Artificial Intelligence · Cross-listed from cs.CL
Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering
Felix Feldman, Joshua Harris, Timothy Laurence et al. (+7 more)
Large
language models (LLMs) achieve promising results on medical question answering
benchmarks, yet their use in public health is constrained by
hallucinations and the rapid
evolution of official guidance.
Retrieval-Augmented Generation (RAG) mitigates these risks by grounding responses in an explicitly maintained corpus, but end-to-end performance depends critically on
retrieval configuration and on evaluation beyond multiple-choice formats. We extend PubHealthBench, a question answering (QA)
benchmark of 7,929 questions derived from UK Government public health guidance, into a
retrieval-augmented setting and systematically evaluate
retrieval and generation choices. We compare dense, sparse, and hybrid
retrieval across multiple
embedding models and corpus variants, and show that hybrid
retrieval consistently improves recall and ranking quality, with chunk length and topic interacting with ranking performance. Providing retrieved context substantially increases multiple-choice accuracy across a diverse set of LLMs, enabling smaller open-weight models to match or outperform larger models used without
retrieval, with gains primarily driven by
retrieval quality and careful context selection. To assess realistic free-form answering, we introduce a rubric-based LLM-as-a-judge covering faithfulness, completeness, clarity, and factual consistency, and validate it against dual human annotations. Judge-human agreement is strongest for faithfulness and completeness, while factual consistency and clarity are less reliably reproduced, motivating caution when interpreting those dimensions at scale. Overall, our results highlight
retrieval as a primary lever for reliable public health QA and provide practical guidance for building and evaluating RAG systems grounded in official guidance.
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All authors Felix Feldman, Joshua Harris, Timothy Laurence, Leo Loman, Ollie Higgins, Fan Grayson, Poonam Soma, Bethany Pace-Bonello, Michael Borowitz, Toby Nonnenmacher
Artificial Intelligence · Cross-listed from cs.LG
What a World Model Represents Is Three Questions
Donna Vakalis
World models learn task-relevant information through many routes: observation reconstruction, recurrent state, temporal filtering, and explicit task supervision. Different routes can make different variables available. The same variable can also be available through several routes at once. When it is, looking at which route would increase the training loss most if removed does not tell you which route the model actually uses. The questions are reachability, whether a training signal can identify a task-relevant direction; admission, whether that direction is recoverable from the latent; and assignment, which eligible route carries it. We test them in environments with a known set of required coordinates. A direction cannot enter the latent unless some training signal can identify it. Reconstruction, recurrence, or filtering may already recover some of those coordinates; a reward or value head then has no residual direction to admit. For what remains, how many independent predictions the target supplies is how many coordinates install: one through four independent predictions admit one through four directions, including through the value head. Reachability is not admission: a temporal second-moment coefficient can remain absent under next-
token prediction when accumulating it is a fraction of a percent of that loss, and a head that predicts the coefficient restores it. Assignment is a different test. Two routes that each carry the same variable when trained alone do not swap the carrier when we reverse which is more costly to remove. A recurrent model trained on a
transformer's recorded sequences shows the same pattern. Near the point where the competing route is beginning to clear the probe threshold, independent training runs disagree. What a world model represents is therefore three questions: what information is reachable, what supervision admits, and which competing route carries it.
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Artificial Intelligence · Cross-listed from cs.LG
At-Grok Is Not Converged:A Measurement-Validity Audit for Grokking Representation Metrics
Truong Xuan Khanh
On modular arithmetic, a network's
embedding keeps compressing for tens of thousands of steps after it has already generalized. Reading effective rank at the grokking transition overstates the converged value by 3-5x on an MLP, and by 1.3-1.5x on a
transformer trained to convergence; on the MLP it also erases which
cells compress at all. Compression lags the accuracy transition by an amount on the order of the time-to-grok, at least 10,000 steps, rather than coinciding with it. A one-variable ablation shows what sets the lag size: adding LayerNorm to an otherwise identical
transformer moves the fraction of compression done by the grok step from 0.87 to 0.25, and a pre-registered control rules out scale invariance as the mechanism. We package this as an audit that separates onset from compression, flags censoring, excludes boundary
cells that never fully generalize, and checks that the reference floor has plateaued, with an
adversarial suite that caught a false-confidence bug in our own branch. A secondary, MLP-specific depth law linking norm budget to converged floor fails a generality test on a
transformer and flips sign under free weight decay. Code and the toolkit are released.
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Artificial Intelligence · Cross-listed from cs.SE
Specification Grounding Drives Test Effectiveness for LLM Code
Amin Haeri and Mahdi Ghelichi
Large
language models frequently generate code that appears correct on typical inputs yet fails on edge cases, invalid inputs, and other specification-defined corner conditions. A popular fix has the model write its own tests and repair until they pass, but the source of the gain is unclear: does it come from the tests merely existing, or from their grounding in a specification of what the code should do? We isolate this factor. Holding the tester, test budget, and repair loop fixed, we change a single
prompt line that controls whether the tester receives the spec as a checklist of rules. The baseline is strong: it is already told to probe invalid inputs and edge cases. Grounding the tests in the spec produces correct code +38 percentage points more often than this baseline across three Claude tiers (Haiku 4.5, Sonnet 4.6, Opus 4.8), and +36 points on a held-out set. Grounding, not test quantity, is the primary driver: doubling the test budget barely helps, and combining eight independent ungrounded suites plateaus far below grounding. An ablation isolates the spec's content, not its format: given the spec as a plain paragraph the tester recovers 27 of 30 bugs, but asked to plan tests without the spec it recovers only 2 of 30. The effect survives stronger baselines: a property-based generator catches 28 of 30 bugs but invents out-of-spec requirements, and an AlphaCodium-style loop only matches the baseline. It replicates across vendors (GPT-5.3-codex +28, Gemini 3.5 Flash +19), with a task-level sign test over 18 tasks significant at p=0.002. Grounding improves both sensitivity and precision: it catches more real bugs and wrongly rejects far less correct code, cutting the false-alarm rate from 33% (68% against a Python standard-library oracle) to 0%. On well-specified algorithmic problems it neither helps nor hurts.
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Artificial Intelligence · Cross-listed from cs.CV
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
Aavash Chhetri, Bibek Niroula, Eduard Vazquez et al. (+4 more)
In this paper, we address the problem of
multimodal federated learning with missing modality. Existing methods utilize an additional public
dataset or perform naive feature synthesis that is based solely on the available modality. To address these limitations, we propose ProMoE-FL, a Prototype-conditioned Mixture-of-Experts framework for robust missing-modality feature synthesis in
multimodal federated learning. ProMoE-FL builds a global client-aware prototype bank that captures clinically meaningful modality priors across institutions. Our Mixture of Experts is conditioned on these prototypes and modality indices to enable direction-aware expert routing for dynamically synthesizing missing features. We perform extensive quantitative and qualitative evaluations on four public chest X-ray
datasets (MIMIC-CXR, NIH Open-I, PadChest, and CheXpert) and demonstrate that ProMoE-FL consistently outperforms state-of-the-art methods in both homogeneous as well as the more challenging heterogeneous settings.
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All authors Aavash Chhetri, Bibek Niroula, Eduard Vazquez, Yash Raj Shrestha, Prashnna Gyawali, Loris Bazzani, Binod Bhattarai
Artificial Intelligence · Cross-listed from cs.CV
Dynamic-in-Few-Step: Unifying Dynamic Computation and Few-Step Distillation for Efficient Video Generation
Yu Cheng, Siyue Yao, Zhongang Qi et al. (+3 more)
Video Diffusion Models (VDMs) have demonstrated superior generation quality but suffer from prohibitive computational costs. While recent few-step
distillation techniques significantly accelerate inference, they typically enforce a static model architecture across all denoising stages, ignoring the varying computational demands inherent to different noise levels. In this work, we propose a novel post-training acceleration framework that exploits this redundancy by integrating dynamic structural sparsification directly into the
distillation process. Unlike conventional post-hoc compression applied to a fixed
diffusion pipeline, our approach jointly optimizes the denoising steps and structured model sparsity, transforming a pre-trained VDM into a compact, step-specific Mixture-of-Models (MoM). To address the training instability arising from this joint optimization, we introduce a Progressive Training Strategy coupled with an Output Rollout Mechanism, which ensures the coherent learning of structural decisions across timesteps. Furthermore, we develop a specialized inference engine to deploy the resulting MoM efficiently. Our method is orthogonal to existing acceleration techniques and highly effective: On Wan-14B, it removes 24% of the per-step FLOPs on top of 4-step
distillation, adding a 1.2x wall-clock gain and reaching a 30x speedup over the 50-step teacher while preserving competitive generation quality.
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All authors Yu Cheng, Siyue Yao, Zhongang Qi, Shanyan Guan, Wei Li, Fajie Yuan
Artificial Intelligence · Cross-listed from cs.LG
Cross-Trajectory Chimera Interventions Reveal Dissociable Roles of Weight Magnitude and Direction in Grokking
Truong Xuan Khanh
Which properties of a partially trained network are causally portable to a different, independently trained network? Single-trajectory interventions show necessity within one run, not portability across runs. We introduce cross-trajectory chimera interventions: given two runs from different seeds, we split each weight vector into a norm and a unit direction, recombine one run's norm with the other's direction, and continue training. On two modular-arithmetic tasks that grok, the components dissociate. Direction carries a transferable, donor-specific
circuit identity: implanting a donor's direction at the recipient's norm drives the run to the donor's
circuit in 40/40 cases, while an angle-matched random control yields no shift. The transfer is threshold-like, and its location is predicted by the recipient's norm, separating perfectly by norm class over all 20 pairs (joint permutation probability 1.9e-4). Norm carries only a modest, distributed delay effect and no identity signal. An adaptive bisection procedure localizes the threshold to +/-1/64. Direction indexes which solution a trajectory approaches; norm governs how susceptible that identity is to being overwritten.
