All entries

arXiv:2609.21073v1 Announce Type: new Abstract: Behavioral tasks measuring facial affect perception assume that identical stimuli impose equivalent perceptual difficulty across participants. However,

arXiv:2609.21015v1 Announce Type: new Abstract: Improvements to Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) for low-cost autonomous underwater vehicles (AUVs) are critical for tran

arXiv:2609.21621v1 Announce Type: new Abstract: Fine-grained object (FO) manipulation requires robots to distinguish a specified FO from visually similar objects and execute actions reliably despite s

arXiv:2609.20834v1 Announce Type: new Abstract: In the rapidly evolving landscape of cloud-native computing, Organizations are increasingly adopting infrastructure models that emphasize scalability, f

arXiv:2603.16086v2 Announce Type: replace-cross Abstract: While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts o

arXiv:2609.21777v1 Announce Type: new Abstract: This paper presents a novel online coverage path planning (CPP) algorithm, called TRACE, for real-time coverage of unknown environments. TRACE is built

arXiv:2609.21425v1 Announce Type: new Abstract: Zero-shot time-series forecasting (TSF) is often described as forecasting without target-specific parameter updates, but that training-status condition

arXiv:2609.21605v1 Announce Type: new Abstract: Recurrent Transformers increase computational depth through temporal recurrence, feeding each token's high-level hidden state into the computation of th

arXiv:2609.22060v1 Announce Type: new Abstract: Traffic sign recognition (TSR) is an important perception task for autonomous driving and advanced driver-assistance systems, where a system must both l

Training models is becoming easier and easier - just look at this and TRL - especially with agents! You're missing out if you're still using off the shelf models for all your tasks! Introducing Halo,

arXiv:2505.04193v2 Announce Type: replace Abstract: Simplicity is a critical inductive bias for designing data-driven controllers, especially when robustness is important. Despite the impressive resul

arXiv:2603.27922v2 Announce Type: replace Abstract: Procedural knowledge in algorithm design is embedded in source code and rebuilt for each new domain. We introduce Generative Executable Algorithm Kn

arXiv:2510.02259v2 Announce Type: replace Abstract: Computational simulations play a central role in scientific discovery, and machine learning (ML) has emerged as a promising alternative to tradition

arXiv:2609.20833v1 Announce Type: new Abstract: This paper presents the Transsion Speech Team submission to Task 1 of the MLC-SLM 2026 Challenge, which focuses on speaker-attributed transcription for

arXiv:2609.21859v1 Announce Type: new Abstract: Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on

arXiv:2609.21085v1 Announce Type: cross Abstract: Gaussian processes (GPs) provide principled probabilistic predictions while encoding prior knowledge, including equivariances. Yet, their use in large

arXiv:2609.20989v1 Announce Type: cross Abstract: As generative AI is increasingly used as a source of personal financial guidance, understanding how people appraise such advice is important for suppo

NVIDIA Earth‑2 is an AI‑driven data‑assimilation platform that ingests observations from proprietary or third‑party sources—such as satellites, radar, and in‑situ sensors—to generate more frequent wea

arXiv:2607.16894v2 Announce Type: replace Abstract: Many complex systems, including brain networks, financial markets, and gene-regulatory circuits, are better described by interaction structures that

arXiv:2603.03279v2 Announce Type: replace-cross Abstract: Achieving autonomous and versatile whole-body loco-manipulation remains a central barrier to making humanoids practically useful. Yet existing

Governments need to rein in increasingly capable AI agents before their risks are fully understood, a United Nations scientific panel warned in the global organization's first major assessment of Open

I shipped something I've been building for the last few weeks : phantom-kv , a refusal-removal system for large language models that doesn't touch a single weight. Instead of editing the model, it loa

arXiv:2609.20905v1 Announce Type: cross Abstract: In this work, we present an uncertainty-driven training framework for three-dimensional computed tomography (CT) lung nodule classification, where val

arXiv:2609.21744v1 Announce Type: new Abstract: Robot assistance is particularly crucial in unfamiliar tasks, where users must understand task requirements while coordinating with the robot. Previous

arXiv:2505.05145v4 Announce Type: replace-cross Abstract: To perform few-shot learning, language models extract signals from a few input-label pairs, aggregate them into a learned prediction rule, and

arXiv:2609.21450v1 Announce Type: new Abstract: Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains d

arXiv:2512.11881v2 Announce Type: replace-cross Abstract: From the relative scarcity of training data to the lack of standardized benchmarks, the creation of effective foundation models for polymers f

arXiv:2504.06647v4 Announce Type: replace Abstract: Safety-critical autonomous driving motivates the effective use of prior information. For online vectorized HD map construction, temporal predictions

Yash Roy / Bloomberg: US Treasury Secretary Scott Bessent says OpenAI's Hugging Face hacking incident is “the responsibility of the OpenAI management, not a bunch of agents” — Treasury Secretary Scott

arXiv:2609.22067v1 Announce Type: cross Abstract: Users increasingly delegate work to autonomous AI agents, yet evaluations typically measure task completion rather than the values users prioritize. U

arXiv:2609.21323v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong scene understanding and semantic judgment across diverse tasks, but their appropriate role in coo

arXiv:2609.21257v1 Announce Type: cross Abstract: Large language model (LLM) agents can propose, implement, and evaluate model changes. Autoresearch loops demonstrate this capability through minutes-s

arXiv:2609.21225v1 Announce Type: new Abstract: Parametric CAD reconstruction requires recovering both precise geometry and editable modeling operations from visual observations, making it challenging

arXiv:2609.21804v1 Announce Type: new Abstract: Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visua

arXiv:2609.21521v1 Announce Type: cross Abstract: While Video Large Language Models (Video-LLMs) have recently demonstrated strong performance, reliably evaluating their fine-grained video understandi

arXiv:2609.21883v1 Announce Type: new Abstract: Air-ground autonomy becomes harder when the unmanned aerial vehicle (UAV) must remain observable by a gimbal light detection and ranging (LiDAR) mounted

arXiv:2609.20843v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) enables models to answer natural-language questions through structured graph reasoning and has achieved substa

arXiv:2609.21212v1 Announce Type: cross Abstract: Learned navigation policies typically consume observations as a temporally ordered history, with positional encodings tying each observation to when i

arXiv:2609.21729v1 Announce Type: new Abstract: As robots transition from performing repetitive tasks to collaborating with humans, understanding human intent becomes crucial to effective interaction.

arXiv:2609.21246v1 Announce Type: cross Abstract: Vision-language-action (VLA) models map visual observations and natural-language instructions to robotic actions, but distribution shifts can compromi

arXiv:2609.20995v1 Announce Type: cross Abstract: Natural spoken interaction requires more than streaming ASR, language generation, and speech synthesis: a system must react to overlap without canceli

arXiv:2609.21498v1 Announce Type: new Abstract: Recent feed-forward 3D Gaussian Splatting (3DGS) methods typically regress pixel-aligned Gaussian primitives, often causing excessive overlap and artifa

arXiv:2505.10202v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their ex

arXiv:2609.21858v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved state-of-the-art performance across a wide range of tasks, motivating two important aspects of deployment:

We put Grok 4.7 from @SpaceXAI to work on a 2 million insurance claim where one deductible error alone changes the calculation by 143,000. In this Box Agent preview, @grok reconciles the claim against

arXiv:2609.21567v1 Announce Type: cross Abstract: Quantum Signal Processing is a powerful quantum framework for generating and approximating univariate polynomials. However, QSP is often limited by ci