Search: “reasoning”

arXiv:2604.13356v1 Announce Type: new Abstract: Mechanisms for continued self-improvement of language models without external supervision remain an open challenge. We propose Peer-Predictive Self-Trai

arXiv:2509.20490v4 Announce Type: replace-cross Abstract: Agentic systems offer a potential path to solve complex clinical tasks through collaboration among specialized agents, augmented by tool use a

arXiv:2604.13502v1 Announce Type: new Abstract: Social Determinants of Health (SDOH) refer to environmental, behavioral, and social conditions that influence how individuals live, work, and age. SDOH

VLM Performance：Qwen3.6 is natively multimodal, and Qwen3.6-35B-A3B showcases perception and multimodal reasoning capabilities that far exceed what its size would suggest, with only around 3 billion a

Banger paper from NVIDIA. Agentic reasoning needs models that are not just capable, but efficient at long-context inference. The agent model layer is moving toward open, long-context, high-throughput

arXiv:2603.28325v3 Announce Type: replace-cross Abstract: Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, pr

arXiv:2604.13010v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teach

arXiv:2604.10507v1 Announce Type: new Abstract: Psychological client simulators have emerged as a scalable solution for training and evaluating counselor trainees and psychological LLMs. Yet existing

arXiv:2604.10425v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain rem

arXiv:2604.10470v1 Announce Type: cross Abstract: Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-qu

arXiv:2604.11188v1 Announce Type: cross Abstract: Synthesizing high-quality mathematical reasoning data without human priors remains a significant challenge. Current approaches typically rely on seed

arXiv:2509.22403v2 Announce Type: replace Abstract: Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale an

arXiv:2604.10516v1 Announce Type: new Abstract: Selecting the right knowledge is critical when using large language models (LLMs) to solve domain-specific data analysis tasks. However, most retrieval-

arXiv:2604.10291v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promising performance in time series modeling tasks, but do they truly understand time series data? While multip

arXiv:2604.11661v1 Announce Type: cross Abstract: Large language models (LLMs) have recently gained significant attention as a promising approach to accelerate scientific discovery. However, their app

arXiv:2604.07927v2 Announce Type: replace Abstract: Deep research requires reasoning over web evidence to answer open-ended questions, and it is a core capability for AI agents. Yet many deep research

arXiv:2604.08788v1 Announce Type: new Abstract: Patient-clinician communication is an asymmetric-information problem: patients often do not disclose fears, misconceptions, or practical barriers unless

(thinking traces are always summaries of the internal reasoning, since the actual internal chain of thought is a trade secret, but that doesn't mean they aren't useful when summarized, especially when

arXiv:2604.07814v1 Announce Type: new Abstract: Accurate and interpretable plant disease diagnosis remains a major challenge for vision-language models (VLMs) in real-world agriculture. We introduce A

arXiv:2510.18034v2 Announce Type: replace-cross Abstract: Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution semantic anomalies. While VLMs have emer

arXiv:2510.12184v2 Announce Type: replace Abstract: Recently, efficient Multimodal Large Language Models (MLLMs) have gained significant attention as a solution to their high computational complexity,

arXiv:2604.04344v2 Announce Type: replace Abstract: We establish a computation-substrate-agnostic inference architecture in which domain is an explicit first-class computational parameter. This produc

arXiv:2604.08322v1 Announce Type: new Abstract: Fundus imaging such as CFP, OCT and UWF is crucial for the early detection of retinal anomalies and diseases. Fundus image understanding, due to its kno

I strongly suspect that Claude Mythos is a looped language model, as described in the paper 'Scaling Latent Reasoning via Looped Language Models' from ByteDance The authors of that paper called out gr

arXiv:2604.07148v1 Announce Type: new Abstract: Emerging computation-intensive applications impose stringent latency requirements on resource-constrained mobile devices. Mobile Edge Computing (MEC) ad

arXiv:2604.06375v1 Announce Type: new Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches ofte

arXiv:2610.21393v1 Announce Type: new Abstract: Vehicle Routing Problems (VRPs) are fundamental to many real-world applications. Recent advances in large language models (LLMs) have spurred progress i

arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and inter

arXiv:2610.21895v1 Announce Type: new Abstract: Surgical teams could benefit from assistants that watch the video for requested events, such as 'tell me when the clip applier appears during Calot tria

arXiv:2610.14389v1 Announce Type: new Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86 o

arXiv:2610.17043v1 Announce Type: new Abstract: Recent multimodal deep search agents have advanced tool use and visual content retrieval, yet they still struggle to convert intermediate visual evidenc

Hello! We've been busy at Telperion with a lot of experimental research (more coming soon). Today, we're announcing the release of our latent attention variants of Qwen3.8-27B. Our latent attention va

arXiv:2603.13759v2 Announce Type: replace Abstract: Category-level multi-object trackers estimate trajectories for all detected instances, whereas some applications require propagating only targets sp

arXiv:2610.13695v1 Announce Type: new Abstract: Any automation within reliability-critical contexts such as medicine or law has to be transparent and verifiable. Despite their advantages, current LLM-

arXiv:2610.11171v1 Announce Type: cross Abstract: Long-form video understanding often involves multiple questions about different aspects of the same recording. Yet existing video agents typically pro

arXiv:2610.10782v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for post-training vision-language models (VLMs), but it typically a

arXiv:2610.08829v1 Announce Type: new Abstract: Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than

arXiv:2610.09746v1 Announce Type: new Abstract: Mental imagery, ``seeing with the mind's eye'' is an essential aspect of human cognition. Despite rapid progress Large Language Models (LLMs) and Vision

arXiv:2610.08813v1 Announce Type: new Abstract: X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potenti

arXiv:2610.09418v1 Announce Type: new Abstract: Pointing-gesture visual grounding requires connecting hand geometry with the visual identity and extent of a referred object. A central challenge for co

arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while pr

arXiv:2506.05766v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedi

arXiv:2610.03615v1 Announce Type: new Abstract: Recent advances in frontier models enable robot manipulation from only a few demonstrations, but high inference latency limits their use for real-time r

arXiv:2610.03016v1 Announce Type: cross Abstract: In this paper, we propose a Role-aware Stimulus-guided (RASG) framework for interlocutor emotion recognition, which predicts listener emotions from li

arXiv:2610.00277v1 Announce Type: new Abstract: Federated multimodal graph foundation models (GFMs) aim to adapt pretrained multimodal models to decentralized graph data, where each client owns a priv

arXiv:2610.01076v1 Announce Type: new Abstract: Structured electronic health records (EHRs) contain a patient's clinical trajectory as a sequence of clinical codes. Answering clinical questions from s