Knowledge catalogue
arXiv:2608.26449v1 Announce Type: new Abstract: Byte-level BPE tokenizers that use the HuggingFace ByteLevel pre-tokenizer inherit GPT-2's word regex, where a word is defined as p{L}+, one or more Uni
arXiv:2608.26239v1 Announce Type: new Abstract: Generative world models provide robots with predictive models of how the world evolves under interaction, with growing potential for simulation, plannin
arXiv:2608.27260v1 Announce Type: new Abstract: LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain co
arXiv:2510.25732v2 Announce Type: replace-cross Abstract: Unlearning in large language models (LLMs) is usually evaluated as whether an 'unlearned' fact can be recovered. We instead ask whether a fact
arXiv:2608.27187v1 Announce Type: new Abstract: Peer effects are difficult to estimate when interaction graphs evolve because pre-assignment network history, dynamic peer exposure, and post-assignment
arXiv:2608.26319v1 Announce Type: new Abstract: The performance of textual neural models often degrades when their inputs are corrupted by noise such as typos, OCR errors, or dropped words. We study t
arXiv:2608.26877v1 Announce Type: new Abstract: Prior analyses by Derezinski and Warmuth established all-size sampling identities, selected-OLS unbiasedness, and inverse moments for ordinary volume sa
arXiv:2608.26895v1 Announce Type: cross Abstract: Agentic recommender systems ground each decision of a large language model (LLM) in a persistent memory of the user, and in existing agents that memor
arXiv:2608.26655v1 Announce Type: new Abstract: Model merging promises to construct a single multi-task model from independently fine-tuned task models without accessing the original task data. This m
arXiv:2608.27176v1 Announce Type: cross Abstract: Understanding spoken dialogue requires joint reasoning over lexical content and paralinguistic acoustic signals such as emotion and conversational int
arXiv:2608.26187v1 Announce Type: cross Abstract: Whether large language models (LLMs) can perform the abductive leap from evidence to a new system of axioms, commonly referred to as a jump, has recen
arXiv:2608.27146v1 Announce Type: new Abstract: Tool-augmented LLM agents must rely on untrusted runtime Observations to complete open-ended tasks; however, when tool outputs no longer merely provide
arXiv:2608.26123v1 Announce Type: new Abstract: Large language models (LLMs) are trained predominantly on English-language internet text that over-represents certain cultural narratives, raising conce
arXiv:2608.26638v1 Announce Type: new Abstract: Across various non-verifiable tasks, human evaluation is reliable but expensive, while automatic metrics are more scalable but often biased. Building on
arXiv:2608.26632v1 Announce Type: new Abstract: Gait recognition has achieved remarkable progress, yet existing methods remain confined to rigid visual matching and often overlook the potential of nat
arXiv:2608.26144v1 Announce Type: new Abstract: Explainable AI (XAI) is now a major theme in NLP; however, Arabic NLP remains under-explained in three connected senses. First, there is a method gap: A
arXiv:2608.26182v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for verbal interaction in social robots, yet prompt design in human-robot interaction (HRI) remains u
arXiv:2608.26974v1 Announce Type: cross Abstract: Kernels measure similarity or correlation in tasks such as regression and classification. The Gaussian kernel, other names of which include squared ex
arXiv:2608.26385v1 Announce Type: cross Abstract: Volume-based accuracy rewards retrieval-augmented generation (RAG) systems for guessing: a system that answers everything outscores one that declines
arXiv:2608.27454v1 Announce Type: new Abstract: Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers
arXiv:2608.27360v1 Announce Type: new Abstract: Speaker anonymization suppresses speaker-identifying attributes from speech while preserving linguistic content and quality. We propose repurposing XTTS
arXiv:2608.26480v1 Announce Type: cross Abstract: Multi-agent large language model systems are widely reported to beat single-model baselines, but the evidence is mixed, and comparisons are usually co
arXiv:2608.26476v1 Announce Type: new Abstract: Zero-shot image restoration methods with text-to-image latent diffusion models have achieved great success in universal image restoration tasks without
arXiv:2507.09993v4 Announce Type: replace Abstract: Camera-based perception in connected and autonomous vehicles remains exposed to physical adversarial attacks. Prior attacks often either optimize im
arXiv:2603.07587v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction, but its quality often degr
arXiv:2608.25956v1 Announce Type: new Abstract: Current world action models (WAMs) typically operate on 2D visual data. These models can achieve exceptional visual quality, but they lack explicit spat
arXiv:2608.25479v1 Announce Type: new Abstract: Generative video models now synthesize footage nearly indistinguishable from reality. Their promise as interactive tools hinges on fine-grained control
arXiv:2512.21852v4 Announce Type: replace Abstract: The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL ob
arXiv:2608.25744v1 Announce Type: new Abstract: Neural operators applied to transient-dynamics PDEs with strong discontinuities exhibit autoregressive instability: in concrete-penetration stress-field
arXiv:2608.25601v1 Announce Type: new Abstract: Multi-camera systems are foundational to modern media production, and multi-camera editing is a critical task. This involves the proper selection of the
