Knowledge catalogue
arXiv:2610.18410v1 Announce Type: cross Abstract: Dense vector retrieval can miss evidence connected through intermediate entities, while direct graph retrieval requires access to the graph at query t
arXiv:2610.20504v1 Announce Type: new Abstract: Long-horizon precise manipulation produces mixed-quality data for post-training vision-language-action (VLA) models: even successful demonstrations and
arXiv:2610.19549v1 Announce Type: new Abstract: Graph retrieval-augmented generation (GraphRAG) uses document-derived knowledge graphs to assemble evidence for large language model (LLM) answers. Its
arXiv:2610.19996v1 Announce Type: new Abstract: Large language models often produce fluent but factually incorrect output. Gray-box detectors identify such hallucinations from the token-probability st
arXiv:2610.14792v1 Announce Type: new Abstract: Online, same-day delivery requires operators to constantly update their routes, and the routing cost for deliveries increases with the number of custome
arXiv:2610.19100v1 Announce Type: cross Abstract: Constructing robust GUI agents relies on multimodal visual grounding: the ability to precisely map natural language intents to actionable on-screen co
arXiv:2610.19541v1 Announce Type: new Abstract: Image-goal navigation requires a global planner that connects visually specified endpoints while providing observations that a local policy can follow.
arXiv:2610.15350v1 Announce Type: cross Abstract: We present Gugak-VocalSet, the first publicly available audio dataset for Korean traditional singing with dense annotations of sigimsae, a structured
arXiv:2610.16108v1 Announce Type: new Abstract: Maintaining up-to-date clinical practice guidelines (CPGs) is essential for evidence-based medicine, but rapid evidence growth and complex expert consen
arXiv:2610.19654v1 Announce Type: new Abstract: Simulated students let researchers test tutoring systems and teaching strategies before real learners are available, but only if the simulation reproduc
arXiv:2610.15993v1 Announce Type: new Abstract: We study how initial model selection, later handoff, and training targets affect success and cost in LLM tasks that span multiple turns. We compare seve
arXiv:2610.19524v1 Announce Type: new Abstract: Accurate path loss (PL) estimation is essential for wireless network planning and optimization. This paper proposes HankelKANet, a lightweight physics-i
arXiv:2610.15150v1 Announce Type: new Abstract: Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback.
arXiv:2610.20261v1 Announce Type: new Abstract: Deploying a general-purpose model as an agent means running it inside a product-specific runtime harness that decides which actions are executable, how
arXiv:2610.15221v1 Announce Type: new Abstract: AI agents can evolve by using environment feedback to revise their harnesses, the software managing model inputs, tool use, memory, and execution, while
arXiv:2610.15091v1 Announce Type: cross Abstract: Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledge
arXiv:2610.19184v1 Announce Type: new Abstract: Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as te
arXiv:2610.16078v1 Announce Type: new Abstract: Recent advances in self-improving LLM agents have demonstrated the potential of AI systems that drive their own intelligence. However, many domain-speci
arXiv:2610.20209v1 Announce Type: new Abstract: Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement opti
arXiv:2610.15449v2 Announce Type: new Abstract: Despite significant advances in language understanding, Large Language Models (LLMs) can still miss meaning hidden in plain sight, failing to identify h
arXiv:2610.20607v1 Announce Type: cross Abstract: Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-d
arXiv:2610.19518v1 Announce Type: new Abstract: While multi-fingered dexterous robot hands have the potential to provide human-level dexterity, using them for functional manipulation remains difficult
arXiv:2610.19513v1 Announce Type: cross Abstract: Feel-good Thompson sampling (FGTS) augments the posterior of vanilla Thompson sampling with an exploration term that favors reward functions with high
arXiv:2610.20497v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual r
arXiv:2610.14489v1 Announce Type: new Abstract: Reusable wearable PPG representations should capture local pulse morphology and broader physiological regularities. Existing pretraining objectives, how
arXiv:2610.19315v1 Announce Type: new Abstract: Sequential screening policies must use acquired feedback effectively under a fixed experiment budget. We study acquisition-time residual adaptation: a s
arXiv:2610.20121v1 Announce Type: new Abstract: Rapid progress in AI agents and robotics makes it a matter of the near future that humans are served by a team of robots. Such a team will receive sever
arXiv:2610.18230v1 Announce Type: cross Abstract: While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused
arXiv:2610.19502v1 Announce Type: new Abstract: Spatial intelligence requires scalable 3D environments that are both photorealistic and semantically grounded, yet existing indoor datasets are dominate
arXiv:2504.01627v2 Announce Type: replace-cross Abstract: Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces
arXiv:2610.19427v1 Announce Type: new Abstract: Legal reasoning is a fundamental capability for large language models (LLMs) to complete legal AI tasks such as legal judgment prediction. In legal prac
arXiv:2605.05348v2 Announce Type: replace-cross Abstract: Audio description (AD) narrates visual elements in video for blind and low-vision audiences. Recent work has shown that giving novice describe
arXiv:2610.19025v1 Announce Type: new Abstract: Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters. We
arXiv:2610.19064v1 Announce Type: new Abstract: Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of si
arXiv:2610.19033v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) applies every rank-one component of its update to every token, and mixture-of-experts variants replace this with a learned ro
arXiv:2610.18201v1 Announce Type: cross Abstract: Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs) r
arXiv:2610.14772v2 Announce Type: new Abstract: Camera-first agents must act on the visual world as it unfolds. We argue that the central obstacle for future camera-first agents is not recognition abi
arXiv:2610.19185v1 Announce Type: new Abstract: Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them.
