Artificial Intelligence · Cross-listed from cs.LG
Private Learning with Public Feature Conditioning
Shuli Jiang, Walid Krichene and Nicolas Mayoraz
We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features – common in applications such as recommendation and advertising systems. While such label-DP or semi-sensitive-feature settings have been primarily explored in the context of classification, effective approaches for regression remain underexplored. We introduce Cond-DP, a conditioned variant of DPSGD that leverages the structure of public feature
matrices to improve optimization under
privacy constraints. Motivated by the observation that these public features often exhibit rapidly decaying spectra, Cond-DP incorporates a data-driven conditioning
matrix to reshape the optimization landscape and accelerate convergence. We provide convergence guarantees for convex, strongly convex, and non-convex settings, and recover standard DPSGD as a special case when the conditioning
matrix is the identity. We show how to construct an effective conditioning
matrix for Cond-DP directly from public features, enabling provably faster convergence than DPSGD in private linear regression without incurring additional
privacy cost. Empirically, Cond-DP with this conditioning
matrix consistently outperforms state-of-the-art baselines across a wide range of
datasets and model architectures under label DP, demonstrating strong and robust performance in practice.
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Artificial Intelligence · Cross-listed from cs.RO
Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs
Chris Lee, Flora Salim, Benjamin Tag et al. (+1 more)
Expressive gestures are essential for natural and effective communication, complementing
speech when verbal cues alone are insufficient (e.g., pointing). For social
robots such as the
humanoid Pepper, producing natural and expressive movements is critical for improving human-
robot interaction (HRI) and long-term acceptance. However, generating gestures remains challenging due to reliance on expert-authored animations, resulting in rigid behaviors that are impractical for dynamic and diverse environments. Alternatively, machine learning approaches often struggle to capture perceived naturalness, becoming increasingly challenging with more degrees of freedom. Consequently, producing expressive
robot gestures requires a system that can adapt to the environment while adhering to social norms and physical constraints. Recent advances in large
language models (LLMs) enable dynamic code generation, offering new opportunities for runtime gesture synthesis from natural
language. In this paper, we integrate ChatGPT into the
humanoid robot Pepper to generate co-
speech gestures aligned with conversational output. While this baseline enables flexible gesture generation, the resulting motions are often perceived as stiff and unnatural. To address this limitation, we introduce an iterative reinforcement learning with human feedback (RLHF) system that finetunes gesture generation based on user evaluations, leveraging an iterative user study to compare Pepper's generated gestures. Our results show that RLHF improved the LLM's co-
speech generative capabilities, producing more expressive, relevant and fluid movements.
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All authors Chris Lee, Flora Salim, Benjamin Tag, Francisco Cruz
Artificial Intelligence
What Must Generalist Agents Remember?
Khurram Yamin, Namrata Deka, Maitreyi Swaroop et al. (+3 more)
This paper develops a formal account of what generalist
agents must store in
memory in order to act near-optimally across multiple environments and goals. It shows that when two domains share an observational bottleneck but require incompatible optimal actions, any uniformly near-optimal policy must induce distinct
memory distributions at that bottleneck. The result yields a separation
theorem: sufficiently successful
agents cannot rely only on current state observations, but must preserve domain-relevant information in
memory. The paper further shows that if an
agent's
memory contains enough information to estimate values for related goals, then that
memory can be used to approximately reconstruct the
agent's local transition dynamics. Together, these results characterize
memory as the substrate that supports domain disambiguation, transition-model reconstruction, and planning for generalist
agents.
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All authors Khurram Yamin, Namrata Deka, Maitreyi Swaroop, Albert Ting, Jeff Schneider, Bryan Wilder
Artificial Intelligence · Cross-listed from cs.SE
SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents
Qiao Zhao, JianYing Qu, Jun Zhang et al. (+3 more)
Realistic coding-
agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or
benchmark-driven model selection. Fully synthetic tasks avoid direct historical replay, but can drift away from real repository needs. We propose SWE-Future, a forecast-conditioned data synthesis method for future-oriented coding tasks. Given a forecast snapshot at time $T_0$, the method uses only pre-$T_0$ repository evidence to forecast future feature implementation/enhancement, bugfix, and refactor task families. We first validate this
forecasting step retrospectively: after forecasts are fixed, later pull requests are used only to measure whether the predicted task families match future repository work. In an 80-repository study, the forecaster achieves 58.1% future-work relevance under the main semantic matching metric. We then use validated forecast families as conditioning signals to synthesize a 200-task coding-
agent dataset across 61 repositories from a task-generation snapshot, rather than replaying the later pull requests used for validation. SWE-Future shows that repository-
evolution forecasts can guide realistic, future-oriented coding-task synthesis while reducing direct dependence on historical pull-request replay.
