Search: “concepts”

arXiv:2606.11570v1 Announce Type: cross Abstract: We propose a spectral-based, unsupervised representation learning framework to derive low-dimensional embeddings for clinical concepts and patients in

arXiv:2606.12234v1 Announce Type: new Abstract: Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved t

arXiv:2606.11599v1 Announce Type: new Abstract: Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depen

arXiv:2606.10170v1 Announce Type: new Abstract: This paper extends the concept of Learning Entropy (LE) from temporal adaptive systems to spatial learning in multilayer perceptron networks (MLPs) appl

arXiv:2606.07564v1 Announce Type: cross Abstract: This report explores detector design considerations for the Future Circular Collider in its electron-positron mode (FCC-ee) through an extended dialog

arXiv:2606.08303v1 Announce Type: new Abstract: This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Succes

arXiv:2606.07760v1 Announce Type: new Abstract: Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single-cell RNA sequenci

arXiv:2606.07720v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Contin

arXiv:2606.06790v1 Announce Type: cross Abstract: This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw an

arXiv:2606.06908v1 Announce Type: new Abstract: Modern image-analysis pipelines often convert images into structured semantic variables, such as facial attributes, object concepts, and scene descripto

arXiv:2602.07025v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) exhibit puzzling failures in multi-object visual tasks, such as hallucinating non-existent elements or failing t

arXiv:2606.05167v1 Announce Type: cross Abstract: Realism is a central yet seemingly under-theorized concept in Agent-Based Modelling. This paper presents a Systematic Literature Review, aiming to ide

arXiv:2505.05026v5 Announce Type: replace Abstract: User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept. While recent

arXiv:2606.05949v1 Announce Type: new Abstract: Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts a

arXiv:2606.04033v1 Announce Type: new Abstract: The validation of advanced nuclear reactor designs and fuel concepts requires critical experiments with high neutronic similarity to the target technolo

arXiv:2606.03471v1 Announce Type: new Abstract: This paper proposes, for the first time, a rigorous formal definition of the concept of Machine Theory of Mind, based on principles supported by evidenc

arXiv:2606.03453v1 Announce Type: cross Abstract: Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generati

arXiv:2506.22881v3 Announce Type: replace Abstract: Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weig

arXiv:2606.00269v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models can be steered at test time by intervening on semantically meaningful internal directions, but existing methods use

arXiv:2606.02168v1 Announce Type: new Abstract: Compositional visual question answering (VQA) represents a challenging yet fundamental task that requires models to comprehend novel combinations of pre

arXiv:2606.01909v1 Announce Type: cross Abstract: We present Echo, a proof-of-concept audio system built around a single 25 M-parameter ViT encoder. The encoder is pretrained with a JEPA objective and

arXiv:2606.00356v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary

arXiv:2512.09730v3 Announce Type: replace Abstract: Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two compleme

arXiv:2606.01060v1 Announce Type: cross Abstract: Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes intern

arXiv:2602.10117v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We cal

arXiv:2605.30745v1 Announce Type: new Abstract: Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. Howe

arXiv:2508.16687v2 Announce Type: replace Abstract: Traditional embeddings represent datapoints as vectors, which makes similarity easy to compute but limits how well they capture hierarchies and comp

arXiv:2511.20934v2 Announce Type: replace Abstract: Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and conce

arXiv:2605.27594v1 Announce Type: cross Abstract: We study the problem of computationally efficient proper agnostic learning of multidimensional concept classes under the Gaussian distribution. In thi

arXiv:2605.28210v1 Announce Type: new Abstract: Drawing on Ullmann-Margalit's concept of opting (transformative, irrevocable, and shadowed by foreclosed alternatives), we show that current AI systems

arXiv:2605.26703v1 Announce Type: cross Abstract: The classic concept of 'calibrated forecasts' and its more recent refinement, 'calibeating,' are defined with respect to the standard quadratic scorin

arXiv:2605.26548v1 Announce Type: cross Abstract: Large language models (LLMs) now support automated software security tasks, including vulnerability discovery and proof-of-concept (PoC) generation. E

arXiv:2508.05305v2 Announce Type: replace Abstract: The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-s

arXiv:2506.08543v3 Announce Type: replace Abstract: High-level representations have become a central focus in enhancing AI transparency and control, shifting attention from individual neurons or circu

arXiv:2605.25962v1 Announce Type: cross Abstract: Machine unlearning removes designated concepts or knowledge from pre-trained models. Recent work has extended this paradigm to speaker identity unlear

arXiv:2605.25171v1 Announce Type: new Abstract: In most existing AI humor research, humor was treated as either 'present' or 'not present.' We explore the concept of humor as a social interaction with

arXiv:2605.24773v1 Announce Type: new Abstract: Annotator disagreement in emotion classification reflects ambiguity intrinsic to emotion concepts and is essential for predictor-quality assessment in s

arXiv:2605.23861v1 Announce Type: cross Abstract: Causal generative modeling is essential for developing reliable and transparent AI systems capable of counterfactual reasoning. While existing approac

arXiv:2605.22658v1 Announce Type: new Abstract: While large language models provide strong compositional reasoning, existing reasoning segmentation pipelines fail to transparently connect this reasoni

arXiv:2605.20434v1 Announce Type: cross Abstract: We study the contradiction graphs associated with binary concept classes. For a class H subseteq {0,1}^X, the order-m contradiction graph G_m(H) has a

arXiv:2605.20272v1 Announce Type: new Abstract: While humans readily generalize abstract concepts to more complex or larger tasks, building Reinforcement Learning (RL) systems with this ability remain

arXiv:2605.20635v1 Announce Type: new Abstract: This paper proposes a general machine learning framework called the localization method, which is fundamentally built on two core concepts: localization

arXiv:2603.09024v2 Announce Type: replace Abstract: Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient is rar

arXiv:2602.13466v2 Announce Type: replace-cross Abstract: The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is

arXiv:2602.11910v2 Announce Type: replace-cross Abstract: Audio diffusion models can synthesize high-fidelity music from text, yet achieving fine-grained control over specific musical attributes remai

arXiv:2601.01593v2 Announce Type: replace Abstract: Manual font design is an intricate process that transforms a stylistic visual concept into a coherent glyph set. This challenge persists in automate