Knowledge catalogue
arXiv:2608.29970v1 Announce Type: new Abstract: Artistic Text Recognition (ATR) remains challenging because word images often combine decorative fonts, curved layouts, object-like characters, clutter,
arXiv:2608.30792v1 Announce Type: cross Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificial
arXiv:2602.05863v3 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) remains the dominant critic-free approach for fine-tuning LLMs and VLMs, but its compatibility with
arXiv:2608.29847v1 Announce Type: cross Abstract: Text-to-image models learn associations between concepts - in the case of this paper, people's professions, which we refer to as roles - and visual at
arXiv:2608.30986v1 Announce Type: cross Abstract: Activation steering has emerged as a lightweight approach for controlling model refusal at inference time. A growing line of research explores trainab
arXiv:2608.30382v1 Announce Type: new Abstract: Popular adaptive stochastic gradient descent (SGD) methods to train artificial intelligence (AI) systems include the RMSprop, the Adam, and the AdamW op
arXiv:2608.30768v1 Announce Type: new Abstract: Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved enco
arXiv:2608.28958v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps.
arXiv:2608.29613v1 Announce Type: new Abstract: Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specif
CrowdStrike Holdings Inc. today unveiled Falcon Guardian, software that finds the artificial intelligence agents running inside a company and shuts down the ones security teams have not approved. The
arXiv:2608.28843v1 Announce Type: cross Abstract: We show that smooth two-layer feed-forward networks (FFNs) expose an additional structural model extraction channel under a chosen-input raw-output or
arXiv:2608.30386v1 Announce Type: cross Abstract: Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substan
arXiv:2608.30301v1 Announce Type: new Abstract: Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deployed on constrained edge devices. Flexible
arXiv:2608.29939v1 Announce Type: cross Abstract: Streaming-potential-mediated transport of viscoelastic fluids has attracted research attention owing to its applications in electrokinetic energy conv
day 1 of recreating iconic tech from my childhood :) I rebuilt Cher’s iconic closet as a full-stack app in @Replit 💛 the frontend uses React + TypeScript, with an Express backend. I used Replit’s Cler
arXiv:2608.29465v1 Announce Type: cross Abstract: Deployed decisions are often optimized once and retained because updates impose operational, regulatory, or switching costs. As operating conditions c
DeepSeek V4 Flash now comes with native vision on Fireworks. Available now on serverless and priority tiers. Better benchmarks than the text-only 0731 version at the same low price: 0.22 input / 0.66
arXiv:2608.28691v1 Announce Type: cross Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surroundi
arXiv:2310.02423v3 Announce Type: replace Abstract: We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call Delta-amortized inference (Delta-A
arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying
arXiv:2608.30405v1 Announce Type: new Abstract: Medical knowledge changes continually, making large language models vulnerable to relying on outdated yet clinically plausible information. We study whe
arXiv:2608.30877v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated exceptional performance in protein-ligand interaction prediction, but state-of-the-art
arXiv:2608.30413v1 Announce Type: new Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of ne
arXiv:2608.28986v1 Announce Type: new Abstract: LLMs often struggle with modern Korean poetry, producing outputs that resemble 'line-broken prose.' We address two coupled tasks: detecting whether a Ko
arXiv:2608.29307v1 Announce Type: cross Abstract: Language models increasingly answer questions by consulting retrieved documents rather than memory alone, a design now common in search assistants and
arXiv:2608.29096v1 Announce Type: cross Abstract: The ongoing changes in software engineering requirements have created a substantial need for automated tools which can create secure source code from
arXiv:2601.18486v3 Announce Type: replace Abstract: Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes w
arXiv:2505.16990v3 Announce Type: replace Abstract: In this work, we propose Dimple, the first Discrete Diffusion Multimodal Large Language Model (DMLLM). We observe that training with a purely discre
arXiv:2608.28824v1 Announce Type: new Abstract: Currently, in biological systems Neural Correlates of Consciousness (NCCs) are characterized in terms of EEG and FMRI signals. Unfortunately, this chara
arXiv:2608.28615v1 Announce Type: cross Abstract: Synthetic personas based on large language models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet systematic validat
arXiv:2608.28635v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have advanced document understanding, visual question answering, and text extraction. However, their r
arXiv:2608.30327v1 Announce Type: new Abstract: Fine-tuning can improve legal question-answering accuracy without improving how models use law supplied in context. We study this distinction in bilingu
arXiv:2608.29037v1 Announce Type: cross Abstract: Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of Multimoda
arXiv:2608.29434v1 Announce Type: cross Abstract: JEPA world models make latent-space planning a practical route to control, but they are built almost exclusively on images. Whether latent prediction
arXiv:2608.29739v1 Announce Type: new Abstract: Geometric eye trackers can provide the spatial accuracy required for gaze-based interaction and multimodal studies, but their measurements remain sensit
arXiv:2608.31029v1 Announce Type: new Abstract: End-to-end autonomous driving models plan future trajectories from raw sensor input. While earlier driving benchmarks often measured deviation from the
arXiv:2608.29711v1 Announce Type: new Abstract: While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leavin
arXiv:2608.29252v1 Announce Type: new Abstract: Reinforcement fine-tuning (RFT) is increasingly used to strengthen the reasoning abilities of large models, yet its effectiveness is bound by how traini
arXiv:2608.29177v1 Announce Type: new Abstract: The integration of novel view synthesis (NVS) and open-vocabulary segmentation (OVS) has recently yielded powerful feed-forward 3D foundation models. Ho
arXiv:2608.30730v1 Announce Type: cross Abstract: Long-horizon agentic tasks go beyond chaining short tasks over more interaction turns. Their evolving dynamic environments and long-range dependencies
arXiv:2608.29763v1 Announce Type: new Abstract: Broad Learning System (BLS) is an efficient alternative to deep architectures due to its fast training, analytical learning, and strong generalization u
arXiv:2608.30893v1 Announce Type: new Abstract: Electrocardiogram (ECG) interpretation requires knowledge of cardiology, electrophysiology, clinical diagnosis, ECG waveforms, signal acquisition, and i
arXiv:2608.28791v1 Announce Type: new Abstract: Real-time decision-making for enhanced geothermal systems (EGS) is challenging because long-term production periods involve high-dimensional control spa
arXiv:2608.28730v1 Announce Type: cross Abstract: Latest JPEG restoration systems achieve strong quality with large models, yet often remain too slow and expensive for efficient on-device deployment.
arXiv:2602.23188v2 Announce Type: replace Abstract: We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM) that attains accuracy comparable to full retraining while
arXiv:2601.16690v2 Announce Type: replace Abstract: We introduce EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games. Rather than
arXiv:2608.29896v1 Announce Type: new Abstract: A robot's effective ``mind'' need not reside in a single policy. It can emerge when specialized components perceive, reason, predict, act, verify, and r
arXiv:2608.29035v1 Announce Type: new Abstract: Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to pro
arXiv:2608.29890v1 Announce Type: new Abstract: Biomedical Named Entity Recognition (NER) is fundamental to healthcare AI applications, including clinical decision support and medical information extr
arXiv:2608.30462v1 Announce Type: cross Abstract: Large language models exhibit substantial performance variation across languages, even when solving semantically equivalent tasks. Existing analyses o
arXiv:2608.28709v1 Announce Type: cross Abstract: Accurate boundary delineation of brain tumors in Magnetic Resonance Imaging (MRI) is a critical yet formidable challenge in neuro-oncology due to inhe
arXiv:2608.30021v1 Announce Type: cross Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are ofte
arXiv:2608.29929v1 Announce Type: new Abstract: Vehicle attribute recognition is an important task in intelligent transportation systems, particularly when Automatic License Plate Recognition (ALPR) i
arXiv:2608.30980v1 Announce Type: cross Abstract: We study self-modeling: an LLM's ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a pr
arXiv:2608.30884v1 Announce Type: cross Abstract: While gender and racial biases in language models have been widely studied, anti-LGBTQ biases remain underexplored, particularly beyond English. Exist
arXiv:2608.29543v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, ofte
arXiv:2510.26861v4 Announce Type: replace-cross Abstract: Multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. In practic
arXiv:2608.28714v1 Announce Type: cross Abstract: Objective: Deep learning accelerates brain MRI four- to tenfold, but models can erase lesions or synthesize false tissue - failures pixel-averaged met
arXiv:2608.29431v1 Announce Type: new Abstract: Localized Representation Steering (LRS) is widely used to correct reasoning pathologies in large language models. However, standard benchmark evaluation
arXiv:2608.29376v1 Announce Type: new Abstract: Code generation increasingly relies on large transformer models, whose capability advances with scale. Yet such a scale is costly, creating demand for s