Knowledge catalogue
arXiv:2604.09752v1 Announce Type: cross Abstract: During the deployment of Large Language Models (LLMs), the autoregressive decoding phase on heterogeneous NPU platforms (e.g., Ascend 910B) faces seve
arXiv:2601.02149v3 Announce Type: replace-cross Abstract: We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this k
arXiv:2604.10454v1 Announce Type: new Abstract: Affective Image Manipulation (AIM) aims to evoke specific emotions through targeted editing. Current image editing benchmarks primarily focus on object-
arXiv:2603.18492v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token compute, but the deployment still requires stori
arXiv:2604.10528v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) demonstrate remarkable zero-shot recognition capabilities across a diverse spectrum of multimodal tasks, it yet rema
arXiv:2604.10585v1 Announce Type: cross Abstract: Modern large language models (LLMs) are increasingly fine-tuned via reinforcement learning from human feedback (RLHF) or related reward optimisation s
arXiv:2604.10420v1 Announce Type: new Abstract: Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on wea
arXiv:2604.11790v1 Announce Type: cross Abstract: Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet rem
arXiv:2604.10426v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While
arXiv:2604.10496v1 Announce Type: new Abstract: Outliers have emerged as a fundamental bottleneck in preserving accuracy for low-precision large models, particularly within Mixture-of-Experts (MoE) ar
Ollama is a local inference engine designed for running pre-built LLMs on your own machine, and it does not include fine-tuning capabilities — this distinction is correct. Unsloth (and its Unsloth Stu
arXiv:2506.18438v2 Announce Type: replace Abstract: Editing natural images using textual descriptions in text-to-image diffusion models remains a significant challenge, particularly in achieving consi
arXiv:2512.21877v3 Announce Type: replace-cross Abstract: Cricket is the second most popular sport worldwide, with billions of fans seeking advanced statistical insights unavailable through standard w
arXiv:2603.23916v2 Announce Type: replace-cross Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-sta
arXiv:2504.05902v2 Announce Type: replace-cross Abstract: Backdoor attacks pose a serious threat to deep neural networks (DNNs), allowing adversaries to implant triggers for hidden behaviors in infere
arXiv:2509.05342v3 Announce Type: replace Abstract: We propose Delta Rectified Flow Sampling (DRFS), a novel inversion-free, path-aware editing framework within rectified flow models for text-to-image
arXiv:2604.09596v1 Announce Type: new Abstract: Dermatologic diseases impose a large and growing global burden, affecting billions and substantially reducing quality of life. While modern therapies ca
arXiv:2604.10425v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain rem
arXiv:2503.16771v3 Announce Type: replace-cross Abstract: As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. Howe
arXiv:2604.10437v1 Announce Type: new Abstract: Vision--language models (VLMs) for radiology report generation (RRG) can produce long-form chest CT reports from volumetric scans and show strong potent
ERNIE Image Turbo is an open text-to-image generation model developed by Baidu's ERNIE-Image team, serving as the distilled release of the full ERNIE-Image model and built on a single-stream Diffusion
arXiv:2604.09585v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as foundation models for IoT applications such as human activity recognition (HAR). However, directly applyi
arXiv:2511.11653v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasonin
here’s a good application of harness permissions Programmatically Enforced Auto-Research: - auto-research loops usually expose a set of files that the agent is allowed to edit to hill climb a metric/e
arXiv:2604.10675v1 Announce Type: new Abstract: We propose a method to learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling the implicit placement know
arXiv:2510.07794v2 Announce Type: replace-cross Abstract: Agentic RAG is a powerful technique for incorporating external information that LLMs lack, enabling better problem solving and question answer
arXiv:2604.09960v1 Announce Type: new Abstract: The rapid adoption of large language models has introduced a new class of AI-generated fake news that coexists with traditional human-written misinforma
arXiv:2604.10392v1 Announce Type: cross Abstract: Large language models are increasingly used to generate code from natural language, but ensuring correctness remains challenging. Formal verification
arXiv:2604.10403v1 Announce Type: new Abstract: We address jailbreaks, backdoors, and unlearning for large language models (LLMs). Unlike prior work, which trains LLMs based on their actions when give
arXiv:2604.10667v1 Announce Type: cross Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limit
arXiv:2604.10946v1 Announce Type: new Abstract: Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key c
arXiv:2604.10557v1 Announce Type: cross Abstract: This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy. As LLMs become increasingly deployed in
arXiv:2601.14706v3 Announce Type: replace Abstract: In this paper, we present LookBench (We use the term 'look' to reflect retrieval that mirrors how people shop -- finding the exact item, a close sub
arXiv:2604.10862v1 Announce Type: new Abstract: The rapid advancement of diffusion-based generative models has made face forgery detection a critical challenge in digital forensics. Current detection
arXiv:2601.15498v2 Announce Type: replace Abstract: Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent me
arXiv:2601.22361v2 Announce Type: replace-cross Abstract: Assessing the veracity of online content has become increasingly critical. Large language models (LLMs) have recently enabled substantial prog
arXiv:2604.11012v1 Announce Type: new Abstract: The quality of text generated by large language models depends critically on the decoding sampling strategy. While mainstream methods such as Top-k, Top
arXiv:2604.10755v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced clinical tasks for common conditions, but their performance on rare diseases remains largely unte
This Reddit thread from r/ollama features a user seeking community recommendations for motherboard, CPU, and RAM components to build a system optimized for running Ollama locally without a dedicated G
arXiv:2604.10347v1 Announce Type: new Abstract: Vision foundation models have been shown to be effective at processing satellite imagery into representations fit for downstream tasks, however, creatin
arXiv:2511.04812v2 Announce Type: replace-cross Abstract: Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB i
arXiv:2405.16091v2 Announce Type: replace Abstract: Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explor
arXiv:2505.04278v3 Announce Type: replace-cross Abstract: Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing De
arXiv:2604.10064v1 Announce Type: new Abstract: Multimodal Transformers serve as the backbone for state-of-the-art vision-language models, yet their quadratic attention complexity remains a critical b
arXiv:2604.11808v1 Announce Type: new Abstract: Generating high-fidelity 3D indoor scenes remains a significant challenge due to data scarcity and the complexity of modeling intricate spatial relation
arXiv:2604.10475v1 Announce Type: new Abstract: Modeling household-level trip generation is fundamental to accurate demand forecasting, traffic flow estimation, and urban system planning. Existing stu
arXiv:2604.11120v1 Announce Type: new Abstract: Personality imbuing customizes LLM behavior, but safety evaluations almost always study prompt-based personas alone. We show this is incomplete: prompti
arXiv:2604.10857v1 Announce Type: cross Abstract: Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by mi
arXiv:2603.24853v2 Announce Type: replace Abstract: Ethical debates in AI have primarily focused on back-end issues such as data governance, model training, and algorithmic decision-making. Less atten
arXiv:2604.09860v1 Announce Type: cross Abstract: The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid
arXiv:2503.05534v3 Announce Type: replace Abstract: Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred bo
arXiv:2508.11290v3 Announce Type: replace Abstract: LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that seemingly resemble harmful
arXiv:2604.10306v1 Announce Type: new Abstract: As Earth-observation workloads move toward onboard and edge processing, remote-sensing segmentation models must operate under tight latency and energy c
arXiv:2604.10718v1 Announce Type: new Abstract: Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly p
arXiv:2604.11244v1 Announce Type: new Abstract: Advances in Multimodal Large Language Models (MLLMs) are transforming video captioning from a descriptive endpoint into a semantic interface for both vi
arXiv:2604.10152v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs)
arXiv:2604.09557v1 Announce Type: cross Abstract: Speculative Decoding (SD) has emerged as a critical technique for accelerating Large Language Model (LLM) inference. Unlike deterministic system optim
arXiv:2509.22220v2 Announce Type: replace Abstract: Prevalent semantic speech tokenizers, designed to capture linguistic content, are surprisingly fragile. We find they are not robust to meaning-irrel
arXiv:2604.09737v1 Announce Type: cross Abstract: Structured prediction requires models to generate ontology-constrained labels, grounded evidence, and valid structure under ambiguity, label skew, and
arXiv:2604.10228v1 Announce Type: new Abstract: Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this