TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction
Sparse-view 3D reconstruction is increasingly addressed with feed-forward splatting networks that predict explicit primitives directly from images. Yet most existing methods remain...
Research Papers
Latest AI/ML research from ArXiv and Papers with Code
WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation
Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill...
Claw-Anything: Benchmarking Always-On Personal Assistants with Broader Access to User's Digital World
Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Yet current systems operate o...
SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges
Sparse encoders offer high-precision retrieval by representing term importance within a vocabulary space, yet their English-centric structures pose a critical impediment to languag...
Toward Native Multimodal Modeling: A Roadmap
Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders...
Can LLMs Introspect? A Reality Check
Can large language models detect and report their own internal states? A number of studies have argued that the answer to this question is yes. We argue, based on lessons from huma...
CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation
Customized image editing aims to equip pre-trained diffusion models with specific visual effects using limited paired data, typically via Low-Rank Adaptation (LoRA). As the number ...
CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents
Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents...
Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals
Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white-box interpretability techniques. However, uncertainty...
Rethinking VLM Representation for VLA Initialization
Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is ...
InstructSAM: Segment Any Instance with Any Instructions
In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions. We formulates instruction-driven...
Pixel-Level Pavement Distress Assessment Using Instance Segmentation
Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization of thin, branching, and irregu...
Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning
Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-source inputs, existing approaches...
Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction
Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typically trained and evaluated un...
Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents
Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are dif...
From Model Scaling to System Scaling: Scaling the Harness in Agentic AI
This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures aro...
When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges
Customizing an LLM judge to a specific task or domain often involves optimizing its prompt across multiple evaluation criteria simultaneously. Textual gradient methods automate thi...
ControlLight: Towards Controllable, Consistent, and Generalizable Low-Light Enhancement
Existing deep learning-based low-light enhancement methods are typically trained on limited datasets with single enhancement targets, which restricts their generalization ability a...
Helix4D: Complex 4D Mesh Generation
Current video-to-4D methods struggle with complex topology changes, transparent materials, thin structures, and inner surfaces. We present Helix4D, a dynamic mesh generation framew...
On-Policy Adversarial Flow Distillation for Autoregressive Video Generation
Autoregressive video generators are attractive for streaming, long-horizon, and interactive applications, but distilling strong black-box teachers into causal students remains diff...
Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one benchmarks miss a basic syst...
Advancing Creative Physical Intelligence in Large Multimodal Models
Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded...
ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence
Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evalua...
Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents
While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after explicit user prompts. This par...