Search: “reasoning”

arXiv:2606.28164v1 Announce Type: new Abstract: Echocardiography is the most widely used non-invasive cardiac imaging modality, providing essential information for cardiovascular diagnosis. Interpreti

arXiv:2603.09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they

arXiv:2606.27981v1 Announce Type: new Abstract: We introduce a new, contextual, multilingual dataset called ToxiREX: Toxic REasoning in ConteXt. The dataset consists of threads of Reddit comments and

arXiv:2606.26530v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC;~itealp{chollet2019measure}) contains tasks that require summarizing patterns from limited grid samples and

arXiv:2606.26875v1 Announce Type: cross Abstract: Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling an

arXiv:2509.01412v3 Announce Type: replace Abstract: Large language models (LLMs) show strong reasoning via chain-of-thought (CoT) prompting, but the process is opaque, which makes verification, debugg

arXiv:2606.18974v2 Announce Type: replace Abstract: Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an

arXiv:2606.25524v1 Announce Type: cross Abstract: Large language models (LLMs) reach high accuracy in mathematical reasoning, but individual traces on the same problem diverge; some arrive at the corr

arXiv:2606.25325v1 Announce Type: new Abstract: We find that current emotion-oriented Omni-MLLMs still lack reliable omni-modal perception: they (i) underutilize multimodal cues in their reasoning tra

arXiv:2606.25178v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematic

arXiv:2606.24281v1 Announce Type: cross Abstract: Reasoning language models are increasingly asked not only to answer difficult questions, but also to estimate their likelihood of success. Existing me

arXiv:2606.24539v1 Announce Type: new Abstract: Pointing-based visual grounding requires models to precisely locate target objects by deciphering complex spatial relationships between the visual scene

arXiv:2606.24279v1 Announce Type: new Abstract: In Description Logics (DLs), reasoning under Rational Closure (RC) is a well-known and widely accepted non-monotonic formalism to handle defeasible know

arXiv:2606.24124v1 Announce Type: new Abstract: Multi-step reasoning with Chain-of-Thought (CoT) prompting remains fragile: logical errors or hallucinations in early steps silently propagate, producin

arXiv:2606.20599v1 Announce Type: cross Abstract: Tree of Thought (ToT) search has become a promising direction for improving the reasoning capabilities of large language models, but deploying these m

arXiv:2606.14470v2 Announce Type: replace-cross Abstract: Large language model reasoning leaves no trace once it is done. The steps of a chain of thought disappear when the context window closes, a pr

arXiv:2606.21734v1 Announce Type: new Abstract: Understanding long videos requires fine-grained perception and multi-step, higher-order reasoning over complex, long-range spatio-temporal dynamics. Vis

arXiv:2606.21119v1 Announce Type: new Abstract: Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality

arXiv:2606.21830v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has driven rapid progress in mathematical and code reasoning, but when extended to science, existi

arXiv:2606.22938v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated that reinforcement fine-tuning of pretrained base models can lead to significant gains

arXiv:2606.20905v1 Announce Type: new Abstract: Robots operating in open-world environments must seamlessly integrate localization, spatial reasoning, navigation, and long-horizon planning. While spec

arXiv:2606.12065v1 Announce Type: new Abstract: Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primar

arXiv:2606.11745v1 Announce Type: cross Abstract: Visual causal reasoning is essential for understanding and intervening in the physical world, requiring identification of causal variables from visual

arXiv:2509.14860v2 Announce Type: replace-cross Abstract: Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fi

arXiv:2606.11609v1 Announce Type: new Abstract: Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorica

arXiv:2510.04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-tho

arXiv:2606.10254v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved near-perfect performance in solving high-school mathematics, their ability to evaluate the diverse reas

arXiv:2606.09937v1 Announce Type: cross Abstract: We introduce RKSC (Reasoning-Aware KV Cache Sharing), a training-free inference framework that eliminates two structural redundancies in multi-branch

arXiv:2606.09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is veri

arXiv:2512.11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in

arXiv:2601.03093v2 Announce Type: replace Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (

arXiv:2606.09092v1 Announce Type: new Abstract: Theory of Mind (ToM) is a must-acquire skill for modern foundation model systems to operate effectively and safely in the real world. Recent works have

arXiv:2602.21172v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures. However,

arXiv:2606.08234v1 Announce Type: new Abstract: LLM-based scientific agents have shown strong capacity for autonomous research, yet their safety layers remain structurally divorced from core reasoning

arXiv:2606.08992v1 Announce Type: cross Abstract: Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in ord

arXiv:2606.07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the mod

arXiv:2606.06840v1 Announce Type: cross Abstract: Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevan

arXiv:2606.07190v1 Announce Type: new Abstract: Reasoning prefixes shape the future trajectory of LLM problem solving, yet existing process reward models usually evaluate them through local step corre

arXiv:2602.11201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) explanations are widely used to interpret how language models solve complex problems, yet it remains unclear whether these st

arXiv:2606.07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and

arXiv:2606.07436v1 Announce Type: new Abstract: This paper explores agentic 3D spatial understanding, i.e., MLLM agents performing 3D reasoning through tool use. Existing methods often misuse tools an

arXiv:2606.05382v1 Announce Type: new Abstract: Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables

arXiv:2606.05533v1 Announce Type: cross Abstract: Existing robot planning systems rely on appearance-based reasoning, where visual observations are encoded into latent spaces organized around object a

arXiv:2510.13272v3 Announce Type: replace Abstract: Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for domains like math and code, recent work has begun

arXiv:2606.04223v1 Announce Type: new Abstract: Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation. We argue th

arXiv:2606.04474v1 Announce Type: new Abstract: Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this modality gap is not a uniform cognit