Knowledge catalogue
arXiv:2609.27195v1 Announce Type: cross Abstract: Non-speech interference can change a speech representation without causing comparable task loss. We test eight frozen encoders on four tasks, adding n
arXiv:2404.07239v2 Announce Type: replace-cross Abstract: Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroi
arXiv:2609.28262v1 Announce Type: new Abstract: Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inf
arXiv:2009.13987v3 Announce Type: replace Abstract: We introduce a class of random polytopes which simultaneously generalizes several known constructions. While being fairly general, these polytopes a
arXiv:2609.27855v1 Announce Type: new Abstract: AI systems increasingly claim to optimize prompts, policies, architectures, plans, tool-use trajectories, reasoning traces, and test-time computation. T
arXiv:2609.27173v1 Announce Type: new Abstract: Solving complex tasks in domains such as science, medicine, law, and finance often requires assembling interdependent information scattered across vast,
arXiv:2609.26942v1 Announce Type: cross Abstract: Current literature evaluates large language models (LLMs) on multilingual kinship understanding using multiple choice benchmarks, treating it as a rec
arXiv:2609.28081v1 Announce Type: cross Abstract: Conventional image registration algorithms are robust to domain shifts and achieve low errors, but they are slow and computationally expensive. Deep-l
arXiv:2609.28080v1 Announce Type: new Abstract: Evaluating open-ended text generation involves understanding how different properties of a continuation relate to its perceived quality. We present a re
arXiv:2609.27346v1 Announce Type: new Abstract: Reliable localization of non-line-of-sight (NLOS) pedestrians is critical for safe urban autonomous driving, yet it remains highly challenging in ego-dy
arXiv:2609.27612v1 Announce Type: new Abstract: Long-horizon robot execution requires a clear distinction between a model's proposal, a controller's termination, and verified task completion. We prese
arXiv:2602.23006v3 Announce Type: replace-cross Abstract: Simulating a Gaussian process requires sampling from a high-dimensional Gaussian distribution, which scales cubically with the number of sampl
arXiv:2609.27035v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) and related policy-gradient methods for training language model agents collapse an entire multi-turn rollout i
arXiv:2609.27986v1 Announce Type: new Abstract: Accurate remaining discharge time (RDT) prediction is challenging in real-world battery applications because future load profiles are unknown and highly
arXiv:2609.27890v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) fre- quently generate text that is inconsistent with the input image. While object- and attribute-level hallu
arXiv:2609.27202v1 Announce Type: cross Abstract: The growing deployment of Internet of Things (IoT) devices has increased the need for privacy-preserving intrusion detection systems that operate dire
arXiv:2609.27913v1 Announce Type: new Abstract: We study the problem of aggregating opinions from multiple black-box experts in noisy, conflict-prone settings where expert reliability varies across in
arXiv:2609.27938v1 Announce Type: new Abstract: Enabling operators to perceive and interact with remote environments naturally is a fundamental challenge in robotic teleoperation. This is especially c
arXiv:2609.28425v1 Announce Type: cross Abstract: Recursive state estimation often executes approximate numerical solutions inside a feedback loop, where highly accurate local steps do not guarantee b
arXiv:2609.27756v1 Announce Type: new Abstract: Large language models are increasingly asked to analyze data and report what the results mean, a task distinct from the belief- or preference-alignment
arXiv:2609.27248v1 Announce Type: new Abstract: Next-token prediction has enabled highly fluent autoregressive language models, but it represents global structure only indirectly through sequential fa
arXiv:2609.28263v1 Announce Type: new Abstract: The growth of large language model (LLM) inference and search services increases the scale of online linear programming problems, motivating computation
arXiv:2609.26979v1 Announce Type: new Abstract: Gaussian processes (GPs) provide a flexible framework for learning unknown functions from noisy measurements while quantifying predictive uncertainty, m
arXiv:2602.16715v2 Announce Type: replace Abstract: We explore the potential of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph-based RAG (GraphRAG) for generating Design
arXiv:2603.08166v2 Announce Type: replace Abstract: Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological r
arXiv:2609.27982v1 Announce Type: cross Abstract: In this paper, we are concerned with matrices formed by block-diagonal factors interleaved with fixed permutations -- a flexible family of structured
arXiv:2609.27981v1 Announce Type: new Abstract: KV-cache eviction is typically evaluated through average quality-memory trade-offs, yet a small average loss can hide requests whose utility degrades ma
arXiv:2609.28145v1 Announce Type: new Abstract: Reinforcement learning (RL) improves reasoning, but its performance depends on the policy from which training begins. We study on-policy distillation (O
arXiv:2609.27677v1 Announce Type: new Abstract: Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal memory can recover weak observations, but reusing m
arXiv:2609.27475v1 Announce Type: new Abstract: As robots remain in public spaces over extended periods, they must maintain interaction continuity by preserving and correctly applying context as peopl
arXiv:2609.27667v1 Announce Type: new Abstract: Reinforcement learning (RL) achieves strong performance in sequential decision-making but remains brittle under dynamic uncertainty and distributional s
arXiv:2511.18683v3 Announce Type: replace Abstract: Autonomous surface vehicles (ASVs) are influenced by environmental disturbances such as wind and waves, making accurate trajectory tracking a persis
arXiv:2609.27546v1 Announce Type: cross Abstract: Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution,
arXiv:2505.10080v3 Announce Type: replace-cross Abstract: Scrambling quantum systems have attracted attention as effective substrates for temporal information processing. Here we consider a quantum re
arXiv:2609.26951v1 Announce Type: cross Abstract: We introduce Rolling Conformal Prediction (rolling-CP), a distribution-free predictive inference method for the setting of sequential model training.
arXiv:2609.28300v1 Announce Type: new Abstract: Ill-posed inverse problems require priors to constrain the solution space toward plausible outcomes. In inverse rendering, learned priors modeling the d
arXiv:2609.27289v1 Announce Type: cross Abstract: Conventional Japanese automatic speech recognition (ASR) is supervised by an orthographic transcript, although the same written form can correspond to
arXiv:2609.27413v1 Announce Type: new Abstract: Alignment-free RGB-T salient object detection (RGB-T SOD) aims to identify salient objects from unregistered RGB and thermal image pairs without costly
arXiv:2604.01173v2 Announce Type: replace-cross Abstract: Uncertainty quantification is essential when deploying learning-based control methods in safety-critical systems. This is commonly realized by
arXiv:2609.27816v1 Announce Type: cross Abstract: Safe coordination in heterogeneous machine-to-machine (M2M) robotic systems is challenging when robots differ in sensing capabilities, environmental a
arXiv:2603.17373v2 Announce Type: replace Abstract: Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety i
arXiv:2609.27463v1 Announce Type: new Abstract: Distributed model predictive control (DMPC) often constructs both predictions and collision constraints from neighbor trajectories, so packet loss can r
arXiv:2609.26865v1 Announce Type: cross Abstract: Conversational AI systems can pose safety risks to their users such as hallucination, sycophancy, overconfidence, and anthropomorphism, but these risk
arXiv:2609.26986v1 Announce Type: new Abstract: For multimodal large language models, when images or speech conflict with accompanying text, measured text reliance can entangle modality preference wit
arXiv:2609.27678v1 Announce Type: new Abstract: Contract inference requires multiple judgments about a shared document, but aggregate accuracy can conceal changes in the individual decisions. Repeated
arXiv:2607.11039v2 Announce Type: replace-cross Abstract: Young job seekers frequently explore their career possibilities by browsing peers' posts that share job-seeking experiences. However, static b
arXiv:2609.27442v1 Announce Type: new Abstract: 3D reconstruction from satellite imagery is essential for large-scale topographic analysis, yet the lack of high-fidelity training datasets with accurat
arXiv:2609.27209v1 Announce Type: cross Abstract: Subgraph sampling reduces the training cost of large-scale graph neural networks, but sampling criteria may overlook the geometric roles of edges. We
arXiv:2609.28117v1 Announce Type: cross Abstract: In this paper, we introduce a gradient-based head attribution strategy where the Token-level Max-Margin loss is backpropagated to the attention maps.
arXiv:2609.27166v1 Announce Type: new Abstract: Capability and efficiency are two key dimensions of reasoning in large language models (LLMs). Capability refers to the ability to solve a given problem
arXiv:2609.27891v1 Announce Type: cross Abstract: Repository-level coding benchmarks have become the standard for evaluating coding agents, yet they inherently suffer from data leakage because they ar
arXiv:2609.27958v1 Announce Type: new Abstract: Implicit neural representations (INRs) have emerged as a promising paradigm for video compression, providing compact neural representations with flexibl
arXiv:2609.27175v1 Announce Type: cross Abstract: Multimedia verification requires not only accurate decisions but also traceable evidence, reliable human correction, and safe reuse of prior experienc
arXiv:2601.13566v2 Announce Type: replace-cross Abstract: Can language models improve their accuracy without external supervision? Methods such as debate, bootstrap, and internal coherence maximizatio
arXiv:2609.27854v1 Announce Type: cross Abstract: Multi-source remote sensing image classification has attracted increasing attention due to the complementary spectral, structural, and geometric infor
arXiv:2609.27671v1 Announce Type: new Abstract: 4D radar complements dense image semantics with long-range geometry and radial motion, but existing radar--camera detectors largely solve where to align
arXiv:2609.28006v1 Announce Type: new Abstract: Decoder-only Transformer language models cache keys and values (KV) to reuse past computation during generation. Sharing KV across layers saves storage
arXiv:2609.27008v1 Announce Type: cross Abstract: We study the long-time behavior of Wasserstein gradient flows for interaction energies [ mathsf E[mu] = frac12iint_{Mimes M}K(x,y),mathrm dmu(x),mathr
arXiv:2609.28372v1 Announce Type: cross Abstract: Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using 'Tool-Lab,' an adaptation of
arXiv:2609.27621v1 Announce Type: new Abstract: Physical modeling and inverse design require computation that can continue from reusable state. We introduce SHRAV, an architecture-independent computat