EvoFlock employs multi-objective genetic algorithms to automatically optimize 15 parameters of multi-agent flocking models, achieving behaviors aligned with user-defined metrics.

Craig Reynolds

FLUX3D employs Diffusion-Aligned Structured Latents and sparse-structure-aware diffusion transformer to generate high-fidelity 3D Gaussian point clouds, outperforming SOTA methods.

Haorui Ji, Weizhe Liu, Hongdong Li et al.

Proposes a multidimensional evaluation framework combining technical metrics and user-centered methods for six AAC problem spaces, enhancing AI system fairness and adaptability.

Blade Frisch, Will Wade, Dylan Gaines et al.

Introduces a structural certification framework using deep goal composition to filter specific transitions, with an error bound of O(1/n)+δ, enabling local validation of internal world models.

Yikai Lu, Yifei Wu, Xinyu Lu et al.

This paper critically analyzes Andreas & Günther's seven definitions of actual causation, revealing their logical equivalence and exposing flaws in their categorical distinctions.

Sander Beckers

Grad Detect leverages layer-wise gradients for single-pass hallucination detection, outperforming confidence baselines with 94-99% accuracy.

Anand Kamat, Daniel Blake, Brent M. Werness

Proposes a four-module analytical framework to evaluate 12 agent memory systems across diverse workloads, revealing workload-architecture matching importance.

Wei Zhou, Xuanhe Zhou, Shaokun Han et al.

Introducing inertia into Dirac-Frenkel dynamics enhances robustness for nonlinearly parametrized solutions, providing well-posedness and error bounds, especially in ill-conditioned scenarios.

Matteo Raviola, Benjamin Peherstorfer

ReTVL leverages sparse retry event supervision to capture local mistake-recovery dynamics, outperforming progress-based models in fine-grained value estimation.

Xinyao Qin, Junjie Lu, Kaixin Wang et al.

Proposes a semantic browsing framework for controllable diversity in image generation, leveraging rich textual representations to enable structured, interpretable exploration.

Sara Dorfman, Maya Vishnevsky, Omer Dahary et al.

Proposes Success Visitation Matching (SVM) rewards, transforming sparse rewards into dense signals, doubling RL finetuning speed in robotic tasks.

Raymond Tsao, Andrew Wagenmaker, Sergey Levine

See2Act employs diffusion models for active perception, coupling viewpoint inference with action prediction, achieving 95% success in occluded manipulation tasks.

Kuancheng Wang, Vaibhav Saxena, Shuo Cheng et al.

LangMAP extends UnigramLM with language-specific MAP estimation, enabling multi-language tokenization from a shared vocabulary, improving morphological boundary alignment.

Clara Meister, Suchir Salhan, Andrzej Szablewski et al.

VeriEvol employs iterative prompt evolution and offline answer verification to scale visual math reasoning data from 10K to 250K, boosting accuracy by 19.31%.

Haoling Li, Kai Zheng, Jie Wu et al.

Using BGE-M3 embeddings, analysis of 66 brands across 12 European languages reveals significant language bias in AI-generated brand reputation, with model choice affecting stability more than language.

Dmitrij Żatuchin

Genetic algorithm optimization of reservoir hyperparameters (size, spectral radius, etc.) reveals structural constraints that enhance spatiotemporal chaos prediction, extending forecast horizon and efficiency.

Nima Dehghani

JanusMesh is a fast, zero-shot framework for generating dual-semantic 3D illusions using cross-space denoising, completing in 3-5 minutes with high geometric and semantic fidelity.

Siang-Ling Zhang, Huai-Hsun Cheng, Tsung-Ju Yang et al.

UNIEGO employs proxy-mediated hierarchical distillation from nine heterogeneous teachers to unify egocentric video representations, achieving state-of-the-art results.

Wenhao Chi, Arkaprava Sinha, Dominick Reilly et al.

Proposes Spatially Speculative Decoding (SSD), leveraging 2D spatial prediction to accelerate autoregressive image generation by up to 13.3×.

Shilong Xiang, Zirui Zhang, Lijun Yu et al.

CalTennis is a large multi-view tennis video dataset with over 11 million frames, used to evaluate monocular-to-3D pose estimation, highlighting challenges in depth and foot contact accuracy.

Ilona Demler, Xinran Xie, Blake Werner et al.