Search: “reasoning”

arXiv:2607.00159v1 Announce Type: new Abstract: Knowledge-Based Visual Question Answering (KB-VQA) aims to evaluate whether Visual Language Models (VLMs) can retrieve, ground, and reason over external

arXiv:2606.29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs). However, directly applyi

arXiv:2606.30128v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting improves LLM reasoning, but the source is contested: do the intermediate steps help because they carry useful semantic

arXiv:2606.14202v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthe

arXiv:2606.29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and

arXiv:2606.28166v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly improved the reasoning capability of large language models, reaching expert or e

arXiv:2606.26904v1 Announce Type: cross Abstract: Video reasoning language models implicitly assume that every input frame is equally reliable. This leads to what we term the Blind Trust Problem: unde

arXiv:2606.26671v1 Announce Type: new Abstract: Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold

arXiv:2606.26443v1 Announce Type: cross Abstract: A robot working alongside people must reason about what they have done, in what order, and with what intent. Video carries the spatial layouts, object

arXiv:2606.26935v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning is widely used in language-model agents, but prior work has shown that verbalized CoT is not always faithful and may in

arXiv:2603.10757v2 Announce Type: replace Abstract: When MLLMs fail at Science, Technology, Engineering, and Mathematics (STEM) visual reasoning, a fundamental question arises: is it due to perceptual

arXiv:2510.14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks

arXiv:2606.10921v1 Announce Type: new Abstract: Long-document question answering (QA) requires large language models (LLMs) to reason over evidence scattered across lengthy documents, where answers of

arXiv:2606.08708v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language M

arXiv:2606.07861v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored.

arXiv:2606.08719v1 Announce Type: new Abstract: ''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoni

arXiv:2606.06036v1 Announce Type: new Abstract: Despite recent progress, LLM agents still struggle with reasoning over long interaction histories. While current memory-augmented agents rely on a stati

arXiv:2606.01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specializati

arXiv:2606.00596v1 Announce Type: new Abstract: Large language models have rapidly evolved in multilingual competence and reasoning capacity, enabling their integration into Social Sciences and Humani

arXiv:2605.31460v1 Announce Type: new Abstract: Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a

arXiv:2605.31561v1 Announce Type: new Abstract: Test-time reasoning has become a significant field of study since the introduction of chain-of-thought reasoning in large language models (LLMs). Howeve

arXiv:2605.30284v1 Announce Type: new Abstract: Scientific discovery is an inherently creative and uncertain process, requiring reasoning beyond the recall of known knowledge. While many benchmarks ha

arXiv:2605.29310v1 Announce Type: new Abstract: Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods for

arXiv:2605.27853v1 Announce Type: new Abstract: We present MolLingo, a multi-agent system that emulates the reasoning process of a chemist to automate molecular design. Existing LLM-based approaches e

arXiv:2605.28566v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, yet their standard generation process -- auto-regressive token predict

arXiv:2605.27190v1 Announce Type: cross Abstract: Recent advances in Large Audio-Language Models (LALMs) have made real-time, streaming spoken interaction increasingly practical. In this setting, reas

arXiv:2605.25244v1 Announce Type: new Abstract: Inference time optimization techniques, such as repeated sampling, have significantly advanced the reasoning capabilities of Large Language Models (LLMs

arXiv:2510.23509v2 Announce Type: replace Abstract: Social robot navigation increasingly relies on large language models for reasoning, path planning, and enabling movement in dynamic human spaces. Ho

arXiv:2602.11499v2 Announce Type: replace Abstract: Multimodal Large Language Models have shown promising capabilities in bridging visual and textual reasoning, yet their reasoning capabilities in Ope

arXiv:2605.18820v1 Announce Type: cross Abstract: Superposition allows Transformers to reason in depth, carrying an entire reasoning frontier in parallel through a bounded-depth forward pass instead o

arXiv:2605.18864v1 Announce Type: cross Abstract: Recent studies observe that reinforcement learning with verifiable rewards (RLVR) reliably improves pass@1 on reasoning tasks, yet often fails to yiel

arXiv:2605.19766v1 Announce Type: cross Abstract: An effective healthcare agent must be able to recall and reason over a patient's longitudinal medical history. However, the absence of datasets with r

arXiv:2603.18003v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skel

arXiv:2509.03403v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves final-answer accuracy on reasoning tasks, but it does not reliably improve reas

arXiv:2508.03018v2 Announce Type: replace Abstract: Large Language Reasoning Models have demonstrated remarkable success on static tasks, yet their application to multi-round agentic planning in inter

arXiv:2509.14004v2 Announce Type: replace Abstract: Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicated problems by generating long chain-of-thoughts (C

arXiv:2605.18261v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language model

arXiv:2605.16480v1 Announce Type: cross Abstract: Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular repre

arXiv:2601.13992v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) reasoning empowers Large Language Models (LLMs) with remarkable capabilities but typically requires prohibitive paramet

arXiv:2509.17680v2 Announce Type: replace Abstract: Table question answering (TableQA) is a fundamental task in natural language processing (NLP). The strong reasoning capabilities of large language m

arXiv:2605.15342v1 Announce Type: new Abstract: Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation

arXiv:2605.15239v1 Announce Type: new Abstract: Safety alignment often improves robustness to harmful queries at the cost of reasoning ability, a tradeoff known as the safety tax. A common cause is di

arXiv:2603.21250v2 Announce Type: replace Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former

arXiv:2605.08175v1 Announce Type: cross Abstract: While significant progress has been made in Video Question Answering and cross-modal understanding, causal reasoning about how visual dynamics drive m

arXiv:2605.08787v1 Announce Type: new Abstract: Recent advances in 3D medical vision-language models have enabled joint reasoning over volumetric images and text, showing strong performance in medical

arXiv:2605.08346v1 Announce Type: cross Abstract: Hallucination detection methods for large language models increasingly operate on chain-of-thought reasoning traces, yet it remains unclear whether th