Search: “reasoning”

arXiv:2606.07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspo

arXiv:2601.10930v3 Announce Type: replace Abstract: A key challenge in contact-rich dexterous manipulation is the need to jointly reason over global geometry and nonsmooth contact dynamics. End-to-end

arXiv:2606.05932v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) improves reasoning even when the reward signal is spurious -- assigning credit to the group-plural

arXiv:2606.06448v1 Announce Type: new Abstract: LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale re

arXiv:2603.16475v2 Announce Type: replace Abstract: In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubrics, checklists, or verification queries -- before

arXiv:2606.06223v1 Announce Type: new Abstract: Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal mode

arXiv:2606.05263v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards improves reasoning and tool use, yet long-horizon language agents still learn unsupported evidence chai

arXiv:2606.05806v1 Announce Type: new Abstract: Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We intr

arXiv:2606.05778v1 Announce Type: new Abstract: Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks th

arXiv:2504.10823v4 Announce Type: replace Abstract: Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been li

arXiv:2606.05753v1 Announce Type: new Abstract: Latent visual reasoning (LVR) inserts supervised latent tokens between perception and answer generation in vision-language models (VLMs). The field uses

arXiv:2606.05949v1 Announce Type: new Abstract: Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts a

arXiv:2606.05471v1 Announce Type: new Abstract: Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this

arXiv:2606.06002v1 Announce Type: new Abstract: Large Vision-Language Models have achieved significant reasoning performance in various tasks.However, there are few studies on text-to-3D indoor scene

arXiv:2606.05404v1 Announce Type: cross Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end

arXiv:2606.06098v1 Announce Type: new Abstract: Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialize

arXiv:2606.06076v1 Announce Type: cross Abstract: While vision-language models excel at general multimodal understanding, they still struggle with visual spatial planning. We attribute this to a perce

arXiv:2606.06080v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards is widely used for post-training reasoning models, but sparse outcome rewards make exploration difficul

We will no longer accept public pull requests. [...] A substantial patch used to imply substantial effort, and that effort was a reasonable proxy for good faith. That assumption no longer holds. [...]

arXiv:2606.05660v1 Announce Type: new Abstract: Embodied AI systems are increasingly expected to reason and act over extended horizons in physical environments. This growing capability brings safety t

arXiv:2606.06079v1 Announce Type: new Abstract: Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at

Google's May 2026 AI updates center on the new 'agentic' era, featuring the Gemini 3.5 model and Gemini Omni for advanced reasoning and creation. Gemini Omni is a new model that can create anything fr

arXiv:2606.05976v1 Announce Type: cross Abstract: Recent work shows that LLM agents struggle to correct errors in their own reasoning traces yet show markedly higher correction rates when identical cl

arXiv:2606.05748v1 Announce Type: cross Abstract: Global-scale video moderation faces a dual challenge: the need for fine-grained multi-modal reasoning and the demand for interpretable outputs to supp

arXiv:2606.06416v1 Announce Type: cross Abstract: Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updati

arXiv:2606.06467v1 Announce Type: new Abstract: Long-context inference in modern LLMs is increasingly constrained by decoding efficiency, especially in reasoning-heavy settings where models generate l

arXiv:2510.15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke. We hyp

arXiv:2606.04874v1 Announce Type: new Abstract: Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeas

arXiv:2606.05106v1 Announce Type: cross Abstract: We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning. Building on the GA

arXiv:2606.04557v1 Announce Type: new Abstract: Large Language Models can reason over long contexts, yet prefilling millions of tokens is wasteful as much of the content remains static across queries.

arXiv:2606.04535v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally s

arXiv:2604.00819v2 Announce Type: replace-cross Abstract: Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact thro

arXiv:2606.04986v1 Announce Type: new Abstract: Recent studies have explored Vision-Language Models (VLMs) for food analysis. However, most existing methods rely primarily on supervised fine-tuning (S

arXiv:2606.04516v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) significantly advances LLM reasoning, yet it faces a dilemma: standard supervised scaling is thr

Hadamard thought in image space World Labs CEO Dr. Fei-Fei Li: 'The world is not made of words.' 'Language models have given machines an extraordinary command of concepts, vocabulary, and reasoning, b

arXiv:2606.04560v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards with GRPO is a standard approach for post-training reasoning LLMs. It remains sample inefficient. Each

The post announces that the author will share insights about their use of dynamic workflows and explain their reasoning behind building a custom implementation. This likely covers practical applicatio

arXiv:2307.00862v3 Announce Type: replace-cross Abstract: Vision-language tasks, such as VQA, SNLI-VE, and VCR are challenging because they require the model's reasoning ability to understand the sema

⚠️Why didn’t the hyperscalers wait until after the IPO in switching to pay-by-usage-charging? My guess is that *they literally could not afford to* —because it would bankrupt them. My reasoning: in “a

A workflow audit is no longer the best way to figure out how to use AI in your job. Despite the advice from AI labs, I'm more convinced, because of AI's reasoning capabilities and long context horizon

Microsoft announced the MAI-Thinking-1 and MAI family of models at their Build conference, representing new advances in their AI model lineup. These models likely focus on improved reasoning capabilit

arXiv:2412.01282v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capab

arXiv:2510.08977v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) efficiently scales the reasoning ability of large language models (LLMs) but is bottlene

arXiv:2606.03331v1 Announce Type: cross Abstract: Consumer device repair is an important but underexplored testbed for large language models (LLMs). Repair tasks require reasoning over incomplete prob

arXiv:2606.03793v1 Announce Type: new Abstract: Multimodal Large Language Models integrate visual perception into language reasoning, introducing a continuous attack surface susceptible to adversarial

arXiv:2512.10999v3 Announce Type: replace Abstract: Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural language and strict knowledge graph schemas by generati