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Artificial Intelligence · Cross-listed from cs.LG
Open-Ended Scenario Reasoning for Specialist Model Adaptation
Youcheng Zong, Runda Jia, Ranmeng Lin et al. (+2 more)
Process industries have accumulated validated specialist models, yet sensor drift, feedstock variation, and regime switching cause these models to degrade systematically in new scenarios. Collecting new labeled data and retraining is costly, while continuing with the original model incurs persistent bias. Existing adaptation methods require modifying model parameters with sufficient labeled data, making rapid response on deployed systems difficult. Using LLMs as direct predictors risks
hallucinations and uncontrollable outputs. Such predictors also cannot incorporate unstructured scenario knowledge from the field. To address these limitations, this article proposes
Reasoning-Driven Open Adaptation for Specialist Models (ROAM), a framework that uses LLM world knowledge and
reasoning to adapt frozen specialist models to unseen scenarios without retraining. ROAM confines all corrections to a low-dimensional, semantically interpretable latent space. LLM-generated scenario judgments and online observations are fused under a unified probabilistic framework. A risk-constrained mechanism suppresses corrections under unreliable LLM evidence or abrupt scenario shifts and falls back to the original frozen model when evidence is insufficient. Experiments on a mineral thickening process and the public IndPenSim penicillin fermentation
dataset show that ROAM reduces MAE by over 20% in major shift settings such as hidden shifts with only 839 additional parameters and under 0.02\,ms per-step overhead. These results indicate that LLM
reasoning can be turned into a conservative adaptation signal for industrial models already in service.
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All authors Youcheng Zong, Runda Jia, Ranmeng Lin, Mingxuan Ren, Dakuo He
Artificial Intelligence
AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
Andrey Podivilov, Vadim Lomshakov, Sergey Savin et al. (+4 more)
We present AgentLens, a production-assessed
benchmark for interactive code
agents. Most code-
agent benchmarks reduce a run to a single bit – did the task pass? – but the people who actually use these
agents experience the entire trajectory: how the
agent follows instructions, uses its tools, verifies its own work, recovers from mistakes, and talks to them along the way. AgentLens evaluates that whole trajectory. It pairs formal verification, where an objective check exists, with LLM-written trajectory reviews and side-by-side comparisons, so that each run yields a readable explanation of why the score is what it is. This makes AgentLens useful for more than ranking models: we use it to diagnose model behavior, compare successive versions of our own
agent, and catch product regressions in a nightly evaluation pipeline. We release the
benchmark as open source at https://github.com/
agent-lens/
agent-lens-bench.
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All authors Andrey Podivilov, Vadim Lomshakov, Sergey Savin, Matvei Startsev, Roman Pozharskiy, Maksim Parshin, Sergey Nikolenko
Artificial Intelligence · Cross-listed from cs.LG
LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
Youcheng Zong, Runda Jia, Mingxuan Ren et al. (+1 more)
Process industries rely on time-series
forecasting and soft sensing to estimate quality variables that are hard to measure online. Labeled data are scarce, operating regimes change frequently, and retraining models or rebuilding
alignment pipelines for each scenario is costly. Such settings often provide variable tables and process documents that record variable names, units, physical meanings, and process roles. However, standard time-series backbones usually treat inputs as anonymous numerical columns. Existing text-enhanced methods also rarely make the semantic-logical relations between input variables and the prediction target available to the model within each numerical window. To address this problem, this article proposes Task-Semantic Field Factorization (TSF), a large
language model (LLM)-guided framework. TSF builds a task-semantic field from task protocols and variable documents before training and uses the LLM only for offline semantic construction. Online training and inference are handled by conventional time-series backbones. During training and inference, the current numerical window activates variable semantics, so semantic information participates in each prediction and supports adaptation to different prediction targets and operating shifts. Across multiple complex industrial
forecasting and delayed soft-sensing tasks, TSF reduces MAE by 3.6% on average. Across all
dataset–backbone pairs, the macro-average reduction is 2.9%, with a maximum reduction of 24.9%. It adds only about 0.7–4.3k parameters, with less than 8\,$\mu$s/sample of additional online inference overhead. These results show that TSF turns existing process documents into measurable
forecasting gains across backbones and semantic generators while remaining lightweight for deployment.
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All authors Youcheng Zong, Runda Jia, Mingxuan Ren, Dakuo He
Artificial Intelligence · Cross-listed from cs.CV
SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts
Haida Feng, Hao Wei, Haolin Wang et al. (+3 more)
Recent
Multimodal Large
Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry. Existing 3D-aware models either rely on costly 3D-specific data or utilize RGB-only inputs with heuristic sampling and monolithic, shallow fusion, which respectively disrupt essential spatiotemporal connectivity and induce modality contention across diverse spatial tasks. To overcome these bottlenecks, we introduce SpaR3D-MoE, an end-to-end framework that enables adaptive spatial
reasoning by equipping MLLMs with geometry-aware capabilities from only sparse RGB inputs. First, we propose an adaptive spatiotemporal
manifold sampling mechanism that constructs a geometry-aware spatiotemporal graph to extract informative keyframes, effectively mitigating sequence redundancy while preserving the scene's topological connectivity. Second, we introduce the heterogeneous geometry-inductive Mixture-of-Experts driven by an instruction-pose aware router, which adaptively routes
multimodal tokens to specialized experts, resolving the cross-modal contention inherent in monolithic fusion. Extensive experiments on VSI-Bench, ScanQA, and SQA3D demonstrate that our method achieves state-of-the-art performance. Notably, SpaR3D-MoE achieves the highest average score of 63.5 on VSI-Bench, outperforming the strongest baseline by 7.8 absolute points, alongside relative improvements of 35.4% and 51.4% in Route Plan and Relative Direction tasks, respectively.
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All authors Haida Feng, Hao Wei, Haolin Wang, Shiwei Li, Chade Li, Yihong Wu
Artificial Intelligence · Cross-listed from cs.CV
Overview of the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation
Shenxi Liu, Kan Li, Mingyang Zhao et al. (+2 more)
Following the CMIVQA, MMI-VQA, and M4IVQA challenges in NLPCC 2023–2025, we introduce the Difficulty-Aware Medical Instructional
Video Question Answering (DA-MIVQA) shared task for NLPCC 2026. DA-MIVQA extends previous
multilingual and
multimodal medical
video benchmarks by explicitly distinguishing questions according to the type and complexity of evidence required for answering. Specifically, simple questions can often be answered from subtitle-based textual cues, whereas complex questions require visual grounding, procedural understanding, and cross-modal evidence integration. The challenge contains three tracks: Difficulty-Aware Temporal Answer Grounding in Single
Video (DA-TAGSV), Difficulty-Aware
Video Corpus
Retrieval (DA-VCR), and Difficulty-Aware Temporal Answer Grounding in
Video Corpus (DA-TAGVC). The
dataset is collected from public medical instructional channels, covers diverse scenarios such as first aid, emergency response, rehabilitation, nursing, and general medical education, and is manually verified with difficulty annotations. This paper presents the task motivation,
dataset construction, evaluation protocol, participation overview, competition results, and representative systems of DA-MIVQA. DA-MIVQA provides a practical
benchmark for evaluating medical instructional
video question answering systems under varying textual, visual, temporal, and procedural
reasoning requirements.
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All authors Shenxi Liu, Kan Li, Mingyang Zhao, Yuhang Tian, Bin Li
Artificial Intelligence · Cross-listed from cs.LG
Inertia-1: An Open Exploration of Wearable Motion Foundation Models
Zongzhe Xu, Aakarsh Anand, Sarah Jiang et al. (+4 more)
Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling principles remain poorly understood. Prior work studies isolated design choices, such as sensor placement or sampling frequency, often under fixed settings and narrow downstream tasks that fail to capture real-world sensing diversity. We introduce Inertia-1, a fully open exploration of wearable motion foundation models. Using massive corpora of accelerometer data from global sources spanning more than 18.2M hours, we build a controlled framework for studying the full lifecycle of wearable motion foundation models, covering data choices such as sensor modality, device placement, sampling rate, window length; model choices such as architectures and model size; and training choices such as pretraining objective and data scale. Extensive evaluations across 15
datasets spanning human activity recognition, freezing-of-gait detection, and disease prediction reveal intriguing findings for building motion foundation models that generalize across tasks and sensing conditions. Collectively, Inertia-1 not only presents state-of-the-art recipes for diverse downstream tasks, but also serves as a comprehensive, practical, and open cookbook for wearable motion representation learning.
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All authors Zongzhe Xu, Aakarsh Anand, Sarah Jiang, Chuntung Zhuang, Zitao Shuai, Sriram Sankararaman, Yuzhe Yang
Artificial Intelligence · Cross-listed from cs.LG
WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
Sangwoo Lee, Sunghwan Park and Jaewoo Lee
Label skew in
federated learning (FL) causes client drift and degrades global accuracy. Synthetic data augmentation can reduce this imbalance; however, full class balancing requires substantial computation cost. We propose FedEAS, a policy that assigns each client an
entropy-adaptive per-class generation budget computed from its local label distribution. The budget jointly decides how much each client generates and WHERE the samples go. Accordingly, the total generation budget follows from the per-client budgets rather than being fixed in advance. FedEAS recovers most of the accuracy gain of full class balancing while reducing the generation budget by 94.1%. At the same total generation budget, it outperforms Uniform allocation by up to 18.82% across CIFAR-10 and CIFAR-100.
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Artificial Intelligence · Cross-listed from cs.LG
STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting
Ye Zihao
Accurate station-level demand
forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex spatio-temporal dependencies and the large scale of urban networks. This paper presents STAGformer, a Spatio-Temporal
Agent Graph
Transformer that achieves efficient global modeling with linear computational complexity. The model introduces a two-step
agent attention mechanism, where a small set of learnable spatial and temporal
agent tokens first aggregate global information and then broadcast it back to individual stations and time steps, effectively capturing long-range interactions while reducing the quadratic cost of standard self-
attention to O(NT). STAGformer integrates four core modules: a spatio-temporal encoder that fuses dynamic node features with external contextual factors (weather, time, points of interest), a graph propagation module for spatial neighbor aggregation, a temporal convolution module for local pattern extraction, and the
agent attention module for global dependency modeling. Extensive experiments on two real-world
datasets – NYC Citi-Bike and Chicago Divvy-Bike – demonstrate that STAGformer consistently outperforms state-of-the-art baselines across multiple prediction horizons, achieving significant improvements in both RMSE and MAE. Ablation studies validate the contribution of each component, with the
agent attention mechanism proving critical for modeling global spatio-temporal dependencies.
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Artificial Intelligence · Cross-listed from cs.CR
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca et al. (+2 more)
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user
privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks. Although recent work has explored secure and Byzantine-resilient FL protocols, they face a fundamental trade-off among
privacy, integrity, and verifiability, and incur substantial computational and communication overhead due to the heavy use of cryptographic primitives. In this work, we propose PRoVeFL-a novel, modular FL framework that is
Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation. PRoVeFL employs multiple servers leveraging multi-key fully homomorphic encryption. Each client encrypts its local model updates and distributes encrypted shares to all servers. This design enables a hybrid computation model in which ciphertext operations are carefully offloaded to the plaintext domain under strict
privacy constraints to efficiently evaluate complex statistical aggregation rules. PRoVeFL is compatible with a wide range of state-of-the-art Byzantine-robust aggregation
algorithms (e.g., Krum, Trimmed Mean, FLTrust, norm clipping, MESAS, and more) and further enhances them with verifiability mechanisms that require minimal trust in at least one honest server. We evaluate it across different settings and demonstrate its scalability with varying numbers of parameters and participants. PRoVeFL improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.
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All authors Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca, Graham Cormode, Carsten Maple
Artificial Intelligence · Cross-listed from cs.CL
Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts
Andrei-George Durdun, Victor Constantinescu and Radu Tudor Ionescu
Automatically recognizing the sentiment, positive or negative, from
speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words. Recent solutions rely on audio foundation models to solve the task, but it remains unclear if such models can take all aspects into account. To this end, we propose a
multimodal solution that integrates audio and text information via cross-modal
transformers, where text transcripts are automatically generated via an automatic
speech recognition (ASR) tool. Moreover, we create multiple text modalities by automatically translating the transcripts into multiple
languages via machine translation tools. Audio and
multilingual text features are combined via a cascaded architecture comprising cross-modal
transformer blocks that integrate modalities one by one. We further distill knowledge from the
multimodal model, called teacher, into a unimodal (audio only) model, called student. We conduct experiments on a large-scale
dataset, demonstrating that the automatically generated textual information can bring significant performance boosts in
multimodal sentiment polarity classification. Our ablation study confirms that both automatic transcripts and automatic translations are helpful. Moreover, we show that the audio-only model can be enhanced via
distillation, boosting performance without any computational overhead during inference. To reproduce the reported results, we publicly release our code at https://github.com/andreidurdun/cross-modal-audio-sentiment.
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Artificial Intelligence · Cross-listed from cs.LG
Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta et al. (+1 more)
Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk,
market dynamics, and practical investment constraints. Existing reliability based portfolio optimization approaches primarily rely on static optimization frameworks and often fail to capture sequential decision making, tail risk, and
market frictions such as transaction costs. To address these limitations, we propose a deep reinforcement learning framework for multi-objective reliability based portfolio optimization (MORP-DRL). The proposed framework jointly optimizes expected return and downside risk using three complementary risk measures: variance, Conditional Value-at-Risk (CVaR), and Entropic Value-at-Risk (EVaR). To model uncertainty and heavy-tailed
market behavior, asset returns are represented using GARCH(1,1), Extreme Value Theory, and a t-copula dependence structure, while realistic scenarios are generated through quasi-Monte Carlo simulation. A Proximal Policy Optimization (PPO) based strategy is developed under practical constraints including transaction costs and portfolio bounds, and is benchmarked against NSGA-II. Experiments on ten global equity indices across pre-COVID, COVID, and post-COVID
market regimes demonstrate that MORP-DRL achieves competitive risk-return performance, reduced downside risk during periods of
market stress, and scalability to high-dimensional portfolio settings.
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All authors Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta, Aditya Gupta
Artificial Intelligence · Cross-listed from cs.LG
D2PO: Optimizing Diffusion Samplers via Dynamic Preference
Jinkyu Kim, Jinyoung Choi and Bohyung Han
We propose D2PO (Dynamic Direct Preference Optimization), a principled framework for optimizing
diffusion sampling policies with respect to timestep schedules and classifier-free guidance (CFG) weights. Our work is motivated by a fundamental limitation of existing student-teacher regression frameworks; low-NFE student samplers are trained to mimic high-NFEteachers, often sacrificing high-frequency texture fidelity while preserving coarse global structures, thereby misaligning the sampler with perceptual quality. D2PO addresses this challenge by reformulating sampler optimization as a preference-based
alignment problem, leveraging the Direct Preference Optimization (DPO) framework. To make DPO applicable to
diffusion samplers, we model the sampling policy as an energy-based model (EBM), transforming preference comparisons into tractable energy differences. We further introduce a novel energy formulation derived directly from the pretrained score network, enabling preference evaluation in perturbed spaces that jointly capture structural consistency and fine-grained details. Moreover, we introduce dynamic preferences, where the preferred samples used for
alignment progressively improve as the sampling policies are learned. This self-improving mechanism replaces rigid static teacher supervision with an iterative, preference-guided refinement process, providing progressively stronger
alignment signals. Extensive experiments demonstrate that D2PO aligns
diffusion samplers with perceptual quality more faithfully, unlocking the full potential of high-quality teachers and consistently outperforming conventional regression-based schedulers under low-NFE constraints.
