Knowledge catalogue
arXiv:2609.24507v1 Announce Type: new Abstract: Visuomotor policies trained from a few demonstrations may reproduce demonstrated trajectories without reliably following changes in object position. Exi
arXiv:2407.16139v2 Announce Type: replace Abstract: Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions.
arXiv:2609.24385v1 Announce Type: new Abstract: Tactile sensing is an essential modality for robots performing contact-rich, dexterous manipulation, particularly under visual occlusion. While pre-trai
arXiv:2609.23688v1 Announce Type: new Abstract: We study norm-constrained linear classification under Eu clidean adversarial perturbations in a Gaussian model with a low-dimen sional informative subsp
arXiv:2609.24591v1 Announce Type: new Abstract: Sea ice forecasts are issued several days ahead, allowing errors to accumulate while new, often sparse sea ice concentration (SIC) observations become a
arXiv:2609.24246v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable progress in natural language understanding, yet their effectiveness in specialized fields like astron
arXiv:2609.22165v1 Announce Type: new Abstract: Automatic synthesis of analog and Radio Frequency (RF) circuits is an emerging area that requires an efficient circuit modeling method. In recent years,
arXiv:2609.23151v1 Announce Type: cross Abstract: Assembly remains a challenging robotic manipulation task in presence of tight tolerances and complex contact interactions. While Learning from Demonst
arXiv:2609.22238v1 Announce Type: cross Abstract: Neural network pruning can be formulated as a combinatorial optimization problem, yet many existing approaches rely on independent filter-importance s
arXiv:2609.22795v1 Announce Type: new Abstract: Different tasks performed by legged robots impose distinct torque and speed requirements on actuators. Existing robotic actuators are generally optimize
arXiv:2609.23580v1 Announce Type: new Abstract: Reactive vision--language--action (VLA) models struggle with long-horizon manipulation when visually similar observations can correspond to different ac
arXiv:2609.22254v1 Announce Type: new Abstract: On-policy distillation (OPD) is a promising approach for transferring knowledge between language models, where a student receives dense token-level supe
arXiv:2609.22221v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent and coherent text that is increasingly difficult to distinguish from human writing, motivating the deve
arXiv:2509.01167v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can ingest only a limited number of video frames, making frame selection a practical necessity. But do current V
arXiv:2609.23701v1 Announce Type: cross Abstract: Probabilistic forecasting requires accurate central predictions and calibrated uncertainty estimates. This paper presents TEMPER, the Temporal Encoder
arXiv:2609.24280v1 Announce Type: cross Abstract: Malware detection is a critical task in cybersecurity, and graph neural networks over control flow graphs have shown promising results for it. However
arXiv:2609.22850v1 Announce Type: new Abstract: Contrastive activation directions are often interpreted from what they decode or how strongly they steer behavior. But what evidence is sufficient to id
arXiv:2609.24821v1 Announce Type: new Abstract: The Linear Representation Hypothesis associates high-level concepts with directions in language models, but it remains unclear how these concept-related
arXiv:2609.22214v1 Announce Type: new Abstract: Long multilingual conversational spoken question answering requires systems to balance long-range transcript semantics with sparse acoustic and speaker-
arXiv:2606.16560v2 Announce Type: replace Abstract: Automatic semantic change detection aims to identify how word meanings shift over time, offering insights into both linguistic and societal change.
arXiv:2609.24885v1 Announce Type: new Abstract: When a language model answers from a curated corpus via graph-based retrieval, a large grounding uplift does not establish reasoning over the retrieved
arXiv:2609.22246v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to forecast real-world events by retrieving and reasoning over news. We show that this dependence on
arXiv:2609.22216v1 Announce Type: cross Abstract: Quantization enables deployment of large language models on resource-constrained clinical edge devices, but its effect on clinical accuracy and safety
arXiv:2609.23374v1 Announce Type: new Abstract: Reinforcement learning (RL) offers a natural language for healthcare decisions whose conse- quences unfold over time, yet most reported progress remains
arXiv:2609.23837v1 Announce Type: cross Abstract: We study the complexity of finding (elta,epsilon)-Goldstein stationary points of nonsmooth nonconvex Lipschitz functions. By now, it is known that ran
arXiv:2609.22156v1 Announce Type: new Abstract: Speculative decoding in Mixture-of-Experts (MoE) models faces the problem of unstable verification cost caused by input-dependent expert loading. To stu
arXiv:2609.23934v1 Announce Type: new Abstract: We present a two-part neural framework for forced three-dimensional incompressible Navier--Stokes flow. Part~I develops the computational forcing system
arXiv:2609.23988v1 Announce Type: cross Abstract: Renewable forecasts are evaluated by statistical skill (e.g., CRPS), but grid operators pay for realized dispatch cost. We diagnose what drives dispat
arXiv:2609.23254v1 Announce Type: new Abstract: Self-calibrating monitors adapt their threshold online to guarantee a prescribed long-run false-alarm rate under arbitrary drift. We compute the price o
arXiv:2609.23369v1 Announce Type: new Abstract: Generation-free world action models (WAMs) retain future-video prediction during training but act from internal video features at inference, leaving unc
arXiv:2609.22198v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) creates new opportunities for strategic content generation on online platforms, including potentially
arXiv:2609.23092v1 Announce Type: new Abstract: Adversarial multi-objective bandits hold the potential to help us optimize choices (arms) whose reward is a multidimensional vector chosen by an adversa
arXiv:2602.01161v2 Announce Type: replace Abstract: The global deployment of large language models (LLMs) has raised concerns about cultural misalignment, yet the linguistic properties of fine-tuning
arXiv:2609.22812v1 Announce Type: cross Abstract: We construct the Yukawa couplings of the quark sector as overlap integrals of Gaussian wave functions in extra spatial dimensions. Assuming that the w
arXiv:2609.22195v1 Announce Type: new Abstract: Large language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals
arXiv:2609.24322v1 Announce Type: new Abstract: We show that a quantized model that keeps its classification accuracy still changes 14 to 46% of its top-1 retrieval results, and that aggregate ranking
arXiv:2609.22554v1 Announce Type: new Abstract: Backpropagation (BP) has driven the remarkable success of modern deep learning by enabling large hierarchical networks to learn complex functions end-to
arXiv:2609.24136v1 Announce Type: new Abstract: Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented
arXiv:2609.24682v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models map observations to actions with no objective that accounts for how the world responds, so their robustness is bou
arXiv:2609.23886v1 Announce Type: new Abstract: Software delegates more of its branches to models every year: which queue a ticket enters, whether a command is safe to run, whether a claim clears with
arXiv:2609.23916v1 Announce Type: new Abstract: Large language models (LLMs) drift out of date the moment their pretraining ends, yet retraining from scratch is prohibitively expensive. Continued pret
arXiv:2512.07624v2 Announce Type: replace Abstract: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of d
arXiv:2609.23132v1 Announce Type: new Abstract: Soft everting robots can traverse long, confined paths by continuously growing, yet active interaction remains limited to a single tool fixed at or near
arXiv:2609.22805v1 Announce Type: new Abstract: Natural language generation (NLG) tasks span the spectrum of conditional entropy, ranging from highly constrained machine translation to open-ended dial
arXiv:2609.22097v1 Announce Type: new Abstract: The evaluation of large language models (LLMs) on coding tasks has primarily focused on performance metrics such as pass@k. As LLMs continue to advance,
arXiv:2609.22943v1 Announce Type: new Abstract: Efficient large language model (LLM) instruction tuning requires selecting response supervision with supporting prompt context. Existing methods typical
arXiv:2609.24903v1 Announce Type: new Abstract: Tone languages constitute over 50-70% of the world's languages, but the vast majority are low-resource, lacking the large transcribed corpora needed for
arXiv:2609.23435v1 Announce Type: cross Abstract: Multi-omics sequences contain complex biological patterns, yet deciphering their mechanisms for automated scientific discovery remains challenging. As
arXiv:2609.22218v1 Announce Type: cross Abstract: As LLM agents are exposed to hundreds to tens of thousands of skills, tools, and API functions, full-library prompting becomes costly, slow, and less
arXiv:2609.24379v1 Announce Type: new Abstract: Mechanistic interpretability of vision transformers seeks to decompose model computation into human-readable units, but learned representations entangle
arXiv:2609.23944v1 Announce Type: new Abstract: Vision-language-action (VLA) policies can struggle with manipulation tasks with complex obstacle geometries due to partial observability. These complex
arXiv:2609.23163v1 Announce Type: cross Abstract: Comparing probability measures in machine learning trades transport geometry against computational cost: Wasserstein distances encode the geometry of
arXiv:2609.24660v1 Announce Type: new Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot han
arXiv:2609.24787v1 Announce Type: new Abstract: Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current models
arXiv:2609.24554v1 Announce Type: new Abstract: Concepts are commonly defined as abstract, compact representations of knowledge and treated as basic units of intelligent behavior. Yet, cognition, psyc
arXiv:2609.22113v1 Announce Type: new Abstract: Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predi
arXiv:2609.24055v1 Announce Type: new Abstract: Robots can recover from failures by asking bystanders for help, but effective human-in-the-loop recovery requires communication that accounts for differ
arXiv:2609.22292v1 Announce Type: cross Abstract: Unmanned aerial vehicle (UAV) swarms deployed in mission-critical applications must simultaneously track a mobile aerial target and maintain reliable
arXiv:2609.22463v1 Announce Type: new Abstract: Sleep monitoring using wearable data has shown promise for personal health, yet large language model (LLM)-based summarization and question answering re
arXiv:2609.22870v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key technique for improving the reasoning and agentic abilities of large language models (LLMs). Although FP8 q