+1 more)
Off-road traversability is direction-dependent and vehicle specific, yet most global maps assign a single isotropic cost to each location. Existing learned
estimators are also commonly trained independently for each vehicle; this preserves vehicle-specific behavior but prevents vehicles from sharing common terrain representations. DGT-MAP addresses both limitations through a self-supervised framework that learns global, directional, and vehicle-conditioned traversability costmaps from RGB-D observations and locomotion signals. A shared multi-task backbone learns common terrain features across training vehicles while vehicle-specific prediction heads preserve platform-dependent responses. At inference, DGT-MAP produces a heading-indexed costmap that can be used by a direction-aware planner. We evaluate DGT-MAP in simulation by integrating it into a Hybrid A*
navigation stack and measuring downstream task success on challenging terrains, including slopes that are traversable downhill but not uphill and a ridge obstacle that is traversable by some vehicles, but not by others. Across evaluated tasks, DGT-MAP achieves the highest or tied-highest
navigation success rate when compared against
geometric, binary, and learned direction-agnostic baselines.
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All authors Jaskrit Singh, Kashif K. Noori, Jing Xiao, Constantinos Chamzas
Robotics
Realizability Is Not Enough: Encoding, Liveness, and Auditing of Synthesized Robot Supervisors
David C. Conner, Joshua Luzier, William J. Doyle et al. (+7 more)
High-level robotic supervisors coordinate capabilities whose reported outcomes determine the
robot's next action. Reactive synthesis can generate such supervisors with formal guarantees, but deployment requires more than proving a Generalized Reactivity (1) (GR(1)) specification realizable. Designers must encode failure-prone capabilities, choose liveness assumptions that match retry intent, audit strategies, and translate them into
robot software. We present an open-source pipeline for
Robot Operating System (ROS) 2 Flexible Behavior Engine (FlexBE) supervisors that generates capability-based GR(1) specifications, analyzes assumptions before synthesis, audits strategies, reduces states with a behavior-preservation proof, and emits executable state machines. Across four case studies (six comparisons), including hardware on two quadcopter platforms, we compare enumerated and one-hot encodings and two liveness formulations. Under the tested backend, enumerated encoding usually synthesizes faster, although fewer propositions do not reliably predict smaller controllers or lower symbolic cost. System-Goal without pending
memory is the only liveness treatment confirmed to yield executable controllers under both encodings across the reported grid; Fair-Outcome can permit realizable cycles without designer-intended completion. For this backend and model, we recommend enumerated encoding with System-Goal and
auditing every realized strategy, since proposition count and realizability do not measure deployability. The auditor is sound and complete for four structural defect classes (protocol violations, deadlocks, bounded-failure violations, goal-unreachable traps) but is not a general liveness verifier, and the reduction preserves capability-level behavior. Together, these stages narrow the gap between formal realizability and controllers that pass protocol and structural-progress checks.
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All authors David C. Conner, Joshua Luzier, William J. Doyle, Emma R. Faith, Aubrie B. Kooiker, Andrew J. Farney, Sebastian Fox, Evangelina Grimes, Ian G. Conner, Kyle Bloom
Robotics
VkVIO: Cross-platform GPU Acceleration for Visual-Inertial Odometry with Vulkan
Ole Hoffmann, Mateo de Mayo and Daniel Cremers
Perception in robotics and XR fundamentally relies on good state estimation. Visual-inertial odometry (VIO) and Simultaneous Localization and Mapping (VI-SLAM) are proven ways of achieving this goal in a cost-effective and accurate manner. Efficiency in these systems allows for smaller, cooler, and lighter devices. GPU acceleration is a natural approach for reducing latency, thanks to their wide availability in platforms like embedded computers, mobile phones, and XR headsets. However, previous works in the literature have limited themselves to the use of CUDA for this task, significantly reducing deployment options to a single vendor. We instead leverage the vendor-agnostic Vulkan API, originally designed for the strict performance requirements of 3D graphics applications. In this work, we present VkVIO, the first, to the best of our knowledge, cross-platform GPU-accelerated VIO method. We provide state-of-the-art accuracy with causal estimates required for real-time operation. We deploy VkVIO on a diverse range of devices spanning a workstation, a laptop, and an extremely inexpensive single-board computer, while outperforming CUDA-based systems on the same hardware. VkVIO enables possibilities for low-latency, low-power, and low-cost VIO in robotics and XR.
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Artificial Intelligence
Shai Dickman, Mert Cemri, Landon Butler et al. (+1 more)
Reward maximization
alignment methods for discrete
diffusion models have primarily focused on steering the reverse process, either by influencing
token logits or by selecting favorable sequences at intermediate steps. These approaches largely treat inference as a unidirectional process, lacking mechanisms for revisiting undesirable
token selections. We introduce Spectral Feedback, an
algorithm that selects edit-positions in a feedback loop, allowing the model to iteratively correct its own generations. This approach leverages the mask structure of discrete
diffusion models by re-masking and re-sampling
tokens, analogous to image editing methods that reintroduce noisy latents and re-run the reverse process. While prior
alignment methods focus on what
token labels to assign to maximize a target reward, we instead treat which
tokens to revisit as the central
alignment problem. Selecting edit-positions is challenging because edit effects are interdependent: the impact of modifying one
token depends on which others are edited simultaneously. We define an edit-set as a set of
token positions to re-mask and re-sample. Motivated by prior work on sparse interactions in biological systems, we find empirically that edit-set value functions for
protein inverse folding admit sparse
Fourier representations. This structure enables Spectral Feedback to efficiently learn and optimize the value functions for edit-position selection. Spectral Feedback is model-agnostic and can be applied to pretrained, test-time aligned, and fine-tuned
diffusion models. For all of these models, the
algorithm improves
alignment performance without modifying the underlying generative process. Applied to inverse folding with a
protein stability reward oracle, it achieves a 32.3% increase in stable
proteins for a pretrained model, 24.8% for Best-of-10, and 5.8% for a state-of-the-art RL fine-tuned
diffusion model.
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All authors Shai Dickman, Mert Cemri, Landon Butler, Kannan Ramchandran
Machine Learning
Auditing System-1 Models on Biosecurity-Relevant Benchmarks: Calibration, Selective Prediction, and Permutation Instability in a Non-Generative Model
Kimon Antonios Provatas and Ilias Georgakopoulos-Soares
Non-generative "System-1" models return structured probabilistic decisions in a single forward pass, without autoregressive decoding, at a small fraction of the inference cost of a generative model. This makes them of interest as inexpensive components in larger pipelines, but their reliability on biosecurity-relevant tasks has not been systematically examined. We audit one commercial System-1 model on 6,020 multiple-choice items drawn from the Weapons of Mass Destruction Proxy (WMDP), a paraphrase-robust WMDP-Bio variant, and six LAB-Bench subtasks, measuring accuracy, calibration, error detection, selective prediction, and sensitivity to the order in which answer options are presented. Accuracy is strongly task-dependent. Once the vendor's uncertainty field is correctly interpreted, the model is reasonably well calibrated (pooled expected calibration error 0.034) and its top-1 probability separates correct from incorrect predictions (pooled AUROC 0.820), though both degrade substantially on the weaker tasks. Under four cyclic rotations of the answer options, 37.4% of WMDP-Cyber items receive different answers; a control using byte-identical repeated calls attributes most of this to option order rather than run-to-run variation. Averaging probabilities across rotations improves WMDP-Cyber accuracy by 3.8 percentage points, and applying it only to low-confidence items recovers most of that gain at well under the cost of averaging every item.
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Systems and Control
Practical Algebraic Parameter Estimation for Noisy Data via Gaussian Process Regression
Oren Bassik, Alexander Demin and Alexey Ovchinnikov
Parameter estimation for ordinary differential equation (ODE) models is a fundamental task that is often complicated by the limitations of conventional optimization-based methods. In theory, differential-algebraic approaches offer an appealing alternative: they reduce the problem to
polynomial system solving and do not require user-supplied initial guesses for parameter values. In practice, however, algebraic methods have been limited by their sensitivity to measurement noise, because they require accurate derivatives of observed outputs. In this work, we integrate Gaussian Process Regression (GPR) into the differential-algebraic method and derive a first-order error analysis in terms of noise level and algebraic sensitivity. We evaluate the method across several noise levels on a
benchmark of 25 dynamical systems arising in applications including mechanical engineering and systems biology. The proposed method achieves the highest aggregate performance among the methods considered, recovering all sought parameter values and initial conditions to within 10% relative error in 88.5% of runs. These results demonstrate that robust derivative estimation can make differential-algebraic parameter estimation practical for dense, noisy synthetic data while retaining key advantages of the algebraic formulation.