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Artificial Intelligence · Cross-listed from cs.CR
Security and Privacy in Agentic AI: Grand Challenges and Future Directions
Adam Jenkins, Agnieszka Kitkowska, Caterina Maidhof et al. (+22 more)
We present key challenges and future research directions in the security and
privacy of agentic AI, based on a horizon-scanning exercise that brought together thirty leading international experts from academia, industry, and government to engage in focused discussions and collaborative exercises on the emerging risks associated with the growing agency of AI.
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All authors Adam Jenkins, Agnieszka Kitkowska, Caterina Maidhof, Diego Paracuellos, Francesco Sovrano, Gonzalo Gabriel Mendez, Guillermo Suarez-Tangil, Hana Kopecka, Isabel Wagner, Isabel Barbera, Javier Carnerero-Cano, Jide Edu, Jose Luis Martin-Navarro, Jose Such, Josep Domingo-Ferrer, Juan Carlos Carrillo, Kopo Marvin Ramokapane, Mark Cote, Pablo Vellosillo, Ramon Ruiz-Dolz, Rongjun Ma, Ruba Abu-Salma, Sameer Patil, William Seymour, Xiao Zhan
Artificial Intelligence · Cross-listed from cs.LG
NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts
Lanhao Li, Bingshu Xie, Lijun Sun et al. (+3 more)
Accurate long-term
forecasting in complex systems is frequently compromised by
dataset-level distribution shifts, where diverse underlying behavioral modes and evolving system states drive the dynamic multivariate time-series. While existing methods predominantly focus on local temporal shifts, they fail to explicitly model the global structural challenge where
datasets are composites of distinct operational regimes. In this paper, we propose NEST, a specialized framework designed to model and recompose these evolving structures through a two-phase dense MoE architecture. NEST first facilitates structural specialization by partitioning the
dataset into distinct operational regimes through unsupervised clustering in a principled moment-
entropy space. We introduce a regime-oriented router mechanism that generates initial expert weights based on temporal content, subsequently refined through
geometric modulation to regime centroids. Crucially, rather than acting as monolithic predictors, individual experts function as specialized kernels that capture regime-specific dynamics by evolving unique variate-
attention patterns. Extensive evaluations on diverse
benchmarks, including heterogeneous network traffic and physical phenomena, demonstrate that NEST consistently achieves state-of-the-art performance. Our code and
datasets are available at https://github.com/Aaralshin/NEST
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All authors Lanhao Li, Bingshu Xie, Lijun Sun, Xin Xue, Haoyi Zhou, Jianxin Li
Artificial Intelligence · Cross-listed from cs.CV
Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? – A Theoretical and Empirical Study
Sangwon Jung, Sumin Yu, Sanghyuk Chun et al. (+1 more)
The notion of algorithmic
fairness has been actively explored from various aspects of
fairness, such as counterfactual
fairness (CF) and group
fairness (GF). However, the exact relationship between CF and GF remains to be unclear, especially in image classification tasks; the reason is because we often cannot collect counterfactual samples regarding a sensitive attribute, essential for evaluating CF, from the existing images (\eg, a photo of the same person but with different secondary sex characteristics). In this paper, we construct new image
datasets for evaluating CF by using a high-quality image editing method and carefully labeling with human annotators. Our
datasets, \oursceleb and \ourslfw, build upon the popular image GF
benchmarks; hence, we can evaluate CF and GF simultaneously. We empirically observe that CF does not imply GF in image classification, whereas previous studies on tabular
datasets observed the opposite. We theoretically show that it could be due to the existence of a latent attribute $G$ that is correlated with, but not caused by, the sensitive attribute (\eg, secondary sex characteristics are highly correlated with hair length). From this observation, we propose a simple baseline, Counterfactual Knowledge
Distillation (CKD), to mitigate such correlation with the sensitive attributes. Extensive experimental results on \oursceleb and \ourslfw demonstrate that CF-achieving models satisfy GF if we successfully reduce the reliance on $G$ (\eg, using CKD).
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All authors Sangwon Jung, Sumin Yu, Sanghyuk Chun, Taesup Moon
Artificial Intelligence · Cross-listed from cs.LG
TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation
Andrii Balashov and Olena Ponomarova
Conditional computation can decouple
language model quality from per-
token inference cost, yet leading techniques act on a single axis in isolation: Mixture-of-Experts (MoE) sparsifies the FFN, Mixture-of-Depths (MoD) skips whole
transformer blocks, and KV-cache quantization compresses
attention memory. We argue these three decisions (
attention resolution, expert selection, and cache bit-width) are strongly coupled and should be made jointly: a
token rare enough to warrant full
attention may also need high-precision caching regardless of which expert processes it. We introduce TriRoute, a single lightweight controller shared across all three axes that, for every
token at every layer, emits a coordinated policy: (i) an
attention mode (skip/local/full), (ii) a sparse set of FFN experts (with a null expert recovering MoD), and (iii) a KV-cache bit-width. The controller trains end-to-end via a heterogeneous relaxation (Gumbel-Softmax with straight-through estimation for categorical decisions and load-balanced top-k gating for experts) under a Lagrangian budget constraint that turns the average compute and
memory cost into a controllable knob. We identify a cross-axis routing-collapse cascade in naive joint training, where collapse on one axis propagates to the others, and address it with per-axis normalization and a coupling-aware balancing loss. On decoder-only models from 160M to 1.3B parameters at compute-optimal
token counts, TriRoute Pareto-dominates the best independent MoD+MoE+KV-quantization combination at matched inference FLOPs and
memory, while better preserving tail-case robustness on rare entities, code, and arithmetic that pure perplexity optimization erodes. Post-hoc analysis reveals interpretable structure: the controller allocates full
attention and high-precision cache to sentence-initial positions, rare subwords, and named entities, while cheaply routing function words.
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Artificial Intelligence · Cross-listed from cs.CV
MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices
Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand and M. Hassan Najafi
Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection. Recent deep learning methods have greatly improved accuracy, yet even the smallest models require several megabytes of
memory, exceeding low-cost MCU capacity. This work investigates the maximum achievable accuracy under a sub-megabyte budget. We propose MiLSD, a detector tailored for MCU-level constraints, and systematically compare three output representations within a compact fully-convolutional backbone. Our study shows that the proposed F-Clip center-with-length-and-angle formulation learns most effectively at small model sizes. We find that 8-bit quantization preserves full-precision performance, while 4-bit quantization causes significant degradation, particularly in angle regression, with quantization-aware training recovering only part of the loss. With a one-megabyte activation budget and inference enhancements including sub-pixel decoding, test-time augmentation, and a lightweight verifier, MiLSD improves sAP10 on ShanghaiTech Wireframe from 10.6 (25k parameters, 0.25 MB) to 24.1 within 1 MB. Rather than competing with GPU-scale parsers, we map the accuracy
memory trade-off across representations, bit-widths, capacities, and post-processing strategies for embedded
vision systems.
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Artificial Intelligence · Cross-listed from cs.CV
ELSA3D: Elastic Semantic Anchoring for Unified 3D Understanding and Generation
Tianjiao Yu, Xinzhuo Li, Yifan Shen et al. (+4 more)
Unified 3D foundation models aspire to generate 3D assets and reason about them in
language within a single backbone, but their text-3D interaction remains largely implicit. Existing methods concatenate text and 3D
tokens into a flat sequence and rely on self-
attention, collapsing coarse structural cues and fine
geometric details into one undifferentiated representation. We introduce ELSA3D, a unified 3D model that addresses this with elastic semantic anchoring, structuring
language and
geometric reasoning jointly along matched abstraction scales. ELSA3D represents geometry with a scale-aware octree tokenizer and introduces Anchor
Tokens, sparse cross-modal units that select semantic cues, route them to the most relevant 3D scale, retrieve scale-specific
geometric evidence, and write the fused signal back into the unified representation, keeping interaction sparse yet precise. A lightweight per-block router makes both computation and
reasoning elastic, choosing which text
tokens instantiate anchors at which
geometric scale so that cross-modal capacity concentrates where
alignment is most needed. ELSA3D achieves state-of-the-art performance across image-to-3D generation, text-to-3D generation, and 3D captioning, outperforming the strongest unified baseline while roughly halving FLOPs and inference latency relative to the non-elastic version of the same model.
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All authors Tianjiao Yu, Xinzhuo Li, Yifan Shen, Onkar Susladkar, Yuanzhe Liu, Xiaona Zhou, Ismini Lourentzou
Artificial Intelligence · Cross-listed from cs.LG
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Shervin Khalafi, Igor Krawczuk, Sergio Rozada et al. (+3 more)
Denoising graphs is a fundamental problem in graph learning and the core operation of graph
diffusion models.
Attention-based architectures such as graph
transformers have shown promise for this task, yet the mechanisms through which
attention denoises graphs remain poorly understood. We show that, under a graph denoising objective, standard
attention scores learn an average spectral denoising filter across the training distribution and are suboptimal when the graph spectra vary. To address this limitation, we introduce spectral
attention scores, which are
attention scores that depend on the input graph spectrum. We characterize the improvement over standard
attention scores in terms of the spectral diversity of the graph distribution. We then propose graph convolutional
attention (GCA), a practical realization of spectral
attention that uses graph-filtered queries and keys. For asymptotically large
stochastic block models, we show that GCA matches the optimal denoising loss of spectral
attention under suitable conditions. We also show that softmax can further reduce denoising error by approximately projecting noisy eigenvectors onto the clean eigenspace. Across synthetic and real
datasets, replacing standard query–key projections with GCA improves graph denoising and
diffusion, with denoising gains correlated with spectral diversity. Furthermore, replacing the DiGress architecture with a GCA-based architecture within the DiGress generative framework yields comparable generation quality with significantly faster inference.
