arXiv:2510.25976v1 Announce Type: cross Abstract: Reconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, current methods often lack faithfulness to the actual seen images. We present “Brain-IT”, a brain-inspired approach that addresses this challenge through a Brain Interaction Transformer […]
GUI Knowledge Bench: Revealing the Knowledge Gap Behind VLM Failures in GUI Tasks
arXiv:2510.26098v1 Announce Type: new Abstract: Large vision language models (VLMs) have advanced graphical user interface (GUI) task automation but still lag behind humans. We hypothesize this gap stems from missing core GUI knowledge, which existing training schemes (such as supervised fine tuning and reinforcement learning) alone cannot fully address. By analyzing common failure patterns in […]
DARTS: A Drone-Based AI-Powered Real-Time Traffic Incident Detection System
arXiv:2510.26004v1 Announce Type: cross Abstract: Rapid and reliable incident detection is critical for reducing crash-related fatalities, injuries, and congestion. However, conventional methods, such as closed-circuit television, dashcam footage, and sensor-based detection, separate detection from verification, suffer from limited flexibility, and require dense infrastructure or high penetration rates, restricting adaptability and scalability to shifting incident hotspots. […]
Gistify! Codebase-Level Understanding via Runtime Execution
arXiv:2510.26790v1 Announce Type: cross Abstract: As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is central. We propose Gistify, a task where a coding LLM must create a single, minimal, self-contained file that can reproduce a specific functionality of a codebase. The coding LLM is given full […]
Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis
arXiv:2510.26014v1 Announce Type: cross Abstract: Survival analysis is a task to model the time until an event of interest occurs, widely used in clinical and biomedical research. A key challenge is to model patient heterogeneity while also adapting risk predictions to both individual characteristics and temporal dynamics. We propose a dual mixture-of-experts (MoE) framework for […]
Beyond Benchmarks: The Economics of AI Inference
arXiv:2510.26136v1 Announce Type: new Abstract: The inference cost of Large Language Models (LLMs) has become a critical factor in determining their commercial viability and widespread adoption. This paper introduces a quantitative “economics of inference” framework, treating the LLM inference process as a compute-driven intelligent production activity. We analyze its marginal cost, economies of scale, and […]
RADRON: Cooperative Localization of Ionizing Radiation Sources by MAVs with Compton Cameras
arXiv:2510.26018v1 Announce Type: cross Abstract: We present a novel approach to localizing radioactive material by cooperating Micro Aerial Vehicles (MAVs). Our approach utilizes a state-of-the-art single-detector Compton camera as a highly sensitive, yet miniature detector of ionizing radiation. The detector’s exceptionally low weight (40 g) opens up new possibilities of radiation detection by a team […]
Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
arXiv:2505.11730v2 Announce Type: replace Abstract: Test-time scaling (TTS) has proven effective in enhancing the reasoning capabilities of large language models (LLMs). Verification plays a key role in TTS, simultaneously influencing (1) reasoning performance and (2) compute efficiency, due to the quality and computational cost of verification. In this work, we challenge the conventional paradigms of […]
Rethinking Cross-lingual Alignment: Balancing Transfer and Cultural Erasure in Multilingual LLMs
arXiv:2510.26024v1 Announce Type: cross Abstract: Cross-lingual alignment (CLA) aims to align multilingual representations, enabling Large Language Models (LLMs) to seamlessly transfer knowledge across languages. While intuitive, we hypothesize, this pursuit of representational convergence can inadvertently cause “cultural erasure”, the functional loss of providing culturally-situated responses that should diverge based on the query language. In this […]
Reasoning Curriculum: Bootstrapping Broad LLM Reasoning from Math
arXiv:2510.26143v1 Announce Type: new Abstract: Reinforcement learning (RL) can elicit strong reasoning in large language models (LLMs), yet most open efforts focus on math and code. We propose Reasoning Curriculum, a simple two-stage curriculum that first elicits reasoning skills in pretraining-aligned domains such as math, then adapts and refines these skills across other domains via […]