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All authors Qiao Zhao, JianYing Qu, Jun Zhang, Yehua Yang, Hanwen Du, Zhongkai Sun
Artificial Intelligence · Cross-listed from cs.RO
Two-Phase Bilevel Search for the Moving-Target Traveling Salesman Problem with Moving Obstacles
Allen George Philip, Anoop Bhat, Sivakumar Rathinam et al. (+1 more)
The Moving-Target Traveling Salesman Problem (MT-TSP) seeks a minimum cost trajectory for an
agent that departs from a static depot, visits a set of moving targets, each within one of their assigned time windows, and returns to the depot. In this article, we study the Moving-Target Traveling Salesman Problem with Moving Obstacles (MT-TSP-MO), a generalization of the MT-TSP where the
agent trajectory must avoid moving obstacles. We present a Mixed-Integer Conic Programming (MICP) formulation that can be solved using off-the-shelf solvers, as well as a fast and scalable Two-Phase Bilevel Search (TPBS)
algorithm that computes high-quality feasible solutions for the problem. We evaluate our approaches against an existing baseline
algorithm on a broad range of problem instances with up to 40 targets and 40 obstacles. The results demonstrate that both the proposed methods significantly outperform the baseline with respect to success rates, solution costs, and computation time.
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All authors Allen George Philip, Anoop Bhat, Sivakumar Rathinam, Howie Choset
Artificial Intelligence · Cross-listed from cs.LG
Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring
Fang Wang and Ernesto Damiani
Structurally constrained event sequence generation remains challenging because generated paths must preserve transition feasibility, temporal order, termination, and attribute consistency. In predictive process monitoring (PPM), this challenge appears as full event sequence generation, whereas existing work mainly addresses component tasks such as next activity, remaining time, outcome, and attribute prediction. This paper proposes the Graph Grounded Cross
Attention Transformer Neural Network (GGATN) for this unified PPM task. GGATN uses a global process graph as structured activity
memory, contextualizes sequence positions through
Transformer self
attention, and injects process
topology through graph grounded cross
attention. Unlike autoregressive decoding, GGATN generates activities, timestamps, length, and event level and sequence level attributes in a single pass, followed by Viterbi style graph constrained decoding for feasible paths and explicit termination. Experiments on six
benchmark event logs show more reliable generation quality than local instruction prompted LLM baselines. GGATN achieves strong performance on sequence similarity, Damerau Levenshtein similarity, bigram based control flow similarity, and duration distribution, while maintaining zero hallucinated activities and zero sequence level attribute inconsistency. Ablation analyses confirm the global graph encoder as a stable structural prior. Interpretability analyses show how graph structure, sequence context, feedback refinement, and constrained decoding shape generation.
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Artificial Intelligence · Cross-listed from cs.CL
Morpheus: A Morphology-Aware Neural Tokenizer and Word Embedder for Turkish
Tolga Şakar
Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern
language models split words by corpus statistics, fragmenting semantically loaded suffixes and – in the case of WordPiece and rule-based analyzers – failing to decode their output back to the original text. This paper presents Morpheus, a neural morpheme-boundary model for Turkish that is at once a lossless, morphology-aware tokenizer and a word-
embedding producer. A differentiable Poisson-binomial dynamic program turns per-character boundary probabilities into soft morpheme memberships during training and exact segments at inference, with no string normalization, so $\mathrm{decode}(\mathrm{encode}(w)) = w$ holds by construction. Because the model is neural, the same forward pass that tokenizes also emits a structured word
embedding. Among reversible tokenizers – the only ones valid for generation – Morpheus attains the lowest bits-per-character ($1.425$), roughly doubles the gold morphological
alignment of the subword family (MorphScore macro-F1 $0.61$ vs.\ ${\sim}0.32$), and uses ${\sim}19\%$ less GPU
memory than 64K-vocabulary subword tokenizers. As an embedder, frozen Morpheus vectors lead on lexical
retrieval (root-family MAP $0.85$) and same-root verification (ROC-AUC $1.00$), surpassing the
multilingual retriever BGE-M3 and BERTurk; on context- and inflection-dependent tasks (NER, case/number probing) the heavier contextual encoders remain ahead – a trade-off we attribute to Morpheus's root-centric geometry. Code: https://github.com/lonewolf-rd/TurkishMorpheus; model: https://huggingface.co/lonewolflab/Morpheus-TR-50K; interactive demo: https://huggingface.co/spaces/lonewolflab/morpheus-tr-demo.
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Artificial Intelligence · Cross-listed from cs.HC
Human-AI Agent Interaction in a Business Context
Kathrin Paimann, Elizangela Valarini and Sebastian Juhl
As AI
agents are increasingly integrated into core business processes, understanding and designing effective interaction patterns between humans and AI
agents becomes crucial for value creation. This study identifies and evaluates principles and criteria for a positive User Experience (UX) with AI
agents, along with methods for its measurement. We identify user expectations and needs to facilitate adoption, build trust, and support user-centered decision-making by development teams. Using a mixed-methods approach that combines qualitative and quantitative techniques, we explore interaction patterns between humans and AI
agents. The findings from this exploratory research serve as the basis to develop a survey experiment which evaluates the effectiveness of specific design elements on a larger scale. This foundational research contributes to the development of more intuitive and effective human-AI
agent interactions in business settings.
