Search: “reasoning”

arXiv:2602.13294v3 Announce Type: replace Abstract: Evaluating whether Multimodal Large Language Models (MLLMs) genuinely reason about physical dynamics remains challenging. Most existing benchmarks r

arXiv:2605.20946v1 Announce Type: new Abstract: The thinking-while-speaking paradigm aims to make AI communication more human. A key challenge is maintaining fluent speech while performing deep reason

arXiv:2605.21299v1 Announce Type: new Abstract: Humans effortlessly go beyond literal meanings: If you mow the lawn, I will give you fifty dollars, is typically understood as implying that the speaker

arXiv:2605.20364v1 Announce Type: new Abstract: Automatic evaluation of long-form literary writing remains challenging, as generic LLM-as-Judge approaches may not fully capture creativity-related dime

arXiv:2605.19418v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have demonstrated strong reasoning and decision-making capabilities that consistently surpass those of single LLM ag

arXiv:2605.19092v1 Announce Type: cross Abstract: Reasoning systems increasingly separate intermediate computation into private and public channels, creating evaluation cases that look similar in tran

arXiv:2605.19506v1 Announce Type: new Abstract: First-person dynamic spatial reasoning requires models to track continuous motion and precise geometric structure, but the quadratic attention cost of T

arXiv:2605.20128v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into high-stakes decision-making. Inspired by the theory of inattentional blindness in human co

This video from the World Science Festival, shared by cognitive scientist Gary Marcus, examines the gap between AI systems' claimed reasoning capabilities and their actual mechanisms, likely arguing t

arXiv:2604.07035v2 Announce Type: replace Abstract: Open reasoning language models are often compared under mixed sample sizes, partially standardized prompts, and accuracy-centered summaries, which m

arXiv:2605.18734v1 Announce Type: new Abstract: Egocentric memory is widely used in embodied intelligence, but it may be insufficient for comprehensive spatial-temporal reasoning. Inspired by human re

arXiv:2605.16302v1 Announce Type: cross Abstract: Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on sparse terminal rewards, leading to poor credit assi

arXiv:2605.18162v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile: models that answer an original input correctly

arXiv:2605.17537v1 Announce Type: new Abstract: 3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reaso

arXiv:2603.01683v2 Announce Type: replace-cross Abstract: Injecting new reasoning knowledge into Large Language Models (LLMs) via post-training often induces catastrophic forgetting. Recent studies em

arXiv:2605.17113v1 Announce Type: cross Abstract: Existing deception datasets label completed outputs as honest or deceptive, treating deception as a property of the final response rather than a funct

arXiv:2602.17831v2 Announce Type: replace Abstract: Evaluating the reasoning capabilities of Large Language Models is increasingly challenging as models improve. Human curation of hard questions is hi

arXiv:2510.18814v3 Announce Type: replace-cross Abstract: Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training? We show

arXiv:2602.19069v2 Announce Type: replace Abstract: Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs

arXiv:2605.16080v1 Announce Type: new Abstract: The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting the need for efficient, generalizable image forgery

arXiv:2602.04265v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for enhancing reasoning in Large Language Models (LL

Energy-Based Models (EBMs) have demonstrated renewed competitiveness in formal reasoning tasks, with Aleph achieving leading performance on major benchmarks in this domain. This represents a significa

arXiv:2602.13770v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong semantic reasoning across multimodal domains. However, their integration with graph-base

arXiv:2605.13872v1 Announce Type: cross Abstract: This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is operationalized as a hormonal

arXiv:2605.14704v1 Announce Type: cross Abstract: In real-world scenes, target objects may reside in regions that are not visible. While humans can often infer the locations of occluded objects from c

arXiv:2512.10931v3 Announce Type: replace Abstract: Many state-of-the-art LLMs are trained to think before giving their answer. Reasoning can greatly improve language model capabilities, but it also m

arXiv:2510.08992v3 Announce Type: replace Abstract: While researchers have made significant progress in enabling large language models (LLMs) to perform multi-step planning, LLMs struggle to ensure th

arXiv:2605.13632v1 Announce Type: cross Abstract: In this paper, we propose GTA-VLA(Guide, Think, Act), an interactive Vision-Language-Action (VLA) framework that enables spatially steerable embodied

arXiv:2605.12975v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on m

arXiv:2605.12518v1 Announce Type: cross Abstract: The proliferation of online news poses a challenge to extracting structured timelines from unstructured content. While recent studies have shown that

arXiv:2605.13530v1 Announce Type: cross Abstract: Surgical scene understanding is a cornerstone of computer-assisted intervention. While recent advances, particularly in surgical image segmentation, h

arXiv:2605.11304v1 Announce Type: new Abstract: Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-

arXiv:2605.11019v1 Announce Type: new Abstract: Although large language models rely on chain-of-thought for complex reasoning, the overthinking phenomenon severely degrades inference efficiency. Exist

arXiv:2605.12374v1 Announce Type: new Abstract: Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools

arXiv:2605.11556v1 Announce Type: cross Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit chain-of-thought (CoT) rationa

arXiv:2602.02408v4 Announce Type: replace Abstract: Model editing aims to correct errors in large, pretrained models without altering unrelated behaviors. While some recent works have edited vision-la

arXiv:2605.09675v1 Announce Type: new Abstract: Clinical reasoning agents based on large language models (LLMs) aim to automate tasks such as intensive care unit (ICU) monitoring and patient state tra

arXiv:2605.08755v1 Announce Type: new Abstract: Large reasoning models (LRMs) reach competition-level math and coding accuracy via long autoregressive decoding, making per-token decoding cost a primar

arXiv:2605.10268v1 Announce Type: cross Abstract: To tackle long-context reasoning tasks without the quadratic complexity of standard attention mechanisms, approaches based on agent memory have emerge

arXiv:2605.08876v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents that execute tool-augmented, multi-step tasks, where latency is a critical f

arXiv:2504.12334v2 Announce Type: replace Abstract: Large language models (LLMs) face significant challenges in specialized biomedical tasks due to the inherent complexity of medical reasoning and the

arXiv:2505.20381v4 Announce Type: replace Abstract: Referring Multi-Object Tracking (RMOT) aims to track targets specified by language instructions. However, existing RMOT paradigms heavily rely on ex

arXiv:2605.09614v1 Announce Type: new Abstract: Long chain-of-thought (CoT) reasoning improves large vision--language models, but visual information often fades during generation, limiting long-horizo

arXiv:2605.09008v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting symbolized a huge improvement of reasoning capabilities of Large Language Models (LLMs). However, scaling up test-tim

arXiv:2605.06241v2 Announce Type: replace Abstract: Reinforcement learning has become the standard for improving reasoning in large language models, yet evidence increasingly suggests that RL does not

arXiv:2605.09266v1 Announce Type: new Abstract: We introduce SeePhys Pro, a fine-grained modality transfer benchmark that studies whether models preserve the same reasoning capability when critical in