Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a function of actio...
Diffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve sample quality...
Inspired by the emergent behaviors in large language models that generalized human intelligence, the research community is pursuing similar emergent capabilitie...
Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the...
We present LongLive-2.0, an NVFP4-based parallel infrastructure throughout the full training and inference workflow of long video generation, addressing speed a...
Medicine is inherently pluralistic. Principles such as autonomy, beneficence, nonmaleficence, and justice routinely conflict, and such ethical dilemmas often sh...
Diffusion models have been shown to implicitly generate visual content autoregressively in the frequency domain, where low-frequency components are generated ea...
We present PIXLRelight, a feed-forward approach for physically controllable single-image relighting. Existing methods either provide limited lighting control (e...
Egocentric memory is widely used in embodied intelligence, but it may be insufficient for comprehensive spatial-temporal reasoning. Inspired by human recall fro...
Autoregressive video generation has improved rapidly in visual fidelity and interactivity, but it still suffers from long-term inconsistency and memory degradat...
While scaling laws govern aggregate large language model performance, no scaling law has linked factual recall to both model size and training-data composition....
Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene, choose a ...
Post-training has split large language model (LLM) alignment into two largely disconnected tracks. Online reinforcement learning (RL) with verifiable rewards dr...
Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training par...
Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise that invalidate the stationary covari...
Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execut...
Tabular foundation models (TFMs) achieve strong performance on health datasets, but their inference cost and infrastructure requirements limit practical use. We...
Compound AI applications, which compose calls to ML models using a general-purpose programming language like Python, are widely used for a variety of user-facin...
Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration, and main...
Major deployed generative AI advertising systems preserve a visible boundary between commercial content and AI-generated responses. Yet empirical research shows...
As LLM benchmarks saturate, the evaluation community has pursued two strategies to increase difficulty: escalating knowledge demands (GPQA, HLE) or removing kno...
Central to human-aligned AI is understanding the benefits of human-elicited labels over synthetic alternatives. While human soft-labels improve calibration by c...
Coding agents now run autonomously with shell, file, and network privileges. When a user issues a benign request, the agent sometimes does more than asked: it d...
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CFTC Ramps Up AI‑Driven Enforcement in Prediction Markets The Commodity Futures Trading Commission CFTC says it is ramping up efforts to catch insider trading...
In retrieval-augmented coding, failures often begin when the relevant file is absent from the retrieved context. Under frozen generic tokenization, where a BM25...
The brain learns abstract representations of high-dimensional sensory input, but the plasticity rules that enable such learning are unknown. We study biological...
The market for AI notetaking devices has exploded in the U.S., generating over $600 million in revenue last year, according to a Menlo Ventures reporthttps://me...
This work presents a novel variant of the Firefly Algorithm (FA) for data clustering, addressing limitations of traditional methods like K-Means that struggle w...
Vehicular edge computing (VEC) enables latency-sensitive vehicular applications by offloading computation-intensive tasks to nearby edge servers. However, real-...
Multimodal optimization requires finding many optima rather than merely keeping a diverse population. Yet most niching-based evolutionary algorithms rely on dis...
Behavioral studies of LLM-based software engineering agents extract operational rules about which trajectory shapes correlate with higher resolution rates: that...
Software repositories accumulate large amounts of unstructured knowledge in commit messages, pull-request discussions, and issue threads, but developers and AI ...
Real-time magnetic resonance imaging (rtMRI) of speech production enables non-invasive visualization of dynamic vocal-tract motion and is valuable for speech sc...
Apple in iOS 27 will include an enhanced Siri with a dedicated app that gives users options to keep conversations in memory for a limited time, according to Blo...
Collecting large-scale egocentric video datasets with dense spatial and temporal annotations is costly, slow, and often constrained by environmental biases, pri...
Spatial question answering over egocentric video is a challenging task that requires Vision-Language Models (VLMs) to reason about 3D object positions, scene af...
Current approaches to 3D scene graph generation rely on dedicated depth sensors, such as LiDAR or RGB-D cameras, for metric 3D reconstruction. This limits deplo...
While Multi-Modal Large Language Models (MLLMs) demonstrate impressive capabilities in general reasoning, their embodied spatial intelligence remains hampered b...
바이라인 주간 프리미엄 트렌드 리포트 461호가 발간됐습니다. 이번 호 딥다이브에서는 ‘개인정보 유출 후폭풍 맞은 쿠팡, 업계 반사효과 있었나’를 다뤘습니다. 지난 한 주 동안 놓치지 말아야 할 국내·외 IT 이슈들을 분야별 핫토픽과 옐로스토리, 딥다이브에서 확인하세요. !바이라인 리포...
Partnership Overview OpenAI and Dell Technologies are collaborating to help more enterprises deploy Codex in the environments where their most important data,...
!https://media-cldnry.s-nbcnews.com/image/upload/t_focal-758x379,f_auto,q_auto:best/mpx/2704722219/2026_05/f_vert_brk_prayer_rally_260518.00_00_14_14.Still002-2...
Large Language Models (LLMs) demonstrate strong potential for automated code generation, yet their ability to iteratively refine solutions using execution feedb...
Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficult to debu...
Production log analytics in self-hosted, resource-constrained environments requires natural-language access to massive log streams without the cost of routing e...
Deploying adaptive intelligence at the edge remains challenging due to the high computational and energy cost of training neural models. Spiking Neural Networks...
Optimization problems in real-world applications across the medical and engineering domains often involve potential risks when evaluating candidate solutions. S...
!https://cdn.platum.kr/wp-content/uploads/2026/05/unnamed-1-5-735x1024.jpg 프로그램 개요 딥테크 액셀러레이터 블루포인트파트너스가 글로벌 헬스케어 기업 인바디와 함께 헬스케어 및 라이프스타일 분야의 예비·초기 창업자를 발굴한다....
