Skill-based LLM agents select reusable procedures from an external library to solve complex tasks, yet their routing decisions rely entirely on text-level signals such as task descriptions, verbal reflections, and experience-derived rules, while the model's own internal representational state remains unobserved. Recent interpretability work has shown that LLMs maintain linear emotion representations that causally influence behavior; however, these representations have been exploited only for post-hoc analysis or direct output steering, and have not been used to inform agent-level decision-making.
Omnimodal language models (OLMs) enable unified audio-visual understanding, but processing long joint token sequences makes inference computationally prohibitive. While recent token compression methods attempt to alleviate this burden, compressing modalities in isolation often destroys the temporal cross-modal anchors necessary for coherent reasoning.
Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence.
Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform task sampling allocates compute without regard to differences in how tasks respond to optimization. Existing task-valuation methods mostly rely on snapshot-based signals such as current pass rate or reward, which estimate how solvable a task is under the current policy.
Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs. Existing approaches either require manual specification of the feature vocabulary or automate discovery only partially, leaving the gap between dataset-level correlation and model-level exploitation unaddressed.
Large language models (LLMs) are increasingly expected to follow long lists of constraints in complex instructions, and synthesizing instructions from a reference document (i. e.
AI video generation has advanced rapidly and entered widespread commercial use. As a result, quality differences among videos produced by state-of-the-art AI video generation models~(AIVGMs) have become increasingly difficult to discern using conventional evaluation criteria, such as visual fidelity and semantic instruction following.
Reconstructing 3D shapes from a single image remains a fundamental yet challenging problem in computer vision. Traditional monocular 3D generation pipelines typically synthesize multiple views from a single input image before applying Neural Radiance Field (NeRF)-based reconstruction.
arXiv:2608. 06595v1 Announce Type: cross Abstract: Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states.
By Mohamed Ghanem, Bernd Finkbeiner
arXiv:2608. 06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior.
By Srinivasan Subramanian, Md. Abdullah Al Hafiz Khan, Kazi Aminul Islam
arXiv:2608. 06650v1 Announce Type: cross Abstract: Reduced-order models based on Cosserat-rod theory are now well established, and modeling theory is no longer the primary bottleneck in soft-robot control.
By Maximilian St\"olzle, Solange Gribonval, Daniel Feliu-Talegon, Vito Daniele Perfetta, Michele Martini, Chuhan Zhang, Kiwan Wong, Mohammed Tarnini, Anup Teejo Mathew, Federico Renda, Daniela Rus, Cosimo Della Santina
arXiv:2608. 06690v1 Announce Type: cross Abstract: Most language-model access controls regulate behavior while leaving the same computation available to every request.
By Zhuoheng Huang, Mukesh Singh
arXiv:2604. 00310v2 Announce Type: replace-cross Abstract: Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions.
By Anurag Kumar, Raghuveer Peri, Jon Burnsky, Alexandru Nelus, Rohit Paturi, Srikanth Vishnubhotla, Yanjun Qi
arXiv:2608. 06884v1 Announce Type: cross Abstract: Biological specimen records collected by natural history institutions constitute a rich source of temporal geographic knowledge, capturing biodiversity information about regional landscapes as they were recorded at different times.
By Aneesha Fernando, Surangika Ranathunga, Kristin Stock, Raj Prasanna, Christopher B. Jones
arXiv:2608. 06938v1 Announce Type: cross Abstract: The visual reasoning ability of multimodal large language models (MLLMs) is crucial for downstream applications, particularly counter-commonsense reasoning, which requires models to reason beyond common assumptions.
By Chen Ling, Hanqian Li, Dongnan Liu, Keyu Qian, Jungang Li, Xinglong liu, Shiyi Wang, Xin Dong, Pengcheng Zhu, Wei Zhou, Linjian Mo, Nai Ding
arXiv:2608. 06554v1 Announce Type: new Abstract: Hidden Markov models (HMMs) are widely used probabilistic models for discrete sequential data but can be limited when hidden dynamics are complex.
By Ning Ning
arXiv:2608. 06549v1 Announce Type: cross Abstract: LLMs are increasingly being applied to tasks involving institutional and political texts, but existing benchmarks evaluate them on isolated documents or single tasks.
By Debodeep Banerjee, Amitangshu Dasgupta
arXiv:2607. 25232v2 Announce Type: replace Abstract: Digital phenotyping (DP) using smartphones and wearable devices has shown considerable potential for mental health monitoring.
By Quoc-Cuong Pham, Hoang-Thuy-Duong Vu, Thi-Thanh-Huong Ha, Huy-Hieu Pham
arXiv:2608. 07250v1 Announce Type: cross Abstract: Confirmed oncogenic microbes contribute significantly to cancer burden.
By Kaela Kokkas, Hairong Wang, Richard Klein, Nazir A. Ismail, Natalie Irwin, Mohammad Z. Moonsamy, Kubendran Naidoo, Jeremy Nel, Ekene E. Nweke, Raveen Parboosing, Emmanuel K. Sekyi, Rebecca T. van Dorsten, Bruce A. Bassett, Robert F. Breiman
arXiv:2506. 06793v2 Announce Type: replace-cross Abstract: Reward assignment from scarce demonstrations is a key challenge in both offline and online imitation learning.
By Zixuan Dong, Yumi Omori, Keith Ross