Artificial Intelligence · Cross-listed from cs.SD
Evaluating Loss Functions in Differentiable Out-of-Domain Sound-Matching with Partial Parameter Distance
Amir Salimi, Daniel Penner, Kalvin Eng et al. (+2 more)
In out-of-domain (OOD) sound-matching, a synthesizer is optimized to mimic a sound it did not generate. OOD evaluation of loss functions is underexplored in part because the standard "parameter loss" metric requires a shared parameter space between target and imitator, which OOD settings lack. We introduce Partial Parameter Distance (PPD), which applies parameter loss only to the critical parameters that mismatched synthesizers share (e.g., filter cutoffs), enabling automatically evaluated OOD experiments; we verify its results with blinded listening tests. Across seven scenarios involving band-pass filtering, amplitude modulation, and pitch-bending, we evaluate four differentiable loss functions (SIMSE_Spec, L1_Spec, JTFS, DTW_Envelope). Loss-function effectiveness remains tightly coupled to the method of synthesis: SIMSE_Spec excels at filter-cutoff recovery, DTW_Envelope at amplitude-modulation recovery, and JTFS at smooth pitch trajectories. Parameter-based evaluation agrees with listening tests on the top-ranked loss function in five of seven scenarios, demonstrating its utility as a diagnostic tool.
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All authors Amir Salimi, Daniel Penner, Kalvin Eng, Abram Hindle, Osmar R. Zaïane
Artificial Intelligence · Cross-listed from eess.IV
CARDINAL Predicts Cardiovascular Risk From Non-contrast Cardiac CT
Roy Gabriel, Nattakorn Kittisut, Jamshid Hassanpour et al. (+6 more)
Cardiovascular risk prediction remains limited by incomplete clinical data and imaging biomarkers that reduce computed
tomography (CT) to a small number of handcrafted features. We developed CARDINAL (Cardiovascular Assessment via Representation learning from Deep Imaging with Nested Anatomical Latent
embeddings), a clinically grounded framework that learns compact representations from routine non-contrast cardiac CT for major adverse cardiovascular event (MACE) prediction. In 17,659 patients, CARDINAL was evaluated for 1-, 3-, 5-, and 10-year MACE prediction against American Heart Association (AHA) pooled cohort equations (PCE), AHA predicting risk of cardiovascular disease events (PREVENT), coronary artery calcium (CAC),
segmentation-derived CT biomarkers, and 70-feature structural radiomics. Gains were largest at longer horizons. At 10 years, CARDINAL (joint) achieved an area under the receiver operating characteristic curve (AUROC) of 0.866 $\pm$ 0.020 and an area under the precision-recall curve (AUPRC) of 0.890 $\pm$ 0.015, compared with an AUROC of 0.826 $\pm$ 0.023 and an AUPRC of 0.826 $\pm$ 0.022 for structural radiomics, the strongest baseline. CARDINAL also achieved the highest survival concordance index (C-index), 0.753 $\pm$ 0.015, and high-versus-low risk-tertile hazard ratio, 10.78 $\pm$ 3.16, with favorable reclassification and exploratory calibration. These findings suggest that non-contrast cardiac CT contains prognostic information beyond conventional risk equations, CAC scoring, and engineered imaging biomarkers.
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All authors Roy Gabriel, Nattakorn Kittisut, Jamshid Hassanpour, Michael Galarnyk, Abanoub Abdelmalak, Marly van Assen, Carlo N. De Cecco, Arshed Quyyumi, Ali Adibi
Artificial Intelligence
Agents for Everyone: A Workshop Framework for Building Agentic AI Capabilities in a Distributed Curation Community
Seth Carbon, Sierra Moxon, Kimberly Van Auken et al. (+2 more)
Agentic AI has the potential to accelerate curation of biological databases and knowledge bases. However, uptake has been hindered by a number of challenges and obstacles, including access to
agents and appropriate training. Here we describe how we have attempted to address and mitigate these challenges and obstacles through the deployment of a cloud-based agentic environment, and the development of an interactive training workshop for the Gene Ontology Consortium. Our cloud environment for agentic-assisted curation was based on the JupyterHub platform, and utilized Claude Code as a universal harness. This allows curators to interact with an
agent session through a terminal running in the browser, and has additional benefits such as centralization of access through a single API gateway, removing the need for participants to manage subscriptions or install software locally. We created four training modules, walking participants through basic agentic tool use first and then working up to agentic biological pathway curation using the existing GO-CAM (GO Causal Activity Model) curation tool. Thirty-seven participants took part in the four-hour workshop. Our key takeaway from this workshop is that building community capability with agentic AI is primarily a problem of access, workflow design, and training. Removing technical barriers, introducing capabilities gradually, grounding exercises in familiar curation tasks, and giving curators direct experience evaluating
agent output can provide a practical route toward building shared agentic AI capability in distributed scientific communities.
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All authors Seth Carbon, Sierra Moxon, Kimberly Van Auken, Pascale Gaudet, Christopher J. Mungall
Artificial Intelligence · Cross-listed from cs.CL
First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents
Syed Mahbubul Huq and Pranava Madhyastha
We present Qwen-GuidePlay-2B, a 2B-parameter
language model for dialogue-game interaction. We fine-tune Qwen3.5-2B using three steps: a) SFT on only successful game trajectories from Playpen, b) weighted turn-level SFT, and c) teacher-guided SFT. The teacher model (which is a larger model) is only used to fix formatting and evaluate examples, but does not create new gold actions. Our final model scores 57.12 clemscore and 42.68 statscore on the public Playpen validation. In the officially released challenge results, our model obtains the second-highest Playpen clemscore delta among submitted systems (which is approximately +36 over its base model). Our findings suggest that imitating full trajectories helps with playability, while turn-level and teacher-guided training usually improve decision-making and increase the overall score. Alternative procedurally heavy approaches like replay-repair and hard-example mining did not help, which suggests that small models are performant simply by using careful curation strategies rather than aggressive changes. We make available both the model and the code for reproducibility.