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Artificial Intelligence · Cross-listed from cs.CL
TW-LegalBench: Measuring Taiwanese Legal Understanding
Fei-Yueh Chen, Chun Huang Lin, Chan Wei Hsu et al. (+4 more)
Large
language models (LLMs) have shown impressive capabilities across diverse tasks, yet their performance on jurisdiction-specific legal
reasoning remains underexplored. We present TW-LegalBench that utilizes Taiwanese legal system's rich official corpus open to the public to fill the gap in evaluating LLMs on Taiwanese law, among common-law
benchmarks that focus on English sources and civil-law
benchmarks focusing on sources of Simplified Chinese. TW-LegalBench comprises three task types: (1) over 16,000 multiple-choice questions (MCQs) across five years of official examinations in 18 professional domains; (2) 117 open-ended essay questions (OEQs) from examinations for legal professionals with official scoring rubrics; and (3) more than 14,000 legal judgment prediction (LJP) instances covering hundreds of crime categories. We evaluate 13 LLMs using accuracy for MCQs, a decomposed LLM-as-Judge framework based on the scoring rubric points for OEQs, and metrics for sentencing accuracy and statute citation for LJP. Our results reveal that top-performing models exceed the passing threshold for qualified lawyers (passing rate: 11%) but fall short of that for judges and prosecutors (passing rate: 1~2%). For LJP, while models demonstrate reasonable verdict type accuracy and sentence prediction capability, they struggle to cite exact legal articles. These findings highlight that reliable legal text generation remains challenging for LLMs, even though their performance on qualification examinations approaches human level.
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All authors Fei-Yueh Chen, Chun Huang Lin, Chan Wei Hsu, Kuan Hsuan Yeh, Zih-Ching Chen, Kuan-Ming Chen, Patrick Chung-Chia Huang
Artificial Intelligence · Cross-listed from cs.RO
Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets
Alexander Belyaev and Oleg Kushnarev
The energy-based method remains a comparatively underexamined approach for surface classification in mobile robotics, despite promising results in constrained environments. This study evaluated the viability of using energy-derived features as either a standalone classification modality or as supplementary input to inertial data. A comprehensive evaluation was conducted across three publicly available
datasets, comparing the performance of modern deep learning architectures including recurrent neural networks, convolutional neural networks, encoder-only
transformers, and Mamba state-space models, under automated hyperparameter tuning and input sequence length optimization. The models achieved higher accuracy than previously reported values on all evaluated
datasets, with the convolutional neural network yielding the highest overall performance. When relying exclusively on energy-based features, the models attained classification accuracies in the range of 85-90%, approximately 5-10% lower than those achieved when combined with inertial features (96-99%). Augmenting inertial data with energy features resulted in a consistent mean accuracy improvement of 1-2%. These findings indicate that classifiers relying solely on energy features offer sufficient accuracy for standalone deployment, while also providing a consistent gain when used in combination with other sensing modalities.
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Artificial Intelligence · Cross-listed from cs.LG
Dual-Channel Grounded World Modeling (DCGWM): Structural Prevention of Objective Interference Collapse via Heterogeneous External Grounding with Inward-Only Gradient Flow
Akshay Hazare
Joint
Embedding Predictive Architectures (JEPAs) are a leading approach to world model representation learning. We identify a failure mode in JEPA-based world models grounded against two qualitatively distinct external signals: physical dynamics (sparse, high-magnitude, constraint-satisfying gradient corrections) and social-behavioral dynamics (diffuse, distribution-matching corrections). We term this Objective Interference Collapse (OIC): we argue that joint learning in a shared latent space causes the dominant channel to systematically collapse the subordinate channel's representational subspace, in a manner not resolvable by loss weighting alone. We propose Dual-Channel Grounded World Modeling (DCGWM), designed to structurally prevent OIC through a partitioned latent space (physical subspace Z_p, behavioral subspace Z_b) with inward-only gradient flow. A Physical Grounding Channel updates only Z_p via VICReg-style
alignment to physical measurements; a Social-Behavioral Grounding Channel updates only Z_b via
alignment to trajectories from an emergent multi-
agent simulation. An Inter-Channel Interface Module couples the subspaces at the task level without cross-subspace gradients. An Asymmetric Grounding Adherence Loss penalizes rollout drift with a hard hinge for physical violations and a soft KL for behavioral divergence. A Generative Rendering Layer is architecturally isolated from the latent world model. We present three theoretical results: the partition removes the gradient-interference pathway implicated in OIC; each grounded subspace inherits anti-collapse guarantees from its
alignment objective; and generative isolation is necessary under a stated assumption on the generative objective's geometry. This manuscript establishes the problem formulation and architecture; experimental validation is ongoing and will be reported in a future revision.
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Artificial Intelligence
Jaeho Lee, Nick Merrill and Ezra Karger
Forecasting benchmarks for general-purpose AI systems usually inherit the constraints of the real world: outcomes resolve slowly, tail events are rare, and counterfactual questions are difficult to score. We introduce ForecastBench-Sim, a simulated-world
forecasting benchmark built on game rollouts from Freeciv, a turn-based strategy game modelled on the Civilization series. Forecasters receive a fixed world report (a structured snapshot of the current game state) and answer questions about hidden future states; the
benchmark then continues the simulation and scores forecasts. Because the world is simulated, the same setup can generate continuous or binary
forecasting questions at arbitrary time horizons, paired intervention worlds for conditional or causal questions, and resolved examples of rare or disruptive outcomes. We describe the
benchmark pipeline, question families, scoring protocol, and release artifacts, and report validation slices from model evaluations and an anonymized human pilot. ForecastBench-Sim is intended to complement real-world
forecasting benchmarks by providing controlled, immediately resolvable tasks for studying probabilistic
reasoning under dynamic world states.
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Artificial Intelligence · Cross-listed from cs.LG
Bounded Context Management for Tabular Foundation Models on Stream Learning
Jinmo Lee, Doyun Choi, Moongi Choi et al. (+1 more)
Tabular stream learning requires predictions on sequentially arriving examples under distribution shift. While standard methods adapt by updating model states, tabular foundation models (TFMs) make predictions conditioned on a labeled context in an in-context manner, making them a natural alternative for stream learning. This shifts the challenge from how to update the model to how to manage the context. We propose a future information view that yields three practical requirements for context management: preserve recent examples, retain uncertain examples, and remove redundant examples. We instantiate these requirements as CURE (Context management via Uncertainty-aware admission and Redundancy aware Eviction), a context-managing policy with
entropy-gated admission and redundancy-aware eviction. Across seven streams, CURE shows up to 27.0% relative improvement over classical stream learners, remains robust across multiple TFM backbones, and ranks first among other policy variants. Code and
datasets are available at https://github.com/morcellinus/CURE-ICML-FMSD.
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All authors Jinmo Lee, Doyun Choi, Moongi Choi, Jaemin Yoo
Artificial Intelligence · Cross-listed from cs.LG
scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
Jinke Wu, Yifan Wang, Siyu Yi et al. (+5 more)
Single-
cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of
cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA-seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA-seq data. Toward this end, in this paper, we propose a novel single-
cell RNA-seq clustering framework via deep Siamese Graph
Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for
cell clustering. In particular, we formulate scRNA-seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph
transformer network is employed to explicitly incorporate shortest-path information and node-wise distances for capturing richer structural relationships between
cells. Finally, we employ an optimal transport strategy to guide the
cell clustering in a self-supervised manner. Extensive experiments on multiple
benchmark scRNA-seq
datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W-RMSL/scGTN.
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All authors Jinke Wu, Yifan Wang, Siyu Yi, Caiyang Yu, Ziyue Qiao, Nan Yin, Jiancheng Lv, Wei Ju
Artificial Intelligence · Cross-listed from cs.SD
NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization
Yizhuo Yang, Junqiao Fan, Shenghai Yuan et al. (+1 more)
Reliable sound source localization is fundamental to
robot audition, enabling autonomous
robots to perceive spatial cues and operate effectively in dynamic environments. Classical methods such as Multiple Signal Classification (MUSIC) offer strong theoretical foundations but degrade under low signal-to-noise ratios. While deep learning-based approaches achieve promising performance, they often struggle with limited generalization across conditions. To address these challenges, we propose NeuralMUSIC, a hybrid neural-subspace framework for robotic sound source localization. Specifically, a neural network first estimates the spatial covariance
matrix from multichannel microphone observations. The predicted covariance is then integrated into a classical MUSIC pipeline with
eigenvalue decomposition (EVD) and pseudo-spectrum computation, followed by a Frequency
Attention Fusion (FAF) module to produce the final DOA estimates. To improve data efficiency, we further introduce a Self-supervised Spatial Correlation Learning (SSCL) strategy that leverages unlabeled acoustic data to capture spatial structure. Extensive experiments across different robotic tasks demonstrate that NeuralMUSIC achieves competitive localization accuracy while exhibiting improved robustness and cross-domain generalization.
