Briefing
The fast feed: breaking AI news from the global tech press, deduplicated and time-ordered.
The fast feed: breaking AI news from the global tech press, deduplicated and time-ordered.
Latest in Briefing
30 storiesNeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization
Reliable sound source localization is fundamental to robot audition, enabling autonomous robots to perceive spatial cues and operate effectively in dynamic envi…
EARS: Explanatory Abstention for Reliable Sub-Agent Modeling in Large-scale Multi-Agent Systems
In large-scale enterprise settings, centralized multi-agent systems (MAS) are increasingly adopted, in which a coordinator delegates user requests to lightweigh…
UniTemp: Unlocking Video Generation in Any Temporal Order via Bidirectional Distillation
Autoregressive video diffusion models have emerged as a promising approach for long video generation, achieving strong performance in streaming settings. Howeve…
LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment
Item discrimination is a fundamental psychometric property of educational assessment, which measures whether an item meaningfully distinguishes students with hi…
Trainable Photonic Measurement for Physics-Informed PDE Learning
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scie…
LegalWorld: A Life-Cycle Interactive Environment for Legal Agents
Civil litigation is inherently a life-cycle process: what a lawyer drafts on day one constrains what unfolds at trial months later. Yet existing legal benchmark…
SMART: A Flexible, Interpretable, and Scalable Spatio-temporal Brain Atlas from High-Resolution Imaging Data
We introduce SMART, a framework for learning a flexible, interpretable, and scalable spatio-temporal brain atlas from longitudinal high-resolution 3D medical im…
SpectralDiT: Timestep-Conditioned Spectral Residual Correction for Flow-Matching DiTs
We propose SpectralDiT, a lightweight modification to flow-matching Diffusion Transformers that adds timestep-conditioned spectral correction to the MLP residua…
SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval
With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information …
ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling b…
Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in training LLMs for reasoning tasks, but representative methods such as G…
Reinforcement Learning Foundation Models Should Already Be A Thing
Foundation models for language and vision are powered by internet-scale data, while structured domains (tabular prediction, time-series forecasting, graph learn…
Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation
Self-distillation improves reasoning in large language models by using the model's own rollouts as training signal, typically through implicit logit-level align…
Scaling Learning-based AEB with Massive Unlabeled Data
This paper studies how to scale learning-based automatic emergency braking (AEB) with massive unlabeled fleet data under production constraints. Our approach is…
SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration
Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients do not functio…
SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety
Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to l…
SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents
Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify ea…
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costl…
A Controlled Benchmark of Quantum-Latent GAN Augmentation for Brain MRI
Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been pro…
FOSC-X: An Extended Framework for Optimal Local Cuts and Non-Horizontal Cluster Selection from Clustering Hierarchies
Extracting a flat clustering solution from a hierarchy is a common task in practical cluster analysis and can be formulated as an optimisation problem. Existing…
Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning
Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an order-of-ma…
G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
Idioms are difficult to transfer across languages due to their non-compositionality and weak surface-form grounding, making literal mappings unreliable. We pres…
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior …
Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science
Research methods are essential carriers of knowledge contribution in academic papers. Automatic multi-label classification of research methods can support knowl…
Structure Over Nonlinearity: Explicit Interaction Architectures for Dynamical Learning
Most learning architectures for dynamical systems rely on generic nonlinear function approximation, often requiring high model complexity to capture structured …
Smoothness-Based Derandomization of PAC-Bayes Bounds
We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic pred…
JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling
Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer…
Towards an Agent-First Web: Redesigning the Web for AI Agents
The World Wide Web was built on an assumption held for three decades: the primary consumer of web content is a human being. This permeates every layer; its acce…
Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning
Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clien…
On Local Population-Risk Certificates
This paper develops local certificates for population-risk increments around a current model. For a local candidate set \(\mathcal D\), the certificate is a two…