Search: “concepts”

arXiv:2606.24808v1 Announce Type: cross Abstract: Quantum computers could outperform classical machines on important problems, but only if the errors that pervade quantum hardware can be corrected at

arXiv:2606.21935v1 Announce Type: new Abstract: Diffusion models excel at capturing multi-modal action distributions in robot imitation learning. However, in multi-task and long-horizon scenarios, mon

arXiv:2411.19700v5 Announce Type: replace Abstract: Explainable AI methods for vision models aim to identify the parts of the input that are important for the final prediction and subsequently relate

arXiv:2606.11218v1 Announce Type: cross Abstract: Ethical deliberation is often misunderstood as a search for single right or wrong answers, creating difficulties for non-ethically trained personnel w

arXiv:2603.02237v2 Announce Type: replace-cross Abstract: Representation steering offers a lightweight mechanism for controlling the behavior of large language models (LLMs) by intervening on internal

arXiv:2605.31053v1 Announce Type: cross Abstract: Controllable music editing is to modify high-level attributes while strictly preserving rhythmic and melodic structures. However, this task is challen

arXiv:2601.22943v2 Announce Type: replace Abstract: Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characte

arXiv:2605.30671v1 Announce Type: new Abstract: Recovering ego-camera orientation from manipulation video is a prerequisite for disentangling hand motion from camera motion, a key step in imitation le

arXiv:2605.28215v1 Announce Type: new Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples. Yet, how these models use th

arXiv:2603.25722v2 Announce Type: replace Abstract: Contrastive vision-language (V&L) models remain a popular choice for various applications. However, several limitations have emerged, most notably t

arXiv:2509.03738v4 Announce Type: replace-cross Abstract: We introduce sparse autoencoder neural operators (SAE-NOs), a new class of sparse autoencoders that operate in function spaces rather than fix

arXiv:2508.13650v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preser

arXiv:2604.21511v1 Announce Type: cross Abstract: Learned Sparse IR models, such as SPLADE, offer an excellent efficiency-effectiveness tradeoff. However, they rely on the underlying backbone vocabula

arXiv:2604.16930v1 Announce Type: new Abstract: Visual Question Answering (VQA) requires models to identify the correct answer options based on both visual and textual evidence. Recent Mixture-of-Expe

arXiv:2604.14489v1 Announce Type: new Abstract: Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but req

arXiv:2602.05971v2 Announce Type: replace Abstract: Semantic representations can be framed as a structured, dynamic knowledge space through which humans navigate to retrieve and manipulate meaning. To

arXiv:2604.11373v1 Announce Type: cross Abstract: Robots are increasingly entering human-interactive scenarios that require understanding of quantity. How intelligent systems acquire abstract numerica

arXiv:2608.07886v1 Announce Type: cross Abstract: Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization proble

arXiv:2608.05558v1 Announce Type: cross Abstract: This paper examines Turing's 1948 report, 'Intelligent Machinery', as an important conceptual source for the later imitation games. Its first contribu

arXiv:2608.01585v1 Announce Type: new Abstract: Language model benchmarking is a difficult task. Outcome reasoning alone does not test the model's conceptualization of language and popular open-source

arXiv:2607.10285v2 Announce Type: replace Abstract: We study how unsupervised autoencoders trained on microscopic spin configurations from the Ising model learn macroscopic, theory-relevant variables

arXiv:2607.23694v1 Announce Type: new Abstract: Efficient surgical segmentation empowers clinical diagnosis, intraoperative monitoring, and downstream robotic pipelines for reconstruction and simulati

arXiv:2607.20993v1 Announce Type: cross Abstract: Large vision-language models are becoming increasingly dominant in 3D medical image interpretation, but we rarely know which internal units encode cli

arXiv:2607.21325v1 Announce Type: cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authenticat

arXiv:2607.08233v1 Announce Type: new Abstract: A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and de

arXiv:2607.07737v1 Announce Type: cross Abstract: GNSS-denied unmanned aerial vehicles require occasional absolute position fixes to bound the drift of visual-inertial odometry. Cross-view image retri

arXiv:2411.17077v2 Announce Type: replace-cross Abstract: As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applicat

arXiv:2607.01465v1 Announce Type: new Abstract: Large language models are trained to predict the next token, not to act inside a specific API. In niche enterprise SaaS workflows -- where success means

arXiv:2607.01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge. Language models can p

arXiv:2607.00023v1 Announce Type: cross Abstract: Dense sentence embeddings are fundamental to modern Retrieval-Augmented Generation (RAG) systems but suffer from a lack of interpretability due to fea

arXiv:2607.00578v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide interpretability by routing predictions through a layer of human-understandable concepts. However, defining an

arXiv:2606.27382v1 Announce Type: new Abstract: While the primary function of computers lies in computation and processing, the core value of the Internet is rooted in sharing and collaboration. Compu

arXiv:2606.25149v1 Announce Type: cross Abstract: The last few years have seen a significant rise in interest in highly autonomous and proactive systems, fueled by advances in AI. Systems that anticip

arXiv:2606.21252v1 Announce Type: new Abstract: Video-based seizure detection is essential for the management of epilepsy patients, offering a non-invasive complement to electroencephalography. While

arXiv:2606.12088v1 Announce Type: new Abstract: Most fairness research in NLP assumes direct access to protected attributes such as gender, race, or nationality. In practice, however, such information

arXiv:2602.12542v2 Announce Type: replace-cross Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under

arXiv:2603.01372v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting

arXiv:2606.02342v1 Announce Type: new Abstract: Dynamic aspects of handwriting are critical for assessing developmental disorders such as dysgraphia and are typically captured using digitizing tablets

arXiv:2606.00102v1 Announce Type: new Abstract: Over the centuries, probability theory has grown from the calculus of games of chance into a central framework for reasoning under uncertainty. This art

arXiv:2605.26001v1 Announce Type: cross Abstract: Evaluating generative AI (GenAI) systems is challenging because many targets of evaluation are broad, contested concepts, such as 'reasoning,' 'fairne

arXiv:2605.24057v1 Announce Type: cross Abstract: Neural networks acquire structured representations at specific moments during training, yet identifying these transitions typically relies on retrospe

arXiv:2605.23895v1 Announce Type: new Abstract: Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized

arXiv:2605.22616v1 Announce Type: new Abstract: Understanding how conceptual knowledge is grounded in bodily experience, and to what extent machine systems can acquire such knowledge without direct se

arXiv:2601.23206v2 Announce Type: replace Abstract: Large language models (LLMs) offer promise for dynamic game content generation, but they face critical barriers, including narrative incoherence and

arXiv:2605.17651v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) provide actionable recourse, but most methods assume a static framework with fixed data and a trained classifier. Thi

arXiv:2605.12532v1 Announce Type: cross Abstract: Conventional algorithmic trading systems are grounded in deterministic heuristics or offline-trained statistical models that cannot adapt to the seman