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All authors Yizhuo Yang, Junqiao Fan, Shenghai Yuan, Lihua Xie
Artificial Intelligence · Cross-listed from cs.CV
LandslideAgent with Multimodal LandslideBench: A Domain-Rule-Augmented Agent for Autonomous Landslide Identification and Analysis
Chengfu Liu, Dongyang Hou, Junwu Xiang et al. (+5 more)
Intelligent landslide hazard interpretation is critical for disaster prevention, yet current paradigms struggle to simultaneously extract visual features and high-level geoscientific semantics, while general-purpose
vision-
language models (VLMs) suffer from perceptual limitations and domain
hallucinations in complex geological scenarios. To address these challenges, we propose an instruction-driven agentic framework comprising three components. First, LandslideBench, a
multimodal fine-grained
dataset with seven subtype labels, high-resolution imagery, pixel-level masks, and high-quality textual descriptions, is constructed via multi-VLM cross-validation and interactive annotation. Then, LandslideVLM, a landslide-oriented VLM, is fine-tuned via LoRA on LandslideBench to enhance geological semantic understanding. Finally, LandslideAgent, a domain rule-enhanced
agent taking LandslideVLM as its cognitive backbone, employs a dual-rule controller incorporating structured report metadata constraints and cross-validation identification constraints to regulate automated tool invocation. Experiments demonstrate that LandslideBench provides effective baselines across five mainstream models on fine-grained classification and semantic
segmentation. LandslideVLM achieves accuracy improvements of 10.96%, 32.87%, and 15.91% on landslide discrimination, fine-grained classification, and semantic description quality, respectively. LandslideAgent further enables autonomous multi-source spatial data inference, realizing full-process intelligence for landslide identification and analysis.
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All authors Chengfu Liu, Dongyang Hou, Junwu Xiang, Cheng Yang, Xuezhi Cui, Zeyuan Wang, Liangtian Liu, Zelang Miao
Artificial Intelligence · Cross-listed from eess.AS
Augmenting Dysarthric Speech Severity Assessment with MOS Supervision
Kaimeng Jia, Minzhu Tu, Zengrui Jin et al. (+2 more)
Dysarthria is a
speech disorder marked by reduced intelligibility and communicative effectiveness. Automatic utterance-level assessment of dysarthric
speech can support scalable
speech monitoring and therapy-related analysis. Yet training such systems is bottlenecked by the scarcity of clinically annotated dysarthric
speech. This work proposes to augment dysarthric
speech assessment using data from
speech synthesis evaluations, specifically human-annotated utterances with Mean Opinion Score (MOS) labels from the QualiSpeech corpus. Experiments show that fine-tuning on
speech synthesis assessment data consistently improves performance on both intelligibility and naturalness prediction, while joint training yields gains primarily on naturalness. These results suggest that synthesis artifacts and dysarthric
speech share perceptual commonalities, and
speech synthesis evaluation corpora offer a practical augmentation source that reduces reliance on scarce clinical annotations.
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All authors Kaimeng Jia, Minzhu Tu, Zengrui Jin, Siyin Wang, Chao Zhang
Artificial Intelligence · Cross-listed from cs.CL
PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes
Yingyu Shan, Zeming Liu, Silin Li et al. (+4 more)
Recent advancements in Large
Language Models (LLMs) have empowered home assistants with natural
language interaction capabilities. However, current assistants overlook the progressive omission that occurs in human dialogue as shared context accumulates, leading to more elliptical expressions for efficient communication. Thus, current assistants still struggle to interpret such elliptical expressions accurately, which limits their effectiveness in real-world applications. In practical smart home scenarios, assistants face two major challenges caused by elliptical commands: (1) referential ambiguity caused by different environmental expectations among multiple users; and (2) intention ambiguity resulting from user preferences that evolve over time or change with the environment. To address these challenges, we introduce PEC-Home, the first simulated home
dataset specifically designed for interpreting progressively elliptical commands in smart homes. Extensive experiments on various LLMs, including GPT-4o, show that existing home assistants struggle to execute user-intended operations based solely on elliptical commands. Even when equipped with tools for storing and retrieving user dialogue history, execution accuracy remains below that achieved with complete commands.}.
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All authors Yingyu Shan, Zeming Liu, Silin Li, Boao Qian, Jiashu Yao, Yuhang Guo, Haifeng Wang
Artificial Intelligence · Cross-listed from cs.RO
EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
Zecheng Yin and Benedict Jun Ma
To locate a target object while exploring the unknown environment is a fundamental capability for autonomous
agents, with applications ranging from search-and-rescue to field
robots. A simplified version of such task is Object Goal
Navigation (ObjNav). In ObjNav, successful arrival at the target object provides a basic measure of performance; however, the efficiency of the
navigation trajectory is equally important, as it indicates how intelligently the
agent explores and how much time remains for subsequent tasks. In unknown environments, the key to efficient
navigation lies in deciding where to explore next. While many prior works aim to address this core challenge and achieved promising performance in certain settings, recent training-based models and non-training frameworks still suffer from generalization and efficiency issues respectively, which in the worst cases can lead to excessive exploration of already-visited areas or redundant back-and-forth motion. We evaluate EffiNav on two widely used simulation
benchmarks Habitat Matterport 3D (HM3D) and Open-Vocabulary Object goal
Navigation (OVON), and further validate its effectiveness on physical
robots in real-world settings. We conduct failure analysis on massive simulation episodes. With minimal modification, we also extend EffiNav to a
memory-augmented ObjNav task on the GOAT-BENCH
dataset, demonstrating its adaptability beyond standard ObjNav settings. Across two standard metrics–Success Rate (SR) and Success weighted by Path Length (SPL), EffiNav matches or outperforms recent baselines, reflecting its efficiency, robustness, and practical applicability. Recognizing the different emphases of the two
datasets, the performances reveals this framework is more balanced and generalizable for efficient ObjNav.
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Artificial Intelligence · Cross-listed from cs.CL
BCL: Bayesian In-Context Learning Framework for Information Extraction
Haoliang Liu, Chengkun Cai, Xu Zhao et al. (+7 more)
Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large
language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL (
Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with
Bayesian updates to systematically refine label representations across IE tasks. Through four steps initialization, observation, weight update, and resampling, BCL generalizes to both sequence labeling and relation classification paradigms. Extensive experiments demonstrate substantial and consistent improvements over existing approaches.
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All authors Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
Artificial Intelligence · Cross-listed from cs.CR
Code-Augur: Agentic Vulnerability Detection via Specification Inference
Zhengxiong Luo, Mehtab Zafar, Dylan Wolff et al. (+1 more)
The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM
agents are uncovering critical vulnerabilities in fundamental software underpinning digital society. Many of these vulnerabilities remained masked for years, surfacing only now with AI
agents. Yet the
reasoning behind these discoveries remains alarmingly opaque and unvalidated. What assumptions did the
agent make about a function's inputs when it deemed that function to be secure? Failures in
reasoning and incorrect assumptions can lead to missed vulnerabilities and reduce trust in agentic analysis. We propose a security-specification-first paradigm that (1) exposes the
agent's tacit assumptions explicitly as security specifications and (2) continuously refines those specifications via runtime falsification. We realize our approach in Code-Augur, a novel harness for agentic vulnerability detection. Given a codebase, Code-Augur analyzes each component of the system for vulnerable code. When it deems a component to be secure, it commits the local invariants behind that judgment as in-source assertions. In parallel, Code-Augur leverages a guided fuzzer to attempt to falsify those assumptions. When the fuzzer triggers an assertion, this either reveals a genuine vulnerability or a flawed specification to refine. In both cases, this process grounds the
agent's understanding, aligning its view of code intent with how the code actually behaves. On real-world subjects, Code-Augur effectively leverages security specifications to detect more vulnerabilities than other state-of-the-art
agents. Additionally, Code-Augur found 22 new vulnerabilities in key open-source projects. Compared to curated specialized models like Claude Mythos, Code-Augur offers effective agentic vulnerability detection built on widely available LLMs like Sonnet and DeepSeek.
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All authors Zhengxiong Luo, Mehtab Zafar, Dylan Wolff, Abhik Roychoudhury
Artificial Intelligence · Cross-listed from cs.CY
AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice
Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Clara Brandt et al. (+2 more)
There exist numerous tutor training platforms. However, few provide AI-driven training and evaluation for human tutors based on real-life performance. We present an AI-driven system that assesses both open responses during training and authentic real-life tutoring. Unlike platforms that only assess learning through online training or simulations, our system utilizes Generative AI (Gemini-2.5-pro) to analyze transcriptions of authentic tutoring, measuring the transfer of tutor skills to real-life application. Human tutors instructing students remotely in math (N=86) completed six scenario-based lessons, averaging a significant 7.4% learning gain. Using mixed-effects models across 405 session-to-lesson pairs, we found that training performance significantly predicted real-life transcript scores with an effect size of 0.25 SD. Model comparison (AIC/BIC) indicated averaging open response and multiple choice performance during training predicted real-life tutor performance best, although open responses were comparatively more predictive. Exploratory analysis showed that after training, tutors were significantly more likely to encounter pedagogical opportunities to apply their skills (61.1% to 68.9%) and demonstrated higher execution quality within those opportunities (65.5% to 68.1%). Interrupted time series analysis suggested that these tutor improvements were part of a gradual trend over time rather than an immediate intervention effect of training. We illustrate an AI-driven method to link tutor training with real-life assessment. In doing so, we contribute open
datasets, AI
prompts, and scoring rubrics to support transparency and reproducibility.
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All authors Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Clara Brandt, Conrad Borchers, Kenneth R. Koedinger
Artificial Intelligence · Cross-listed from cs.CL
Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
Tianming Du, Peijie Yu, Sihan Shang et al. (+12 more)
The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR system interaction, or patient communication. Physician assistance instead requires coordinating these capabilities within the same interaction, where physicians issue underspecified requests, patients describe symptoms ambiguously, and EHR systems demand precise tool use. We introduce PhysAssistBench, a
benchmark for interactive doctor-patient-EHR assistance. Built from real MIMIC-IV cases, PhysAssistBench uses a scalable pipeline to construct agentic patients: interactive, record-grounded
agents that turn static EHR records into multi-turn clinical scenarios while preserving clinical factuality. PhysAssistBench provides a curated bilingual evaluation set of 1,296 manually reviewed and physician-validated turns. Experiments with leading LLMs show that current models remain unreliable in this setting, which exposes a key bottleneck for clinical LLMs: reliable assistance requires coordination across knowledge, communication, and systems, not isolated gains in any of them.
