Artificial Intelligence · Cross-listed from cs.SD
AudioLens: Multi-Perspective Speech Clustering with Reasoning Audio-Language Models
Wenjun Huang, Qiaosong Chu, Tiger Shao et al. (+11 more)
Audio clustering is a fundamental task for organizing rapidly growing
speech collections, supporting applications such as conversational analysis and
speech-driven discovery. However, existing methods rely on fixed acoustic similarity metrics or ASR-based text pipelines, limiting their ability to reorganize the same audio collection under different user-specified perspectives, especially when clustering depends on both linguistic and paralinguistic cues. We introduce audio multi-perspective clustering, where a model directly partitions
speech recordings according to a natural-
language perspective while inferring both the number of clusters and their assignments. To study this setting, we construct AudioLens-Bench, a
benchmark spanning multiple application domains and evaluating both in-perspective and cross-perspective generalization. We further propose AudioLens-R1, an end-to-end large audio-
language model trained with
reasoning distillation and preference optimization. Experiments show that AudioLens-R1 consistently outperforms all baselines, improving overall ARI by 12.99 points and V-measure by 11.62 points. These results demonstrate the promise of native audio-
language models for flexible, perspective-conditioned structure discovery over
speech collections.
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All authors Wenjun Huang, Qiaosong Chu, Tiger Shao, Pengfei Zhang, Yutong Song, Hanning Chen, Yezi Liu, Weiyi Wu, SungHeon Jeong, Ryozo Masukawa, Sanggeon Yun, Yang Ni, Jiang Gui, Mohsen Imani
Artificial Intelligence · Cross-listed from cs.LG
Bayesian Flow Networks for Offline Trajectory Planning
Ludvig Killingberg and Helge Langseth
Offline reinforcement learning (RL) leverages static
datasets to learn decision policies without real-time environment interaction. While recent sequence-modeling approaches rely on continuous
diffusion models for trajectory synthesis, applying these methods to discrete planning tasks requires a categorical formulation rather than the standard Gaussian construction. We present BFN-RL, a unified generative modeling framework for offline RL based on
Bayesian Flow Networks (BFNs). By iteratively evolving distribution parameters rather than noisy data instances, BFN-RL natively models both discrete and continuous trajectory spaces within a single probabilistic formulation. The categorical planner generates future state sequences, and a learned inverse-dynamics model converts consecutive generated states into actions. Evaluations in discrete planning and continuous control show that BFN-RL can generate effective trajectories across both categorical and continuous state spaces. Our results establish BFNs as a versatile generative foundation for offline trajectory planning across data modalities.
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Artificial Intelligence
FuzzingBrain-Bench V1: Evaluating Open-Ended Bug Discovery by LLMs
Ze Sheng, Aleksandar Kezic, Zhicheng Chen et al. (+1 more)
Evaluating the ability of large
language models (LLMs) to discover software bugs is increasingly important. Existing
benchmarks typically evaluate this capability by asking the model to generate a proof-of-concept input that triggers a predefined target vulnerability. However, this setup may overlook valid crashes discovered by the model when they do not match the predefined target. As a result, the evaluation may not reflect the model's real capability. We present FuzzingBrain-Bench, a
benchmark for assessing AI models' ability to discover bugs in open-source software. Models are given an open-source project and a sanitizer-instrumented harness in a self-contained Docker image. Their goal is to generate inputs that trigger as many distinct crashes as possible through the harness. A model's performance on each challenge is scored based on the number of distinct crash signatures it produces, capped at a predefined maximum and weighted by a difficulty coefficient. FuzzingBrain-Bench V1 consists of 77 challenges drawn from 43 open-source projects, with 36 C, 32 C++, and 9 Java/JVM challenges. We evaluate Claude Haiku 4.5, Claude Sonnet 4.6, and Claude Opus 4.8 on the full
benchmark. Claude Opus 4.8 performs best, triggering crashes in 60 of 77 challenges and achieving a score of 196 out of 579. None of the three models triggers a crash in 13 challenges. The FuzzingBrain-Bench corpus and harnesses are publicly available at https://github.com/fuzzingbrain/FuzzingBrain-Bench.
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All authors Ze Sheng, Aleksandar Kezic, Zhicheng Chen, Jeff Huang
Artificial Intelligence · Cross-listed from cs.CL
Belief Cascades Drive Persuasion in LLM Agent Networks
Haoyi Qiu, Genglin Liu, Pranav Narayanan Venkit et al. (+4 more)
Multi-
agent LLM systems increasingly debate answers, coordinate research, simulate users, and mediate information flows, making
agent-to-
agent persuasion a basic but undermeasured capability. We introduce a controlled testbed for studying how goal-directed persuaders shift elicited stances in networks of LLM
agents grounded in real-world ego-network
topologies. Across four LLM backbones, five graphs, and 55 policy statements, we find that persuasion dynamics depend on the interaction between
topology, competition, topic, and model prior. Additionally, we show that direct exposure reliably predicts next-round stance change in competing runs, and peer relays carry smaller but measurable influence, showing that
agents not assigned to persuade can still transmit persuasive force. Finally, analyzing post text alone misses important movement: planned strategies are only partly realized in executed messages, action choices can diverge from message content, and persuadees rarely state the stance shifts detected by probes. These results argue for evaluating multi-
agent persuasion as a trajectory- and exposure-level process, using belief probes, exposure provenance, and action logs to identify who influenced whom and whether visible
language reflects underlying stance movement.
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All authors Haoyi Qiu, Genglin Liu, Pranav Narayanan Venkit, Kung-Hsiang Huang, Saadia Gabriel, Chien-Sheng Wu, Nanyun Peng
Artificial Intelligence · Cross-listed from cs.CV
Can You Trust Frozen Hematology Foundation Models under Acquisition Shift?
Jai Kumar Sharma and Peeyush Tapadiya
Frozen hematology foundation-model (FM)
embeddings reach near-saturated in-domain white-blood-
cell (WBC) accuracy, but clinical deployment demands reliability across scanners, sites, stains and preparation pipelines. We audit 15 frozen encoders (hematology, pathology, and general
vision) across four public single-
cell acquisition domains along two axes: accuracy robustness and calibration. In-domain linear-probe macro-F1 is saturated (0.98-0.997), yet cross-
dataset macro-F1 drops 34-72% and rankings re-order: DinoBloom-L, the in-domain best, falls to 10th of 15 on the most-shifted target (MLL23) at the
benchmark's shared 224-px input, behind RedDino and several general and pathology encoders. Rank transfer is probe-dependent: 1-NN
retrieval is more stable on average than a source-fitted linear head (median $\rho$ 0.65 vs 0.45), but neither probe universally predicts target robustness. Calibration also collapses: source-trained probes are nearly calibrated in-domain (expected calibration error, ECE, 0.004) but confidently wrong off-domain (ECE 0.35), and source-fitted temperature scaling transfers poorly. We further audit pretraining exposure and identify MLL23 as DinoBloom's internal cohort; because DinoBloom's only held-out
dataset is also our source domain, this
benchmark cannot isolate exposure from scanner-associated shift. Label-free adaptation and marginal-
entropy-based model selection appear safe under balanced evaluation but fail under realistic WBC class-prior shift. Class-Balanced Re-standardization (CBR), a training-free pseudo-label-balanced feature normalization, improves all evaluated target-prior scenario means and partially improves calibration, although encoder-level exceptions and residual miscalibration remain. Hematology FM
benchmarks must therefore jointly audit accuracy, calibration, exposure, and class-prior robustness.
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Artificial Intelligence · Cross-listed from cs.RO
SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring
Weijiang Xiong, Lan Feng, Alexandre Alahi et al. (+1 more)
Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degrades severely when applied to a new environment zero-shot, because of domain shifts in traffic regulations, road layout and driving behaviors. Therefore, adapting a trajectory planner to a new city typically requires resource-demanding local data collection with a vehicle sensor suite. In this work, we show that driving behavior can be learned from a scalable and efficient alternative. We introduce SkyDrive, a framework that utilizes drone-based traffic monitoring to provide efficient supervision for autonomous driving
agents in a new environment. While vehicle-based data collection logs the ego and its surroundings, an aerial platform naturally observes many road users simultaneously over an extended field of view. As a result, every vehicle can be a data source with grounded driving behavior, effectively scaling up the amount of supervision. Based on 137 hours of aerial traffic monitoring footage, we extract 650K driving samples and construct a
benchmark for trajectory planners and motion predictors. Zero-shot experiments with multiple models reveal significant cross-city domain gaps, but many of them can be alleviated by limited supervision from the sky, e.g., 30 minutes of monitoring per location. Our findings show that aerial traffic monitoring is an efficient and scalable data source for adapting autonomous driving systems in new cities. Data and code will be made publicly available.
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All authors Weijiang Xiong, Lan Feng, Alexandre Alahi, Nikolas Geroliminis
Artificial Intelligence · Cross-listed from cs.LG
Drift Variation Autoencoder: Unifying Generation and Representation Learning through Conditional Posterior Flow Matching
Jiarui Cao
Stochastic masking, cropping, or modality removal makes deterministic reconstruction an incomplete target: one observation can admit many clean completions. This work takes the corresponding posterior $P(X\mid C)$ as the common statistical object for conditional generation and generatively sufficient representation learning. Drift Variation autoencoder trains a masked encoder $Z=E(C)$ and a conditional flow decoder with one clean-prediction Flow Matching loss. The analysis first decomposes the ideal conditional KL into generator approximation and the representation deficiency $I(X;C\mid Z)$. It then derives orthogonal risk decompositions for conditional Flow Matching. For an affine Gaussian path, the clean-prediction representation gap is zero if and only if $P(X\mid Z)=P(X\mid C)$. Thus the encoder-dependent excess clean-prediction risk induced by Flow Matching and the profiled ideal conditional KL have the same posterior-sufficient zero set, without being numerically equal objectives. An exact conditional field with a zero-noise endpoint then generates $P(X\mid Z)$ and hence $P(X\mid C)$ at a joint ideal optimum. The result extends to continuous
multimodal product spaces when the complete modality tuple remains the Flow target for every observation mask. On CrossGeom-4, an 18-run controlled
benchmark, observable factors have linear-probe $R^2$ of $0.9990$-$0.9992$, shuffling the joint model's encoder condition increases conditional error by $13.5\times$-$15.7\times$, and joint target
attention reduces disagreement on an unobserved factor shared by two outputs by $90.1$-$92.8\%$ relative to independent target decoders. Visible modalities are also generated and reconstructed, directly validating the full-tuple objective. Unconditional mode balance remains imperfect, delimiting the empirical claim to a controlled
multimodal proof of concept.
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Artificial Intelligence · Cross-listed from cs.LG
When Does Context Routing Help? A Systematic Study of Multi-Modal Fusion in Time Series Forecasting
Ruizhe Zhou, Gaoyuan Du, Xiaoyang Liu et al. (+4 more)
Multi-modal time series
forecasting methods integrate auxiliary context into temporal predictions through increasingly sophisticated fusion mechanisms. A growing body of work reports substantial gains, yet it is often unclear whether they reflect genuine use of the context or incidental architectural effects. We ask a narrower, checkable question: when can auxiliary context help a forecaster at all? We identify two
dataset-level conditions that must both hold: (1) the target is not dominated by a last-value shortcut (low autocorrelation rho_h), and (2) the context carries information about the target beyond history (non-zero conditional mutual information delta; when delta=0 no predictor can benefit—a distribution-free result). Through controlled experiments on MoME (a 14.3B-parameter mixture-of-experts model, 6
datasets, 10 seeds) and four additional fusion mechanisms implemented within a single-backbone testbed (5
datasets), we find that when both conditions hold, text-conditioned expert modulation contributes a sizeable MSE reduction; when either fails, the contribution collapses to the capacity floor of the modulation pathway and carries no context-attributable signal. We establish causality through two interventions: adding a shortcut to MoME suppresses routing contribution by 77-93% across 3
datasets; progressively corrupting context quality drives the context-specific benefit from +44% to negative. We validate the autocorrelation component of our diagnostic on 27 Monash Archive
datasets. We provide a calibrated pre-training diagnostic that, on the
datasets we test, yields no false positives in well-powered settings. We are explicit about the asymmetry of our evidence: the negative arm is broadly reliable, while the large positive magnitudes come from a single model family (MoME) and are corroborated only in direction by the testbed.
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All authors Ruizhe Zhou, Gaoyuan Du, Xiaoyang Liu, Haoqi Yao, Deepayan Chakrabarti, Jiating Lin, Yixuan Shen
Artificial Intelligence · Cross-listed from cs.CL
SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation
Ben Lagnese and Manas Gaur
Retrieval-augmented generation (RAG) improves large
language models by incorporating external knowledge without retraining, but existing methods often underuse the relational structure encoded in knowledge graphs. Graph-based RAG can capture entity relationships, yet supervised graph
retrieval typically requires labeled question-answer data that may not be available for newly constructed graphs. We address this limitation with SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever. The generated questions capture multi-hop paths and local neighborhoods, providing relational supervision without manual annotation. Experiments on multi-hop question answering and classification
benchmarks show that SelfGraphRAG improves
retrieval precision and downstream
reasoning performance over
embedding-based baselines. These results suggest that knowledge graph structure can provide useful supervision for training graph retrievers when labeled data are unavailable.
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Artificial Intelligence · Cross-listed from cs.LG
Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events
Heyang Gong
Machine learning is usually formalized through samples, while the persistent individual to which multiple observed or possible events refer often remains implicit. We propose the unit as an explicit primitive at the level of task semantics. A learning task first declares a population of persistent referents and a sameness criterion; the realized value $u$ denotes the selected referent. Supervised learning is the main formal specialization. Its semantic object is a family of unit-conditioned response laws. Homogeneity is the special case in which those laws coincide; a sample-only conditional is silent as to whether the world is homogeneous or the observed law is only the marginal of a heterogeneous family. What is learned from data is a pair $(T_\phi,R_\theta)$: a tokenizer that produces a contextual unit
token and one shared response-law form that reads it. The structured class takes that form to be a simple relation in the
token; a linear predictor is the running instance. The
token is the learner-side representation through which the task-side unit affects prediction, while a learner specification that omits unit information is unit-insensitive; homogeneity remains a property of the world-side response family. When identity is unresolved, the world-side law mixes unit-conditioned targets, while the learner composes its shared form with a
token. A trusted resolver may fix the unit and supply a lookup
token; otherwise unit abduction forms a
token of the same type from factual evidence. Unlinked single-row observations can fail to distinguish a heterogeneous unit world from a homogeneous pooled world; trusted same-unit pairs separate a restricted witness. The formal results concern this supervised specialization.
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Artificial Intelligence · Cross-listed from cs.LG
GRAPE: Gradient Refinement and Progress-Aware Exploitation for Query-Efficient High-Dimensional Bayesian Optimization
Richard Cornelius Suwandi and Feng Yin
Optimizing expensive, high-dimensional black-box functions remains a central challenge in modern machine learning and scientific discovery. While local
Bayesian optimization mitigates the curse of dimensionality, existing techniques often prioritize the probability of descent over the magnitude of progress. This leads to overly conservative steps that yield negligible improvement, wasting queries on directions that are nearly certain to descend but offer little decrease. We introduce Gradient Refinement and Progress-Aware Exploitation (GRAPE), a two-stage framework that first sharpens the local gradient posterior via a closed-form acquisition function, then selects update directions by maximizing the expected decrease conditional on descent. Theoretical analysis proves that this gradient refinement stage monotonically minimizes local uncertainty and that the progress-aware direction converges to true steepest descent as the posterior sharpens. Empirically, GRAPE demonstrates superior query efficiency across high-dimensional tasks: in black-box
adversarial attacks, it achieves an average 5.4$\times$ speedup over baselines, and on large
language model
prompt optimization tasks, it outperforms the second best method by a reduction of 3.8 log-units in the final average regret.
