Human-Computer Interaction
Supporting Allyship in Virtual Collaboration with Artificial Intelligence: A Scenario-Based Study
Crescentia Jung, Ricardo E. Gonzalez Penuela, Prashita Biswas et al. (+2 more)
Recent research has revealed that accessibility in virtual collaboration is not only a technical problem but also depends on allyship: the informal, interpersonal practices through which people support others' accessibility needs. Yet little research has examined how artificial intelligence (AI) might support allyship in collaborative accessibility contexts. To address this gap, we conducted a study where 18 participants (10 disabled, 8 non-disabled) reflected on four allyship scenarios with novel AI tools. Participants valued AI designs that they anticipated could help teammates express, interpret, and coordinate around access needs by reducing repeated disclosure and making allyship more actionable. At the same time, participants raised concerns when AI acted without consent, misrepresented users' intentions, or displaced the interpersonal work of allyship. Based on our findings, we introduce allyship-support tools as a class of accessibility technologies, offer design guidelines, and discuss AI in particular as a means of supporting allyship.
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All authors Crescentia Jung, Ricardo E. Gonzalez Penuela, Prashita Biswas, Seungyoon Kwon, Shiri Azenkot
Systems and Control
AI Data Centers Meet Electrical Grids: A Review of Power Challenges and Coordinated Solutions
Mariam Mughees, Yuzhuo Li, Yize Chen et al. (+1 more)
Artificial intelligence (AI) workloads are creating large, power-electronic loads whose effects on power systems depend on location, temporal variability, controllability, and interactions with other loads and resources. Annual electricity consumption alone cannot capture these effects. This review examines how AI data centers affect power systems stability, renewable-energy integration, and low-carbon planning by tracing the power-delivery chain from accelerators and workloads through rack converters, uninterruptible power supplies (UPS), storage, and microgrids to the transmission point of interconnection. Reported oscillatory events and multi-gigawatt load transfers highlight how facility responses can amplify disturbances, while programmable workloads and controllable power converters offer opportunities for demand flexibility. Challenges and mitigation strategies are organized across device, rack, facility, and power systems levels, spanning millisecond-to-year timescales. A bandwidth-matching framework relates disturbances to the response capabilities of mitigation resources. The review distinguishes computational energy efficiency from power systems and sustainability outcomes: lower energy consumption per
token does not necessarily reduce peak demand, electrical disturbances, or carbon emissions. It examines how coordinated workload scheduling, grid-interactive UPS systems, storage, high-voltage DC distribution, and flexible interconnection can support reliability, efficiency, and decarbonization. Key research needs include dynamic load models, high-bandwidth telemetry, carbon-and grid-aware scheduling, and mechanisms for valuing flexible computational demand.
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All authors Mariam Mughees, Yuzhuo Li, Yize Chen, Yunwei Ryan Li
Machine Learning
EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks
Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt et al. (+7 more)
Sparse GNN training reduces computation, but deciding which edges to keep can be costly. Reusing one sparse graph is cheap, but locks training to a fixed
topology, while varying it across epochs can require repeated sampling or recomputation. We introduce EDiS (Edge-Disjoint Subgraph sparsification framework), which separates one-time structural extraction from per-epoch graph composition. EDiS decomposes the graph once into cacheable edge-disjoint subgraphs, then recombines them into graphs with edge-budget constraints across epochs and retention ratios without re-extracting structure. Our default construction uses feature-based scores and successive maximum score covering forests, while the same composition mechanism also supports alternative edge selection rules. We provide a combinatorial analysis of the per-epoch sampler, the composition step that draws a training graph from the cached decomposition. We show that, under the default covering-forest selector, the stored decomposition deterministically preserves high-score cut edges, and we derive a selector-agnostic conditional bound on high-score cut survival in composed training graphs. Across 19 homophilic, heterophilic, and large-scale node classification
benchmarks against 17 baselines under the same edge budget, EDiS achieves the highest mean
benchmark score (accuracy/ROC-AUC) and the lowest average rank and gap-to-best among ranked methods. Ablations show the clearest benefits of structural decomposition and epoch variation at tight edge budgets.
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All authors Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt, S M Ferdous, Ryan A. Rossi, Nesreen K. Ahmed, Baris Coskunuzer, Alex Pothen, Lakshman Tamil, Mahantesh M Halappanavar
Machine Learning
Learning Cross-Model Activation Alignments with Explicit Many-to-Many Layer Maps
Alina Sudakov, Guy Bar-Shalom, Fabrizio Frasca et al. (+1 more)
LLMs are released at a rapid pace, raising a natural question: how do two independently trained models relate, both in which layers correspond and in how features transform between them? We study this by learning an activation
alignment, a map from a source model's layerwise activations to a target's. Our method, MATCHA, factors this map into a layer map, whose output is an explicit target-by-source
matrix that can be extracted and inspected, and a layer-shared feature map between hidden spaces. Most of prior work fixes the layer correspondence in advance, pairing layers at roughly the same relative depth; in contrast, we learn both factors jointly from
prompts. Across 42 pairs of seven models spanning three different families, MATCHA reconstructs the target's activations more faithfully and improves
retrieval-based metrics substantially, w.r.t. previous approaches. The recovered maps are broadly monotone in depth but, in contrast with most previous approaches, are consistently many-to-many: each target layer draws on a band of source layers. Our
alignments also enable transfer of activation-space interventions, allowing steering vectors and probes developed for one model to transfer to another.
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All authors Alina Sudakov, Guy Bar-Shalom, Fabrizio Frasca, Haggai Maron
Artificial Intelligence
Epistemic Uncertainty-Aware Defect Detection for Quality Control in Medical Device Manufacturing
Raham A. Butt, Marco Romanelli and Roche C. de Guzman
Objective: We investigate whether accounting for epistemic uncertainty can improve the reliability of automated defect detection in medical device manufacturing. Methods: We consider a machine learning framework that operates on heterogeneous manufacturing and device-report data represented with Knowledge Graphs. To mitigate errors arising from uncertainty in the decision model, we analyze a principled rejection strategy to abstain from predictions whose estimated epistemic uncertainty exceeds a specified threshold. We evaluate the approach using standard synthetic
benchmarks and real-world medical device report data. Results: The theoretical results establish the validity of the method characterizing the regimes under which it is expected to be effective. Empirically, the rejection strategy enables explicit control of coverage, that is, the proportion of samples for which the model issues predictions, while improving performance on the retained samples. On 266,170 real-world FDA MAUDE device reports, a 10% abstention rate reduces classification error by 48%, and more aggressive rejection (approximately 70% coverage) yields near-perfect accuracy on the retained samples. On standard synthetic manufacturing
benchmarks, abstaining on 9% of the decisions, our approach reduces the risk up to 63% compared with the standard no-abstention approach. Conclusions: Abstaining from predictions with high epistemic uncertainty can provide a practical tool for controlling the reliability of machine learning-based defect detection, especially in high-stakes medical device manufacturing applications. Significance: Uncertainty-aware defect detection may support safer and more reliable quality assurance in medical device manufacturing by identifying cases that require additional inspection rather than issuing potentially harmful predictions.
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Machine Learning
A Geometry-Based Capacity Theory for Finite-Feature Associative Memory
Jianhai Zhang, Donghao Zhang, Pattarawut Charatpangoon et al. (+4 more)
We develop a geometry-based capacity theory for exact-key
retrieval in compressed finite-feature Hebbian associative
memory. For random or approximately isotropic values,
retrieval interference separates into finite-feature noise, which decreases with feature dimension, and structural interference, which is determined by squared kernel overlap among stored keys and persists in the infinite-feature limit. This yields a fit-free prediction of
retrieval quality, reveals a geometry-dependent capacity ceiling, and predicts the feature budget required for a target
retrieval quality. When stored values are correlated, we show that
retrieval depends jointly on the key kernel and value Gram
matrix, and derive finite-feature approximations that account for this interaction. We validate the theory on synthetic, visual, and medical-image representations. Overall, the framework links representation geometry directly to
memory capacity and distinguishes when performance can be improved by increasing the feature budget and when the representation itself must be changed. Across these settings, the predicted
retrieval curves closely match empirical behavior and correctly identify changes in the preferred
memory design.
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All authors Jianhai Zhang, Donghao Zhang, Pattarawut Charatpangoon, Bijoy Menon, M. Ethan MacDonald, Wu Qiu, Aravind Ganesh
Robotics
MimicX: Policy-in-the-Loop Supervision Refinement for Video-Driven Humanoid Motion Tracking
Shuaijun Liu, Chenglong Zhang, Xuhao Liu et al. (+7 more)
Human
videos provide rich motion targets for
humanoid learning, yet visually plausible references can still produce persistent failures under physics-based execution. These failures reveal where training supervision should change. We present MimicX, a policy-in-the-loop framework that uses execution feedback to refine
video-driven
humanoid motion tracking. Starting from reconstructed and retargeted motion, MimicX localizes difficult transitions and affected body regions, then jointly adapts tracking objectives and the reset curriculum for policy continuation. Repeated rollout verification selects execution-priority improvements subject to tracking guards. Across four core
video tasks, MimicX consistently improves tracking accuracy and Robust Execution Horizon relative to the Fixed Reference baseline. Task-averaged results show a 25.7% reduction in body-tracking error and a 255.6% increase in execution horizon. Additional
video, motion-reference, and collision-scene studies evaluate the method beyond the core tasks, while MimicX-HLoop accelerates feedback through heterogeneous execution. Overall, MimicX turns policy failure into actionable supervision for deciding what to refine and which refinement to retain.
