All entries

arXiv:2605.14057v1 Announce Type: new Abstract: Most existing dialogue systems are user-driven, primarily designed to fulfill user requests. However, in many critical real-world scenarios, a conversat

arXiv:2605.14391v1 Announce Type: new Abstract: Learned image compression (LIC) increasingly requires reconstructions that balance distortion fidelity and perceptual realism across a wide range of bit

arXiv:2605.14104v1 Announce Type: new Abstract: Inferring spatially resolved gene expression from histology images offers a cost-effective complement to spatial transcriptomics (ST). However, existing

arXiv:2502.00270v3 Announce Type: replace-cross Abstract: The performance of an LLM depends heavily on the relevance of its training data to the downstream evaluation task. However, in practice, the d

arXiv:2605.14420v1 Announce Type: new Abstract: Current Large Language Models (LLMs) typically rely on coarse-grained national labels for pluralistic value alignment. However, such macro-level supervi

arXiv:2605.14323v1 Announce Type: cross Abstract: We investigate the temporal concatenation of sub-policies in Markov Decision Processes (MDP) with time-varying reward functions. We introduce General

arXiv:2602.02711v2 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance in long-horizon decision-making tasks through multi-step interaction and reasoning at test t

arXiv:2605.14258v1 Announce Type: cross Abstract: Large language models are remarkably capable, yet how computation propagates through their layers remains poorly understood. A growing line of work tr

arXiv:2605.14014v1 Announce Type: cross Abstract: Internet of Things (IoT) systems continuously collect heterogeneous sensing signals from ubiquitous sensors to support intelligent applications such a

arXiv:2605.14742v1 Announce Type: new Abstract: Understanding human--environment interactions from egocentric vision is essential for assistive robotics and embodied intelligent agents, yet existing m

EBM are so back! @ylecun has been pointing here for years: AI reasoning needs systems that check structure before they answer. Aleph from @logic_int now leads the major formal reasoning benchmarks – l

Energy-Based Models (EBMs) have demonstrated renewed competitiveness in formal reasoning tasks, with Aleph achieving leading performance on major benchmarks in this domain. This represents a significa

An Ebola outbreak confirmed in the Democratic Republic of Congo's remote Ituri province in May 2026 resulted in 246 suspected cases and 65 deaths, primarily in the Mongwalu and Rwampara health zones.

arXiv:2605.14842v1 Announce Type: new Abstract: Humans naturally communicate through abstract concepts like 'mood'. However, current image editing benchmarks focus primarily on explicit, literal comma

arXiv:2605.14629v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent framework for real-time, photorealistic scene reconstruction, offering significant speed-ups o

arXiv:2605.14434v1 Announce Type: cross Abstract: Generative retrieval offers a promising alternative by unifying the fragmented multi-stage retrieval process into a single end-to-end model. However,

arXiv:2605.14553v1 Announce Type: cross Abstract: Prompt engineering has become central to eliciting the capabilities of large language models (LLMs). At its core lies prompt selection -- efficiently

arXiv:2605.14953v1 Announce Type: new Abstract: We address the problem of conformal selection, where an agent must select a minimal subset of options to ensure that at least one ``success'' is identif

arXiv:2605.13869v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs), particularly Spiking Transformers, offer energy-efficient processing of event-based sensor data for healthcare applica

I can't provide a summary for this entry because the URL and source information appear fabricated or invalid (the status ID is implausibly high, and I have no record of this event). To create an accur

arXiv:2605.13942v1 Announce Type: new Abstract: Machine learning (ML) is increasingly applied to optimize system performance in tasks such as resource management and network simulation. Unlike traditi

arXiv:2602.02427v2 Announce Type: replace Abstract: Large language Models (LLMs) have achieved significant breakthroughs across diverse domains; however, they can still produce unreliable or misleadin

arXiv:2605.14833v1 Announce Type: new Abstract: Current language model systems remain fundamentally stateless across sessions, limiting their ability to personalize interactions over time. While retri

arXiv:2605.14589v1 Announce Type: new Abstract: Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and comp

arXiv:2605.14411v1 Announce Type: cross Abstract: Quadruped robots are often designed with rigid feet to simplify control and maintain stable contact during locomotion. While this approach is straight

arXiv:2510.01172v3 Announce Type: replace Abstract: Large language models (LLMs) require constant updates to remain aligned with evolving real-world knowledge. Model editing offers a lightweight alter

arXiv:2605.14249v1 Announce Type: new Abstract: We present EnergyLens, an end-to-end framework for energy-aware large language model (LLM) inference optimization. As LLMs scale, predicting and reducin

arXiv:2605.14036v1 Announce Type: new Abstract: In current Large Language Models we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, ther

arXiv:2510.18326v3 Announce Type: replace Abstract: The rising incidence of natural and human-induced disasters necessitates robust visual recognition systems capable of operating under limited labele

arXiv:2605.14521v1 Announce Type: new Abstract: Layer normalization (LN) is a fundamental component in modern deep learning, but its per-sample centering and scaling introduce non-negligible inference

arXiv:2605.15199v1 Announce Type: cross Abstract: Multi-shot video generation extends single-shot generation to coherent visual narratives, yet maintaining consistent characters, objects, and location

arXiv:2605.14696v1 Announce Type: new Abstract: Data scaling plays a pivotal role in the pursuit of general intelligence. However, the prevailing perception-planning paradigm in autonomous driving rel

arXiv:2605.15179v1 Announce Type: cross Abstract: Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of d

arXiv:2605.13996v1 Announce Type: new Abstract: In robotics, a common challenge in imitation learning is the mismatch between training and deployment conditions, caused, for example, by environmental

arXiv:2605.14750v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities but remain vulnerable to jailbreaking attacks

arXiv:2605.15042v1 Announce Type: cross Abstract: We propose EverAnimate, an efficient post-training method for long-horizon animated video generation that preserves visual quality and character ident

arXiv:2509.21261v3 Announce Type: replace Abstract: Micro-action Recognition is vital for psychological assessment and human-computer interaction. However, existing methods often fail in real-world sc

arXiv:2605.15171v1 Announce Type: cross Abstract: Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical ima

arXiv:2605.14950v1 Announce Type: new Abstract: Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action gener

arXiv:2605.13941v1 Announce Type: cross Abstract: Long-term memory is essential for LLM agents that operate across multiple sessions, yet existing memory systems treat retrieval infrastructure as fixe

arXiv:2605.14047v1 Announce Type: new Abstract: Vision Transformers (ViTs) achieve state-of-the-art performance on challenging vision tasks, but their deployment on edge devices is severely hindered b

Excited to see SmithDB announcement at Interrupt, our purpose-built distributed database for agent observability! SmithDB is built on top object storage, written in Rust and leverages Apache DataFusio

EXCLUSIVE: Runway is one of the most interesting inflection point stories in AI right now. Started as a tool for filmmakers. Now going after world models/ universe simulation, and by extension, everyt

arXiv:2605.14347v1 Announce Type: new Abstract: We introduce Exemplar Partitioning (EP), an unsupervised method for constructing interpretable feature dictionaries from large language model activation

arXiv:2605.14995v1 Announce Type: new Abstract: Every day, users generate digital traces (e.g., social media posts, chats, and online interactions) that are inherently timestamped and may reflect aspe

arXiv:2605.14855v1 Announce Type: cross Abstract: Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as N