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Artificial Intelligence
A Framework for Object-Centric Predictive Monitoring of Collaborative Processes
Daniel Calegari, Andrea Delgado, Leonel Peña et al. (+1 more)
Predictive Process Monitoring (PPM) of collaborative, inter-organizational processes requires
reasoning over multiple interdependent entities, including participants, messages, local executions, and the global collaboration case. Existing approaches extend traditional event logs with collaboration attributes but retain a single-case perspective, leaving much of this structure implicit. Object-centric process mining (OCPM) provides an alternative by representing these entities as first-class objects with explicit relations and multiple notions of case. This study connects collaborative PPM and OCPM through three contributions: (i) a formal semantic mapping from extended collaborative event logs to an OCED-conformant object-centric representation, serialized in OCEL 2.0; (ii) a reformulation of collaborative prediction tasks as object-centric prediction tasks; and (iii) a reproducible converter and prediction pipeline implementing the proposed mapping. We evaluate the framework on four public collaborative event logs and a fifth derived from the BPI Challenge 2013 incident-management log by executing the fourteen reformulated tasks using five predictive strategies across tabular, sequential, and graph-native encodings. We further discuss the benefits, limitations, and threats to the approach's validity. The representation makes collaboration structure explicit and makes it natural to state prediction targets based on object relations that fall outside the case-centric taxonomy, at the cost of increased relational complexity and dependence on object-centric tooling.
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All authors Daniel Calegari, Andrea Delgado, Leonel Peña, Martín Rubio
Artificial Intelligence · Cross-listed from cs.CR
Semantic Watermarking with Order-Robust Detection over Sub-sentence Units
Abdulrahman Diaa, Jonathan Petit and Florian Kerschbaum
Semantic watermarks tie the mark to sentence meaning rather than
token choices, promising robustness to content-preserving edits. However, the detector only observes attacker-supplied text, which can be reworded, reordered, or resegmented to evade detection without content loss. Rewording, reordering, and resegmentation all cause
embedding displacement: detection tests
embeddings different from those selected during watermarking and can therefore lose the mark. Our adaptive
embedding displacement attack (EDA) admits all three edits under a single objective that maximizes this displacement. It uses a public paraphraser and surrogate encoder without access to the provider's generator or secret key. At a 5% false-positive rate (FPR) and content-preservation threshold $\bar{q}=90\%$, EDA successfully removes the mark on between 32.6% and 47.9% of documents across four schemes, the highest among the tested attacks. Therefore, EDA evaluates the schemes' robustness more thoroughly than passive paraphrasing. To address these vulnerabilities, we design (k)-SwordStamp: semantic watermarks with order-robust detection over sub-sentence units, reducing sensitivity to attacker-chosen structure at a small quality cost. Against k-SwordStamp, the strongest no-box attack we test is an EDA variant adapted to its design, with a 10.8% attack-success rate. A stronger EDA with access to the provider's detector and secret key reaches a 39.7% attack-success rate, compared with 65.5% on k-SemStamp. Our code is available at https://github.com/D-Diaa/SwordStamp.
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Artificial Intelligence · Cross-listed from cs.CL
Knowing Before Answering: Decoding Language Models for Reliable RAG
Syed Mahbubul Huq, Christopher Child, Tillman Weyde et al. (+1 more)
In
Retrieval-Augmented Generation (RAG),
retrieval may provide insufficient or conflicting information needed to answer a question. The system should not only know when to answer but also be able to identify cases in which the documents provided in RAG are insufficient or contain conflicting information. This can be framed as a three-way classification problem, where we use the model's internal signals to determine whether the provided information in the input can be classified as sufficient, insufficient, or conflicting. We create a controlled
benchmark dataset that replicates a RAG setup with fictitious information and labels each instance as answerable, insufficient, or conflicting. We use hidden activations and
attention-derived features as inputs to train a lightweight linear model to distinguish among the three classes. Across 16
language models spanning different architectures and a range of model sizes, our feature-based router consistently outperforms prompting-based baselines and the performance of specialised RAG-models. We further conduct analyses into the information dynamics of the models. We show that the most informative signals for the classification are available in the middle layers, with hidden activation states being more effective than
attention values or the MLP-feature outputs in most of the tested models. Overall, our results suggest that
language models internally encode whether retrieved evidence is sufficient to support answering, and that this signal can be decoded reliably for RAG triage.
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All authors Syed Mahbubul Huq, Christopher Child, Tillman Weyde, Pranava Madhyastha
Artificial Intelligence
If Agents Were Angels, No Governance Would Be Necessary: Out-of-Band Policy Enforcement at a Trusted Tool Boundary
Marc Millstone, Tyler Akidau, Johannes Brüderl et al. (+1 more)
Give an
agent a human's credential and it inherits the person's reach without the judgment that limits its use. It can sweep every reachable record into model context, where hidden instructions steer its next call, and every request stays credential-valid while the
agent exceeds its job or absorbs a secret.
Prompts are a brittle guardrail: one fallible reasoner interprets the task and enforces its limits. We present Out-of-Band Policy Enforcement (OBPE), a trusted boundary outside
agent reasoning. It authorizes the typed operation and resource, narrows the query before the backend call, then filters records and fields or masks values in the response. Semantic gating can deny or hold an authorized call on argument values or external state. A data policy owner sets the maximum grant;
agent policy can only narrow it. We prove, under stated conditions, that the policy plan is order-independent and
agent policy cannot widen the ceiling. Field removal covers one execution; masking and history rules claim less. We release an HTTP proxy prototype simplified from our production system, with conformance tests tying its typed Cedar policy core to the model. Against Jira and ServiceNow mocks, our
benchmark compares prompted
agents with and without OBPE on four models, including 20 adaptive red-team tasks. A trace failure means protected data entered
agent context, an exact value appeared in the answer, or a forbidden effect completed. In 3,621 trials it fell from 57.6% to 0.2%, a cluster-weighted reduction of 41.2 points [95% CI: 27.7, 54.9]; fulfillment fell from 79.1% to 60.9%, while paired safe-useful completion rose 21.8 points [9.5, 35.2]. Some answers reconstructed a value that never entered context or used filtered row counts as an oracle: shaping one execution is not noninterference. Write controls, durable approval, and temporal and aggregate policies lie outside this evaluation.