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All authors Shuaijun Liu, Chenglong Zhang, Xuhao Liu, Feiyang You, Yifan Liao, Shuyang Hao, Chaozhe Zhang, Chengyu Wu, Zhen Sun, Ningxin Su
Artificial Intelligence
Justice After Identity: Large Language Models and the View from Everywhere
W. Russell Neuman
The search for a common view of justice and
fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of social position, personal benefit, cultural inheritance, and historical circumstance. John Rawls famously attempted to overcome this limitation through popularizing a philosophical tradition known by the phrase "the original position" - a thought experiment by which people select principles of justice without knowing the identities or advantages they will possess. Critics, however, have long questioned whether people can meaningfully suspend their social identities and suppress morally relevant forms of lived experience. Artificial intelligence engaged to calculate algorithmic and agentic
fairness introduces a novel possibility. LLMs have no singular class, race, gender, nationality, or biography, yet their parameters encode linguistic representations of a vast range of human identities and moral traditions. Perhaps the ethical judgments of LLMs could approximate an integrative original position - a "view from everywhere" generated not by excluding social identities but by computationally incorporating their diversity.It is unlikely that humankind will "hand over the keys" to computational systems by simply delegating complete agentic control of distributive and procedural collective processes. But AI may play a role, perhaps a positive one, interacting with individual and collective human judgment as we often confront increasingly polarized views on what is fair and just. We present data comparing human, base model, and frontier/fine-tuned model judgments about classic moral dilemmas while systematically varying identity relationships and Rawlsian constraints on identity. We conclude by speculating whether, if advanced AI systems provide humans with thoughtful advice, humans would actually be likely to accept it.
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Machine Learning
BeatFlow-ECG: Rectified Flow for ECG Reconstruction from Indirect Wearable Signals
Mohamed Kamel, Sahar Selim, Walaa Medhat et al. (+1 more)
Continuous cardiac monitoring outside clinical settings requires signals that are both informative and practical to collect during daily life. Electrocardiography (ECG) provides rich information about cardiac rhythm and waveform morphology, while wearable photoplethysmography (PPG) is easier to acquire continuously but is only an indirect cardiovascular measurement and is highly sensitive to motion. We present BeatFlow-ECG, a conditional rectified-flow model for reconstructing single-channel ECG from synchronized PPG and inertial measurements. BeatFlow-ECG models reconstruction as conditional transport from noise to ECG using a convolutional encoder-decoder with a
transformer bottleneck and explicit flow-time conditioning. Motion information is incorporated through IMU-derived conditioning features, motion-dependent loss weighting, and an easy-to-hard training curriculum. We evaluate the model under leave-one-subject-out protocols on PPG-DaLiA and WESAD. BeatFlow-ECG achieves the best results among the evaluated deterministic,
adversarial, and
diffusion-based baselines across all reported waveform and beat-timing metrics, with Pearson correlations of 0.983 and 0.986 and R-peak F1 scores of 0.946 and 0.955, respectively. Compared with Conditional DDPM-1D, L1 error decreases from 0.085 to 0.062 on PPG-DaLiA and from 0.074 to 0.055 on WESAD. Additional analyses on PPG-DaLiA show higher correlation in fixed R-peak-relative waveform regions and lower reconstruction error across low-, medium-, and high-motion subsets.
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All authors Mohamed Kamel, Sahar Selim, Walaa Medhat, Tamer Nadeem
Machine Learning
Davis Wertheimer, Haochen Shen, Ahan Gupta et al. (+6 more)
The large KV-cache size of modern LLMs creates a barrier to efficient deployment. Recent work has explored replacing
attention layers' RoPE positional
embeddings with alternative decay-based mechanisms, which can then be used to prune KV-cache during inference. However, these decay functions have limited expressivity, and in practice devolve into sliding-window-like eviction patterns. In this work, we propose a unifying framework for complementary and novel decay mechanisms, capturing complex key statistics and interactions while preserving expressive RoPE
embeddings and Softmax
attention. The resulting Universal
Attention is a highly expressive and end-to-end trainable architecture, whose composite decay mechanism acts as a natural, $\textit{adaptive}$
pruning criterion, removing
tokens that contribute least to
attention computation. Experimentally, Universal
Attention achieves state-of-the-art $10\times$ compression on natural
language and synthetic task data, while $\textit{improving}$ downstream performance compared to both state-of-the-art baselines and unpruned oracles. It further demonstrates superior long-context generalization with unprecedented $25\times$ compression at length 16k.
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All authors Davis Wertheimer, Haochen Shen, Ahan Gupta, Derrick Liu, Yu Chin Fabian Lim, Mudhakar Srivatsa, Raghu K. Ganti, Minjia Zhang, Naigang Wang
Data Structures and Algorithms
Efficient Algorithms for Online Subadditive Combinatorial Allocations
Calum MacRury, Rian Neogi, Kanstantsin Pashkovich et al. (+3 more)
For the online combinatorial allocation problem with subadditive valuations, Correa and Cristi (STOC 2023) proved the existence of a $6$-competitive online
algorithm, improving on the previous best $O(\log\!\log m)$-competitive online
algorithm due to Dütting, Kesselheim, and Lucier (FOCS 2020), where $m$ is the number of items. However, Correa and Cristi's result is existential, and it was left open whether a constant competitive ratio is attainable via an efficient online
algorithm that uses a
polynomial number of demand oracle queries. In this work, we answer this affirmatively, giving an expected-
polynomial-time $(6 + \epsilon)$-competitive online
algorithm for any constant $\epsilon > 0$. Our techniques also recover, in the offline setting, the $(2 + \epsilon)$-approximation result of Feige (STOC, 2006). Finally, when the buyers' valuations are drawn from identical distributions, we exploit
symmetry to obtain an improved $(60/11+\epsilon)$-competitive
algorithm. Starting with the natural configuration LP relaxation for the problem, our main technical contribution is a recursive Bundle Score Generator (BSG) that resolves item conflicts by assigning correlated scores to the items requested by each buyer. Unlike the Random Score Generator whose existence is shown by Correa and Cristi, our BSG is efficiently computable in expected
polynomial time. Moreover, it satisfies a
stochastic dominance property that is sufficient to recover both Feige's offline result and Correa and Cristi's online result.
