arXiv:2608.11344v3 Announce Type: replace-cross Abstract: Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight. Yet agentic AI…
arXiv:2608.04765v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control. However, existing VLA models still face major…
arXiv:2608.02628v3 Announce Type: replace-cross Abstract: Symbolic regression (SR) aims to discover underlying mathematical expressions from data while preserving interpretability. Most existing learning-based SR methods primarily optimize…
arXiv:2608.01301v4 Announce Type: replace-cross Abstract: Human pairwise comparison provides a direct basis for perceptual infrared-visible image fusion assessment, but dense annotation becomes costly as method pools grow. We present the Learned…
arXiv:2608.00569v3 Announce Type: replace-cross Abstract: Deploying billion-parameter Vision-Language-Action (VLA) policies on mobile robots creates a systems conflict: semantic reasoning benefits from cloud GPUs, whereas closed-loop control must…
arXiv:2607.20002v3 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deployment…
arXiv:2607.19305v4 Announce Type: replace-cross Abstract: Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific manifolds, rely on Euclidean approximations, or…
arXiv:2607.19190v4 Announce Type: replace-cross Abstract: Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover scene…
arXiv:2607.15740v3 Announce Type: replace-cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI. Existing…
arXiv:2607.15196v3 Announce Type: replace-cross Abstract: We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but instead consequences, of higher-level modelling…
arXiv:2606.17702v3 Announce Type: replace-cross Abstract: Characterising the TME from routine H&E-stained histology images requires simultaneous cell segmentation, biological feature extraction, and interpretable clinical reporting. We present…
arXiv:2606.16847v5 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality. While revocable decoding strategies attempt…
arXiv:2606.09430v2 Announce Type: replace-cross Abstract: Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass constraints…
arXiv:2606.07951v2 Announce Type: replace-cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news…
arXiv:2606.03852v2 Announce Type: replace-cross Abstract: Large language models often generate code with bugs. Existing methods rely on feedback signals such as test failures and self-critiques to iteratively refine the generated code. Such signals…
arXiv:2606.03077v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a standard post-training paradigm for shaping large language models (LLMs) into capable agents. In agentic RL, the rollout stage generates…
arXiv:2606.01670v2 Announce Type: replace-cross Abstract: Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indices (SIDs). Owing to the exceptional…
arXiv:2606.00435v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can produce confident answers without relevant visual evidence, a failure mode known as mirage reasoning (Asadi et al., 2026). To that end, we study pre-release…
arXiv:2605.16692v3 Announce Type: replace-cross Abstract: We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is a planner…
arXiv:2605.06850v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encompassing frameworks like RLHF and RLAIF…
arXiv:2604.14862v3 Announce Type: replace-cross Abstract: Constrained decoding is widely used to make large language models produce structured outputs that satisfy schemas such as JSON. Existing work mainly treats schemas as structural constraints…
arXiv:2604.13954v2 Announce Type: replace-cross Abstract: Existing agent-safety evaluation has focused mainly on externally induced risks. Yet agents may still enter unsafe trajectories under benign conditions. We study this complementary but…
arXiv:2604.12044v2 Announce Type: replace-cross Abstract: Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Deviation. This is because as training…
arXiv:2604.11507v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with…
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