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All authors Shervin Khalafi, Igor Krawczuk, Sergio Rozada, Charilaos Kanatsoulis, Antonio G Marques, Alejandro Ribeiro
Artificial Intelligence
Rethinking Indic AI from a Lens of Cultural Heritage Preservation
Aparna Madva, Sharath Srivatsa, Srinath Srinivasa et al. (+1 more)
As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts the linguistic and cultural foundations of this civilization. AI is seen as a ”double-edged sword” where on the one hand, it can enable access and inclusion for a large population, on the other, it can homogenize worldviews and exclude underrepresented
languages and worldviews. In this paper, we try to characterize this problem by addressing the extensive characteristic nature of Indian linguistics and the way they closely connect to cultural practices and worldview. We then perform a longitudinal survey of how Natural
Language Processing (NLP) techniques have evolved in this space, tracing the historical development of Indic NLP, covering key milestones, methodological shifts, and resource creation efforts. In addition, the paper also examines the structural and sociolinguistic characteristics of Indian
languages, such as rich morphology, complex scripts and grammar rules, diglossia, and large dialectal variation, and explains how these create unique challenges for building AI foundation models. We then discuss the growing role of Indic foundation models and analyze how these models address these long-standing resource and representation gaps. Finally, we propose a research direction called 'Culture Sensing', which re-imagines AI based on hermeneutic
reasoning. Culture Sensing aims to address open problems such as ensuring equitable performance across low-resource
languages and producing outputs that are culturally meaningful. By bringing together past work, current techniques, and emerging trends, this paper outlines research directions that can guide the next phase of Indic NLP and contribute to the development of more robust and inclusive Indic foundation models.
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All authors Aparna Madva, Sharath Srivatsa, Srinath Srinivasa, Tulika Saha
Artificial Intelligence
The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
Ghassen Marrakchi and Basarab Matei
- Objective:
Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference. To address this inflexibility, we propose the Large Cancer Assistant (LCA), a model-agnostic, post-hoc orchestration framework designed for scalable clinical decision support. - Methods: The LCA is mathematically formalized as a 7-tuple architecture grounded in the principle of Algorithmic Impermeability, ensuring the orchestration logic remains strictly independent of underlying black-box AI models. We introduce the Entry Theory, leveraging
Geometric Deep Learning (GDL) to standardize
multimodal patient data along distinct structural and medical axes. The system dynamically orchestrates data via a Cancer Switching Module and intentionally isolates the core AI execution from volatile hospital IT infrastructures by outputting a Standardized Intermediate Payload (SIP). - Results: A Proof of Concept (PoC) validated the orchestration logic across four technical scenarios. The framework executed a nominal flow with negligible orchestration overhead. It empirically demonstrated algorithmic impermeability by maintaining an invariant routing projection during AI model swaps, and it validated strict failure-safety by achieving a 100% recall rate in generating targeted Supplementary Data Requests (SDR) under injected data anomalies. Multi-protocol execution capability was also successfully verified. - Conclusion: By structurally decoupling
multimodal ingestion from feature inference, the LCA provides a highly adaptable and modular orchestration foundation. The SIP establishes a clear architectural boundary, natively setting the stage for downstream Electronic Medical Record (EMR) interoperability as an independent future paradigm.
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Artificial Intelligence · Cross-listed from cs.CL
RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
Sambaran Bandyopadhyay and Ananth Muppidi
Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the semantic gap where intermediate nodes lack lexical overlap with the query. To address this, we propose RSF-GLLM, a framework decoupling differentiable graph
reasoning from answer generation. Our Recurrent Soft-Flow (RSF) module employs a GRU-guided query updater to propagate continuous relevance scores, utilizing a dynamic gating mechanism to traverse semantically dissimilar bridge nodes via structural cues. We introduce flow sparsity regularization to theoretically guarantee convergence from soft probabilities to discrete
reasoning paths. These paths are extracted and textualized to fine-tune a Large
Language Model (LLM), ensuring generation is grounded in factual
topology. Experiments on WebQSP and CWQ demonstrate that RSF-GLLM achieves competitive performance with superior inference efficiency compared to LLM based computationally expensive approaches.
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Artificial Intelligence
DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression
Anna Cordoba, Adam Puente Tercero, Nerea Angulo Hijo et al. (+4 more)
Long-context
language model inference is increasingly limited by the
memory bandwidth and capacity required to store key-value caches, yet existing compression methods often apply uniform budgets across layers or
tokens and degrade
retrieval when lexical cues and semantic states require different preservation. We introduce DepthWeave-KV, a
token-adaptive cache compression method that factorizes key and value states across neighboring
transformer layers using shared low-rank channel bases while retaining lightweight
token-specific residuals where
attention behavior is sensitive. DepthWeave-KV combines cross-depth residual factorization with a
token-conditional depth router that allocates higher reconstruction rank to instruction-bearing and
retrieval-critical
tokens, and uses calibration-free online error tracking from
attention-output probes to adapt compression during generation without retraining the base model. A fused CUDA implementation jointly performs basis lookup, residual dequantization, and
attention projection to reduce decode-time
memory traffic. Across LongBench, Needle-in-a-Haystack, L-Eval, and long-form QA and summarization
benchmarks, DepthWeave-KV achieves near-full-cache task quality with substantially lower
memory use, improving average score and
retrieval accuracy over prior compressed caches while reaching 8.3x KV
memory reduction and 72.8
tokens per second at 64K context.
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All authors Anna Cordoba, Adam Puente Tercero, Nerea Angulo Hijo, Mar Linares Tercero, Julia Barrientos, Ainhoa Miranda, Jesus Olivera