Knowledge catalogue
arXiv:2610.02840v1 Announce Type: cross Abstract: World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate a
arXiv:2610.02925v1 Announce Type: new Abstract: Safety monitors help safeguard language-model agents interacting with external tools and environments, but conservative monitoring can generate many fal
arXiv:2610.02967v1 Announce Type: cross Abstract: Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because n
arXiv:2610.02511v1 Announce Type: new Abstract: Advancements in high-resolution numerical weather prediction (NWP) and data assimilation (DA) have shaped the developments in deep learning (DL) archite
arXiv:2602.10273v3 Announce Type: replace-cross Abstract: Reasoning ability in large language models is often attributed to distribution sharpening: concentrating output probability on high-likelihood
arXiv:2610.02303v1 Announce Type: new Abstract: Language models increasingly assist people with power-related requests, so systematic differences in whom they help could shift the distribution of powe
arXiv:2610.03199v1 Announce Type: cross Abstract: Large language models fine-tuned from a shared base can be merged by averaging their task vectors, but some merges collapse far below the base model,
arXiv:2610.03163v1 Announce Type: cross Abstract: Adapting large language models to an individual author's style from a few examples is challenging, and scientific writing sharpens the difficulty: for
arXiv:2610.03201v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioner
arXiv:2610.03098v1 Announce Type: new Abstract: Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic
arXiv:2610.03467v1 Announce Type: cross Abstract: Accurate colon segmentation from CT images is essential for colorectal disease analysis, yet deep learning based methods often produce disconnected pr
arXiv:2610.03326v1 Announce Type: new Abstract: Diffusion language model (dLLM) compression faces a known challenge because calibration is typically performed on clean, fully visible activations, wher
arXiv:2610.03664v1 Announce Type: new Abstract: Autoregressive (AR) video models excel at causal generation, but their reliance on next-chunk prediction confines them to a short-sighted, reactive para
arXiv:2610.02911v1 Announce Type: cross Abstract: Methods for training language models on stale samples are judged by comparisons against importance-corrected baselines. We show that details of the ex
arXiv:2610.03512v1 Announce Type: new Abstract: Humans draw progressively: a few strokes, a look at the result, a stroke erased, a prompt revised. Image generators do not work this way. They typically
arXiv:2610.02413v1 Announce Type: new Abstract: LLM agents increasingly rely on activation probes as runtime monitors for prompt injection, jailbreaks, and unsafe requests, reading the model's own hid
arXiv:2610.02759v1 Announce Type: new Abstract: Generative robot policies predict short action chunks but lack explicit long-horizon intent. Recent methods expose longer-horizon structure through lang
arXiv:2610.02740v1 Announce Type: cross Abstract: Reinforcement learning for long-horizon agents relies on purely retrospective training signals: credit is assigned only after observing environmental
arXiv:2610.02299v1 Announce Type: cross Abstract: We introduce Psi-Resilience, a model-free feature importance method that derives explanations directly from the data itself via 1D topological signals
arXiv:2610.02885v1 Announce Type: new Abstract: Mental health disorders affect a substantial proportion of the global population, yet a persistent shortage of trained practitioners leaves the majority
arXiv:2610.02615v1 Announce Type: new Abstract: Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties affect average-case learning per
arXiv:2509.10756v4 Announce Type: replace-cross Abstract: We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providin
arXiv:2610.02875v1 Announce Type: cross Abstract: Multilingual encoders can exhibit reduced retrieval effectiveness when queries and relevant documents differ in language, despite strong same-language
arXiv:2610.02251v1 Announce Type: new Abstract: Speculative sampling accelerates diffusion generation by verifying inexpensive draft states in parallel while preserving the target law. Recent tree-bas
arXiv:2610.02683v1 Announce Type: new Abstract: Can programmable radio propagation serve as computational depth rather than only as a communication channel or one-shot analog transform? We introduce t
arXiv:2610.02979v1 Announce Type: new Abstract: Unstructured discharge notes in Electronic Health Records (EHRs) often carry signal complementary to structured medical codes, holding patient-specific
arXiv:2610.03538v1 Announce Type: new Abstract: Tactile sensing is particularly valuable for contact-rich robotic manipulation. Recent work has made substantial progress in tactile representation lear
arXiv:2610.02601v1 Announce Type: new Abstract: Event cameras have microsecond-level temporal resolution and high dynamic range which make them more resilient to motion blur and poor illumination than
arXiv:2607.16310v2 Announce Type: replace Abstract: Motor impairments affecting the upper limb significantly reduce autonomy in daily activities, particularly for tasks involving object manipulation.
