Text-Vision Co-Instructed Image Editing
Existing image editing methods can be generally categorized into textual instruction-based and visual prompt-based ones. Textual instructions are semantically expressive, but are l...
Research Papers
Latest AI/ML research from ArXiv and Papers with Code
Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences
In this report, we present LOGOS (Language Of Generative Objects in Science), a scientific generative language model that unifies heterogeneous tasks across the natural sciences wi...
Taylor-Calibrate: Principled Initialization for Hybrid Linear Attention Distillation
Hybrid linear attention models offer an appealing path to faster long-context inference: they reduce the quadratic cost and KV-cache burden of full softmax attention while retainin...
ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
Vision-Language-Action (VLA) models benefit from large-scale and diverse embodied data, yet scaling robot trajectory collection is costly and labor-intensive. Recent advances show ...
ProCUA-SFT Technical Report
Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data ...
A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization
Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but GRPO-style optimization remains prone to collapse. We analyse this instability through ...
LectūraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching
Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction...
RepSelect: Robust LLM Unlearning via Representation Selectivity
Making large language models (LLMs) deeply forget specific knowledge and values without sacrificing general capabilities remains a central challenge in unlearning. However, current...
CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies
As LLM agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important. Unlike existing benchmarks tha...
Implicit Reasoning for Large Language Model-based Generative Recommendation
Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this kn...
Tangram: Unlocking Non-Uniform KV Cache Compression for Efficient Multi-turn LLM Serving
Multi-turn LLM serving accumulates dialogue history whose Key-Value (KV) cache grows with every turn and every user, quickly exceeding the model weights themselves and making memor...
MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents
Current benchmarks for computer-use agents evaluate models in impersonal environments. This leaves a gap between evaluation and deployment where personal assistants are expected to...
Thinking with Visual Grounding
Visual thinking should not only sound right; it should show its evidence. While recent vision-language models (VLMs) can produce natural-language reasoning traces, these traces oft...
SciOrch: Learning to Orchestrate Expert LLMs for Solving Frontier Multimodal Scientific Reasoning Tasks
Frontier scientific reasoning remains a major challenge for large language models (LLMs), where even the strongest commercial systems fall short of expert-level performance. A clos...
LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change th...
The Data Manifold under the Microscope
A significant gap exists between theory and practice in deep learning. Generalization and approximation error bounds are often derived for simplified models or are too loose to be ...
You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences
Progress in AI has largely been driven by methods that assume less. As compute and data increase, approaches with weaker inductive biases generally outperform those with stronger a...
AC-ODM: Actor--Critic Online Data Mixing for Sample-Efficient LLM Pretraining
Optimizing pretraining data composition is pivotal for LLM generalization. While dynamic mixing outperforms static strategies by capturing evolving training dynamics, current metho...
Selective Synergistic Learning for Video Object-Centric Learning
Typical video object-centric learning (VOCL) approaches employ slot-based frameworks that rely on reconstruction-driven encoder-decoder architectures, where learning is mediated by...
SpatialAvatar-0: High-Quality 4D Head Avatar with Multi-Stage Reconstruction
High-quality 4D head avatars from one or a few source portraits are central to telepresence, AR/VR, and digital-human interaction. 3D Gaussian Splatting (3DGS) has emerged as the d...
Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMs
Standard accuracy benchmarks are designed to test how closely large language models (LLMs) approach correct answers, but are not suitable for testing whether LLMs stick with a corr...
Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time
Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in bot...
Track2View: 4D-Consistent Camera-Controlled Video Generation via Paired 3D Point Tracks
Re-rendering an existing video from a novel camera viewpoint requires the output to follow the prescribed camera trajectory while preserving the appearance and dynamics of the orig...
Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations
In-context learning (ICL) is the standard method for low-resource classification, yet its efficacy in specialized domains remains largely unexplored. We address the challenge of cl...