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Artificial Intelligence · Cross-listed from cs.LG
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Yan Gao, Mohammad Naseri, Javier Fernandez-Marques et al. (+19 more)
Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Existing evaluations are often tied to custom infrastructure, released as incomplete research code, and conducted primarily in simulation, which limits portability and practical relevance. We present Flower Hub, a platform for publishing, discovering, and executing decentralized and
federated applications. We show how it enables reproducible benchmarking by packaging
benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows. We instantiate this approach with a multi-domain
benchmark suite spanning cross-silo and cross-device settings, and including tasks in medical imaging, financial tabular learning, legal instruction tuning, phishing URL detection, and audio tagging. We further demonstrate that the same benchmarking application can run across both simulation and deployment runtimes without changing the application code, enabling unified evaluation across varying learning environments. Beyond model quality, our
benchmark design supports system-aware reporting, including runtime and communication metrics. This work advances benchmarking in FL settings from ad hoc code artifacts towards portable, executable, and reusable
benchmark applications.
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All authors Yan Gao, Mohammad Naseri, Javier Fernandez-Marques, Dimitris Stripelis, Lorenzo Sani, Davide Eynard, Fan Zhang, Hong Jia, Ting Dang, D. B. Emerson, Fatemeh Tavakoli, Ole Werger, Lars Wulfert, Petros Demetrakopoulos, Sofia Tsekeridou, InSeo Song, KangYoon Lee, Honghao Li, Lingjuan Lyu, John P Dickerson, Daniel Janes Beutel, Nicholas D. Lane
Artificial Intelligence
Towards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning
Xuzhong Wang, Maiqi Jiang, Tejal Nair et al. (+3 more)
Large
Language Models (LLMs) are powerful but limited by static parametric knowledge that becomes outdated once pretraining ends. Knowledge editing addresses this problem by updating model behavior on target facts without full retraining. In particular, in-context knowledge editing has gained
attention because it is training-free and readily applicable to black-box LLMs. Recent reinforcement learning (RL)-based approaches improve over fixed
retrieval strategies by adapting
prompt construction to the quantity-quality trade-off. Despite initial success, they fail to model the
prompt as a structured entity under the distinct and often competing objectives of reliability, generality, and specificity. Previous methods largely optimize a single objective and make decisions over only part of the
prompt construction process, thereby overlooking both the balance of different objectives and the global organization of demonstrations. We propose Multi-Objective In-context Knowledge Editing (MO-IKE), a multi-objective RL
algorithm that formulates
prompt construction for in-context knowledge editing as a Constrained Markov Decision Process. MO-IKE trains a dynamic retriever to optimize competing objectives in knowledge editing, enabling more balanced and globally coherent
prompt construction. On Llama-3.2, MO-IKE improves edit success (reliability) from 85.0% to 92.0%, paraphrase consistency (generality) from 77% to 79%, while increasing retention rate (specificity) by 23.0% compared to prior RL-based methods.
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All authors Xuzhong Wang, Maiqi Jiang, Tejal Nair, Girija Bhusal, Yanfu Zhang, Haipeng Chen
Artificial Intelligence
PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?
Ruoran Xu, Wending Gao, Liyunfeng Chen et al. (+5 more)
Understanding how (
multimodal) large
language models perform on physics problems requires
benchmarks that reflect the difficulty and breadth of expert-level physical
reasoning. Existing physics
benchmarks remain limited in the following two important ways: (1) short of high-difficulty
datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current
datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present PhysElite, a large-scale bilingual
multimodal benchmark for Olympiad-level physics
reasoning. PhysElite contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese-English solution derivations, and the final answer. We
benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the
reasoning chain. Our
datasets are released at https://huggingface.co/
datasets/physelite/PhysElite.
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All authors Ruoran Xu, Wending Gao, Liyunfeng Chen, Aixin Shi, Haoyu Cheng, Zixiang Fang, Yiqiang Zou, Qiufeng Wang
Artificial Intelligence · Cross-listed from cs.LG
Understanding the Energy Scaling of Large Language Model Inference Across Context Lengths and Attention Architectures
Molka Chkir, Syed Muhammad Danish, Jos Höll et al. (+1 more)
The growing adoption of large
language models (LLMs) has raised increasing concerns about the energy consumption and environmental impact of inference. This paper presents a systematic empirical study of decode-phase energy consumption across representative open-source LLMs employing Multi-Head
Attention (MHA), Grouped Query
Attention (GQA), and Grouped Query
Attention with Sliding Window
Attention (SWA) to characterize how
attention architecture influences decode-phase energy consumption under varying inference workloads. We evaluate four models across different context lengths, batch sizes, and generation workloads while measuring GPU energy using NVIDIA hardware counters. We examine the effects of context length,
attention mechanism, Key-Value (KV) cache growth, and batching on decode-phase energy consumption. Results show that
attention mechanism is the primary factor governing how decode energy scales with context length. MHA models exhibit substantially steeper energy growth than GQA models, whereas GQA with SWA maintains nearly constant energy consumption. We further show that model size primarily determines absolute energy consumption, while batching reduces both energy per generated
token and request latency by up to 87%. These findings provide practical guidance for selecting energy-efficient LLM architectures and inference configurations.
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All authors Molka Chkir, Syed Muhammad Danish, Jos Höll, Arghavan Asad
Artificial Intelligence
Auto-Policy, not Auto-Skill: Compiled Agent Skills for the Physical World
Zhonghao Zhan and Hamed Haddadi
Self-evolving Skill harnesses (AutoSkills, Hermes
Agent) generate more advisory orchestration automatically; their reported gains are efficiency, not safety. This misses the actual gap: a Skill describes how an
agent should behave; a Policy decides which behavior is allowed to become an action. Today's format covers the first with markdown and scripts; the second is left to the model. Generating more Skills scales the gap, not the safety, especially when a wrong invocation can unlock a door or move money. Two adjacent attacks are documented: malicious skills compromising cloud software, and jailbroken LLM-controlled
robots causing physical harm. Their intersection, malicious
agent skills causing physical harm, follows directly but has not been reported. We name this class Borrowed Authority: Skills format gives the receiving
agent no typed way to reject an inter-
agent permission claim, so a malicious or misused Skill can drive actuation by attaching one. We propose Edge Skillguard, a typed authority layer that lives inside the Skill artifact rather than between tools as workflow engines do, with guards over world state and sensor evidence. On a live edge control-plane testbed, the guards reject 60/60 borrowed-authority requests across five attack variants without blocking benign requests, and the result holds at 5x scale and across hosts over a Tailscale mesh. These results suggest that high-risk Skills should co-package typed invocation policy with procedural knowledge, so that physical actions depend on machine-checkable evidence rather than peer-
agent claims.
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Artificial Intelligence · Cross-listed from cs.LG
The Von-Neumann State-Space Transformer for neural decoding
Morteza Sarafyazd
Cortical computation is strikingly low-dimensional: a handful of latent variables, carried in a neural population's activity, steer the higher-dimensional responses of individual
neurons. Our aim is sample efficiency-models that decode well from limited data and at small parameter budgets. In a standard
Transformer layer, the feed-forward block applies the same operator to every
token. We suggest a von-Neumann inspired hypothesis of efficient computation as an alternative for neural decoding: a controller decodes an instruction and then executes a
token-specific operator; the usual realization-a soft mixture of experts-only blends their outputs, not operators. We introduce a von-Neumann State-Space
Transformer (VN-SST), a
memory-augmented
Transformer whose feed-forward block is a low-rank instruction bank: a shared base operator plus a small set of learned low-rank instructions, from which a per-
token code synthesizes the weight
matrix actually used at that
token. The code is read from a low- dimensional projection of a carried state-space
memory, so a slow latent trajectory acts as an instruction pointer-mirroring how low-dimensional dynamics may route cortical computation. On three motor-cortex neural-decoding
benchmarks, VN-SST is far more data-efficient than a modern
Transformer, each jointly predicting spikes and decoding behavior. This model wins by a wide margin on the scarcest
benchmark, leads on the other two, and turns longer context into rising rather than falling accuracy. We evaluated that the network compresses a large instruction bank to a few bits per
token, so program capacity acts as a control channel, not an accuracy lever. The same model is also more parameter-efficient on two small text
benchmarks used for
language modeling (LLMs), suggesting a generic mechanism.
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Artificial Intelligence · Cross-listed from cs.LG
NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates
Muyan Anna Li, Manikandan Ravikiran and Aditi Gautam
Time series
forecasting models are widely used in high-stakes settings, yet their predictions remain difficult to interpret because existing post-hoc methods often ignore temporal dependence and fail to provide horizon-specific explanations. We propose a model-agnostic explainability framework that explains
forecasting predictions by attributing each forecast horizon to temporally relevant historical lags. The framework models
forecasting as a latent trajectory and introduces semantic flow to quantify how information evolves across time in the model's internal representations. By aggregating semantic flow, it constructs a lag-horizon attribution
matrix that captures horizon-resolved temporal influence. To improve explainability, we further generate structure-preserving perturbations and fit sparse local surrogate models, producing human-readable and temporally coherent explanations. We evaluate the method using faithfulness and stability diagnostics across multiple
benchmark datasets. Results show that the semantic-flow variant achieves competitive or superior faithfulness compared to standard post-hoc baselines, while being substantially more computationally efficient. Stability analysis further demonstrates that the explanations are robust and identifies regimes where interpretation should be applied with caution.
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Artificial Intelligence · Cross-listed from cs.LG
DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task Learning
Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi et al. (+1 more)
Digital mobility outcomes (DMOs) derived from wearable sensors characterise mobility in daily life and offer a promising means of monitoring disease progression. However, existing DMO studies have typically focused on either a single disease or a single visit. To the best of our knowledge, we are the first to define and study the practical problem of cross-disease longitudinal DMO modelling. We argue that this problem should satisfy at least two requirements. First, the temporal progression of DMOs should be modelled within each disease, as mobility-limiting diseases evolve over time. Second, multiple mobility-limiting diseases should be modelled jointly, as different diseases affect different aspects of human mobility. To address this problem, we propose DeMMO, an interpretable framework for longitudinal, multi-disease, and multi-outcome learning. Its central technical contribution is an interpretable cross-disease and cross-outcome relation-learning mechanism that infers signed relations directly from learned longitudinal DMO-outcome mappings, thereby enabling selective information sharing across cohorts without requiring paired participants. We evaluate DeMMO on the recently released, large-scale, multicentre Mobilise-D
dataset, which presents a challenging modelling setting involving longitudinal observations, multiple clinical outcomes, and four participant-disjoint cohorts representing distinct mobility-limiting diseases. Compared with eight strong structural longitudinal and deep-regression baselines, DeMMO achieves the best overall predictive performance and outperforms the baselines for most individual outcomes. Stability selection further identifies reliable longitudinal DMO patterns that can inform subsequent clinical validation and disease monitoring. The implementation code is available at https://github.com/menghui-zhou/DeMMO.
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All authors Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi, Po Yang
Artificial Intelligence · Cross-listed from cs.CL
HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench
Matthew Flathers, Phuong Anh Nguyen, Jill Noorily et al. (+7 more)
General-purpose health
benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, making domain-specific performance hard to isolate. Mental health is of acute public-health concern as millions of people turn to LLMs for psychological support, and most existing evaluations are bespoke academic
benchmarks that are difficult to integrate into developer workflows. We introduce HealthBench-Psych and HealthBench-Psych-Hard. We screened HealthBench's 5,000 physician-rubric conversations for mental-health relevance with a transparent LLM-applied rubric, then validated the subset through two rounds of blinded clinician review with concealed known-exclude controls, yielding 610 conversations (12.2% of the corpus). Evaluating 20 frontier and open models under a cross-vendor panel of three LLM judges, we find a statistically tied frontier cluster, measurable refusal behavior in two models, and near-identical rankings across judges ($\tau \ge 0.92$). We release the subset, pipeline, model responses, grades, and analysis code as a reusable resource.
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All authors Matthew Flathers, Phuong Anh Nguyen, Jill Noorily, Julian Herpertz, Meiting Chen, Jasreen Multani, Samuel Powell, Mason Granof, Mark Kalinch, John Torous
Artificial Intelligence
SimVerity: When Does Simulated Agent Success Survive Physical Deployment?
Zhonghao Zhan, Yefan Zhang, Krinos Li et al. (+1 more)
Simulated evaluation is widely used to
benchmark AI
agents, yet how much evidence a simulated pass provides about physical deployment has not been systematically quantified. We present SimVerity, a verdict-transfer assurance framework: it replays matched scenarios on target smart home deployments and cross-validates
agent execution against independently qualified physical witnesses. Our evaluation highlights that deployment success is a real-world process, not a static property in simulation: completion, reported state, observable effect, and settled outcome diverged within the same execution. Although an advanced simulator cleared all 240 light trials, a camera caught 42 sub-second failures invisible to settled-state checks. False clearance was predictable: a risk profile learned from measured trials and locked before evaluation predicted failures on a path it never physically measured, beating a property-blind baseline in all eleven held-out sessions across two cohorts.
Agent auditability was also measurable: switching one
agent loop's model-client/serving configuration raised its scenario-matching share from 52-88% to 100%. Finally, a second qualified simulator added no independent cross-check: it never disagreed on any overlapping case, and only physical measurement exposed their shared blind spots. SimVerity turns verdict transfer into an explicit decision: clear, abstain, or escalate before deployment.
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All authors Zhonghao Zhan, Yefan Zhang, Krinos Li, Hamed Haddadi
Artificial Intelligence · Cross-listed from cs.AR
FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration
Geraldo F. Oliveira, Arash Tavakkol, Xiangyu Zhu et al. (+10 more)
LLM inference is increasingly constrained by accelerator
memory capacity rather than compute throughput. This constraint is especially acute in single-accelerator and small-node inference systems, where limited on-package
memory capacity restricts the size of deployable models. HBF is an emerging 3D-stacked NAND flash technology that provides multi-terabyte near-accelerator capacity, making it a promising capacity tier for storing LLM weights. However, existing HBF-based proposals face three adoption challenges: they (1) rely on coarse-grained static prefetching for LLM weights aiming to hide the microsecond-level read latency of the NAND flash device while maximizing HBF's read throughput, (2) expose NAND flash management tasks (e.g., refresh operations) to the accelerator-visible critical inference path, and (3) miss optimization opportunities to specialize and optimize the flash-management mechanisms to the workload behavior. Our goal is to design an efficient HBF substrate that integrates HBF as a
memory-capacity tier alongside HBM while addressing these three challenges. To this end, we propose FLINT, a workload-driven HBF substrate for capacity-scalable LLM inference. FLINT introduces three mechanisms: (1) a hardware burst-buffer controller that dynamically coalesces and pipelines HBF reads aiming to utilize existing NAND flash buffers while sustaining high HBF bandwidth, (2) a phantom-plane refresh mechanism, which removes refresh from the critical inference path by moving refresh-related NAND flash operations outside the read foreground back via low-cost resource duplication, and (3) a read-only FTL, which replaces SSD-class support for arbitrary writes with a compact table that translates logical weight bursts to physical HBF locations.
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All authors Geraldo F. Oliveira, Arash Tavakkol, Xiangyu Zhu, Ahmet Caner Yüzügüler, Vamanan Arulchelvan, Lukas Cavigelli, Renzo Andri, Mohammad Sadrosadati, Jia Xinglei, Onur Mutlu, Zhou Ke, Shai Bergman, Ji Zhang
Artificial Intelligence · Cross-listed from cs.CL
DataKernelBench: Can LLMs Optimize Database Queries on GPUs?