arXiv:2610.18435v1 Announce Type: cross Abstract: Referees increasingly use large language models (LLMs), while finance and accounting journals disagree about whether to allow it. We ask which part of
arXiv:2610.20400v1 Announce Type: cross Abstract: Head-related transfer function (HRTF) spatial upsampling from sparse measurements is a fundamental challenge in personalized binaural audio. We formul
arXiv:2610.20351v1 Announce Type: new Abstract: Human demonstrations are today used largely for pretraining vision-language-action models, with little ability to use the same data for target-embodimen
arXiv:2610.14960v1 Announce Type: cross Abstract: Can an autonomous AI agent reverse engineer a binary as well as a skilled human analyst? Do they adopt the same strategy, and can agents scale to real
arXiv:2610.20412v1 Announce Type: new Abstract: To reach top accuracy, current fully supervised instance segmentation methods severely rely on large-scale pixel-wise labeled datasets. They are usually
arXiv:2610.19742v1 Announce Type: new Abstract: Autonomous navigation of planetary rovers is challenged by visually repetitive, featureless terrain. SLAM systems based on 3D Gaussian Splatting (3DGS)
arXiv:2610.20409v1 Announce Type: new Abstract: We develop a self-supervised reinforcement learning algorithm that learns to build towers of up to eight cubes from scratch, without prior data, rewards
arXiv:2610.20375v1 Announce Type: cross Abstract: Given n distributions (hypotheses) mathcal H={H_1,ldots,H_n} and samples from an unknown target distribution P, proper hypothesis selection seeks a di
arXiv:2610.19526v1 Announce Type: new Abstract: Temporal action detection in long untrimmed videos suffers from inaccurate localization and low-quality proposals, especially with sparse annotations an
arXiv:2610.16319v1 Announce Type: new Abstract: Real-world hybrid web tasks need both information gathering and page interaction, yet search-based and operation-based agents are developed and evaluate
arXiv:2405.01259v2 Announce Type: replace Abstract: Argumentation is an important cognitive activity that involves the scrutiny of arguments and counterarguments. When given an argument, it is desirab
arXiv:2601.07871v2 Announce Type: replace-cross Abstract: Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed c
arXiv:2610.19129v1 Announce Type: new Abstract: Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily appl
arXiv:2610.16125v1 Announce Type: new Abstract: An agent can retain successful trajectories without learning which part of an experience should repair a new discrepancy. We formulate passive imitation
arXiv:2610.19803v1 Announce Type: new Abstract: Active domain adaptation researches focus on alleviating the annotation cost, which comprises source data and target data labelling, as well as enhancin
arXiv:2610.15520v1 Announce Type: cross Abstract: We investigate the problem of allocating indivisible chores among agents with arbitrary positive weights and additive nonnegative cost functions. An a
arXiv:2610.19481v1 Announce Type: new Abstract: Wrist-worn inertial measurement units (IMUs) provide a common sensing modality for tasks ranging from activity and gesture recognition to pose estimatio
arXiv:2610.20138v1 Announce Type: new Abstract: Batch black-box optimization is important for expensive systems where many candidates can be evaluated in parallel, yet only a few rounds of evaluation
arXiv:2610.15373v1 Announce Type: cross Abstract: We adapted Nemotron-3-Nano-30B-A3B for eight Indian languages without changing its backbone architecture. We expanded the vocabulary in place from 131
arXiv:2505.12010v5 Announce Type: replace-cross Abstract: In federated learning (FL), a center trains a shared model from updates that clients compute on their private data. Collecting and training on
arXiv:2610.20432v1 Announce Type: new Abstract: Infant fingerprint recognition is hindered by a combination of physiological characteristics, such as small ridge width and spacing, and acquisition cha
arXiv:2610.20341v1 Announce Type: new Abstract: Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predict