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All authors Marc Millstone, Tyler Akidau, Johannes Brüderl, Marat Pekker
Artificial Intelligence
Generative AI Expands the Intellectual Reach of Course Based Undergraduate Research Experiences (CUREs)
Aditi Babar, Kristin J. Davin and Alex Dornburg
Course-based undergraduate research experiences (CUREs) broaden access to authentic scientific inquiry through responsive instructor support as research problems become increasingly complex. Generative artificial intelligence (GenAI) may extend this support by providing individualized assistance that can adapt as student needs change. However, how
embedding GenAI within a CURE to provide support across the research process impacts student inquiry, collaboration, and scientific
reasoning remains unresolved. Here we use longitudinal qualitative data collected across three semesters of a bioinformatics and genomics CURE to show that GenAI expanded the intellectual reach of the research experience in three distinct ways. First, personalized, on-demand scaffolding allowed students to move beyond the boundaries of instructor expertise and transform their own interests into researchable inquiry, with all teams developing distinct self-directed projects rather than selecting instructor-provided topics. Second, GenAI became part of the distributed cognitive system of research teams, helping novice researchers communicate and coordinate across differentiated expertise without eliminating specialization. Third, expanded capability did not replace the need for disciplinary judgment. Students increasingly validated, revised, or rejected AI-generated contributions, such that research independence emerged through retained intellectual responsibility. Together, these findings suggest that GenAI can extend the reach of CUREs by expanding what novice researchers can investigate, how they can collaborate, and the level of responsibility they can assume while preserving human judgment central to authentic scientific inquiry.
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Artificial Intelligence · Cross-listed from cs.LG
Curvature-Aware Radius Shrinkage for Adaptive Nearest Neighbor Classification
Alexandre L. M. Levada
Nearest neighbor classification relies fundamentally on how locality is defined, yet conventional $k$-NN imposes the same neighborhood cardinality throughout the feature space. This assumption can be inadequate for data whose local geometry varies substantially across the underlying
manifold. We introduce
Curvature-Aware Radius Shrinkage for Adaptive Nearest Neighbor Classification (CARSANN), a geometry-driven framework that adapts the spatial support of each neighborhood according to local
geometric complexity. CARSANN first estimates intrinsic dimensionality using TwoNN and constructs an intrinsic representation through principal component analysis. Local mean
curvature is then estimated using a shape-operator-based formulation and controls neighborhood scale: highly curved regions receive stronger radius shrinkage, whereas approximately flat regions retain broader spatial support. Unlike methods that modify only the number of neighbors or the local metric, CARSANN explicitly adapts the spatial extent of local evidence. Experiments on more than 70 real-world OpenML
datasets show that CARSANN consistently improves upon standard $k$-NN and is competitive with adaptive nearest-neighbor methods. In a controlled comparison using the same base neighborhood size, CARSANN achieves higher balanced accuracy on 40 of 45
datasets, increasing mean balanced accuracy from 0.6506 to 0.7528. The advantage also persists against $k$-NN with fixed $k=5$. Friedman and Nemenyi tests confirm that the improvements are statistically significant. These results indicate that local
manifold curvature can serve as an effective
geometric control variable for adapting neighborhood support, providing a complementary paradigm to cardinality-based nearest-neighbor adaptation.
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Artificial Intelligence · Cross-listed from cs.CV
Depth-Aware Pothole Detection Using YOLO and RT-DETR at the Edge
Md Monjurul Ahsan Prodhan and Md Nour Hossain
Pothole detection and its severity measurement is still an important challenges in urban infrastructure management, where late maintenance directly contributes to vehicle damage, road accidents, and escalating repair costs. Existing automated approaches depend on 2D RGB images and cannot measure physical depth of potholes. In this paper, we present a depthaware pothole detection framework and then compare five architectures: YOLOv8n, YOLOv8nSeg, YOLOv9t, RTDETRL, and RTDETRX for RGB-D sensor fusion-based detection and automated depth measurement. A custom offline augmentation pipeline is used here to simulate adverse road monitoring conditions. All models are trained on the PothRGBD
dataset with an 80% training and 20% validation split and evaluated using Precision, Recall, mAP@50, and mAP@50_95. Before measuring the depth data, all depth maps are corrected for camera tilt using RANSAC ground-plane orthorectification and all zero-valued sensor pixels are cast to NaN before any statistic is computed. YOLOv8nSeg achieves the highest mAP@50 of 0.9556 and mAP@50_95 of 0.6758 with the most accurate depth estimate of 2.96 cm with the pixel-precise Dseg
algorithm. YOLOv8n achieves the fastest inference at 3.6ms. RTDETRX achieves the highest detection confidence at 92.70%. An important finding is that even after full RANSAC orthorectification, bounding box models overestimate pothole depth by 0.16 to 0.21 cm compared to pixel precise
segmentation masks. This confirms that the pavement inclusion bias is structural rather than a calibration artifact.