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All authors Calum MacRury, Rian Neogi, Kanstantsin Pashkovich, Sahil Singla, Siddharth M Sundaram, Chaitanya Swamy
Machine Learning
Towards Financial World Modeling
Humzah Merchant, Alec Guthrie, Simon Mahns et al. (+2 more)
Building a world model requires a state representation useful for planning and decision-making—potentially over tasks unknown at training time. In the context of financial
markets, planning and decision-making may require a model to reason about
market-wide conditions, asset-specific expected returns, liquidity,
volatility, and cross-asset relationships. Yet financial representation learning has largely been evaluated on individual predictive tasks, oftentimes on a single time period using comparatively narrow
datasets. We address this through three primary contributions. First, we introduce
Market-1T, a
dataset containing nearly one trillion observations across U.S. equities from 2008 to 2025 at 1 Hz resolution. Second, we develop and implement a rigorous evaluation protocol. Third, we conduct a systematic large-scale study of financial representation learning, comparing 18 encoder-training strategies across nearly two decades of
market regimes. We evaluate learned representations both by their predictive utility on common finance tasks and through probes of latent structure. We find that encoders with similar predictive performance can organize
market state very differently. Collectively, we establish a foundation for training and evaluating financial
market representations in support of world models such as DINO-WM, V-JEPA 2, and LeWM.
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All authors Humzah Merchant, Alec Guthrie, Simon Mahns, Randall Balestriero, Bradford Levy
Machine Learning · Cross-listed from stat.ML
Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models
Yuzhen Zhao, Yating Liu and Quentin Guibert
We study the problem of learning transition kernels for time-homogeneous jump-
diffusion processes using conditional
diffusion models, with the goal of generating new sample paths from training data consisting of N independent trajectories observed on a high-frequency discrete time grid. On the theoretical side, we establish non-asymptotic bounds for the conditional score estimation error and for the KL divergence between the laws of the true and generated discretely observed paths. On the numerical side, we first evaluate our method on synthetic data to assess the theoretical findings and
benchmark its performance against the approach of Gao et al. (2025). We then apply our method to real-world data and investigate its performance on a probabilistic
forecasting task.
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Machine Learning · Cross-listed from stat.ML
Quadratic Weak-to-Strong Generalization in Random Feature Networks via Random Matrix Theory
Deborah Oliveira and Elliot Paquette
Weak-to-strong generalization is the phenomenon where a strong student model trained with labels produced by a weak teacher model is able to generalize better than the teacher. In this paper, we study this phenomenon in two-layer random feature networks where the model strength is determined by its width. Using tools from random
matrix theory, we derive deterministic equivalents for the population errors of an optimally trained teacher and a student trained with gradient flow. For ReLU activation and a pure spherical harmonic target, we obtain sharp asymptotics under a Gaussian universality assumption, showing a quadratic improvement: the student error scales as the square of the teacher error. These results attain the general lower bound of Medvedev at al (2025). We also analyze how the student behaves under more general stopping times and targets supported on multiple harmonic degrees, characterizing the regimes in which weak-to-strong generalization occurs and identifying the transition between quadratic, non-quadratic, and no improvement.
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Artificial Intelligence · Cross-listed from stat.ML
Careful Judge: Safe and Efficient Human-AI Collaborative Decision Making
Chenyu Zhang, Rachel Luo, Boyi Li et al. (+3 more)
In human-AI collaborative decision making, human review can prevent unsafe AI decisions, but each human judgment is costly. Treating human intervention after AI abstention as a one-off fallback misses the opportunity to improve future AI decisions for greater automation, yet AI adaptively learning from selectively queried human feedback breaks safety guardrails calibrated for old models. We approach this challenge with CARE—calibrated adaptive rectification and escalation—an end-to-end pipeline that combines AI models and human reviewers to guarantee safe, human-aligned decisions, while continuously learning from human feedback to achieve greater automation with fewer human queries. CARE is principled, general, modular, and works with any black-box AI model. Our novel adaptive calibration module guarantees risk control at every time step for any rectification module. We further show how CARE improves query efficiency when the AI model is well trained and the human-AI misalignment has a clear structure. Experiments on four safety-critical real-world
datasets spanning driving,
language, and robotics demonstrate that CARE achieves human-aligned decisions while reducing human queries by 25-81% relative to baselines.
