Knowledge catalogue
arXiv:2609.18706v1 Announce Type: cross Abstract: Neural decompilers can recover readable and recompilable source code from binaries, but their predictions remain difficult to trust. Matching decompil
arXiv:2609.17953v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence t
arXiv:2609.17916v1 Announce Type: new Abstract: Temporal link prediction on the Temporal Graph Benchmark 2.0 (TGB 2.0) faces a scalability ceiling: on the benchmark's three largest datasets, every exi
arXiv:2609.17655v1 Announce Type: cross Abstract: In an important recent work, Blanc (2026) gave an algorithm for robustly learning Boolean concept classes with respect to a fixed distribution that ou
arXiv:2609.19080v1 Announce Type: new Abstract: As robot capabilities increase, quadratic programming (QP)-based controllers must account for a similarly increasing number of constraints to ensure saf
arXiv:2603.05757v2 Announce Type: replace Abstract: Video generative models (VGMs) pretrained on large-scale internet data can produce temporally coherent rollout videos that capture rich object dynam
arXiv:2609.18385v1 Announce Type: new Abstract: Emotions are an essential aspect of human communication, particularly on social media, where authors frequently combine text and images to convey their
arXiv:2609.17981v1 Announce Type: cross Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Ra
arXiv:2509.16894v2 Announce Type: replace Abstract: Autonomous racing serves as a compelling testbed for advancing autonomous vehicle systems. The 1/10-scale F1Tenth platform is widely adopted for edu
arXiv:2609.18164v1 Announce Type: new Abstract: This paper studies energy-aware manipulation as a physically grounded learning problem. We define a joint-space mechanical-work proxy from joint torque
arXiv:2609.17554v1 Announce Type: new Abstract: In English all-words word sense disambiguation (WSD), the labels, not the models, have become the bottleneck: frontier LLMs are accurate enough that the
arXiv:2609.17532v1 Announce Type: new Abstract: Invasive mechanical ventilation is a lifesaving therapy, but timely, safe discontinuation is essential to preventing extubation failure (EF) and related
arXiv:2512.07179v2 Announce Type: replace Abstract: Intelligent Tutoring Systems (ITS) provide personalized learning paths by diagnosing learners' proficiency. Knowledge Tracing (KT) models play a cen
arXiv:2603.02948v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks (PINNs) incorporate physics into neural networks by embedding partial differential equations (PDEs) into thei
arXiv:2609.19044v1 Announce Type: new Abstract: Sampling can increase response diversity without producing history-dependent behavior. We formalize a different design target, structured unpredictabili
arXiv:2603.16130v5 Announce Type: replace Abstract: Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in visu
arXiv:2609.17885v1 Announce Type: new Abstract: Computer-use agents that operate through screenshots and simulated actions are advancing rapidly, yet their evaluation remains anchored to general deskt
arXiv:2609.18381v1 Announce Type: new Abstract: Despite their high accuracy on point-wise metrics, machine learning weather forecasting models can exhibit different failure modes such as blurring, per
arXiv:2609.19099v1 Announce Type: new Abstract: Self-emulsifying drug delivery systems (SEDDS) can improve the oral bioavailability of poorly soluble drugs, but identifying high-performing formulation
arXiv:2609.18852v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) capture years of patient history across notes, codes, labs, and procedures, and contain evidence needed to
arXiv:2609.17755v1 Announce Type: cross Abstract: The analysis of US political language is usually based on the written form (e.g. presidential addresses) or posts broadcasted on various social networ
arXiv:2609.17590v1 Announce Type: cross Abstract: Evolutionary Ensemble Search (EES) constructs machine-learning procedures through expert-guided program evolution. A role-specialized council turns ta
arXiv:2609.17632v1 Announce Type: new Abstract: Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-
arXiv:2609.18256v1 Announce Type: new Abstract: Reliability under sparse and heterogeneous failures remains a fundamental challenge for medical image segmentation. High average accuracy can conceal a
arXiv:2608.28363v2 Announce Type: replace Abstract: LLM agents increasingly modify their own prompts, tools, middleware, resources, and execution harnesses at runtime. Such self-evolution can improve
arXiv:2609.18047v1 Announce Type: new Abstract: We characterize when compressed vector representations admit exact linear or affine readouts of a finite lexicon's truth conditions: one fixed map per p
arXiv:2609.18900v1 Announce Type: new Abstract: Prototyping and evaluating human-robot teaming (HRT) scenarios in the real-world is costly. Virtual simulation of HRT scenarios has been adopted as an a
arXiv:2609.19135v1 Announce Type: new Abstract: Can a logged dataset visit every hidden state frequently and still be exponentially uninformative about a target policy's value? We show that it can whe
arXiv:2505.15918v3 Announce Type: replace-cross Abstract: In this work, we evaluate the potential of Large Language Models (LLMs) in building Bayesian Networks (BNs) by approximating domain expert pri
arXiv:2609.18238v1 Announce Type: new Abstract: Observational decision-support systems often expose one causal estimate as a recommendation even when plausible estimators disagree. The inherent engine