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Artificial Intelligence · Cross-listed from cs.IR
LitCurate: A Configuration-Driven AI-Assisted Framework for Scientific Database Construction with an Application to Lower-Mantle Equation-of-State Data
Abin Shakya, Wilson Samuels, Dominica Wilson et al. (+4 more)
The growing scientific literature contains decades of experimental and computational results that could support data-driven and physics-based modeling, yet much of this infor- mation remains locked in publications and is not readily usable for large-scale analysis or sci- entific software. Building structured databases from the literature is particularly challenging whenrelevantstudiesmustfirstbediscoveredamonglargecollectionsofpapersandreported quantities must be extracted with enough scientific context to remain usable. We present LitCurate, an open-source framework for building scientific databases from the literature using large
language models within an auditable, stage-wise curation workflow. LitCurate integratesliteraturediscovery, relevancescreening, full-textprocessing, andstructuredinfor- mation extraction while retaining intermediate results and provenance, allowing researchers to inspect and revise individual stages rather than treating automated curation as a black- box process. We apply LitCurate to construct an equation-of-state database of lower-mantle and lower-mantle-relevant high-pressure mineral phases from experimental and theoretical studies, comprising 1,334 entries from 205 papers. The resulting
dataset links reported equation-of-state parameters to mineral phases, compositions, equation formulations, meth- ods, and parameter constraints, and labels values as source-reported or citation-reported when provenance can be determined. The records are available through a searchable web application. By connecting scientific literature to traceable, machine-readable data, LitCu- rate provides a reusable approach for transforming accumulated literature into resources for scientific analysis and computational modeling.
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All authors Abin Shakya, Wilson Samuels, Dominica Wilson, Gioia A. Marchi, Israa Draz, Chenxing Luo, Renata M. Wentzcovitch
Artificial Intelligence · Cross-listed from cs.NE
Tensor-Accelerated Eager Multi-Resolution Grids for Evolving Large-Scale Substrates
Romain Claret, Michael O'Neill, Paul Cotofrei et al. (+1 more)
In neuroevolution, indirect encoding generates neural network connectivity from a compact genome rather than specifying each connection. ES-HyperNEAT automatically discovers where to place hidden nodes by examining CPPN output patterns: it recursively subdivides space using a quadtree, expanding regions where CPPN outputs show high variance. This adaptive approach discovers network
topology without manual substrate specification, extending the fixed-grid HyperNEAT framework built on NEAT. However, the quadtree resists tensorization. Each depth level depends on the parent's variance, forcing sequential evaluation. Different CPPNs produce different subdivision patterns, preventing batching. And variable leaf counts are incompatible with JAX's static shape requirement for JIT compilation. Our prior work confirmed these limits at depths exceeding 5, and a JAX reimplementation of the quadtree yielded only marginal speedup despite batched optimizations, motivating the eager reformulation presented here. We present EMR-HyperNEAT, which evaluates all positions at all resolutions up front, then filters using the same variance criterion: ES-HyperNEAT's subdivide_if(var > $\theta$) becomes eval_all(); filter(var > $\theta$). This performs more CPPN queries than necessary, but all queries become independent and parallelizable across both cores and population members, reducing complexity from \BigO($4^D$) to \BigO($4^D/P$) across $P$ parallel cores. Recurrent substrate configurations become feasible through a connection type taxonomy. The experiments section validates 12-34$\times$ on-device GPU speedup on XOR at depths 5-7, and empirically higher solve rates across
benchmarks.
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All authors Romain Claret, Michael O'Neill, Paul Cotofrei, Kilian Stoffel
Artificial Intelligence · Cross-listed from cs.RO
Davood Soleymanzadeh, Kaidi Zhang, Zhiyuan Zhang et al. (+4 more)
Vision-
language-action models (VLAs) have shown strong promise for general-purpose robotic manipulation by mapping
language instructions and
vision observations directly to actions. However, most VLAs primarily condition action prediction on current observations and lack an explicit mechanism for
reasoning over future task dynamics, which is particularly important for fine-grained, contact-rich manipulation. We present PHR-VLA, a framework that enables planning-horizon
reasoning in VLAs through privileged latent representations of future dynamics. PHR-VLA introduces a lightweight auxiliary future head that, during training, aligns the VLA's internal representations with latent dynamics extracted from future observations. Evaluation results demonstrate that local, contact-centric, patch-level latent dynamics supervision from the wrist camera improves success rate on LIBERO from 84.1% to 88.4% and on real-world disassembly tasks from 63.3% to 82.5%. Patch-level supervision from a third-person camera also improves performance on Meta-World from 56.70% to 57.8%. These results demonstrate that privileged latent dynamics
alignment provides an effective training signal for improving anticipatory
reasoning in VLA policies. Project website: \href{https://davoodsz.github.io/PHR-VLA.github.io/}{https://davoodsz.github.io/PHR-VLA.github.io/}
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All authors Davood Soleymanzadeh, Kaidi Zhang, Zhiyuan Zhang, Bihao Zhang, Xiao Liang, Yu She, Minghui Zheng
Artificial Intelligence · Cross-listed from cs.CV
Quanta Perception as Probabilistic Events
Varun Sundar, Pavan Thodima, Sacha Jungerman et al. (+1 more)
Autonomous systems rely on extracting information from light, yet remain brittle in extreme environments, from nighttime
navigation to high-speed robotics. Conventional sensors aggregate
photons over fixed exposures, imposing trade-offs between sensitivity, dynamic range, and temporal resolution that degrade perception when
photons are scarce or dynamics are rapid. Quanta sensors detect individual
photons, but their streams exceed real-time compute and latency budgets by orders of magnitude. Here we introduce $\textit{probabilistic events}$, a computational primitive for real-time quanta perception from individual
photon detections. By computing the posterior over the time since the last intensity change, we represent
photon streams as recursive belief states. Rather than fixed-threshold event-camera triggers, this recursive
Bayesian formulation yields three low-latency signals: motion-adaptive scene flux, high-fidelity activity maps, and
entropy-based perceptual uncertainty. This representation enables perception in extreme conditions, including pose estimation of a running person at $\sim$0.05 lux—without retraining
vision models. Our approach processes input streams exceeding 50{,}000 quanta frames per second on commodity GPU hardware—yielding kilohertz-scale outputs up to four orders of magnitude faster than state-of-the-art quanta reconstruction baselines, even for megapixel arrays. By replacing frame reconstruction with direct probabilistic inference over
photon streams, this work bridges
photon-counting quanta sensing with robotic
vision.
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All authors Varun Sundar, Pavan Thodima, Sacha Jungerman, Mohit Gupta
Artificial Intelligence
LongGuard: Mechanistic Analysis and Training-Free Mitigation of Long-Context Failure in Safety Guardrails
Ziyang Chen, Xing Wu and Songlin Hu
Safety guardrails serve as the last line of defense against harmful inputs and outputs of large
language models (LLMs), yet they are trained and evaluated almost exclusively on short text. We present LongGuard, a framework that evaluates, mechanistically analyzes, and mitigates long-context guardrail failure. We formulate the task as Safety Needle-in-a-Haystack (SafetyNIAH) over a 0.25k-32k length grid; across 15 mainstream guardrails, unsafe recall drops monotonically by more than 50% on average, and a paired Benign-Fill vs. Needle-Repeat design attributes the failure to proportional dilution of the unsafe needle rather than to absolute length. A three-layer
attention-logit-behavior analysis on six guardrails locates the mechanism:
attention mass on the unsafe needle is diluted, the unsafe-over-safe logit margin is compressed in lockstep, and the detection decision collapses accordingly, with this
attention->logit->behavior chain remaining consistent after partialling out length. We further isolate a sparse set of guard-specialized
retrieval heads that exhibit partial specificity relative to their base models. Building on the analysis, we propose two training-free mitigations - Chunked Detection (CD) and
Attention-Head Sharpening (AHS) - and a deployment protocol, Context-Aware Hyperparameter Routing (CAHR), that selects configurations by context length and audit side. Across five
benchmarks spanning synthetic data, long-context attacks, and
reasoning-model outputs, CAHR-CD and CAHR-AHS improve the six-guardrail average by 22% and 13%, respectively. Code and data are available online.
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Artificial Intelligence · Cross-listed from cs.LG
Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution
Yingqi Feng, Yufei Tang, Min Shi et al. (+1 more)
Multi-label graph learning intends to capture the intrinsic complexity of real-world applications, where one sample is often related to multiple groups or consists of multiple objects. To date, a handful of multi-label graph learning methods exist, but none of them integrate training-time interpretation capability. While post-hoc graph explainers have been developed, they do not explicitly model label-dependent evidence sharing in multi-label graph learners, especially when label pairs are weakly or negatively associated. As a result, post-hoc approaches may miss how evidence should be shared or separated across different labels. This paper advances a new end-to-end self-explainable multi-label graph neural network (SEMGNN), which aims to simultaneously classify multi-labeled nodes and identify edges significantly contributing to each target node w.r.t. predicted labels. Different from post-hoc methods, SEMGNN jointly learns a predictor and a sparse edge-mask explainer within a unified framework and training objective. Label-label correlations are used to improve multi-label node classification and enhance individual label explanations, so that different labels of a node can be supported by distinct yet coherent structural and/or correlated evidence. Experiments and comparisons on synthetic and real-world multi-label networks, in social networking, entertainment, and life sciences, show that SEMGNN achieves competitive or improved predictive performance while providing more faithful and compact label-conditioned explanations.
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All authors Yingqi Feng, Yufei Tang, Min Shi, Xingquan Zhu
Artificial Intelligence
Nemotron 3.5 Content Safety Moderator: A Compact Multimodal, Multilingual, and Reasoning Enabled Content Safety Moderator
Varun Singh, Anuj Doshi, Makesh Narsimhan Sreedhar et al. (+2 more)
Safety moderation for deployed AI applications is moving beyond text-only
prompts: systems increasingly need to judge images, documents, screenshots, and generated responses under policies that vary across domains. Existing guardrails usually cover only part of this setting, making it difficult to combine broad coverage, custom policy control, and low compute cost. We present Nemotron 3.5 Content Safety Moderator, also referred to as Nemotron 3.5 CS in this paper for brevity, a compact 4B
vision-
language safety moderator that jointly classifies user
prompts, images, and assistant responses across 12
languages. Nemotron 3.5 CS returns safety labels for latency-sensitive moderation and can additionally produce concise
reasoning traces that apply supplied custom policies and identify violated categories when
reasoning is requested. We also release a
multimodal and
multilingual safety
dataset for guard training, spanning human-labeled real-image moderation, benign
vision-
language and document tasks, synthetic rare-risk and jailbreak cases, and custom-policy examples. Across evaluations spanning
multimodal safety, text moderation,
multilingual robustness, custom-policy following, benign false positives, and latency, Nemotron 3.5 CS demonstrates a practical coverage tradeoff: it adds image-conditioned and policy-conditioned moderation while remaining broadly competitive with specialized guard models. These results suggest that compact
vision-
language moderators can serve as deployable front-line safety components, with
reasoning used selectively for audit and policy review.
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All authors Varun Singh, Anuj Doshi, Makesh Narsimhan Sreedhar, Shaona Ghosh, Katherine Luna
Artificial Intelligence · Cross-listed from cs.CV
FVeinSyn: Synthetic Finger Vein Image Generator
Yifan Wang, Jie Gui, Adams Wai Kin Kong et al. (+6 more)
A major challenge in finger vein recognition is the lack of large-scale public
datasets. Existing
datasets contain few identities and limited samples per finger, restricting the advancement of deep learning-based methods. To address this, we propose FVeinSyn, a large-scale controllable synthetic data generation framework for finger vein. It explicitly decouples synthesis of vascular
topology and imaging appearance to mitigate the limitations caused by insufficient training samples, such as inadequate identity diversity and restricted realism. Specifically: first, a finger vein identity generator models vascular
topology under physiological and
geometric constraints using
stochastic L-systems, producing anatomically valid and identity-distinctive vascular patterns. Then, a cascaded region-aware GAN renders the topological maps into realistic near-infrared images. Finally, an intra-class diversity generator introduces
geometric and optical perturbations to simulate realistic intra-class variations. Using FVeinSyn, we generated 500,000 images (10,000 vein identities, 50 samples per identity) and conducted extensive evaluations. Results show that FVeinSyn holds significant advantages in realism, identity diversity, vascular pattern consistency, and intra-class diversity. Models trained with FVeinSyn outperform real-data-only baselines a cross eight public
datasets, achieving an average accuracy improvement of 27.43%. The code is available at: https://github.com/EvanWang98/Synthetic-Finger-Vein-Generator.
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All authors Yifan Wang, Jie Gui, Adams Wai Kin Kong, Baosheng Yu, Changsheng Chen, Qi Li, Zhenan Sun, James Tin-Yau Kwok, Alex Kot
Artificial Intelligence
SETU: An Agentic Ecosystem for Multilingual, Persona-Aware Communication Coaching
Jonnalagadda Maruthi Tejas, Uponika Barman Roy, Tilottama Goswami et al. (+2 more)
Corporate training teams need scalable and explainable tools to improve workforce communication in
multilingual settings. Existing systems often score text, audio, or
video in isolation, or produce black-box outputs that are difficult to audit for coaching use. This paper presents SETU, an agentic ecosystem for corporate communication coaching aimed at recruiters, frontline sales professionals and training units who prepare for audience specific conversations. SETU is designed for two scoped scenarios: (i) recruiter-candidate eligibility-and-interest calls with persona context and (ii) sales pitches with target-audience adaptation; owing to limited evaluation resources, this paper reports results on scenario (ii) only. The ecosystem decomposes analysis into specialized
video, audio-
speech, text-relevance, scoring, notification and reporting
agents coordinated through trust-aware orchestration. It generates modality-attributed coaching reports for formative training, with human reviewers retaining final judgment. The name SETU (bridge in several Indic
languages) reflects the goal of bridging communication gaps across regional
languages and audience expectations.
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All authors Jonnalagadda Maruthi Tejas, Uponika Barman Roy, Tilottama Goswami, Samir Goswami, Mousita Dhar
Artificial Intelligence · Cross-listed from eess.IV
Destroy Me: Automatic Artifact Generation for Histopathology Images
Zuzanna Krawczyk-Borysiak, Adam Krawczyk, Mateusz Miller et al. (+4 more)
Deep learning's diagnostic utility in pathology is constrained by model vulnerability to real-world data imperfections. While current strategies favor "perfect data" by filtering low-quality regions, which can lead to the loss of valuable diagnostic context, we propose a paradigm shift: engineering models to thrive in imperfect environments using "Destroy Me", a hybrid framework for realistic artifact synthesis and robust data augmentation. Our approach combines Stable
Diffusion, fine-tuned to preserve morphological continuity by realistically integrating artifacts with the underlying tissue architecture, with physics-based procedural modeling to synthesize six common artifact types: tissue folds, precipitates, blur, stitching errors,
dust, and pen markers. Artifact fidelity is assessed using Kernel Inception Distance (KID) and color Wasserstein distance metrics. Validating this strategy on lung adenocarcinoma pattern classification with an nnU-Net, we confirm that models trained on "destroyed" patches consistently outperform baselines on independent real-world
datasets. Specifically, we observed a 10.5% relative improvement in macro F1-score and a 15% relative increase in the Cohen's Kappa ($\kappa$) coefficient. Crucially, our results demonstrate that selective, impact-weighted augmentation is vital for balancing practical robustness with the preservation of subtle diagnostic features.
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All authors Zuzanna Krawczyk-Borysiak, Adam Krawczyk, Mateusz Miller, Gabriela Kaczmarek, Sławomir Pakuło, Małgorzata Sokół, Żaneta Swiderska-Chadaj
Artificial Intelligence · Cross-listed from cs.CL
Trajectory-Level Speculative Decoding for Diffusion Language Models
Tianxiang Pan, Baitao Gong, Mo Guang et al. (+5 more)
Diffusion-based
language models (dLLMs) enable parallel
token generation through iterative denoising, but existing decoding strategies collapse to single-
token generation under low confidence, severely limiting throughput. Unlike autoregressive models where speculative decoding operates on
token sequences in a fixed left-to-right order, dLLMs require speculating over denoising trajectories-sequences of multi-
token updates with explicit positions and unmasking orders. We develop a trajectory-level speculative framework that constructs draft denoising trajectories via confidence-stratified tree exploration and verifies them through blockwise parallel evaluation with bidirectional
attention masking. Our method further introduces inter-block speculation, exploiting
diffusion models' bidirectional structure to perform cross-block lookahead. We formally characterize when this approach is exact and identify trajectory drift as the fundamental cost of increased parallelism. Building on Fast-dLLM's dual-cache infrastructure, our framework reduces denoising iterations by 30-40% and increases
tokens-per-step from 2.6 to 4.3, achieving 7-14x speedup over vanilla dLLMs and 1.3x over Fast-dLLM with less than 1% accuracy change across
reasoning and code
benchmarks.