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All authors Tianming Du, Peijie Yu, Sihan Shang, Danli Shi, My Linh Nguyen, Shengbo Gao, Guangyuan Li, Yinghong Yu, Yan Jiang, Qianlong Zhao, Behzad Bozorgtabar, Shaoxiong Ji, Jiazhen Pan, Daniel Rueckert, Jiancheng Yang
Artificial Intelligence · Cross-listed from cs.SD
QC-GAN: A Parameter-Efficient Quaternion Conformer GAN for High-Fidelity Speech Enhancement
Shogo Yamauchi, Hideaki Tamori, Makoto Sakai et al. (+2 more)
We propose a parameter-efficient
speech enhancement framework, Quaternion Conformer GAN (QC-GAN), which combines a Quaternion Conformer generator with MetricGAN-based training. The Hamilton product encodes the magnitude and phase via structured weight sharing, reducing the number of layer parameters while preserving their interdependencies. A metric-learning discriminator was employed to maximize perceptual quality by optimizing the approximate perceptual evaluation scores. On the VoiceBank+DEMAND
dataset, QC-GAN achieved a Perceptual Evaluation of
Speech Quality (PESQ) score of 3.48 with only 0.89M parameters, delivering a performance comparable to state-of-the-art models at less than half their size. A 35K-parameter variant achieved a PESQ score of 3.23, surpassing conventional methods with significantly fewer parameters. Evaluation on the DNS-Challenge 3
dataset further confirmed generalization to real-world conditions.
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All authors Shogo Yamauchi, Hideaki Tamori, Makoto Sakai, Yosuke Yamano, Tohru Nitta
Artificial Intelligence · Cross-listed from cs.CL
Steerable Cultural Preference Optimization of Reward Models
Minsik Oh, Advit Deepak, Sophie Wu et al. (+2 more)
It is essential for large
language model (LLM) technology to serve many different cultural sub-communities in a manner that is acceptable to each community. However, research on LLM
alignment has so far predominantly focused on predicting a unified response preference of annotators from certain regions. This paper aims to advance the development of
alignment models with a more global outlook, that are able to accurately represent the preferences of subcommunities and do not exhibit excessive bias towards any of them. We focus on the development of reward models for this purpose and present a novel reward model training
algorithm (SCPO) that can incorporate diverse cultural preferences in a balanced manner. Our method results in performance increases of the minority reward model of up to 7 points over the baseline model across two
datasets, PRISM and GlobalOpinionQA, and across 7 countries. SCPO is up to 280% more training data-efficient than full-data finetuning of reward models. In addition, we perform analysis of bias by separately evaluating on the preference of subcommunities and show that excessive bias is mitigated via our weighting method. Our code is available at https://github.com/minsik-ai/Steerable-Cultural-Preference
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All authors Minsik Oh, Advit Deepak, Sophie Wu, Douwe Kiela, Ekaterina Shutova
Artificial Intelligence · Cross-listed from cs.CR
MIDS: Detecting Stealthy Masquerade and Tampering Attacks on CAN Bus via Bidirectional Mamba
Qiqi Liu, Runhan Song, Lei Cui et al. (+3 more)
The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of encryption and authentication exposes it to a range of security threats. Existing intrusion detection systems are largely tuned to fabrication-style attacks (DoS, fuzzing, ID spoofing realised by frame injection), in which detection signals such as per-ID inter-arrival statistics are readily available. We instead address the harder masquerade setting~\cite{b37}, in which an internal adversary substitutes a legitimate frame in-situ at its original transmission slot, preserving traffic periodicity and rendering traffic-statistic defences ineffective. We propose the Mamba Intrusion Detection System (MIDS), an innovative dual-stream framework that processes CAN identifiers and payloads in parallel and reconstructs their joint temporal semantics through bidirectional selective state-space modelling. To evaluate MIDS, we collected over 100 million CAN frames from a physical Tesla Model 3 across three driving regimes and synthesised 54 masquerade attack variants spanning ID-only, data-only, and combined modifications. MIDS attains an F1 of 96.94% on this
dataset, exceeding the strongest reproducible baseline by more than 8 percentage points, while sustaining a 1.147~ms single-window inference latency – ample headroom for real-time onboard deployment. To verify generalisation, we further evaluate MIDS on four public
benchmarks (ROAD, CrySyS, OTIDS, CT&T) covering both masquerade and injection scenarios; MIDS attains F1 from 93.70% to 99.61%, outperforming the strongest of eight reproduced baselines by up to 13.94 percentage points under a unified 5-fold protocol.
