PhysiFormer: Learning to Simulate Mechanics in World Space
We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion. Unlike video world models that operate in view-dependent pixel space, PhysiFormer represe...
Research Papers
Latest AI/ML research from ArXiv and Papers with Code
JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting
Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it faces a scaling limitation: inc...
Hallucination in World Models is Predictable and Preventable
Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the g...
EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper, we view this t...
Focusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE
Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling diffusion models in visual generation. Recent advancements have focused on adaptively allocat...
How Good Can Linear Models Be for Time-Series Forecasting?
Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that ca...
Ko-WideSearch: A Korean Breadth-Search Benchmark for Exhaustive Set Enumeration by Web Agents
Web-agent benchmarks overwhelmingly measure depth -- pinning one obscure answer behind a chain of constraints -- while breadth, exhaustively enumerating a closed set and filling ea...
To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair
LLM-based agents for program repair are increasingly built on a "generate-run-revise" paradigm, iteratively executing tests to evaluate and refine patches. This execution-based app...
How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring
LLM-based code agents navigate repositories through keyword search but miss the structural relationships, such as call graphs, inheritance hierarchies, and configuration dependenci...
MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
Video generation models aspire to simulate dynamic environments, and several benchmarks now evaluate memory consistency across frames. However, most assess consistency only while t...
PolyFlow: Continuous Topology Embedding Flow Matching for Artist-style Mesh Generation
Autoregressive Transformers dominate high-quality mesh generation by producing artist-worthy topologies, yet their inherent sequential decoding induces substantial computational ov...
LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing
Streaming video editing has made rapid progress, yet practical deployment is still limited by two core issues: maintaining stable backgrounds and non-edited regions over time, and ...
RedVox: Safety and Fairness Gaps in Speech Models Across Languages
Speech-capable models are increasingly deployed in real-world applications across languages. Yet their safety and fairness beyond English settings and under naturalistic conditions...
In-Context World Modeling for Robotic Control
Modern Vision-Language-Action (VLA) models often fail to generalize to novel setups, such as altered camera viewpoints or robot morphologies, because they are typically conditioned...
Confidence-Aware Tool Orchestration for Robust Video Understanding
Video reasoning language models implicitly assume that every input frame is equally reliable. This leads to what we term the Blind Trust Problem: under realistic perturbations such...
ViQ: Text-Aligned Visual Quantized Representations at Any Resolution
A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, representing images as discrete s...
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning
Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermedi...
Information-Aware KV Cache Compression for Long Reasoning
Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling and decoding stages. Existing KV...
Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding. The system placed 1st of 62 teams in the online (simulation) round and 2n...
Boundary-Aware Context Grounding for A Low-Channel EEG Agent
Large language models (LLMs) can make scientific software easier to use. However, a general model does not automatically know which measurements a particular sensor can support, wh...
LISA: Likelihood Score Alignment for Visual-condition Controllable Generation
The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pretrained main network, has sh...
Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement
Multi-agent large language model (LLM) systems often rely on verifier and critic agents to suppress hallucinations, but verification is delayed. During this delay, false claims can...
Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving
Efficient deployment of large language models (LLMs) in production forces a trade-off between accuracy and cost. Operators often default to a single model that is either expensive ...
Qwen-Image-2.0-RL Technical Report
We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual qu...