HuggingFace
InstanceControl: Controllable Complex Image Generation without Instance Labeling
Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visua...
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HuggingFace
Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain r...
HuggingFace
PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking
This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leadi...
HuggingFace
CogSENet: Blind Image Deblurring with Blur-Conditioned Semantic Routing and Explicit Frequency Fusion
Blind image deblurring demands the recovery of high-fidelity details and coherent structures from complex, unknown de...
HuggingFace
SciIR: A Large-scale Training Dataset and Benchmark for Scientific Image Reasoning Generation
While Text-to-Image (T2I) models have shown remarkable success in generating photorealistic visual content, they stil...
HuggingFace
Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation
Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of syntheti...
HuggingFace
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
Repository-level performance-optimization benchmarks such as GSO, SWE-Perf and SWE-fficiency evaluate coding agents b...
HuggingFace
HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents
As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is esse...
HuggingFace
Building to the Test: Coding Agents Deliver What You Check, Not What You Requested
Benchmarks are widely used to evaluate task completion by Large Language Models (LLMs), but this approach has accumul...
HuggingFace
When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling
People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning syst...
HuggingFace
GRPO, Dr. GRPO, and DAPO Are Three Operations on One Number: The Group-Standard-Deviation Identity
Three of the most popular methods for training language models to reason look like three different tricks. They are n...
HuggingFace
Seeing Is Not Sharing: Some Vision-Language Models Overestimate Common Ground in Asymmetric Dialogue
In collaborative dialogue, shared perception does not guarantee shared interpretation. Mutual understanding must be e...