Knowledge catalogue
arXiv:2512.00088v2 Announce Type: replace Abstract: We propose SemImage, a novel method for representing a text document as a two-dimensional semantic image to be processed by convolutional neural net
arXiv:2606.00021v1 Announce Type: cross Abstract: Speculative Decoding (SD) accelerates Large Language Model (LLM) inference by employing a lightweight draft model to propose candidate tokens, which a
arXiv:2606.01746v1 Announce Type: new Abstract: Modern neural networks are highly susceptible to adversarial perturbations. In this work, we identify that part of this vulnerability stems from the sen
arXiv:2606.00084v1 Announce Type: cross Abstract: Online travel platforms generate vast volumes of user-generated hotel reviews, offering rich opportunities to understand traveler experiences at scale
arXiv:2502.08884v3 Announce Type: replace-cross Abstract: We present ShapeLib, the first method that uses the priors of Large Language Models (LLMs) to design libraries of programmatic 3D shape abstra
arXiv:2510.08948v4 Announce Type: replace-cross Abstract: Effective e-commerce risk management requires in-depth case investigations to identify emerging fraud patterns in highly adversarial environme
arXiv:2510.01167v2 Announce Type: replace-cross Abstract: Aligning large language models to human preferences is inherently multidimensional, yet most pipelines collapse heterogeneous signals into a s
arXiv:2606.00928v1 Announce Type: new Abstract: Multiplexed fluorescence microscopy improves tissue segmentation by providing complementary channels including nuclear (DAPI) and membrane (E-cadherin),
arXiv:2606.02355v1 Announce Type: new Abstract: Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or pe
arXiv:2503.05641v4 Announce Type: replace-cross Abstract: Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks. However, task-level expert selection is often too coa
arXiv:2606.00510v1 Announce Type: cross Abstract: Agent skills are callable procedural modules that provide reusable knowledge and execution policies for complex agentic tasks. However, existing metho
arXiv:2606.01311v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks. Existing training-free skill
arXiv:2606.00822v1 Announce Type: cross Abstract: Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to e
arXiv:2606.01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving
arXiv:2606.00664v1 Announce Type: cross Abstract: Embodied world models have emerged as a promising paradigm in robotics by predicting how robot actions affect the surrounding scene. However, the roll
arXiv:2606.00015v1 Announce Type: cross Abstract: Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diver
arXiv:2512.04069v2 Announce Type: replace Abstract: Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required f
arXiv:2606.02380v1 Announce Type: cross Abstract: As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment. However, in practical applications,
arXiv:2606.00593v1 Announce Type: cross Abstract: Large language models are increasingly deployed as tool-augmented agents to acquire information beyond parametric knowledge. While recent work has imp
arXiv:2606.01479v1 Announce Type: new Abstract: Integrating large language models (LLMs) into text-to-speech (TTS) systems has improved speech expressiveness, yet interpretable emotional control remai
arXiv:2606.01446v1 Announce Type: cross Abstract: Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments. I
arXiv:2606.01526v1 Announce Type: new Abstract: In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive, especially when robots have limited communi
arXiv:2606.00572v1 Announce Type: new Abstract: Passenger count data from public transit systems reveals urban mobility patterns and is essential for planning, operation, and optimisation. However, no
arXiv:2606.02427v1 Announce Type: cross Abstract: Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not
arXiv:2512.00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to coll
arXiv:2606.01493v1 Announce Type: new Abstract: Reconstructing a photorealistic 3D face avatar from a single unconstrained photograph is challenging: feed-forward 3D Gaussian Splatting (3DGS) models d
arXiv:2606.00962v1 Announce Type: cross Abstract: Foundational agent interoperability standards, notably the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), have advanced multi-age
arXiv:2602.05435v2 Announce Type: replace Abstract: While flow matching is elegant, its reliance on single-sample conditional velocities leads to high-variance training targets that destabilize optimi
arXiv:2606.01790v1 Announce Type: cross Abstract: Vision-language-model-based graphical user interface (GUI) agents have shown broad automation capabilities, yet deployment is bottlenecked by a key-va