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All authors Chenyu Zhang, Rachel Luo, Boyi Li, Anjali Parashar, Marco Pavone, Apoorva Sharma
Discrete Mathematics · Cross-listed from math.CO
Distinguishing graphs with simple spectrum by homomorphism counts
Takanori Maehara
We study which graph classes determine every graph with simple spectrum up to isomorphism by homomorphism counts from their members. We show that such a class has unbounded treewidth and Euler genus and contains a graph with the complete graph on seven vertices as a minor. To prove these conditions, we construct pairs of non-isomorphic cospectral graphs with simple spectrum, each bipartite, nonsingular and of maximum degree at most six. The construction attaches rooted trees to Cai-Furer-Immerman graphs and preserves homomorphism indistinguishability over every minor-closed class. We then prove that subcubic homomorphism counts determine every graph whose adjacency
matrix has kernel dimension at most one. This extends the known result for nonsingular graphs and applies to all graphs with simple spectrum. We also prove that two graphs have the same subcubic homomorphism counts if and only if they have the same number of vertices and there is a bijective linear isometry between the images of their adjacency
matrices that intertwines the adjacency operators and preserves the coordinatewise trilinear forms. Finally, we prove that homomorphism counts from the single-vertex graph and from subdivisions of any sequence of connected cubic graphs have the same distinguishing power as subcubic homomorphism counts if every complete graph is a minor of some member of the sequence.
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Databases
Building Navigable Graphs Without Search in Three Composable Stages
Édgar Chávez
Navigable graphs can be built without searching for neighbors: partition the data, evaluate every pair inside each part, and select each point's edges from the candidates. We give such a construction in three separable stages and show that the middle one decides the quality. The pool is any partition with a few memberships per point. The ending turns a point's candidates into out-edges; ours keeps a bounded heap, prunes by occlusion with a per-corpus slack, and appends reverse edges, re-
pruning only where a list overflows. The spine is any edge set, exempt from the prune, that keeps the graph reachable from its entry; ours, half-space-proximal edges over a random sample, routes monotonically to every sampled point and replaces a spanning tree at 1/10 to 1/500 of its cost. The ending composes with any partitioner: on PiPNN's own candidate pool it beats PiPNN's ending on each of six corpora from $10^6$ to $10^8$ points, by 3 to 14% in distance evaluations at equal recall, and with 60 to 120 memberships per point the composed build matches or beats a full dense construction at k=10 and k=100 on all six, in 0.5 to 0.9 of its build time, deterministically. The analysis explains why. Once a pool is localised its quality is set by the data: every pool built on GIST lands within 4% of the exact-kNN ceiling, and the pairs a block cover misses are predicted, point by point, by the local clustering of the kNN graph, whose zero-clustering tail sets the memberships a corpus needs and grows with n. All code, patches and logs are public.
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Machine Learning
ORACLE: Optimizer-Relative Alignment for Constrained LEarning
Utkarsh Grover, Wyatt Mackey, Kaixun Hua et al. (+2 more)
Constraint handling methods typically intervene before the optimizer acts, by modifying the objective or the gradient. Yet momentum, adaptive scaling, and structured preconditioning can substantially reshape that signal before it becomes a parameter update. We formulate optimizer relative constrained learning, where constraint compatibility is assessed on the post optimizer update. Building on this view, we introduce ORACLE, which evaluates the native optimizer's realized step through a joint endpoint linearization of heterogeneous constraint families, constructs the resulting
alignment in the optimizer's own geometry, bounds its authority, and commits it only after validation. We evaluate ORACLE across eight Partial Differential Equation
benchmarks and four optimizers spanning Euclidean, diagonal adaptive, and structured preconditioned geometries, where it improves or matches native optimizer in 94% of configurations. Cross model analysis shows the same behavior in 92% of configurations, while matched comparisons show improvements over alternative constraint-handling methods acting at the objective, gradient, and post-optimizer levels.
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All authors Utkarsh Grover, Wyatt Mackey, Kaixun Hua, J. Morris Chang, Xiaomin Lin
Artificial Intelligence
From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents
Mahesh Viswanathan, Joan Rossello, Leticia Fernandes et al. (+1 more)
Large
language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically overlapping, or too numerous for downstream systems and human reviewers to use effectively. We present a
dataset-adaptive post-processing architecture developed for Customer Intent Extraction (CIE), where unstructured customer
language is transformed into stable, traceable intent units. The approach separates recall-oriented extraction from utility-oriented reduction. Source-specific preprocessing first isolates evidence from
multimodal plans, sparse operational records, and structured opportunity data. Candidate intents are then standardized and deduplicated, optionally enriched with metadata for
embedding computation, represented in a shared semantic vector space, and grouped using a clustering strategy selected according to the candidate set's characteristics. Cluster-level keywords provide an explainability layer, while singleton reassignment requires agreement between
embedding and keyword similarity. Finally, constrained
language-model aggregation produces one concise intent per cluster without introducing unsupported concepts, and the resulting unit retains provenance, clustering,
embedding, and generation metadata. This treats post-processing not as cosmetic cleanup, but as a semantic reduction layer converting high-recall LLM outputs into reusable enterprise intelligence. We also describe two downstream applications: Machine-Generated Intents, which infer likely objectives for customers lacking direct evidence from peer customers with similar profiles, and intent-guided semantic
retrieval and mapping, which uses the stable intent as a query against a downstream decision space, illustrated here by mapping customer intents to business outcomes.
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All authors Mahesh Viswanathan, Joan Rossello, Leticia Fernandes, Paul Mutawe
Artificial Intelligence
When the Governor Becomes the Disturbance: Control-Generated Disturbance and Cost-Aware Backoff in Governed Tool-Using Agents
Veronique Ziegler
Supervisory governors can interfere with the tool-using
agents they regulate. We study this possibility in a controlled file-recovery environment where increases in regulatory intensity trigger experimentally imposed tool failures. A cost-blind governor can turn these failures into persistent blocking that prevents task completion. We compare this governor with a backoff rule that reduces intervention probability using a moving average of known induced events. On a hand-coded
stochastic-policy
agent, the failure pattern appears under both result replacement and execution of corrupted tool arguments. For the persistent policy, adaptive backoff improves completion relative to a fixed weak governor with approximately matched intervention frequency. A Gemini 2.5 Flash experiment comprising 576 episodes across 6 tasks also shows reduced blocking and improved completion under backoff; among the tested settings, intermediate backoff strength achieves the highest observed aggregate success. These results identify an interaction between intervention cost and persistent action blocking, together with a possible mitigation. The cost mechanisms are imposed and their induced events are directly observable to the backoff rule; applicability beyond this controlled environment remains an empirical question.
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Artificial Intelligence
Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network
Brandon Ho, Nikola Rogers and Seung-Kyum Choi
Multi-
agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation methods, such as
lattice grids or standard sampling-based approaches, frequently face a trade-off between graph density and the likelihood of finding feasible, high-quality solutions. In this paper, we propose a scalable heterogeneous Graph Neural Network (GNN) framework for the automated generation and evaluation of shared multi-
agent roadmaps. Our model covers the representation of waypoints,
agent locations, and task locations as distinct nodes in a heterogeneous graph, allowing it to reason over global connectivity and inter-
agent interactions. By training on occupation density maps aggregated and collected from expert solver trajectories, the GNN learns to identify critical points of interest and prune redundant nodes and edges. This process produces a compact, coordination-aware roadmap that is invariant to task permutations and is reusable for multi-
agent pick and delivery tasks. Experimental results demonstrate that our framework can reduce planning effort and can potentially find better solutions, reaching at least 40% reduction in runtime and in graph size for dense roadmaps.
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Computation and Language
Quad-State Safety Evaluation of Open-Weight Large Language Models on Non-Canonical Inputs
Pavan Maddula
Standard safety evaluations of large
language models assess harmful requests written in canonical plain text, while models in real-world deployment routinely receive inputs containing emojis, altered spellings, encoded strings, and character-level variations. This work introduces the
Adversarial Surface-Form Robustness
Dataset (ASRD), comprising 2,100
prompts across seven distinct surface-form families. Five open-weight
language models are evaluated across these
prompts, producing 10,500 responses. The Quad-State Evaluation Rubric classifies each response into one of four outcomes: harmful compliance, safe response, comprehension failure, or indeterminate. Emoji and invisible Unicode variations cause almost no comprehension failure, with pooled harmful compliance of 20.27% and 17.20% against a 22.87% baseline that is driven mainly by Mistral 7B, whereas leetspeak, encoded wrappers, and hybrid transformations score 2.40%, 0.13%, and 2.40% while comprehension failure rises to 36.47%, 65.60%, and 34.47%. Inspection of raw model outputs reveals three response behaviors: hallucinated benignity, structural collapse, and
language drift. Project page: www.pavanmaddula.com/quadstate
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Computer Vision
Shape-Bayes: Bayesian Inference of Structured Shapes under Visual Ambiguity
Mani Kumar Tellamekala, Tosh Brown and Michel Valstar
Perceiving structured shapes, such as human faces, from pixels is an inherently ambiguous task in real-world conditions. Yet, shape inference is largely posed as a deterministic regression task predicting fixed spatial coordinates. We find that deterministic regression is brittle when visual evidence is ambiguous or incomplete; under severe occlusions deterministic models exhibit structural collapse, predicting incoherent shapes or reverting to generic averages. To address this, we introduce Shape-Bayes, a probabilistic framework that couples uncertainty-aware visual perception with
Bayesian shape
reasoning. Rather than forcing point estimates, Shape-Bayes dynamically weights visual evidence against
geometric priors to infer a structurally valid shape posterior. Demonstrated on human face shape regression, a rigorous testbed featuring complex non-rigid deformations and strict anatomical constraints, Shape-Bayes comprises: (1) a base model predicting noisy landmarks alongside distilled aleatoric uncertainties; (2) a lightweight
Transformer encoding these observations into an adaptive prior over a PCA shape
manifold; and (3) a differentiable
Bayesian solver computing closed-form posteriors by balancing the noisy predictions against this prior. By guaranteeing complete structural integrity, Shape-Bayes achieves an absolute improvement of up to ~34% IDR over state-of-the-art deterministic models. Simultaneously, it yields highly calibrated uncertainty bounds and reduces relative error by up to 12.5%, establishing a new state-of-the-art for robust 2D face shape regression under severe occlusion. The project page is at https://shape-bayes.github.io.
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Computer Vision
Beyond Explanation: Debugging Medical Imaging Models via Concept Intervention
Samrajya Thapa, Daniel J. Quest, Timothy L. Kline et al. (+3 more)
Medical imaging models often operate as black boxes, limiting interpretability and systematic debugging. We introduce an easy-to-use, plug-and-play framework for concept-based interpretation and model refinement. By aligning a single-modality encoder to BioMedCLIP, we construct a Concept Bottleneck Model (CBM) that enables concept-level interventions. These interventions allow us to isolate causal versus spuriously correlated concepts, validate insights with domain experts, and generate counterfactual samples for targeted fine-tuning. We evaluate our framework on a Mayo Clinic ultrasound
dataset and the CheXpert 5x200 chest X-ray
dataset. Results demonstrate that concept intervention enables reliable model diagnosis while maintaining, and occasionally improving predictive performance via guided fine-tuning. Our findings highlight the practical value of this framework for controlled, interpretable refinement of clinical deep learning models.
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All authors Samrajya Thapa, Daniel J. Quest, Timothy L. Kline, Carrie L. Langstraat, Emanuel C. Trabuco, Wei Le
Cryptography and Security
Cost of Delay for Post-Quantum Migration: Putting Classical and Harvest-Now-Decrypt-Later Risk on One Ordered List
Animesh Shaw
Organisations deciding which assets to migrate to post-
quantum cryptography first, and how that work competes with a backlog of classical findings, lack a common unit: post-
quantum scores are dimensionless and
quantum-only, while classical risk is annualised loss. We express both threats as a cost of delay in currency per year on the same asset. The
quantum term is the rate at which deferring migration commits irreversible harvest-now-decrypt-later loss, computed from required confidentiality duration, recordable traffic share and data turnover, using a
quantum-arrival law fitted in closed form to published expert anchors. A competing-risks factor couples the two terms so the same loss is not counted twice. We prove that the construction is not a weighted sum of a classical and a
quantum score, give a dominance threshold on the classical hazard that is independent of asset value, and show that the induced order is optimal for sequencing work under constant loss rates. We verify the implementation by simulation, adaptive quadrature and a second implementation. On four public systems with parameters assumed from public documentation, the
quantum term reorders the asset list beyond input uncertainty for one system only (signal-to-noise 1.36 against 0.14-0.46), moves specific long-lived, quiet, recordable assets decisively, and otherwise changes magnitudes. Asset value and turnover explain 73-91% of the uncertainty in the
quantum term; the arrival date explains 3-17%. The case studies are illustrative and are not validated against outcomes.
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Cryptography and Security
Visual Memory Attacks Can Persist Through The KV Cache
David Dobre, Leo Schwinn, Gauthier Gidel et al. (+3 more)
Modern
language model systems operate autonomously over increasingly long contexts containing untrusted text and images. Can an
adversarial input continue to steer a model even after that input is removed from its context? We show that attacks can be trained to persist through the key/value (KV) cache of subsequent
tokens, allowing
adversarial influence to outlive direct access to its source.We consider the Visual
Memory Injection (VMI; Schlarmann and Hein, 2026) attack setting, in which an
adversarial image that stays in the context plants a hidden
backdoor: the model behaves normally until a chosen trigger elicits an attacker-chosen response. We first demonstrate persistence in this setting with optimized soft
prompts, which remain effective after we mask the
prompt from
attention. We then introduce Persistent Visual
Memory Injection (P-VMI), which optimizes images to preserve this
adversarial behaviour after they are masked from
attention. These attacks persist over conversations substantially longer than those used during optimization. On Qwen3-VL-8B-Instruct, P-VMI achieves up to approximately $90\%$ target success in its strongest configuration and remains effective under a stricter removal setting that exposes the image only on the first turn. A cache-swap ablation localizes the persistent influence to the KV cache. Finally, we show that these attacks can be trained to survive compaction that retains the KV cache of a summary generated by the same model, demonstrating that
adversarial behaviour can persist in cached state without continued access to its source.
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All authors David Dobre, Leo Schwinn, Gauthier Gidel, Spandana Gella, Perouz Taslakian, Pierre-André Noël
Artificial Intelligence
BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment
Kemal Davaslioglu, Nathan Conger, Sastry Kompella et al. (+2 more)
We present BEACON-SP, an ontology-grounded Graph
Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention, where effective assessment requires integrating heterogeneous clinical, behavioral, social, and temporal evidence. BEACON-SP combines patient knowledge graphs with ontology-guided
retrieval to support multi-hop
reasoning across diagnoses, medications, risk and protective factors, life events, and temporal relationships. The framework is enabled by a comprehensive suicide prevention ontology that integrates the Three-Step Theory, the Integrated Motivational-Volitional Model, and the Suicide Social Determinants of Health Ontology into a unified representation of patient risk factors. We construct ontology-grounded patient knowledge graphs and evaluate BEACON-SP for clinician-facing question answering. Compared with a vector-based
retrieval-augmented generation (RAG) baseline on a 1,500-query
benchmark spanning 15 clinical categories and 100 patients, BEACON-SP improves completeness, clinical relevance, and evidence grounding under a corrected comparative evaluation protocol, with a small gain on factual accuracy. In paired criterion-level comparisons, GraphRAG is preferred in 76.4% of cases. These results demonstrate the potential of ontology-guided GraphRAG to provide structured, contextualized patient evidence for clinical decision support.
