Research
Curated daily papers, lab research blogs and national AI research programs across the US, China, EU, UK, Japan and beyond.
Curated daily papers, lab research blogs and national AI research programs across the US, China, EU, UK, Japan and beyond.
Latest in Research
30 storiesAccelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive l…
Structural Kolmogorov-Arnold Convolutions: Learnable Function on the Values or the Filter Shape as Parameter-Efficient Alternative to Per-Edge Convolutional KANs
Convolutional Kolmogorov--Arnold Networks (KANs) replace the fixed weights of a convolutional kernel with learnable univariate functions. The dominant formulati…
ComputeFHE: A Privacy-Preserving General-Purpose Computation Library
Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical…
Entity Resolution via Batched Oracle Queries
We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity. We study how to interroga…
Cycle-Consistent Neural Explanation of Formal Verification Certificates
Formal verification produces machine-checkable certificates that attest to the satisfaction or violation of temporal properties, yet these certificates remain o…
Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learn…
Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories
Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood. We…
MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching
Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains ins…
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA v…
S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing
We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose image gene…
CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference
Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiting susta…
Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods
WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more chall…
Red-Teaming the Agentic Red-Team
The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However, while the co…
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded rea…
PointVG-R: Internalizing Geometric Reasoning in MLLMs for Precise Pointing Localization via Visual Chain of Thought
Pointing-based visual grounding requires models to precisely locate target objects by deciphering complex spatial relationships between the visual scene and poi…
To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias
As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current literatu…
Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patien…
Infinitesimal Causality
This paper introduces a categorical account of infinitesimal causality in Frobenius Markov categories equipped with tangent-bundle semantics. IDC captures the i…
Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Training safe Reinforcement Learning (RL) systems is inherently challenging, with no guarantee of avoiding unwanted behaviors. The most effective defenses again…
The Warrant Gap: Claim-Conditioned Re-scoring for Fact-Checking
Fact-checking systems built on LLMs achieve high verdict accuracy on standard benchmarks, yet routinely output Supports labels whose cited evidence does not lic…
Extended pseudo-spectral physics-informed neural networks for phase-field models
Phase-field models play a central role in the continuum description of phase separation, in which the bulk free-energy density and the interfacial thickness par…
CN-NewsTTS Bench: a target-level automatic benchmark for raw-input Chinese news TTS pronunciation
Chinese news text contains dense written forms such as scores, hyphenated model names, ranges, unit symbols, percentages, English abbreviations, and mixed Chine…
UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving
Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundam…
Revealing Training Data Exposure in Vision Language Large Models via Parameter Gradients
Vision-Language Large Models (VLLMs) trained on massive crawled corpora raise pressing copyright and data-provenance concerns. These concerns are particularly a…
Grad Detect: Gradient-Based Hallucination Detection in LLMs
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations. Detecting these…
Less is More: Quality-Aware Training Data Selection for Scientific Summarization
Scientific long-document summarization datasets commonly treat author-written abstracts as gold reference summaries, although their quality and alignment with t…
DiffusionBench: On Holistic Evaluation of Diffusion Transformers
Diffusion transformer (DiT) research on image generation has converged to a single evaluation setup: class-conditional generation on ImageNet. While methods imp…
GroundEval: A Deterministic Replacement for LLM-as-Judge in Stateful Agent Evaluation
Before letting an agent operate over real context, can you prove it used the right evidence? GroundEval turns that question into a deterministic test of what th…
Error Highways: Scaling Predictive Coding to Very Deep Networks
Predictive coding networks (PCNs) offer a biologically-plausible, local-learning alternative to back-propagation of errors (backprop). Nevertheless, they have r…
Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts
Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achie…