arXiv:2610.03519v1 Announce Type: new Abstract: A reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers
arXiv:2610.02968v1 Announce Type: new Abstract: Large language models (LLMs) can infer user preferences from interaction histories and reviews, yet the rationales they generate may not reflect the inf
arXiv:2601.22352v2 Announce Type: replace-cross Abstract: Language model agents often appear capable of self-recovery after failing tool call executions, yet this behavior lacks a formal explanation.
arXiv:2605.06639v2 Announce Type: replace-cross Abstract: We introduce Recursive Agent Optimization (RAO), a reinforcement learning approach for training recursive agents: agents that can spawn and de
arXiv:2610.03548v1 Announce Type: new Abstract: Generating progressively harder reasoning problems requires synthesis procedures that adapt as the task distribution evolves. Existing task-level recurs
arXiv:2610.03002v1 Announce Type: new Abstract: Unified multimodal models (UMMs) understand and generate both text and images, which lets a model produce its own training data. Existing self-improveme
arXiv:2610.03320v1 Announce Type: new Abstract: Diffusion language models for text-to-speech combine two forms of computation: model depth (parameters) and refinement steps (inference budget). We ask
arXiv:2610.03356v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly deployed in high-stakes settings such as industrial maintenance and equipment fault troubleshooting,
arXiv:2610.02813v1 Announce Type: new Abstract: Visuomotor imitation policies combine high-dimensional visual observations with compact signals such as proprioception, and their fusion topology determ
arXiv:2610.02452v1 Announce Type: new Abstract: The operation of dynamically polarized targets in nuclear physics experiments relies on continuous tuning of the microwave frequency to compensate for r
arXiv:2610.02976v1 Announce Type: new Abstract: Audio-Visual Large Language Models (AV-LLMs) remain prone to cross-modal hallucinations, where one modality incorrectly affects predictions about anothe
arXiv:2606.12997v2 Announce Type: replace Abstract: Two dominant approaches have emerged for generating probabilistic forecasts of physical systems: generative models, such as diffusion or flow matchi
arXiv:2610.02270v1 Announce Type: cross Abstract: Medical vision-language model (VLM) evaluation is sensitive to workflow design, prompting strategy, and benchmark construction, yet most studies treat
arXiv:2610.02975v1 Announce Type: new Abstract: Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candid
arXiv:2610.03022v1 Announce Type: cross Abstract: Simultaneous Sign Language Translation (SLT) is critical for real-time communication, yet existing methods remain largely confined to sentence-level,
arXiv:2610.03418v1 Announce Type: cross Abstract: Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, exist
arXiv:2610.03012v1 Announce Type: new Abstract: Most human motion generation methods encode motion as tokens on a uniform temporal grid, where every token spans the same fixed time window. Human motio
arXiv:2606.13141v2 Announce Type: replace Abstract: Retrieval-augmented generation is extending beyond text to long videos, where query-relevant chunks can be represented across multiple modalities an
arXiv:2610.03577v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) can generate high-quality images and videos, but generating each sample requires multiple costly DiT forward passes. Two
arXiv:2610.02368v1 Announce Type: new Abstract: Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordin
arXiv:2610.02887v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) suffer from hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable d
arXiv:2610.03656v1 Announce Type: cross Abstract: Multi-pitch estimation in vocal ensembles is challenging because singers occupy overlapping pitch ranges and often sing at closely spaced fundamental
arXiv:2610.02718v1 Announce Type: cross Abstract: Vision-language models such as CLIP exhibit strong semantic generalization, but remain limited in fine-grained visual perception. A recent work named
arXiv:2610.02545v1 Announce Type: new Abstract: Reward serves as the primary learning signal in reinforcement learning (RL). However, while reward magnitudes are typically held fixed throughout traini
arXiv:2610.03079v1 Announce Type: new Abstract: Genuine embodied agency requires robots to turn continuous real-world experience into lasting, transferable skills. This demands continual learning that
arXiv:2610.02492v1 Announce Type: new Abstract: LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreeme
arXiv:2610.02217v1 Announce Type: cross Abstract: Large sparse linear systems from PDE discretizations require correction subspaces whose operator images explain the current residual. We study this re
arXiv:2610.03054v1 Announce Type: new Abstract: Clustered Federated Learning (FL) partitions a client population into groups of similar local distributions and trains one specialized model per cluster
arXiv:2610.02817v1 Announce Type: cross Abstract: Large Language Model (LLM) watermarking provides a lightweight mechanism for identifying text generated by a specific model, but its robustness remain
arXiv:2610.03712v1 Announce Type: new Abstract: Ribonucleic acid (RNA) functions through conformational changes that are not fully captured by static structures. However, large-scale standardized RNA
arXiv:2610.02717v1 Announce Type: new Abstract: A key challenge in bringing embodied intelligence into the real world is transferring capabilities from simulation to reality and enabling agents to con