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All authors Kemal Davaslioglu, Nathan Conger, Sastry Kompella, Yalin E. Sagduyu, Nathaniel D. Bastian
Machine Learning
SPIN: Shadow Predictive Indexer for Sparse Attention
Yao Fu, Jiahan Chang, Ritchie Zhao et al. (+11 more)
Indexer-based sparse
attention reduces the cost of core
attention by passing only a fixed, small number of important
tokens to it. However, the indexer must still score the entire KV cache at every decoding step. This scoring overhead becomes a major bottleneck as the context length grows. We propose
SPIN (Shadow Predictive Indexer) to reduce this indexer overhead.
SPIN uses lightweight, history-based prediction to identify important KV blocks, avoiding the need to score the full KV cache at every decoding step.
SPIN treats KV blocks and speculative decoding as first-class design and implementation considerations. Across extensive evaluations on long-context and agentic
benchmarks,
SPIN achieves 30-40% sparsity while preserving task quality. In end-to-end vLLM serving,
SPIN improves output throughput by up to 14.9% and reduces median inter-
token latency by up to 13.2%.
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All authors Yao Fu, Jiahan Chang, Ritchie Zhao, Bryce Long, Yueying Li, Mahdi Kamani, Samkit Jain, Rahul Raman, Tara Safavi, Shreya Gupta, Parsa Ashrafi Fashi, Minseok Lee, Julien Demouth, Bita Darvish Rouhani
Software Engineering
Evaluating Change Point Detection Methods for Software Performance Regression Analysis
Diego Elias Costa, Michele Tucci, Luca Traini et al. (+8 more)
Performance issues in software systems are a critical quality issue that can erode user trust, violate service-level agreements, and ultimately affect business efficiency. Consequently, software performance engineering has shifted its focus to developing robust techniques to detect performance regressions as early as possible in the development cycle. Performance regression analysis often relies on time series of performance measurements to detect significant changes in performance behavior. Change Point Detection (CPD) methods have been widely used to automate the identification of such changes in various domains, including finance, healthcare, and performance monitoring. However, the effectiveness of these methods for software performance measurements has not been thoroughly evaluated. In this paper, we present a comprehensive study to evaluate the effectiveness of various CPD methods on real-world software performance measurement
datasets. We start by collecting performance measurement data from three large software systems and characterizing the unique properties of performance time series data. Then, we undertake a large-scale effort to annotate and evaluate the consistency of human annotators' identification of potential performance changes. Thereafter, we evaluate the accuracy of twelve distinct CPD methods in detecting potential performance changes, providing insights into their applicability and effectiveness in software performance regression analysis.
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All authors Diego Elias Costa, Michele Tucci, Luca Traini, Daniele Di Pompeo, Thomas Bach, Francois Farquet, David Daly, Simon Eismann, Petr Tůma, Vittorio Cortellessa, André van Hoorn
Artificial Intelligence
Not Every Call Needs a Frontier Model: Per-Call-Site Evaluation of Small Language Models in a Deployed Agentic Home-Automation System
Panagiotis Kasnesis, Christos Chatzigeorgiou, Lazaros Toumanidis et al. (+1 more)
An agentic system issues several structurally different kinds of LLM calls. It routes intent, classifies actions, grounds
language in a device registry, plans multi-
agent pipelines and writes the Python code those pipelines run. The difficulty of these call sites varies by an order of magnitude, yet in practice a single model, chosen for the hardest site, serves all of them. In this work, we evaluate 9 models from 0.8B to a frontier hosted model across the five call sites of a deployed open-source home-automation framework (Wactorz), using its unmodified production
prompts and two real Home Assistant installations (280 cases, 2520 scored calls). We find that capability is not ordered the same way at every site, and that larger models are not uniformly better: one 4B model is worse than its 2B sibling at grounded actuation. Paired testing shows the best local model to be statistically indistinguishable from both hosted models at four of five sites. Only code generation separates them, against a small hosted model (p = 0.039) as well as a frontier one (p = 0.002). Aggregate accuracy also hides a safety failure specific to actuation, where small models resolve the accuracy/refusal trade-off in degenerate ways: one model (Gemma4 E2B) actuates on 87.2% of requests for devices the site does not own, while another refuses every request it receives. Routing each site to its best local model reaches 91.8% against 95.4% at no per-call cost. In a live deployment judged by a user, hosting only the two generative sites matches hosting everything (39/43 against 39/43) for 28% of the spend, and the actuation gap the
benchmark predicted appears as exactly one case in twenty-six.
Benchmark, harness and all records are released at https://github.com/waldiez/slm-callsite-eval.
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All authors Panagiotis Kasnesis, Christos Chatzigeorgiou, Lazaros Toumanidis, Amalia Contiero Syropoulou