arXiv:2606.00762v1 Announce Type: new Abstract: Autonomous target search is crucial for deploying Micro Aerial Vehicles (MAVs) in emergency response and rescue missions. Existing approaches either foc
arXiv:2510.09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-i
arXiv:2606.01086v1 Announce Type: cross Abstract: Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during inference due to num
arXiv:2606.00509v1 Announce Type: new Abstract: Recovering surface normals from single view polarization images in complex media remains challenging. This paper focuses on ice as a representative comp
arXiv:2602.02416v2 Announce Type: replace Abstract: Self-correction in language models remains elusive. In this work, we explore whether language models can explicitly localize errors in incorrect rea
arXiv:2601.20803v2 Announce Type: replace Abstract: This paper presents several strategies to automatically obtain additional examples for in-context learning, effectively transforming relation extrac
arXiv:2606.00087v1 Announce Type: cross Abstract: Effective pre-polysomnography screening for obstructive sleep apnea-hypopnea syndrome (OSAHS) requires combining clinical risk factors with visible cr
arXiv:2606.00825v1 Announce Type: new Abstract: AI glasses present a compelling platform for AI agents to serve as personalized memory assistants. To be genuinely useful, such systems must move beyond
arXiv:2606.01843v1 Announce Type: cross Abstract: Deepfake detection suffers from poor generalization across forgery methods, as existing models tend to rely on spurious method-specific shortcuts that
arXiv:2606.00999v1 Announce Type: new Abstract: Large-scale vision foundation models have driven substantial gains on dense prediction tasks such as semantic segmentation, but their size makes deploym
arXiv:2510.17303v2 Announce Type: replace Abstract: Symmetries are known to improve the empirical performance of machine learning models, yet theoretical guarantees explaining these gains remain limit
arXiv:2606.00851v1 Announce Type: cross Abstract: Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or a
arXiv:2606.00282v1 Announce Type: cross Abstract: Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditio
arXiv:2606.00673v1 Announce Type: new Abstract: Thermal imaging offers a powerful alternative to visible-spectrum vision under challenging conditions such as low illumination and adverse weather, yet
arXiv:2504.04718v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs). However,
arXiv:2606.00503v1 Announce Type: cross Abstract: Modifying an attribute in tabular data often introduces an unnatural instance by breaking its relationships with other attributes. The modified instan
arXiv:2505.19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affe
arXiv:2606.00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal
arXiv:2606.01031v1 Announce Type: cross Abstract: Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temp
arXiv:2602.09651v2 Announce Type: replace-cross Abstract: Diffusion models do not recover semantic structure uniformly over time. Instead, samples transition from semantic ambiguity to class commitmen
arXiv:2511.01938v3 Announce Type: replace-cross Abstract: Grokking is a puzzling phenomenon in neural networks where full generalization occurs only after a substantial delay following the complete me
arXiv:2606.00261v1 Announce Type: new Abstract: Police-reported crash statistics remain the standard input for urban road-safety assessment, but their incompleteness and reporting lag limit their usef
arXiv:2606.01847v1 Announce Type: cross Abstract: Diffusion-based Vision-Language-Action policies achieve remarkable success in robotic manipulation, yet commit a fundamental geometric error we term t
arXiv:2606.00674v1 Announce Type: cross Abstract: Large Language Models (LLMs) aligned via outcome-based Reinforcement Learning (RL) frequently exhibit a critical failure mode: they achieve high perfo
arXiv:2510.10943v2 Announce Type: replace-cross Abstract: Bias in large language models (LLMs) remains a persistent challenge, often leading to stereotyping and unfair treatment across social groups.
arXiv:2606.00232v1 Announce Type: new Abstract: We study fact-level repair for multimodal generation, where a fluent output may contain specific facts that are not supported by the input. Existing inf
arXiv:2606.01670v1 Announce Type: cross Abstract: Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indic
arXiv:2606.01540v1 Announce Type: cross Abstract: Shapley values are a widely used tool for attributing importance and interactions among input variables in black-box models, but their computation inv
arXiv:2606.01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create
arXiv:2606.00427v1 Announce Type: new Abstract: State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity. This excludes a co
arXiv:2606.00014v1 Announce Type: cross Abstract: Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to dim