Search: “reasoning”

arXiv:2605.15559v1 Announce Type: new Abstract: Recent years have witnessed significant progress in autonomous navigation using reinforcement learning. However, existing approaches largely emphasize r

arXiv:2605.15436v1 Announce Type: new Abstract: This paper presents a comprehensive analysis of neural activation patterns across six distinct large language model (LLM) architectures, examining their

Note that as of last summer Waymo said that LLMs/VLMs were experimental, and that their foundation model then could “process only a small amount of image frames, does not incorporate accurate 3D sensi

arXiv:2605.15603v1 Announce Type: cross Abstract: Model-based reinforcement learning (RL) offers a compelling approach to offline RL by enabling value learning on imagined on-policy trajectories. Howe

arXiv:2602.10687v3 Announce Type: replace-cross Abstract: Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and vi

arXiv:2605.15229v1 Announce Type: cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a des

arXiv:2605.15609v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising masked token sequences. Although dLLMs can predict all masked positions i

arXiv:2511.09884v2 Announce Type: replace Abstract: Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, determinist

arXiv:2505.12601v2 Announce Type: replace Abstract: As large language models (LLMs) grow in scale and specialization, routing--selecting the best model for a given input--has become essential for effi

arXiv:2605.15792v1 Announce Type: new Abstract: The long-standing goal of multimodal AI is to build unified models in which visual understanding and visual generation mutually enhance one another. Des

arXiv:2605.15607v1 Announce Type: new Abstract: Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from

arXiv:2605.15611v1 Announce Type: new Abstract: Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) casc

arXiv:2605.15735v1 Announce Type: cross Abstract: Vision--language--action (VLA) models are typically built by fine-tuning a pretrained vision--language model (VLM) on action data. However, we show th

arXiv:2605.15876v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text

arXiv:2505.11708v3 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) agents are increasingly used to simulate sophisticated cyberattacks, but their decision-making processes remain op

arXiv:2605.15677v1 Announce Type: new Abstract: Despite the rapid advancements in Vision-Language Models (VLMs), a critical gap remains in their ability to handle structured, controllable diagrammatic

arXiv:2605.15672v1 Announce Type: cross Abstract: Vision-language models (VLMs) achieve strong performance on multimodal benchmarks, but may still lack robust control over basic visual operations. We

arXiv:2605.15604v1 Announce Type: cross Abstract: Modern language models often need to optimize a primary accuracy objective while also accommodating secondary behavioral preferences, such as verbosit

arXiv:2512.00778v2 Announce Type: replace Abstract: Preference optimization (PO) is indispensable for large language models (LLMs), with methods such as direct preference optimization (DPO) and proxim

Every device, user, and microservice generates data. Ingesting this data, extracting meaning and insights, and driving business decisions in real time has the potential to deliver transformational bus

arXiv:2510.06062v2 Announce Type: replace Abstract: Reinforcement learning (RL) has shown great promise in large language models (LLMs) post-training, which typically rely on token-level clipping to m

where i said same in 2024 (one of many places I made this point): Three o3 predictions: – People will initially be amazed - Once they dive in, they will see that it is not reliable - It will work best

The best feature of @xai Grok Build right now is how it handles subagents and personas. Most people still treat the model like one very smart intern that has to do everything at once. Grok Build took

This is a nice article (not sure how I stumbled upon it a month later) I directionally agree with it in that: ✅ I have a massive bias for slope, grit, and scrappiness in candidates vs. pure experience

This is a Wordle game result posted by Anthropic on X (formerly Twitter) showing an unsuccessful attempt at puzzle 1,792, where the player failed to guess the correct word within six tries despite nar

arXiv:2605.13850v1 Announce Type: new Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus o

arXiv:2605.14266v1 Announce Type: new Abstract: Integration of artificial intelligent (AI) agents in higher education is transforming teaching, learning and administrative processes. Although existing

arXiv:2605.14723v1 Announce Type: new Abstract: Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient physiology. Although large language models (LLMs) en

arXiv:2605.14393v1 Announce Type: new Abstract: We study analogical trajectory transfer, where the goal is to translate motion trajectories in one 3D environment to a semantically analogous location i

arXiv:2605.13897v1 Announce Type: cross Abstract: We propose a novel multimodal deep learning framework for patient-level survival prediction, which integrates whole-slide histology features, RNA-seq

arXiv:2504.07738v3 Announce Type: replace Abstract: In this document, we discuss a multi-step approach to automated construction of a knowledge graph, for structuring and representing domain-specific

arXiv:2605.15082v1 Announce Type: cross Abstract: We study a prototypical situation when a learned predictor can discover useful low-dimensional structure in data, while using fewer samples than are n

arXiv:2605.13935v1 Announce Type: cross Abstract: Diffusion language models are a promising alternative to autoregressive models, yet post-training methods for them largely adapt reward-maximizing obj

arXiv:2605.14102v1 Announce Type: new Abstract: Autonomous language-model agents increasingly combine planning, tool use, document processing, browsing, code execution, and verification loops. These c

arXiv:2605.14795v1 Announce Type: new Abstract: Referring Multi-Object Tracking (RMOT) faces a fundamental structural contradiction between the high-discriminability demand and the sparse semantic sup

arXiv:2605.14752v1 Announce Type: cross Abstract: Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long-tail distr

arXiv:2605.13950v1 Announce Type: cross Abstract: Autonomous language-model agents are increasingly evaluated on long-horizon tool-use tasks, but existing benchmarks rarely capture the complexity and

arXiv:2505.04535v3 Announce Type: replace Abstract: Federated Learning (FL) enables the utilization of vast, previously inaccessible data sources. At the same time, pre-trained Language Models (LMs) h

arXiv:2605.15077v1 Announce Type: cross Abstract: Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. U

arXiv:2605.14897v1 Announce Type: cross Abstract: Despite many successful attempts at explaining Deep Reinforcement Learning policies using distillation, it remains difficult to balance the performanc

arXiv:2605.14053v1 Announce Type: cross Abstract: The application of Large Language Models to Question Answering has shown great promise, but important challenges such as hallucinations and erroneous

arXiv:2511.13397v2 Announce Type: replace-cross Abstract: The remarkable progress of Vision-Language Models (VLMs) on a variety of tasks has raised interest in their application to automated driving.

arXiv:2512.13609v2 Announce Type: replace Abstract: We introduce the Do-Undo task and benchmark to address a critical gap in vision-language models: understanding and generating plausible scene transf

arXiv:2605.14420v1 Announce Type: new Abstract: Current Large Language Models (LLMs) typically rely on coarse-grained national labels for pluralistic value alignment. However, such macro-level supervi

arXiv:2605.14750v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities but remain vulnerable to jailbreaking attacks

arXiv:2605.14801v1 Announce Type: new Abstract: Zero-shot vision-and-language navigation (VLN) has gained significant attention due to its minimal data collection costs and inherent generalization. Th