Painting Change Map as guidance for generating painting processes
Digital art generation has undergone a massive paradigm shift, moving from static image synthesis to dynamic video generation that can mimic the human painting process. While exist...
Research Papers
Latest AI/ML research from ArXiv and Papers with Code
CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?
Model--harness co-evolution has emerged as a prominent paradigm for recursive self-improving (RSI) agents, jointly evolving their execution scaffolds and intrinsic capabilities. We...
SPARC: Spoken Profile Authorship Recognition Corpus
Speech anonymization has focused on the acoustic signal, leaving open how much speaker information the transcribed words themselves retain. In this work, we study pairwise speaker ...
Think Visually Across Space and Time: Grounded Reasoning for Video Segmentation
Video Reasoning Segmentation (VRS) requires a model to identify an implicitly described target and predict its pixel-level mask trajectory, while resolving the spatial relations an...
Design Anything, Design at Scale
As multimodal resources are transformed into media outputs, agentic AI is expected to stay aligned with design intent over long horizons. Designers follow briefs specifying goals, ...
Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents
While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and infe...
Editing Images into Reusable Layers via Compositional Edit-Prior Distillation
High-quality image editing requires precise modification of target regions while preserving the content of unrelated regions. Existing editing models produce flattened outputs that...
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval
Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks. Existing methods rely on unconstrained retrieval or predefine...
HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference
Evaluating software agents on a full benchmark is expensive. A small task subset can preserve agent rankings while yielding inaccurate scores. Historical responses provide useful s...
Bridging Exploration and Persona: Information-Directed Dueling for Test-Time Agent Personalization
Effective test-time personalization requires LLM agents to adapt to query-dependent user preferences from lightweight interaction. We study persona selection with binary comparison...
IF-MAP: Influence-Function-Inspired Agent Prioritization in Multi-Agent Systems
Improving every agent in an LLM-based multi-agent system can be prohibitively costly, making it important to identify which agent should be prioritized to achieve the greatest syst...
Axiomatic World Modeling for Physics Reasoning
Recent research-level physics-reasoning benchmarks for LLMs reveal a failure mode that lies not in knowledge or execution, but in modeling the world being reasoned about. Models of...
EdgeKernelBench: Unified LLM-Driven Kernel Generation for Edge Backends
Recent works show that LLMs can generate kernels with strong performance on server GPUs. Edge deployment presents distinct challenges because the hardware is heterogeneous, the sof...
HybridResearcher: Bridging the Structured-Unstructured Boundary for Deep Research Agents
While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables or unstructured data, real-world analyt...
HumanSkill-4D: Training-Free Language Querying of Animatable Gaussian Avatars in Space and Time
We introduce avatar referring segmentation, which selects regions of animatable Gaussian avatars from language queries. A query can also specify a pose or motion to select matching...
Assessing the CAD Capabilities of General-Purpose AI Models: A Case Study of GPT-6 Astra
Recent advances in general-purpose AI models and agentic systems have enabled them to perform some CAD tasks. A systematic assessment is now needed to establish what these systems ...
UniAVC: Unified Audio-Video Creation via Progressive Training and Asymmetric Scheduling
Unified audio-video creation requires a single model capable of text-conditioned joint synthesis, bidirectional cross-modal generation, and instruction-guided editing across both m...
ECHO: Efficient Causal Self-Recursion Beyond Auxiliary Multi-Token Prediction
Existing Multi-token prediction (MTP) methods are auxiliary by design: the multi-horizon loss mostly trains modules attached to the backbone, and each added horizon costs another m...
Muse Glimmer 30B: An On-Device Agentic Model
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer...
Good Judgment, Bad Advice: Decision-Model Controllers Raise Agent Cost Without Improving Success
Agent scaffolds increasingly delegate small decisions (whether to stop, verify, or change approach) to cheaper models. We ask whether a typed decision model, Jev 1.13, consulted on...
OR-License: Evaluating LLM Agents as Real-World Operations Research Practitioners
Can large language model (LLM) agents deliver expert-level operations research (OR) solutions directly from original client materials, without prior expert reformulation? Existing ...
Learning to Discriminate Scene Structures Makes Self-Supervised Depth Learning Scalable
Self-supervised monocular depth estimation is appealing for its potential to scale with unlabeled data, yet existing methods often degrade when trained on diverse scenes. This stud...
Self-Evolving Coding Agents via Harness-Policy Optimization
Large language model (LLM) agents have shown promising potential in coding tasks, and their performance is jointly determined by the policy model and the harness that mediates thei...
PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas
Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential acti...