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Computer Vision
LensDesigner: A Self-Improving Agent for Optical Lens Design
Lei Sun, Haoran Liang, Dannong Xu et al. (+6 more)
Optical lens design is a complex, non-convex optimization challenge that relies heavily on human experience and intuition. Existing optimized-based automatic lens design methods struggle to navigate this vast parameter space without meticulous manual tuning. In this paper, we present LensDesigner, an autonomous
agent framework that mirrors the problem-solving workflow of expert opticians. To overcome the initial cold start problem, we construct LensLib100K, an extensive optical lens library, and employ Optics-Aware
Retrieval to supply physically valid structural seeds. Within an interactive physical simulation environment, the
agent executes macroscopic orchestration while receiving immediate optical feedback. Furthermore, we introduce a continuous self-evolving mechanism guided by a curriculum
agent. By iteratively solving design tasks with progressively increasing difficulty, the
agent autonomously extracts, accumulates, and reuses design heuristics, effectively evolving its optical lens design expertise over time. At the evaluation level, we introduce LensArena, a standardized evaluation
benchmark comprising $120$ diverse optical design tasks, covering extreme configurations. Extensive experiments on this
benchmark demonstrate that LensDesigner significantly outperforms publicly available baseline
algorithms, achieving superior success rates and optimization efficiency. We hope this work sheds light on the emerging field of intelligent optics. The code will be publicly available.
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All authors Lei Sun, Haoran Liang, Dannong Xu, Yao Gao, Yuyu Geng, Jinjin Gu, Kaiwei Wang, Danda Pani Paudel, Luc Van Gool
Human-Computer Interaction
Who Acts When the User Is Gone? Digital Remains, Survivor Claims, and Post-Mortem Governance
Supriya Khadka, Dhiman Goswami and Sanchari Das
Digital systems continue to govern accounts, devices, data, and recovery channels after an account holder dies, leaving survivors to manage digital remains through mechanisms built around a living user. We examine post-mortem digital governance as a sociotechnical problem of cooperative and contested work, focusing on who acts when the user is gone, what claims they make, and what barriers shape recovery, preservation, closure, and protection. We conducted a content analysis of $800$ Reddit posts about post-mortem digital
privacy and security, coding posts across assets, actors, actions,
privacy tensions, access barriers, policy gaps, emotional contexts, and risks. Findings show that phones/devices often act as gateways to other digital remains, socially connected actors make most claims, and data loss emerges as a central harm. We synthesize these findings into a Post-Mortem Digital Governance Framework for designing mechanisms that support survivor coordination while limiting access by purpose, asset, actor, and context.
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Performance
To Store or To Regenerate? A Cost Model for AI-Generated Content at Scale
Yunjia Zheng, Zirui Wang, Haoran Ni et al. (+4 more)
AI-generated content is becoming a rapidly growing class of digital artifacts. Because these artifacts accumulate over time, their exponential growth creates a substantial storage, energy, and infrastructure cost for operators and society. At the same time, GPU compute cost continues to fall rapidly with each hardware generation. This divergence raises a fundamental question: when does on-demand regeneration become cheaper than persistent storage? This paper develops a cost model for comparing persistent storage and on-demand regeneration for AI-generated artifacts. The model accounts for corpus growth, HDD and tape price trends, drive replacement, electricity, request skew, caching, generator FLOPs, and future GPU price-performance improvements. For image generation, our analysis shows that
prompt-based regeneration does not become cheaper than storage until around 2040, because every cache miss must still rerun the full
prompt-to-artifact generation pipeline. We observe that widely used
diffusion-based generation models operate in latent space, creating an alternative point in the cost tradeoff: instead of storing the final artifact or only the
prompt, operators can store a compact intermediate representation (IR) and perform cheap on-demand decoding. Our analysis shows that caching combined with IR-based regeneration substantially reduces both storage and compute cost, making it at least 2x cheaper than both full-object storage and
prompt-based regeneration even today. On a production image trace with 2.07 billion requests, the same conclusion holds:
prompt-based regeneration is over 100x more expensive than storage, while IR-based regeneration reduces total cost to roughly half that of full-object storage while preserving interactive miss latency.
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All authors Yunjia Zheng, Zirui Wang, Haoran Ni, Tingfeng Lan, Zhaoyuan Su, Yue Cheng, Juncheng Yang
Artificial Intelligence
Predicting Transmembrane Protein Topology from 3D Structure
Sitong Chen and Xiaopeng Mao
This paper presents a novel approach to infer
protein topology using the state-of-the-art graph neural network (GNN), SchNet. The model is trained on the same
dataset used to develop the recent DeepTMHMM model with 5-fold cross-validation. Unlike the conventional approaches based on using only the
protein sequences or the $\alpha$-carbons as features, we have decoded our classifier in this way, so all atom-level
embeddings are used. Without applying any pre-trained weight, the final results have shown great potential that GNNs can be used for topological predictions.
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