arXiv:2411.17629v3 Announce Type: replace Abstract: Motivation: Organic synthesis is fundamental to the chemical industry, particularly in domains such as pharmaceutical development. While artificial
arXiv:2608.25893v1 Announce Type: new Abstract: Foundation models have transformed molecular property prediction, yet it remains unclear whether a molecular foundation model, fine-tuned on a single ca
arXiv:2608.25592v1 Announce Type: cross Abstract: Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulati
arXiv:2606.19660v2 Announce Type: replace-cross Abstract: Prompt injection is ranked as the most critical vulnerability in large language model (LLM) deployments by the OWASP Top 10 for LLM Applicatio
arXiv:2608.24935v1 Announce Type: new Abstract: Accurate identification of early-stage apple fruitlet anatomical structures, including the calyx, fruitlet body, and peduncle, is essential for robotic
arXiv:2608.25388v1 Announce Type: cross Abstract: We describe a systematic approach for spawning and aggregating multi-class cryo-EM reconstruction jobs. This approach formalizes standard ad hoc strat
arXiv:2608.25498v1 Announce Type: cross Abstract: Accurate online traffic prediction is essential for intelligent transportation systems, where forecasting must be performed continuously under imperfe
arXiv:2608.25022v1 Announce Type: new Abstract: As people adopt transformer-based language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such mod
arXiv:2608.26008v1 Announce Type: cross Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks that exploit techniques such as role-playing, obfuscation, code transformation, an
arXiv:2608.25940v1 Announce Type: new Abstract: Physical AI models are evaluated on suites of benchmarks that differ across model reports, leaving the model-by-benchmark matrix sparse and the relation
arXiv:2608.25489v1 Announce Type: cross Abstract: Graph retrieval-augmented generation places retrieved subgraphs into the model's context window at query time, paying a recurring token cost and expos
arXiv:2608.25395v1 Announce Type: new Abstract: Recent vision-language-action models offer a path toward robots with broader repertoires than conventional task-specific systems. Construction deploymen
arXiv:2608.25547v1 Announce Type: new Abstract: To apply the techniques of embodied artificial intelligence to human-oid robots for complex manipulations, dexterous robotic hands are indispensable, wh
arXiv:2608.25643v1 Announce Type: cross Abstract: On-policy distillation (OPD) supervises a student on its own trajectories with token-level signals from a frozen teacher, yet how a sampled loss alloc
arXiv:2608.26095v1 Announce Type: new Abstract: In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from s
arXiv:2608.25315v1 Announce Type: new Abstract: Order-dependent activation statistics are often interpreted as evidence of interaction, but that interpretation can be confounded by where interventions
arXiv:2607.09156v2 Announce Type: replace Abstract: Additive activation steering is calibrated in single-turn chat and then deployed inside agent scaffolds. The quantity usually reported for that move
arXiv:2607.08789v2 Announce Type: replace Abstract: Bayesian and multiplicative-weights updates reweight experts, models, or actions from sequential feedback. We show that the regret of any such updat
arXiv:2608.25524v1 Announce Type: cross Abstract: The Levenberg-Marquardt (LM) algorithm is the most widely used method for solving nonlinear least-squares problems, as it combines the robustness of s
arXiv:2608.25513v1 Announce Type: cross Abstract: Random feature methods provide a scalable approximation to kernel ridge regression (KRR), but the regularization parameter that yields the oracle lear
arXiv:2608.25379v1 Announce Type: new Abstract: Chain-of-thought prompting can expose and amplify demographic stereotypes within an LLM's intermediate reasoning and create a failure mode that final-an
arXiv:2608.25412v1 Announce Type: new Abstract: Multi-vector representations have emerged as an effective paradigm for multimodal retrieval, representing each sample with multiple complementary embedd
arXiv:2608.25559v1 Announce Type: new Abstract: Video deep research answers complex questions by jointly understanding video content and retrieving external knowledge from the open Web. However, diver
arXiv:2604.05876v2 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) in real-world dynamic environments raises the challenge of updating their pre-trained knowledge. While existi
arXiv:2312.10089v2 Announce Type: replace Abstract: Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visua
arXiv:2608.25610v1 Announce Type: new Abstract: Robot policies are predominantly learned with classical parametric variants of imitation learning or RL, where training stores the agent's behavior excl
arXiv:2608.25681v1 Announce Type: new Abstract: Predictive models are widely used in many fields, but are vulnerable to false data injection attacks. To address this, detection schemes and adversarial
arXiv:2608.25477v1 Announce Type: cross Abstract: Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is a promising 6G paradigm, but dynamic multi-UAV ISAC control must
arXiv:2608.24954v1 Announce Type: new Abstract: Large language models (LLMs) hallucinate numerical values when generating high-stakes meteorological text, posing risks for weather communication. We pr
arXiv:2608.26093v1 Announce Type: new Abstract: Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe