Knowledge catalogue
arXiv:2605.28510v1 Announce Type: cross Abstract: Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training example
arXiv:2605.28101v1 Announce Type: cross Abstract: Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive s
arXiv:2605.27610v1 Announce Type: cross Abstract: The rapid growth of scientific publishing has made it increasingly difficult to track how fast-moving areas evolve. Search engines and LLM-based assis
arXiv:2605.28526v1 Announce Type: new Abstract: Masked language modeling has become a standard pretraining objective for training encoder-based language models. In this approach, certain tokens in the
arXiv:2605.27390v1 Announce Type: cross Abstract: Speculative decoding accelerates Large Language Model inference via a draft-then-verify paradigm, yet the output projection layer becomes a bottleneck
arXiv:2605.27929v1 Announce Type: cross Abstract: Active sensing links behavior and learning through an action-perception loop: actions determine the observations used to update internal predictive mo
arXiv:2605.28483v1 Announce Type: new Abstract: Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Manageme
arXiv:2605.28816v1 Announce Type: new Abstract: World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single contr
Managing planned maintenance is a critical part of running a reliable business. But as your cloud footprint grows into hundreds or even thousands of projects, keeping track of every individual update
arXiv:2605.27766v1 Announce Type: new Abstract: LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongsi
arXiv:2605.27799v1 Announce Type: new Abstract: International Classification of Diseases (ICD) is a globally recognized coding system that records diagnostic events during each patient encounter, prov
arXiv:2605.27922v1 Announce Type: new Abstract: LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, perform
arXiv:2605.28308v1 Announce Type: new Abstract: Entity Alignment (EA) is essential for knowledge graph (KG) fusion, but existing benchmarks often allow models to exploit name overlap rather than relat
arXiv:2503.11477v2 Announce Type: replace Abstract: Understanding the factors that trigger or prevent undesirable health outcomes across patient subpopulations is essential for designing targeted inte
arXiv:2605.27758v1 Announce Type: cross Abstract: Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations
arXiv:2605.27646v1 Announce Type: cross Abstract: We propose extbf{Hurwitz Quaternion Multiplicative Quantization (HQMQ)}, a extbf{calibration-free} method for KV cache compression of large language m
arXiv:2605.27374v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) and diffusion models (DMs) have opened new possibilities for AI-generated content. Yet, pers
arXiv:2605.28218v1 Announce Type: new Abstract: Modern translation workflows demand more than semantic equivalence. Users routinely require models to preserve JSON or HTML schemas, honor curated gloss
arXiv:2510.15859v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has driven recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be compute
arXiv:2605.28324v1 Announce Type: new Abstract: In this paper, we propose a novel conditional diffusion-based framework for multivariable time-series solar power forecasting. The proposed method refor
arXiv:2501.01669v4 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) has progressed significantly toward accurately learning the underlying rewards in both discrete and continuous
arXiv:2605.27546v1 Announce Type: new Abstract: Crisis Responders (CRs) rapidly assess thousands of youth SMS conversations each year to identify mental health concerns and guide support. Yet youth di
arXiv:2605.27788v1 Announce Type: cross Abstract: Humans know when to reach for help e.g. 347 imes 28 warrants a calculator while 2+2 does not. Language models do not. Prompt-based approaches can inst
arXiv:2605.28047v1 Announce Type: new Abstract: Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In
arXiv:2605.28617v1 Announce Type: new Abstract: LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime o
arXiv:2605.27734v1 Announce Type: new Abstract: Generative models, from diffusion models to large language models, achieve remarkable performance but at a cost in training data orders of magnitude lar
arXiv:2605.27391v1 Announce Type: cross Abstract: This paper examines whether students are entering the generative AI era with sufficiently strong educational foundations, focusing on the relationship
arXiv:2605.27999v1 Announce Type: cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a set of available human or AI agents. In particular, we focus on the
arXiv:2605.28120v1 Announce Type: cross Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more co
arXiv:2605.27914v1 Announce Type: cross Abstract: Subjective evaluation of LLM behavior -- empathy, restraint, calibrated emotional tone -- is hard. Human inter-rater agreement on such qualities satur
arXiv:2605.28046v1 Announce Type: new Abstract: Existing agent memory systems universally follow what we term a Memory-as-Tool paradigm where a single query triggers one-shot retrieval of flat passage
arXiv:2605.28732v1 Announce Type: cross Abstract: Memory is essential for enabling large language models to support long-horizon reasoning, yet existing memory systems remain unreliable and difficult
arXiv:2605.28604v1 Announce Type: cross Abstract: Identifying key individuals in video scenes is essential for applications such as automated video editing and intelligent surveillance. Current method
arXiv:2605.28591v1 Announce Type: cross Abstract: The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings. Prior work has identified tes
arXiv:2605.28512v1 Announce Type: new Abstract: Self-evolving scientific agents capable of conquering the hard tail of formal mathematics require Compositional Learning Behaviours (CLBs) -- the capaci
arXiv:2605.28717v1 Announce Type: new Abstract: Modern datacenter RDMA is bottlenecked at the network interface, not the wire. A NIC running RoCE or InfiniBand holds per-connection state for every (ap
arXiv:2605.27816v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing p
arXiv:2605.28164v1 Announce Type: cross Abstract: Evolutionary computation offers a variety of tools to solve complex real-world optimization problems. However, research often focuses on smaller, simp
arXiv:2605.28806v1 Announce Type: cross Abstract: Long-term memory is increasingly important for personalized AI agents, yet existing benchmarks and methods remain largely text-centric. Even when imag
arXiv:2510.06974v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns that they may reflect and amplify social biases
arXiv:2605.28058v1 Announce Type: new Abstract: Recent work explored the capabilities of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA) through few-shot prompting, requiring su
arXiv:2601.01616v2 Announce Type: replace Abstract: The textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in i
arXiv:2603.05642v2 Announce Type: replace-cross Abstract: Open-world interactive object search in household environments requires understanding semantic relationships between objects and their surroun
arXiv:2605.28740v1 Announce Type: cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critical. Most e
arXiv:2605.28053v1 Announce Type: new Abstract: Test-time training (TTT) adapts an LLM during generation by reading and updating request-owned state, such as fast weights, low-rank deltas, or streamin
arXiv:2605.28477v1 Announce Type: new Abstract: Self-supervised depth estimation from monocular sequences relies on the joint learning of a depth and a pose network. Despite abundant research done to
arXiv:2605.27399v1 Announce Type: cross Abstract: What looks like acceleration can be a quiet transfer of burden from the present to the future. Attempts to replace human labor with AI systems are oft
arXiv:2605.27921v1 Announce Type: new Abstract: Research on AI-generated text detection has presented a number of approaches to discern human from AI prose, some of which achieving high in-distributio
arXiv:2605.28074v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) mitigates LLM hallucinations but introduces a critical vulnerability: corpus integrity. We present SilentRetrieva
arXiv:2605.28424v1 Announce Type: new Abstract: Equipping large language models with explicit skills has emerged as a promising paradigm for enabling autonomous agents to solve complex tasks. Agent sk
arXiv:2605.28084v1 Announce Type: cross Abstract: Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tas
arXiv:2605.28490v1 Announce Type: cross Abstract: 3D object grounding localizes referred objects in a 3D scene from natural language. Unified instance-centric 3D-LLMs aim to solve grounding together w
arXiv:2605.27393v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligne
arXiv:2605.27686v1 Announce Type: cross Abstract: Transformers process images and videos by flattening space and time into long token sequences. While attention and KV caching preserve past features,
arXiv:2605.28498v1 Announce Type: cross Abstract: Conversational artificial intelligence (AI) provides an efficient and convenient gateway to information access. However, it can cause overreliance whe
arXiv:2605.28647v1 Announce Type: new Abstract: It is well known that LLM guardrails and trained persona dynamics can produce a reality gap: the distance between the world a LLM is permitted or shaped
arXiv:2601.17737v3 Announce Type: replace-cross Abstract: Recent advances in video generation have produced models capable of synthesizing stunning visual content from simple text prompts. However, th
arXiv:2605.28589v1 Announce Type: new Abstract: Several important learning tasks can be formulated as minimizing an entropy-regularized objective over an appropriate space of probability distributions
arXiv:2512.09786v2 Announce Type: replace Abstract: Examples of embedded intelligence include a wide variety of tiny neural networks used on-board wireless sensors and actuators, which are expected to
Today, we're releasing LFM2.5-8B-A1B, a device-optimized model designed to power real-life applications on phones, laptops, PCs, robots, and fast & lightweight server-side use-cases. > 8B MoE, 1.5B ac