Researchers present SenseNova-U1.5, an 8-billion-parameter multimodal model that performs visual understanding, reasoning and generation within a single unified architecture, eliminating…
Researchers introduce NCP-ArchPreview, a latent-space language model that extends standard autoregressive pretraining by adding a Next Concept Prediction objective alongside…
Researchers introduce Recursive Code World Models (RCWM), a framework for reconstructing detailed 3D environments from a single image by representing…
Researchers present MaP-WAM, a framework for non-Markovian robotic manipulation that decomposes memory-dependent world-action modeling into memory-grounded planning and plan-conditioned execution.…
Researchers present X-AuT, a technique for making speech-focused large language models more efficient by compressing their audio encoders through progressive…
Researchers introduce Mi-Ripple, a restoration technique that targets degradation artifacts that accumulate from repeated cycles of AI-based image editing. The…
Researchers present SpatialBlock, an approach for improving how large vision-language models understand 3D spatial relationships from 2D images. The method…
The paper introduces Feedback-Enriched Environments (FEEs), which shift training focus from agent-side improvements to environment-side adaptations rather than relying solely…
The paper introduces Procedural Graphs, a framework that organizes procedural knowledge as structured (procedure, relation, procedure) triplets to guide LLM…
Researchers introduce NeoHorse-1, an approach to recursive self-improvement built on agentic post-training over a heterogeneous model pool with intelligent routing.…
Researchers present a system for accelerating reinforcement learning training by improving speculative decoding through online co-training of draft models. The…
The paper proposes BeaconKV, a training-free technique that compresses key-value caches during inference for large reasoning models. It identifies that…
