Search: “reasoning”

arXiv:2604.05096v2 Announce Type: replace Abstract: Large language models (LLMs) acquire most of their knowledge during pretraining, which ties them to a fixed snapshot of the world and makes adaptati

arXiv:2604.13213v1 Announce Type: cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical sys

arXiv:2604.13549v1 Announce Type: new Abstract: The conversion of 2D freehand sketches into 3D models remains a pivotal challenge in computer vision, bridging the gap between human creativity and digi

arXiv:2512.10696v2 Announce Type: replace-cross Abstract: Procedural memory enables large language model (LLM) agents to internalize 'how-to' knowledge, theoretically reducing redundant trial-and-erro

arXiv:2604.13602v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multi

arXiv:2604.13515v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) followed by Group Relative Policy Optimization (GRPO) is a common post-training recipe. We conduct a controlled ablation ov

arXiv:2604.13861v1 Announce Type: new Abstract: This paper develops a unified Markov Decision Process (MDP) framework for optimising two recurring in-match decisions in T20 cricket namely batting orde

Sorry for the long wait, everyone! As I said, Qwen is going to keep open-sourcing! ⚡ Meet Qwen3.6-35B-A3B：Now Open-Source！🚀🚀 A sparse MoE model, 35B total params, 3B active. Apache 2.0 license. 🔥 Agen

arXiv:2604.13051v1 Announce Type: new Abstract: There is debate about whether LLMs can be conscious. We investigate a distinct question: if a model claims to be conscious, how does this affect its dow

arXiv:2604.13519v1 Announce Type: new Abstract: Tool calling has greatly expanded the practical utility of large language models (LLMs) by enabling them to interact with external applications. As LLM

arXiv:2603.15620v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap prim

arXiv:2603.08639v2 Announce Type: replace Abstract: Ensuring trustworthiness in open-world visual recognition requires models that are interpretable, fair, and robust to distribution shifts. Yet moder

What you need to know about Opus 4.7 * Takes instructions literally * Better vision means improved computer use and producing slides and other visual artifacts * Optimized for large-scale real-world a

arXiv:2604.13349v1 Announce Type: new Abstract: Communication in Large Language Model (LLM)-based multi-agent systems is moving beyond discrete tokens to preserve richer context. Recent work such as L

arXiv:2604.13403v1 Announce Type: new Abstract: In-context learning (ICL) enables models to adapt to new tasks via inference-time demonstrations. Despite its success in large language models, the exte

1/5 When we saw our Reducer (=LLM judge component in Maestro, our agentic framework, that selects the best output from parallel agent runs) consistently picking gold patches, we were sure Claude Opus

4/5 We upgraded our original 3-line “be correct” prompt → a much more detailed prompt that enforced a hierarchy of constraints for correctness, regression safety, and minimality. Basically, get the LL

arXiv:2604.12019v1 Announce Type: new Abstract: Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management, beh

arXiv:2604.12513v1 Announce Type: new Abstract: We study whether a variational language model can support a minimal and measurable form of agentic control grounded in its own internal evidence. Our mo

arXiv:2604.11945v1 Announce Type: cross Abstract: High-fidelity numerical simulation of subsurface flow is computationally intensive, especially for many-query tasks such as uncertainty quantification

arXiv:2604.12138v1 Announce Type: new Abstract: RAG systems have transformed how LLMs access external knowledge, but we find that current implementations exhibit a bias toward factual, objective conte

arXiv:2511.22364v2 Announce Type: replace-cross Abstract: Open-vocabulary mobile manipulation (OVMM) requires robots to follow language instructions, navigate, and manipulate while updating their worl

Boston Dynamics has integrated Google's Gemini and Gemini Robotics-ER 1.6 into its Orbit software platform, specifically its AI Visual Inspection systems, which analyze images captured by the Spot rob

arXiv:2604.12833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies focu

arXiv:2604.12461v1 Announce Type: new Abstract: LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology whi

arXiv:2604.12308v1 Announce Type: new Abstract: Individuals' concerns about data privacy and AI safety are highly contextualized and extend beyond sensitive patterns. Addressing these issues requires

Nous Research's Hermes Agent demonstrates advanced capabilities in generating digital art, showcasing how AI agents can autonomously create or assist in producing visual artwork at a sophisticated lev

arXiv:2604.12490v1 Announce Type: cross Abstract: Deepfakes are synthetic media that superimpose or generate someone's likeness on to pre-existing sound, images, or videos using deep learning methods.

arXiv:2604.12659v1 Announce Type: cross Abstract: Vision-language models(VLMs) are increasingly applied to visual stock price forecasting, yet existing benchmarks inadequately evaluate their understan

arXiv:2603.22607v2 Announce Type: replace Abstract: Recent advances in Virtual Try-On (VTON) and Virtual Try-Off (VTOFF) have greatly improved photo-realistic fashion synthesis and garment reconstruct

arXiv:2509.19695v3 Announce Type: replace-cross Abstract: Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts, leading to inefficient

arXiv:2604.12518v1 Announce Type: new Abstract: Multimodal sentiment analysis (MSA) integrates heterogeneous text, audio, and visual signals to infer human emotions. While recent approaches leverage c

arXiv:2604.12460v1 Announce Type: new Abstract: Machine learning has become a central research area, with increasing attention devoted to explainable clustering, also known as conceptual clustering, w

arXiv:2604.12666v1 Announce Type: cross Abstract: Text-based web agents offer computational efficiency for autonomous web navigation, yet developing robust agents remains challenging due to the noisy

FSD v14.3.1 review after 10 drives and many hours, here are my thoughts (it’s a great one) - v14.3.1 feels like a big jump even compared to FSD v14.3, even for “just” a point release build. Everything

arXiv:2604.12168v1 Announce Type: cross Abstract: The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance, transport

arXiv:2604.12285v1 Announce Type: new Abstract: To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining

arXiv:2507.01770v4 Announce Type: replace-cross Abstract: This paper introduces a numerical method to enclose the global minimum of a nonlinear function subject to simple bounds on the variables. Usin

arXiv:2603.27306v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been proposed as supervisory agents for spacecraft operations, but existing approaches rely on static prompt

This r/ChatGPT thread addresses the well-documented tendency of ChatGPT to be sycophantic — by default, ChatGPT is trained to be polite, non-confrontational, and agreeable — and contrasts it with Clau

arXiv:2604.12115v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal performance, but still suffer from hallucinations caused by unstable visual grounding and

arXiv:2505.19134v2 Announce Type: replace-cross Abstract: Human-annotated data plays a vital role in training large language models (LLMs), such as supervised fine-tuning and human preference alignmen

arXiv:2604.12874v1 Announce Type: new Abstract: The rapid advancement of AI has changed the character of HPC usage such as dimensioning, provisioning, and execution. Not only has energy demand been am

arXiv:2604.12223v1 Announce Type: cross Abstract: Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetl

arXiv:2509.16615v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive ex

arXiv:2604.12096v1 Announce Type: new Abstract: On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training.