Gokul Karthik Kumar, Yotam Perlitz, Corey Lammie et al. (+2 more)
GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel
benchmarks focus on machine learning operators, leaving irregular, heterogeneous, data-movement-heavy database-style operators untested. We introduce DataKernelBench, which translates SQL into validated PyTorch TorchPlan programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair. Across ten proprietary and open-weight models on TPC-H SF10 with an H100 GPU, the strongest full-query CUDA configuration achieves $2.11\times$ speedup over the TorchPlan baseline at full pass rate. We find that higher-performing implementations commonly use kernel fusion and execution-strategy changes, stronger models benefit most from full-query specialization, and workload context matters more than hardware context. To handle data larger than GPU
memory, we extend TorchPlan with Dask-cuDF for on-demand partition loading on TPC-H SF100 with four H100 GPUs, achieving $2.54\times$ speedup. Project page: https://kerneldf.github.io/datakernelbench
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All authors Gokul Karthik Kumar, Yotam Perlitz, Corey Lammie, Andrea Giovannini, Katja Hose
Artificial Intelligence · Cross-listed from cs.SD
SPECTRA: Subspace-Preserving Embedding Calibration, Transport, and Replay for Fully Few-Shot Class-Incremental Audio Classification
Giries Abu Ayoub, Loay Mualem and Simon Korman
Fully few-shot class-incremental audio classification (FFCAC) requires recognizing new sound classes from only a handful of labeled examples per session, without forgetting previously learned classes and without any large base
dataset. Existing methods typically freeze a pre-trained audio–
language encoder and classify with point prototypes, but they suffer from significant performance degradation throughout the sessions due to generic feature representations. We propose SPECTRA, a framework built on a frozen encoder which adds three components. (i) a lightweight trainable adapter that calibrates the generic
embeddings to the task; (ii) subspace feature replay, an exemplar-free anti-forgetting scheme that replays old classes by sampling from the low-rank subspace of their stored features; and (iii) a transductive optimal-transport refinement of prototypes at test time. Our central finding is that the subspace structure of the replay diminishes forgetting and outperforms naive Gaussian replay of equal variance. On three FFCAC
benchmarks (NSynth-100, FSC-89, LS-100), SPECTRA improves average accuracy and reduces forgetting over current state-of-the-art methods, and our ablations statistically validate each component.
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Artificial Intelligence · Cross-listed from cs.AR
Hydra: Phase-Aware Workload Characterization of LLM Inference across Edge SoC Generations, Backends, and Quantization Levels
Amir Taherin, Sana Taghipour Anvari, Charles Amante et al. (+9 more)
Edge LLM deployment is shaped by more than model size and precision: inference backend, hardware platform,
memory traffic, and power management all affect latency and efficiency. We present Hydra, a common-schema, phase-aware workload characterization framework for LLM inference on edge SoCs. Hydra instruments HuggingFace
Transformers and llama.cpp with a shared per-
prompt timing schema and fuses those records with hardware telemetry, enabling a multi-dimensional characterization of performance, system-resource utilization, and efficiency across prefill and decode phases. Using Hydra, we evaluate three consecutive edge System-on-Chip (SoC) generations (AGX Xavier, AGX Orin, and AGX Thor), 13 instruction-tuned LLMs from seven families, five execution formats, and consider input/output-length sensitivity. The resulting artifact contains roughly 107K per-
prompt records and is publicly released with Hydra. Our analysis shows that aggregate latency alone hides key deployment effects: backend structure changes where latency is introduced, quantization reduces
memory traffic and energy but does not predict power monotonically, and SoC generation changes how utilization and efficiency should be interpreted. By connecting phase-level timing with system-resource utilization and efficiency metrics, Hydra enables reproducible, phase-aware characterization of edge LLM inference. Hydra's source code and the collected per-
prompt trace corpus are available open-source at: https://github.com/amirtaherin/hydra
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All authors Amir Taherin, Sana Taghipour Anvari, Charles Amante, Yixiao Chen, Ruben Noroian, Zlatan Feric, Nicolas Bohm Agostini, Pu Zhao, José Cano, Bin Ren, Yanzhi Wang, David Kaeli
Artificial Intelligence · Cross-listed from cs.SE
ARISMA: Guidelines for AI- and LLM-Assisted Systematic Reviews, Scoping Reviews, and Mapping Studies
Mahyar Tourchi Moghaddam and Mina Alipour
Systematic reviews, scoping reviews, mapping studies, and related evidence syntheses are increasingly difficult to conduct with fully manual workflows as search volumes, update cycles, and synthesis requirements continue to expand. At the same time, artificial intelligence, machine learning, and large
language models are rapidly entering review practice across query formulation, screening, extraction, categorization, appraisal support, and reporting. Yet the empirical evidence remains uneven, task-dependent, and insufficient to justify unconstrained automation. Existing standards such as PRISMA 2020, PRISMA-S, PRISMA-ScR, PRISMA-P, PRESS, and SWiM remain essential, but none provides an end-to-end operational standard for when AI use is methodologically appropriate, how it should be validated, which review decisions must remain human-led, and how AI involvement should be reported so that readers can audit it. This paper proposes ARISMA, an AI Reporting and Integration standard for Systematic Methods and Analysis. ARISMA treats AI as an inspected, benchmarked, logged, and reversible assistant rather than an autonomous reviewer. It is built around one governing principle: every consequential scientific decision must remain human-interpretable, human-auditable, and human-accountable. The paper contributes a lifecycle taxonomy, process guidance, stepwise recommendations across the review pipeline, a governance and provenance model, a tool-support framework, an AI-integrated reporting checklist, and a validation
matrix. It also addresses legal,
privacy, infrastructure, and sustainability considerations. The framework was iteratively refined through structured expert consultation. The result is a practical and auditable guideline for responsible AI-assisted evidence synthesis.
