arXiv:2606.30852v1 Announce Type: new Abstract: Reasoning models spend different amounts of useful computation across instances, but it remains unclear when a learned stopping rule improves over…
arXiv:2606.32034v1 Announce Type: cross Abstract: LLM agents increasingly act over long horizons, where a single trajectory can contain hundreds or thousands of actions. In these settings,…
arXiv:2606.31859v1 Announce Type: cross Abstract: Residual connections add every sublayer's proposed update with a fixed coefficient of one; the network never evaluates whether an update is…
arXiv:2606.30709v1 Announce Type: new Abstract: Hierarchical Global Attention (HGA) is a drop-in replacement for dense causal attention in pretrained long-context transformers. HGA preserves the original checkpoint…
arXiv:2507.00263v3 Announce Type: replace-cross Abstract: The rapid growth of vacation rental (VR) platforms has led to an increasing volume of property images, often uploaded without structured…
arXiv:2606.30648v1 Announce Type: cross Abstract: Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways not operating through them. Modern biomedical…
arXiv:2606.30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty…
arXiv:2601.04126v3 Announce Type: replace Abstract: GUI agents that interact with graphical interfaces on behalf of users represent a promising direction for practical AI assistants. However, training…
arXiv:2606.30987v1 Announce Type: new Abstract: Decision-makers routinely rely on expert judgments accompanied by written explanations, yet explanation quality is difficult to measure at scale. Forecasting tournaments…
arXiv:2606.30705v1 Announce Type: new Abstract: Deterministic few-step generation succeeds on continuous image latents but collapses to incoherent text on continuous text latents, and we show the…
arXiv:2509.16959v5 Announce Type: replace-cross Abstract: When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, thereby potentially reducing the…
arXiv:2606.31253v1 Announce Type: new Abstract: The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing…
arXiv:2606.30846v1 Announce Type: new Abstract: Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S). When many models coexist, identifying those that…
arXiv:2606.31272v1 Announce Type: cross Abstract: AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces…
arXiv:2506.17294v3 Announce Type: replace Abstract: The advent of artificial intelligence has propelled AI-Generated Game Commentary (AI-GGC) into a rapidly expanding research area, offering advantages such as…
arXiv:2606.30704v1 Announce Type: new Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed…
arXiv:2606.31581v1 Announce Type: cross Abstract: We investigate the problem of the robustness of a trained neural network to the perturbation of its input values. More specifically,…
arXiv:2606.31063v1 Announce Type: new Abstract: Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where posterior…
arXiv:2606.23672v2 Announce Type: replace Abstract: This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles. In this task,…
arXiv:2606.30887v1 Announce Type: new Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control…
arXiv:2606.30989v1 Announce Type: new Abstract: Warning: This paper contains several toxic and offensive statements. While reasoning generally improves fairness in recent large language models (LLMs), failures…
arXiv:2510.14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation,…
arXiv:2606.31700v1 Announce Type: cross Abstract: Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory. Artificial networks that respect this constraint must…
arXiv:2606.30930v1 Announce Type: new Abstract: Modern deep learning has been shown to operate at the edge of stability, routinely using learning rates far larger than those…