arXiv:2609.17913v1 Announce Type: new Abstract: Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead to
arXiv:2609.17786v1 Announce Type: new Abstract: Fairness-aware model compression requires selecting methods and configurations that balance accuracy, fairness, and deployment cost. These decisions bec
arXiv:2511.16549v2 Announce Type: replace Abstract: As deep learning (DL) techniques become integral to various applications, ensuring model fairness while maintaining high performance has become incr
arXiv:2609.18346v1 Announce Type: new Abstract: Large language models (LLM) deployed as autonomous pricing agents may sustain supracompetitive prices through tacit coordination. We develop a causal gr
arXiv:2609.17534v1 Announce Type: new Abstract: Social desirability and impression management are pervasive sources of response distortion in human personality assessment, yet their effects on Large L
arXiv:2609.18644v1 Announce Type: new Abstract: Fallacy-detection benchmarks pair fallacy classes with a single 'valid' or 'none' class that takes everything data collection did not label as a fallacy
arXiv:2609.17730v1 Announce Type: cross Abstract: Approximate multipliers can reduce hardware area and energy consumption in Deep Neural Network (DNN) inference; however, they introduce computational
arXiv:2609.18901v1 Announce Type: cross Abstract: In physics-informed machine learning, a target function u^* is learned from noisy value observations y_i=u^*(x_i)+ arepsilon_i, together with differen
arXiv:2609.17652v1 Announce Type: cross Abstract: When agentic sessions run to a million tokens with many sessions resident at once, the KV cache and the index that ranks it live in host memory, and t
arXiv:2609.19042v1 Announce Type: cross Abstract: In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privacy
arXiv:2609.18964v1 Announce Type: new Abstract: Federated Reinforcement Learning (FRL) enables collaborative policy learning across distributed agents with heterogeneous environments. While recent met
arXiv:2609.18089v1 Announce Type: new Abstract: Vehicular federated learning (VFL) enables privacy-preserving collaborative model training for intelligent transportation systems, where communication r
arXiv:2609.17946v1 Announce Type: new Abstract: Learned quadruped locomotion policies commonly map observations directly to joint-level actions, leaving the periodic structure of locomotion implicit i
arXiv:2609.18119v1 Announce Type: new Abstract: Many real-world robotic applications require dynamically sensitive manipulation, where success depends not only on reaching a target state but on mainta
arXiv:2609.18651v1 Announce Type: new Abstract: Generalist robot policies offer useful initialization, but refining compact specialists through limited physical interaction requires informative learni
arXiv:2609.18058v1 Announce Type: new Abstract: Finding the object referred to by language in a partially observed 3D scene is a core capability for embodied agents. Existing approaches either couple
arXiv:2601.13874v3 Announce Type: replace-cross Abstract: Accurately and efficiently estimating the variance of the Maximum Mean Discrepancy (MMD) remains challenging, particularly for unbalanced samp
arXiv:2607.17542v2 Announce Type: replace Abstract: Accurately steering a robot to a target configuration is fundamental in engineering, yet remains challenging for nonholonomic mobile robots. Vector
arXiv:2609.18471v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) exhibit strong problem-solving abilities, yet their safety alignment often degrades when handling harmful queries. Existin
arXiv:2609.18623v1 Announce Type: cross Abstract: State-of-the-art vision-language-action models (VLA) for autonomous driving face critical limitations: excessive parameter counts, inefficient high-re
arXiv:2609.19124v1 Announce Type: new Abstract: Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formati
arXiv:2609.17627v1 Announce Type: new Abstract: Overconstrained rational single-loop linkages are efficient, compact, and low-cost custom mechanisms, yet their deployment in industrial settings is lim
arXiv:2609.18983v1 Announce Type: cross Abstract: Adaptive loop filter (ALF) for video coding, which is designed to minimize the mean square error between original and reconstructed samples by using W
arXiv:2609.18242v1 Announce Type: new Abstract: Force-aware Vision-Language-Action (VLA) policies improve contact-rich manipulation, but typically combine task-level motion and contact-dependent adjus
arXiv:2609.18455v1 Announce Type: new Abstract: Ropes, cables, and other deformable linear objects appear in tasks from untangling to cable routing and suturing, yet controlling their shape remains a
arXiv:2609.18107v1 Announce Type: new Abstract: Graph anomaly detection aims to identify graph structures (e.g., nodes, edges, or subgraphs) that deviate significantly from expected patterns, which su
arXiv:2609.17991v1 Announce Type: cross Abstract: Interactive Quantum Classifiers (IQCs) constitute a family of quantum machine learning models inspired by open quantum systems, in which the interacti
arXiv:2609.18766v1 Announce Type: cross Abstract: Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence. Their
arXiv:2609.18068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains such as housing screening. While alignment techniques mitigate explicit
arXiv:2609.18650v1 Announce Type: new Abstract: Learning generalizable robot manipulation policies requires large-scale and diverse interaction data, yet collecting real-world demonstrations remains c