Search: “persistence”

arXiv:2608.23650v1 Announce Type: new Abstract: The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-in

arXiv:2608.24764v1 Announce Type: new Abstract: Large language model agents are moving beyond conventional retrieval-augmented generation toward direct interaction with external corpora. Direct Corpus

arXiv:2608.21676v1 Announce Type: new Abstract: Robotic systems are traditionally designed and deployed in static configurations, with assumptions made at design-time becoming immutable constraints du

arXiv:2608.21867v1 Announce Type: new Abstract: LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a core capability for terminal, software-engineering,

arXiv:2603.02229v2 Announce Type: replace-cross Abstract: Safety post-training has been studied extensively in single-step 'chat' settings where safety typically refers to refusing harmful requests. W

arXiv:2608.12476v1 Announce Type: new Abstract: Long-term agent memory is usually treated as select--store--retrieve, but retrieval does not decide whether contradictory, superseded, retracted, delete

arXiv:2608.08075v1 Announce Type: cross Abstract: Most video-retrieval systems assume a bounded corpus and return ranked files or timestamps. Agents operating over cameras, screens, streams, and archi

arXiv:2608.05884v1 Announce Type: cross Abstract: Existing guidance identifies excessive agency, excessive permission, weak task-bound authorization, and inadequate agent controls as important risks.

arXiv:2608.02560v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) imposes a prefill cost proportional to retrieved context length, and -- with Transformer backbones -- a KV-cache th

arXiv:2607.29167v1 Announce Type: cross Abstract: Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persisten

arXiv:2607.28185v1 Announce Type: new Abstract: Oversmoothing is a fundamental limitation of deep graph neural networks (GNNs), where repeated message passing causes node representations to become inc

arXiv:2606.29857v1 Announce Type: new Abstract: Chatbots have become increasingly prevalent across various domains, offering automated assistance in many areas, especially mental health support. The t

arXiv:2508.19094v3 Announce Type: replace Abstract: Event cameras are a bio-inspired class of sensors that asynchronously measure per-pixel intensity changes. Under fixed illumination conditions in st

arXiv:2606.07555v1 Announce Type: cross Abstract: Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways. When this works, the

arXiv:2606.08671v1 Announce Type: new Abstract: Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually

arXiv:2606.07720v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Contin

arXiv:2606.06635v1 Announce Type: cross Abstract: Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize thes

arXiv:2606.00832v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmark

arXiv:2603.03291v2 Announce Type: replace-cross Abstract: Reward Models (RMs) are crucial for online alignment of language models (LMs) with human preferences. However, RM-based preference-tuning is v

arXiv:2605.31042v1 Announce Type: cross Abstract: LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and wr

arXiv:2605.29380v1 Announce Type: cross Abstract: Mainstream strategies for finetuning pretrained multimodal models often degrade out-of-distribution (OOD) robustness, a phenomenon known as catastroph

arXiv:2605.27787v1 Announce Type: cross Abstract: Multi-agent systems (MAS) have substantially advanced autonomous software engineering (SWE), but their growing inference energy demands raise sustaina

arXiv:2605.22975v1 Announce Type: new Abstract: We ask whether large language models (LLMs) treat queries about religious conversion symmetrically. The answer is no. When asked for advice on hypotheti

arXiv:2605.19338v1 Announce Type: cross Abstract: Frontier AI models and multi-agent systems have led to significant improvements in mathematical reasoning. However, for problems requiring extended, l

arXiv:2605.12838v1 Announce Type: new Abstract: Tracking an interpretable emotional arc of a conversation via the sentiment of individual utterances processed as a whole is central to both understandi

arXiv:2605.10723v1 Announce Type: cross Abstract: Generating long-horizon music videos (MVs) is frequently constrained by prohibitive computational costs and difficulty maintaining cross-shot consiste

arXiv:2605.09513v1 Announce Type: new Abstract: Tracking points in videos is typically formulated as frame-to-frame correspondence, where each point is matched locally to the next frame. While this wo

arXiv:2605.03163v1 Announce Type: new Abstract: Scientific time series often encode predictive geometric structure, including connectivity, cycles, shell-like geometry, directional changes, and nonlin

arXiv:2604.22964v1 Announce Type: new Abstract: Anemia affects over one billion people globally and remains severely under-diagnosed in low-resource regions where laboratory blood tests are inaccessib

arXiv:2602.21750v2 Announce Type: replace Abstract: Protein language models (PLMs) have become widely adopted as general-purpose models, demonstrating strong performance in protein engineering and de

arXiv:2604.20911v1 Announce Type: cross Abstract: LLM agents deployed in production operate under operator-defined behavioral policies (system-prompt instructions such as prohibitions on credential di

arXiv:2604.18022v1 Announce Type: cross Abstract: The inverse Potts problem for estimating evolutionary single-site fields and pairwise couplings in homologous protein sequences from their single-site

arXiv:2505.16176v2 Announce Type: replace Abstract: In mathematical reasoning, data selection strategies predominantly rely on static, externally defined metrics, which fail to adapt to the evolving c

arXiv:2604.12016v1 Announce Type: new Abstract: Large language models map semantically related prompts to similar internal representations -- a phenomenon interpretable as attractor-like dynamics. We

arXiv:2604.11302v1 Announce Type: cross Abstract: We present 3D-Anchored Lookahead Planning (3D-ALP), a System 2 reasoning engine for robotic manipulation that combines Monte Carlo Tree Search (MCTS)

arXiv:2604.10096v1 Announce Type: new Abstract: Current embodied intelligent systems still face a substantial gap between high-level reasoning and low-level physical execution in open-world environmen

arXiv:2604.09588v1 Announce Type: new Abstract: Modern AI agents suffer from a fundamental identity problem: when context windows overflow and conversation histories are summarized, agents experience

arXiv:2610.12118v1 Announce Type: new Abstract: Automated methods have been proposed to overcome the limitations of human verbal deception detection, but evidence remains fragmented across disciplines

arXiv:2610.05207v1 Announce Type: new Abstract: Feed-forward 3D models provide strong multi-view geometric priors, while on- line simultaneous localization and mapping (SLAM) relies mainly on local me

arXiv:2610.02162v1 Announce Type: new Abstract: How can a world model continuously observe regions beyond the actor's current view? Video world models simulate how an environment evolves from an agent

arXiv:2609.35402v1 Announce Type: new Abstract: When pricing agents meet repeatedly on a platform, the platform decides who faces whom. We ask whether that choice moves the prices the agents learn, an

arXiv:2609.26147v1 Announce Type: new Abstract: Modern LLMs are deployed as families of post-trained variants (base, instruct, chat, code) derived from a shared set of pre-trained weights. We present

arXiv:2609.22854v1 Announce Type: cross Abstract: Vision-language-action models often predict actions from only the current observation, which can leave tasks involving object occlusion or visually id

arXiv:2609.22195v1 Announce Type: new Abstract: Large language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals

arXiv:2609.21713v1 Announce Type: cross Abstract: Edge AI accelerators are increasingly deployed in safety-critical environments, where model outputs may control physical actuators, make access-contro

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