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Artificial Intelligence
LifePlanner: Evaluating LLM Agents for Geo-spatial Planning with Social Media Data
Zhen Dong, Yuning Peng, Yutao Shi et al. (+4 more)
Geo-spatial planning, like trip design, is a realistic testbed for LLM
agents because it requires grounded tool use, noisy evidence
retrieval, and multi-constraint
reasoning. Most
benchmarks, however, only provide clean geospatial data and tools, missing the open-ended social signals that people use in daily planning. We introduce LifePlanner, a
benchmark that enriches map data with large-scale local social media posts and provides access through an MCP toolset. LifePlanner provides an evaluation suite spanning four task categories and three difficulty levels. Experiments show frontier LLMs perform well on simple
retrieval but degrade sharply on complex planning, with the Pass Rate dropping to 40.2%. Results show that failures mainly stem from incomplete evidence acquisition from such a large
multimodal database, imprecise tool use, and weak constraint integration rather than model size or
reasoning length, suggesting that future progress requires effective grounded planning instead of scaling alone.
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All authors Zhen Dong, Yuning Peng, Yutao Shi, Lei Zhong, Yongsen Mao, Yuan Liu, Haiping Wang
Artificial Intelligence
Retrieve, Match, Escalate: Accurate and Scalable Product Linking with VLM-Distilled Cross-Encoders and Agentic VLMs
Jian Wang, Steven Xu, Sanjyot Thete et al. (+5 more)
Product linking, the entity-resolution task of mapping merchant product records to canonical catalog products, consolidates fragmented listings so downstream search, recommendation, and advertising see one clean entry per product. At marketplace scale, billions of noisy, multi-category records must be resolved against tens of millions of canonical products, where scoring every candidate with a single model is either too weak for the hard cases or too costly for the easy ones. We present a production retrieve-then-match cascade that spends computation in proportion to difficulty:
retrieval surfaces plausible matches, a lightweight text cross-encoder auto-resolves the high-confidence majority, and an agentic
multimodal vision-
language model settles the ambiguous remainder by inspecting product images and issuing web searches for evidence that is in neither record. The cross-encoder is distilled from millions of dual-VLM-consensus labels, retiring human annotation from the training set, and is calibrated to auto-accept links at a 98% precision bar validated against a smaller operator-certified audit. The
agent is a self-hosted open-weight model that reaches a closed frontier VLM's precision at a four-point recall cost (88% versus 92%) for roughly one-seventh the per-pair cost, with no fine-tuning. Per-pair cost spans nearly five orders of magnitude from the cheap cross-encoder to the frontier VLM, so escalating only the hard tail to the
agent raises end-to-end link coverage from the cheap stage's 68% to 77%.
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All authors Jian Wang, Steven Xu, Sanjyot Thete, Maryam Barouti, Tom Tang, Elaine Wu, Charu Sareen, Kyle MacDonald
Artificial Intelligence
CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Search
Xiaolong Sun, Qichao Wang, Hangyu Li et al. (+1 more)
Multimodal product models can complete missing e-commerce attributes, yet current methods still optimize attribute-answer accuracy without verifying visual support, conflate transient prediction with persistent index admission, and lack explicit risk control over factually incorrect or visually unsupported values. We address these gaps with Counterfactual Visual Evidence-Guided Selective Attribute Indexing (CVE-SAI), which first infers and freezes an ontology-constrained candidate from the primary image and attribute question without catalog text, and then decides whether that candidate should enter the index. Focus-Zone Distortion (FZD) constructs an attribute-specific visual-dependence proxy through a controlled counterfactual intervention, and Evidence-Guided
Attention Redistribution (EGAR) uses the proxy to refine ontology-constrained scoring. The canonical candidate is frozen before evidence necessity, evidence retention, nuisance-transformation stability, and candidate-specific catalog-text conflict audits; catalog text can only tighten admission and cannot revise the candidate. Independent family-level calibration selects one policy with a simultaneous one-sided finite-sample bound under a 5% unsafe-admission budget. Experiments on five visual attributes derived from Amazon Berkeley Objects show that CVE-SAI improves attribute inference and evidence localization, achieves the highest certified admission coverage under the shared risk protocol, and yields the strongest controlled
retrieval performance with the lowest unsafe auto-induced exposure among automatic-admission systems. Separating inference from admission therefore enables visually supported attribute completion to improve
retrieval while limiting persistent index contamination.
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All authors Xiaolong Sun, Qichao Wang, Hangyu Li, Liang Chen
Artificial Intelligence · Cross-listed from cs.CL
A Primer on Computational Semantics for Artificial Intelligence Systems
Casey Kennington
As people adopt
transformer-based
language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such models learn and represent the meaning of the
language, and to be more informed about what
language is. This document is an attempt to help the reader understand how linguistic meaning (i.e., semantics) is approached from different fields of scientific and philosophical examination. I also explain three primary semantic theories: formal semantics, grounded semantics, and distributional semantics then compare how
transformer-based
language models differ from how humans learn
language.
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