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All authors Qiqi Liu, Runhan Song, Lei Cui, Heng Zhang, Yuyan Sun, Limin Sun
Artificial Intelligence
Optimizing Lithium Production Decisions under Geological, Demand, and Pricing Uncertainties: A POMDP Framework for Multi-Objective Decision Making
Anna C. Edmonds, Mansur M. Arief, Robert J. Moss et al. (+2 more)
Decision making in lithium production is challenging, whether from an investor's perspective or a strategic production standpoint. Determining which mines to open and when to open them involves not only geological and price uncertainties, but also complexities around the choice of extraction method, from direct lithium extraction to hard rock mining. Prior work explored models of this problem and different methods to optimize mining decisions; these models did not account for uncertainty in pricing, uncertainty in demand, or different mining technologies to extract lithium. Incorporating different pricing models and extraction technology into these models enables more robust strategies for determining not only when and where to open a mine, but also which method of production to pursue. We frame the problem as a partially observable Markov decision process (POMDP) and solve using belief state planning methods to get optimal decision making. In our study, we show that POMDP solvers outperform human inspired heuristics by dynamically adapting to shifting lithium price regimes (static, linear, exponential, and
stochastic) through belief state planning and explicit uncertainty management. By optimally sequencing exploration, production, and technology choice, the framework achieves higher demand fulfillment and more balanced economic environmental outcomes over the projects lifetime in all different pricing and deposit scenarios.
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All authors Anna C. Edmonds, Mansur M. Arief, Robert J. Moss, Mykel J. Kochenderfer, Jef Caers
Artificial Intelligence · Cross-listed from cs.HC
Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep
Amama Mahmood, Bokyung Kim, Honghao Zhao et al. (+4 more)
Sleep diaries are central to behavioral sleep medicine and cognitive behavioral therapy for insomnia, yet daily completion is difficult to sustain, and static forms often provide limited context for interpreting night-to-night sleep variation. We designed an LLM-powered conversational voice diary that delivers clinically grounded morning and evening sleep diary questions through proactive smart-speaker
prompts, structured conversational intake, and adaptive follow-up dialogue. We evaluated the system in a four-week between-subjects field study with 30 university students, comparing it with a text-based mobile diary using matched diary items, reporting windows, and reminder intervals. Compared with the text-based diary, the conversational voice diary showed higher adherence and elicited more detailed contextual self-report about routines, stressors, environmental conditions, and other sleep-related factors. Participants also described the voice diary as easier to integrate into daily routines, despite longer perceived completion time. However, voice-based conversational intake produced lower completeness for some structured diary fields, revealing a trade-off between expressive richness and structured precision. These findings show both the promise and the challenge of using LLM-powered conversational voice assistants for longitudinal health self-report.
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All authors Amama Mahmood, Bokyung Kim, Honghao Zhao, Molly E. Atwood, Luis F. Buenaver, Michael T. Smith, Chien-Ming Huang
Artificial Intelligence · Cross-listed from cs.RO
Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
Seyed Alireza Azimi, Homayoon Farrahi, Abhishek Naik et al. (+2 more)
In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance. In this study, we evaluate pose increment, pose velocity, joint position increment, and joint velocity across two
vision-based manipulation tasks: object picking and pushing. We train policies in simulation and deploy them to the real world using sim-to-real transfer. We find that action-space representation indeed significantly affects sim-to-real performance. In particular, we find that the joint velocity action space is best for the
vision-based picking and pushing tasks in terms of smoothness and final task performance. We also provide practical guidance for RL practitioners in choosing action spaces for both simulation and real-world experiments.
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All authors Seyed Alireza Azimi, Homayoon Farrahi, Abhishek Naik, Colin Bellinger, A. Rupam Mahmood
Artificial Intelligence · Cross-listed from cs.CL
Dual Dimensionality for Local and Global Attention
Zhiyuan Wang, Xuan Luo, Sirui Zeng et al. (+1 more)
Decoder-only
Transformers compute
attention over the KV cache of preceding
tokens. Keys and Values are typically represented with the same dimensionality, regardless of their distance from the prediction target. In natural
language, however, the next word is most strongly influenced by the immediately preceding
tokens. We hypothesize that local and distant
tokens impose asymmetric demands on representational capacity: local
tokens are more critical for predicting immediate outputs and thus require richer representations, whereas distant
tokens primarily serve as long-range
memory, for which lower-dimensional representations may suffice. We formalize this idea as Distance-Adaptive Representation (DAR), which preserves full-dimensional representations within a local context window while assigning reduced-dimensional representations (e.g. 1/2 or 1/4 of the original dimensionality) to
tokens beyond that window. Across multiple pretraining scales (70M to 410M parameters), as well as continued supervised fine-tuning on a 1B-scale model, this approach closely matches the performance of full-dimensional baselines. In contrast, uniformly reducing dimensionality across all
token positions leads to worse performance. These results challenge the common assumption that key and value dimensionality should be uniform across
token positions. We further develop an efficient realization of DAR that achieves model quality comparable to the MLA baseline while reducing the average KV-cache size by 68.8% at 8K context and achieving up to a $1.9\times$ decoding speedup at 128K context on the evaluated models. Our findings suggest a new direction for designing
attention architectures that allocate representational capacity according to
token distance to reduce KV-cache during inference.
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All authors Zhiyuan Wang, Xuan Luo, Sirui Zeng, Xifeng Yan