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All authors Tianxiang Pan, Baitao Gong, Mo Guang, Hongwei Yong, Tianpeng Jiang, Yaqian Li, Zheng Cao, Kaiwen Long
Artificial Intelligence · Cross-listed from cs.LG
DAMP: Decay-Aware Mixed-Precision Recurrent-State Quantization
Tao Zhang, Jianchao Tan, Pingwei Sun et al. (+6 more)
Complex
reasoning and agentic applications increasingly rely on long-context inference, where growing KV caches increase both
memory usage and decoding overhead. Hybrid models reduce these costs by combining Softmax
Attention with Gated DeltaNet (GDN) or Kimi Delta
Attention (KDA), which maintain fixed-size recurrent states. These states are commonly stored in FP32 and consume substantial GPU
memory, while their updates are limited by
memory bandwidth. Quantization can reduce both storage footprint and
memory traffic, but we find that uniform INT8 and FP8 degrade complex
reasoning accuracy, while INT4 and NVFP4 collapse it to near zero. To our knowledge, this is the first study of post-training recurrent-state quantization for GDN and KDA. Our analysis reveals that outliers in GDN and KDA states are concentrated in particular key channels and value dimensions. Learned decay influences how much quantization error is retained. We find that largely the same GDN heads and KDA key channels exhibit slow decay across tasks. Based on these insights, we propose DAMP, which jointly considers quantization error and decay-based error retention to select high-risk key channels offline. Under a fixed storage budget, it retains these channels in FP16 and stores the remainder in INT8. We evaluate DAMP on Qwen3.6-35B, Kimi-Linear-48B and Kimi-K3 across six
reasoning and code generation
benchmarks. At 9.9 bits per state value, DAMP maintains average accuracy close to FP32. In SGLang, DAMP reduces recurrent-state storage by 69.1%, accelerates the recurrent-state update kernel by up to 2.59x , and lowers full-model time per output
token by up to 19.0%.
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All authors Tao Zhang, Jianchao Tan, Pingwei Sun, Yanqi Yu, Zunhai Su, Zixu Jiang, Yuchen Xie, Xunliang Cai, Ziqian Zeng
Artificial Intelligence · Cross-listed from cs.LG
Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation–Deployment Gap
Jacopo Dardini, Claudio Stanzione, Giordano Colò et al. (+1 more)
Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large
Language Models. When a full-precision source checkpoint is evaluated and quantization is applied downstream without equivalent re-evaluation, this workflow creates a structural validation–deployment gap: because quantization is a many-to-one mapping over parameter space, source-precision certification does not guarantee behavioral equivalence in the deployed configuration. We formalize this gap through Quantization Behavioral Equivalence Classes (QBECs) and prove that QBEC membership does not imply behavioral equivalence, providing a theoretical basis for quantization-triggered
backdoor attacks. Building on a three-stage
adversarial fine-tuning framework, we embed latent malicious payloads into models that satisfy the source-precision checks used in our evaluation, yet activate targeted
adversarial behavior upon INT8 or 4-bit compression. We evaluate this threat in two operationally motivated scenarios, tactical machine translation and political content analysis, extending prior work from decoder-only causal LMs to
multilingual encoder-decoder sequence-to-sequence models. Results show that backdoored translation models move from zero measured friend–foe corruption at repaired FP16 to up to 85.02% inversion after quantization, and that a paired stance classifier measures an ideological shift of up to $\Delta\mathrm{Bias}=0.33$ upon compression. A cross-quantizer transferability analysis further shows that attack persistence varies across quantization schemes and model architectures, rather than being determined by nominal bit-width alone. These findings demonstrate that source-precision
auditing alone does not rule out quantization-triggered behavior and that the final deployed configuration must be included in behavioral certification for trustworthy edge AI.
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All authors Jacopo Dardini, Claudio Stanzione, Giordano Colò, Giuseppe Fenza
Artificial Intelligence
WM-R1: Training GUI Agents to Reason and leverage World Models with Reinforcement Learning
Yu Han and Tianwen Qian
GUI
agents trained with reinforcement learning (RL) have showcased strong environment learning capabilities on mobile platforms. However, RL typically demands extensive real-environment interactions, leading to high resource costs and instability, especially in GUI scenarios. To address these, we propose WM-R1, the first reinforcement learning framework that trains mobile GUI
agents with world models instead of real environments. Specifically, world models serve as the source of state transitions during all rollouts, replacing the real Android environment within the training loop. WM-R1 also embeds world models directly into the thinking process, enabling
agents to reason about the consequences of candidate actions before committing to the final action. Crucially, WM-R1 eliminates the need for real-environment interaction, supports massively parallelized and step-level granularized trajectory generation grounded in world models, and introduces a multi-dimensional rule-based reward that jointly optimizes task success, trajectory efficiency, and world model utilization. For efficient training, we curate a high-quality
dataset of 2000 challenging tasks. Experiments on Android mobile
benchmarks demonstrate that WM-R1-trained
agents significantly outperform GRPO-only baselines and inference-time simulation methods. Code is available at https://github.com/genalyu/WM-R1 .
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Artificial Intelligence · Cross-listed from cs.LG
Marginal Coverage Credit Reduces Redundant Exploration in Parallel State-Entropy Optimization
Junhao Cao, Hongyi Xia, Jianian Wu et al. (+3 more)
Policy Gradient for Parallel State
Entropy maximization (PGPSE) expands state-space coverage by training independently parameterized policies in replicated copies of the same environment. However, its pooled team-
entropy score measures only collective exploration and cannot identify policies that contribute non-redundant coverage. We introduce Marginal Coverage Credit for PGPSE (MCC-PGPSE), which combines leave-one-policy-out coverage with state-owner specialization to estimate policy-specific credit. MCC-PGPSE preserves PGPSE's pooled objective and redistributes non-negative auxiliary intrinsic rewards according to these credits without changing their total mass. This redistribution is designed to discourage redundant visitation and promote complementary coverage. We evaluated MCC-PGPSE in controlled environments, seven public discrete-state
benchmarks, and representative Room and Maze settings from the original PGPSE protocol. Across all tested settings, MCC-PGPSE produced positive final window gains in normalized team state
entropy and state support over the
Entropy baseline. Controlled-task comparisons and the fixed-suite public aggregate were significant, whereas five-seed original-protocol comparisons were directionally consistent. Ablations and credit
alignment controls indicate that most gains arise from leave-one-policy-out coverage rather than non-uniform weighting, mismatched credit, or neural novelty alone. These results support contribution-conditioned auxiliary reward allocation as an interpretable approach to improving complementary coverage among parallel policies in discrete state spaces.
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All authors Junhao Cao, Hongyi Xia, Jianian Wu, Xiaopeng Yi, Lixia Huang, Ping Guo
Artificial Intelligence
Thinking Costs Tokens: When More Structure is Worth the Price
Thomas Nolasque, John Grey, Calista Pham et al. (+1 more)
Adding inference structure to a
language model lets it search, verify, and revise, but these actions consume the very budget they are supposed to use well. In this paper, we investigate whether there exists a
token-budget threshold, below which the overhead of planning and verification hurts performance and above which it helps. We evaluate two systems on FinQA and TAT-QA financial
reasoning tasks, using GPT-5.4 mini across 14 budget tiers ranging from 250 to 42,000 output-equivalent
tokens. The first system is a monolith, which is a single LLM call. The second is a verified search architecture that adds planning, label-blind checking, and repair capabilities. We run 1,000 cases for a total of 28,000 completed
cells. Both systems score 0% at the two lowest tiers, where neither can fit a complete
prompt. At 1,000
tokens, the monolith reaches 18% accuracy while verified search scores near 0%, since the planning overhead leaves no room for an answer. From 1,500
tokens onward, verified search surpasses the monolith and maintains a consistent advantage, reaching approximately 44% at the highest tiers while the monolith reaches approximately 40%. The crossover occurs between 1,000 and 1,500 output-equivalent
tokens, confirmed by a strict intersection-union test ($p \le 0.001$ at both endpoints).
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All authors Thomas Nolasque, John Grey, Calista Pham, Ankit Vani
Artificial Intelligence · Cross-listed from cs.CL
A Survey on Rubric-Guided Reinforcement Learning for Language Models
Zifei Shan and Fangning Shao
Reinforcement learning from human feedback (RLHF) has become the dominant paradigm for aligning large
language models (LLMs) with human preferences. However, traditional RLHF relies on scalar reward signals that lack interpretability and fail to capture the multifaceted nature of response quality. Rubric-guided reinforcement learning addresses these limitations by introducing structured, interpretable evaluation criteria, or rubrics, as the backbone of reward design, feedback generation, and policy optimization. In this survey, we introduce a
Bayesian framework that defines constitutions as prior distributions $P(R)$ over evaluation criteria and rubrics as conditional instantiations $R_x \sim P(R|x)$. Under this unified view, we present a taxonomy of rubric-guided RL along the prior-posterior axis, covering constitutional AI, instance-specific rubrics, process-level supervision, self-evolving rubrics, and their agentic and
multimodal extensions. Furthermore, as rubrics are natural-
language artifacts, we present a linguistic analysis of how granularity trade-offs, semantic drift, and linguistic reward hacking impact
alignment reliability, identifying key open problems for future research.
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Artificial Intelligence
WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
Liyan Tang, Cyrus Rashtchian, Chun-Sung Ferng et al. (+3 more)
Agent skills package specialized knowledge and workflows into reusable resources that extend AI
agent capabilities. Recent work automatically discovers such skills from
agent experience, which enables
agents to progressively adapt through interaction. However, the insights that guide skill development typically remain scattered across optimization histories, limiting their systematic reuse across iterations. We introduce WikiSkill, a framework that co-evolves
agent skills with a persistent knowledge base (wiki). At a high level, WikiSkill separates raw execution experience, accumulated knowledge, and executable skills, while continuously consolidating experience into the wiki, which subsequent skill updates can build on. Across diverse
benchmarks and models, WikiSkill consistently outperforms state-of-the-art skill-
evolution methods and improves over no-skill baselines in most model-
benchmark settings. We find that skill
evolution complements model scaling: larger models generally benefit more from evolved skills, while smaller models with skills can outperform substantially larger models without them. We also find that evolved skills transfer effectively across models and model families, and skills evolved by other models can outperform self-evolved skills. Finally, our ablation studies confirm that persistent knowledge accumulation in the wiki is critical for effective skill
evolution. These results demonstrate the benefits of systematically accumulating and refining
agent experience for developing reusable and transferable skills.
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All authors Liyan Tang, Cyrus Rashtchian, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan, Tu Vu
Artificial Intelligence · Cross-listed from cs.SE
SWE-Prime: Fewer Trajectories, Better Performance
Dewu Zheng, Ruizhe Ye, Yanlin Wang et al. (+7 more)
To improve large
language models' ability to resolve real-world software issues, prior work has focused on constructing large-scale
agent trajectory
datasets and performing supervised fine-tuning (SFT) on successful trajectories. However, task success does not guarantee high-quality supervision: successful trajectories may still contain ineffective, redundant, or risky steps. Directly using such trajectories for SFT can introduce noisy supervision and encourage models to imitate undesirable problem-solving behaviors. Therefore, we propose SWE-
Prime, a multi-granularity, two-stage SFT data selection method that progressively filters training data at the trajectory and segment levels. Specifically, the first stage performs trajectory-level screening based on process quality, result quality, and data representativeness, selecting a high-quality and representative subset of successful trajectories. The second stage performs segment-level selection by grouping consecutive steps into semantic segments and assessing each segment based on its contribution to the final solution, learnability, and potential risks. During SFT, all segments remain in the sequence to preserve context, while only selected segments contribute to the loss computation. Experiments on SWE-Bench Pro and SWE-Bench Verified show that training on the 10% trajectory subset selected by SWE-
Prime outperforms training on the full resolved
dataset, yielding relative performance gains of up to 12.2% and 24.2%, respectively.
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All authors Dewu Zheng, Ruizhe Ye, Yanlin Wang, Yang Ye, Hongyu Zhang, Ensheng Shi, Xilin Liu, Yuchi Ma, Jianxing Yu, Zibin Zheng