Search: “reasoning”

arXiv:2607.20918v1 Announce Type: new Abstract: Omni-modal models can handle text, images, and audio in one system, but improving all of these abilities together remains difficult. Training a single m

arXiv:2607.19054v2 Announce Type: replace Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the diffe

arXiv:2607.20806v1 Announce Type: new Abstract: Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resour

arXiv:2607.20481v1 Announce Type: new Abstract: Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained rout

arXiv:2602.10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyper

arXiv:2607.20431v1 Announce Type: new Abstract: Materials science literature analysis requires simultaneous attention to composition, processing, characterization, and property relationships, yet conv

arXiv:2607.20558v1 Announce Type: cross Abstract: AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services. Yet their real-world behavior remains poor

arXiv:2607.20510v1 Announce Type: new Abstract: We introduce Telco-GAIA, a bilingual, multi-modal benchmark for evaluating tool-using agents on the data of a real-world telecommunications operator. Te

arXiv:2607.21547v1 Announce Type: new Abstract: The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible. Impli

arXiv:2607.20449v1 Announce Type: cross Abstract: LLMs are trained predominantly on human-authored text, yet the structural and narrative conventions embedded in that text are rarely examined as a sou

arXiv:2607.21071v1 Announce Type: new Abstract: Autonomous biomedical laboratories increasingly rely on visual perception to recognize, localize, and manipulate transparent plasticware, yet high-quali

arXiv:2607.21556v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) is promising as it removes the external teacher required by on-policy distillation (OPD), yet it still needs asymme

arXiv:2607.20883v1 Announce Type: new Abstract: Recent one-step text-to-image (T2I) models enable efficient image synthesis and provide new opportunities for real-time image editing. However, existing

arXiv:2607.19954v1 Announce Type: new Abstract: Detecting media bias automatically is difficult because biased framing is often subtle, yet in domains such as news analysis, accurate predictions alone

arXiv:2607.19429v1 Announce Type: new Abstract: Electroencephalographic (EEG) abnormalities arise from dynamic changes in neural synchrony across spatial and temporal scales, yet many computational ap

arXiv:2607.19827v1 Announce Type: new Abstract: Ensuring safety in Physical AI systems operating in real-world environments is a critical challenge, particularly in hospital wards where vulnerable pat

arXiv:2607.20083v1 Announce Type: cross Abstract: Post-training with evaluator feedback on policy-induced samples serves as a major mechanism for improving large language models. As policies improve,

arXiv:2606.19031v2 Announce Type: replace Abstract: Autonomous mobile service robots are often required to complete tours that require navigating through a set of locations in an environment. Example

arXiv:2607.18716v1 Announce Type: new Abstract: Video multimodal large language models have shown strong capability in video understanding, yet their adaptation to sequentially evolving domains remain

arXiv:2506.04122v4 Announce Type: replace Abstract: Open-loop performance evaluation of 3D multi-object tracking in autonomous driving requires matching criteria that effectively penalize translationa

arXiv:2607.19115v1 Announce Type: new Abstract: Edit-conditioned 3D scene retrieval pairs a reference 3D room with a natural-language modification and retrieves rooms from a corpus that satisfy the ed

arXiv:2607.19528v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have triggered the development of end-to-end MLLMs for autonomous driving. However, the ma

arXiv:2607.19699v1 Announce Type: cross Abstract: The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This study examines

arXiv:2607.19674v1 Announce Type: cross Abstract: Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches t

arXiv:2601.00821v4 Announce Type: replace Abstract: A growing class of conversational-memory systems compresses dialogue history into structured artifacts (extracted facts, decisions, or events) on th

arXiv:2607.19899v1 Announce Type: cross Abstract: Disagreement-triggered escalation can create a structural blind spot in multi-agent arbitration: as base learners improve, they tend to converge, weak

arXiv:2607.19592v1 Announce Type: new Abstract: Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own co

arXiv:2607.19027v1 Announce Type: new Abstract: Zero-shot video moment retrieval aims to overcome the limitations of traditional approaches that require large-scale datasets annotated with text and it

arXiv:2607.19288v1 Announce Type: new Abstract: Test-time scaling offers a promising method to improve the inference performance of Vision-Language Models (VLMs) without additional training. Existing

arXiv:2607.20372v1 Announce Type: new Abstract: Humans distill experience into reusable abstractions, e.g., strategies and cautionary reminders, and apply them to gradually solve problems more effecti

arXiv:2607.18850v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgme

arXiv:2607.20121v1 Announce Type: new Abstract: LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra secur

arXiv:2607.19856v1 Announce Type: cross Abstract: FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task tests wheth

arXiv:2607.20389v1 Announce Type: new Abstract: Video captioning requires fine-grained spatio-temporal understanding of videos, including spatial perception of where objects are located and temporal p

arXiv:2605.05598v2 Announce Type: replace Abstract: The proliferation of large language models (LLMs) in educational settings has paradoxically undermined the cognitive processes they purport to suppo

arXiv:2607.19359v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents that interact across sessions, yet current memory benchmarks primarily evaluate single-hop recall, leaving

arXiv:2607.20061v1 Announce Type: new Abstract: Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision.

arXiv:2607.19701v1 Announce Type: new Abstract: VLMs are increasingly deployed in AD systems, creating an urgent need for rigorous safety evaluation under rare yet safety-critical scenarios. Among the

arXiv:2607.20145v1 Announce Type: cross Abstract: Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed trainin

arXiv:2509.24739v4 Announce Type: replace Abstract: Vision-Language Foundation Models (VLMs), trained on large-scale multimodal datasets, have driven significant advances in Artificial Intelligence (A

arXiv:2607.19595v1 Announce Type: cross Abstract: Large language model (LLM) agents are vulnerable to security risks, such as prompt injection attacks from untrusted context that manipulate downstream

arXiv:2607.19781v1 Announce Type: new Abstract: Autonomous driving requires understanding the road as a graph of drivable lanes and their connectivity, beyond the ego lane alone, to follow routes thro

arXiv:2512.02473v2 Announce Type: replace-cross Abstract: Video world models have attracted significant attention for their ability to produce high-fidelity future visual observations conditioned on p

arXiv:2607.13354v1 Announce Type: new Abstract: Autonomous racing demands planning algorithms that balance vehicle dynamics at the limits of handling with strategic decision-making in competitive mult

arXiv:2607.14044v1 Announce Type: new Abstract: By 2030, 59 of every 100 workers will need reskilling or upskilling, yet the average time to close an enterprise skills gap grew from roughly 3 days in

arXiv:2607.13562v1 Announce Type: new Abstract: Knowing when to say 'I don't know' is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments