Search: “reasoning”

arXiv:2511.00371v2 Announce Type: replace Abstract: In Socratic debugging, instructors guide students towards identifying and fixing a bug on their own, instead of providing the bug fix directly. Most

arXiv:2605.09346v1 Announce Type: cross Abstract: The Chain-of-Thought (CoT) paradigm, while enhancing the interpretability of Large Language Models (LLMs), is constrained by the inefficiencies and ex

arXiv:2605.09544v1 Announce Type: new Abstract: Tool-integrated reasoning has emerged as a promising paradigm for enhancing large language models with external computation, retrieval, and execution ca

arXiv:2605.10373v1 Announce Type: cross Abstract: Large language models have significantly improved natural language interfaces to databases by translating user questions into executable queries. In p

arXiv:2605.08709v1 Announce Type: new Abstract: Unified face attack detection (UAD) requires recognizing physical spoofing and digital forgery within a shared decision space, yet existing discriminati

arXiv:2605.08201v1 Announce Type: cross Abstract: Neurosymbolic systems promise to combine deep neural network's (DNN) processing of raw sensor inputs with few-shot performance of symbolic artificial

arXiv:2605.07137v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a highly effective method for improving the reasoning abilities of Large Language Mod

arXiv:2605.07926v1 Announce Type: new Abstract: As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar wo

arXiv:2601.15050v4 Announce Type: replace Abstract: Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from extit{factual myopia}: they relentlessly emphasize factual accuracy

arXiv:2601.21699v2 Announce Type: replace Abstract: Multi-turn reasoning agents solve complex questions by decomposing them into intermediate retrieval or tool-use steps, for accumulating supporting e

arXiv:2605.08077v1 Announce Type: new Abstract: Knowledge Graph Question Answering (KGQA) has shown promise for grounded and interpretable reasoning, yet existing approaches often fail to provide reli

arXiv:2605.06728v1 Announce Type: cross Abstract: Interpreting transcriptomic data is one of the most common analytical tasks in modern biology. Yet most current models either consume expression profi

arXiv:2510.01290v2 Announce Type: replace Abstract: The long-output context generation of large reasoning models enables extended chain of thought (CoT) but also drives rapid growth of the key-value (

arXiv:2605.07593v1 Announce Type: new Abstract: Real-world audio-visual understanding requires chaining evidence that is sparse, temporally dispersed, and split across the visual and auditory streams,

arXiv:2605.06772v1 Announce Type: new Abstract: As large language models (LLMs) show increasing promise on research-level physics reasoning tasks and agentic AI becomes more common, a practical questi

arXiv:2605.04652v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) reasoning aims to predict future events from historical facts. A key challenge lies in jointly capturing two sources of p

arXiv:2605.05126v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models primarily focus on mapping 2D observations to actions, but exhibit notable limitations in spatiotemporal per

arXiv:2605.04941v1 Announce Type: new Abstract: This paper describes our system submitted to SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We present an ef

arXiv:2604.06022v2 Announce Type: replace Abstract: Incorrect information poses significant challenges by disrupting content veracity and integrity, yet most detection approaches struggle to jointly b

arXiv:2601.03731v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into autonomous agents, evaluating repository-level reasoning, the ability to maintain logical consiste

arXiv:2605.02173v1 Announce Type: new Abstract: We evaluate the long-context retrieval and reasoning capabilities of five frontier large language models with advertised 1M-token context windows on a c

arXiv:2605.03788v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly explored as high-level reasoning engines for cyber-physical systems, yet their application to real-time

SenseNova U1 is an open-source unified multimodal model that integrates understanding, reasoning, and generation within a single architecture, with particular strength in creating complex infographics

arXiv:2605.02745v1 Announce Type: new Abstract: Molecular property models increasingly support high-stakes drug-discovery decisions, but their outputs are often difficult to audit: classical predictor

arXiv:2605.02073v1 Announce Type: new Abstract: Mathematical reasoning is a key benchmark for large language models. Reinforcement learning is a standard post-training mechanism for improving the reas

arXiv:2605.01870v1 Announce Type: new Abstract: Large Language Models (LLMs) have substantially advanced the field of Natural Language Processing (NLP), achieving state-of-the-art performance across a

arXiv:2605.01736v1 Announce Type: new Abstract: Understanding the geometric and semantic structure of environments is essential for embodied navigation and reasoning. Existing semantic mapping methods

arXiv:2501.09775v3 Announce Type: replace Abstract: Multiple Choice Question (MCQ) tests are among the most used methods for evaluating large language models (LLMs). Besides checking the correctness o

arXiv:2510.07731v3 Announce Type: replace-cross Abstract: Organic reaction mechanisms are the stepwise elementary reactions by which reactants form intermediates and products, and are fundamental to u

arXiv:2605.02730v1 Announce Type: new Abstract: Despite the success of Large-Vision Language Models (LVLMs), general optimization objectives (e.g., standard MLE) fail to constrain visual trajectories,

arXiv:2602.13595v2 Announce Type: replace Abstract: Neural scaling laws provide a predictable recipe for AI advancement: reducing numerical precision should linearly improve computational efficiency a

arXiv:2504.15077v5 Announce Type: replace Abstract: Large Language Models (LLMs) can translate natural language into SQL, but small models struggle with multi-table and complex queries in Zero-Shot Le

arXiv:2510.05950v2 Announce Type: replace Abstract: Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and in

arXiv:2603.17837v3 Announce Type: replace-cross Abstract: During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal c

arXiv:2602.06037v4 Announce Type: replace Abstract: Recent progress in spatial reasoning with Multimodal Large Language Models (MLLMs) increasingly leverages geometric priors from 3D encoders. However

arXiv:2510.04978v5 Announce Type: replace Abstract: The rapid advancement of embodied intelligence and world models has intensified efforts to integrate physical laws into AI systems, yet physical per

arXiv:2604.28043v1 Announce Type: new Abstract: We present Collaborative Agent Reasoning Engineering (CARE), a disciplined methodology for engineering Large Language Model (LLM) agents in scientific d

arXiv:2603.09117v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from

arXiv:2601.11908v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with reasoning over long contexts where relevant information is sparsely distributed. Although plan-and-execut

arXiv:2604.27850v1 Announce Type: new Abstract: Task-based dialogue systems assist users in achieving specific goals, such as executing actions or retrieving information, through natural language inte

arXiv:2604.25928v1 Announce Type: new Abstract: Professional domain knowledge underpins human civilization, serving as both the basis for industry entry and the core of complex decision-making and pro

arXiv:2510.06002v3 Announce Type: replace Abstract: In high-stakes legal domains, retrieval must preserve not only semantic relevance, but also the hierarchy, temporality, and causal provenance of leg

arXiv:2510.21828v2 Announce Type: replace-cross Abstract: Understanding and reasoning with abstractive information from the visual modality presents significant challenges for current multi-modal larg

arXiv:2604.25053v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly being explored for mental health applications, recent studies reveal that they can exhibit stigma to

arXiv:2604.24831v1 Announce Type: cross Abstract: Deep Learning methods are becoming prominent in automated software bug detection; however, they lack the global understanding of the given code. Conse

arXiv:2601.21225v2 Announce Type: replace Abstract: Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagge