OPMD: On-Policy Mechanism Distillation Beyond Output Supervision
On-policy distillation (OPD) improves language-model by supervising a student on prefixes sampled from its own policy, but its supervision remains confined to the output space. Con...
Papers
HelixWorld: A Real-time Interactive Audio-Visual World Model
World simulation is inherently multisensory, demanding synchronized visual and acoustic dynamics in real time. Yet prevailing interactive world models remain strictly silent, focus...
MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution
Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its ...
PreviewDiff: Multimodal Critic-Guided Search over Diffusion Latents
Diffusion models can produce striking images and videos, but they still struggle with the compositional details that make a generation faithful to a prompt, such as object counts, ...
Evaluating Bounded Autonomy in Regulated Agentic AI: A Diagnostic Harness with Constitutional Rewards, Escalation Labels, and Runtime Governance
We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows. It instruments six trustworthiness signals: citation validity, source grounding, schema...
Rubric Rewards from Item Response Theory
Many language tasks have no single answer that can be checked automatically. Rubrics provide criteria for judging responses to these tasks. For reinforcement learning, the resultin...
How to Loop MoE: Flatten the Experts, Untie the Attention
Looped Transformers reuse one block of layers several times: by spending extra computation they push a model of fixed size further, and so use its parameters more fully; while spar...
Improving Test-Time Scaling with Adaptive Looped Transformers
Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-looped models at matched paramet...
Nereus: Adaptive Parallelism for LLM Post-Training
Reinforcement learning (RL) post-training for large language models (LLMs) coordinates multiple models across generation, inference, and training on GPU clusters. Several factors m...
The Endless Exam: Mathematical Constructions from Today's Models toward Superintelligence
We introduce the Endless Exam, a benchmark spanning fourteen parameterised families of mathematical construction problems, with verifiable scores that distinguish progress before a...
FlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching
Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially generate reasoning, tool selection...
PyroAdapt: Adapting Wildfire Prediction under Spatial Heterogeneity and Temporal Shift
Prediction of wildfire occurrence is a rare-event problem compounded by spatial heterogeneity and temporal distribution shift, as fire occurrences are vastly outnumbered by non-occ...
Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence
Vision-language-action (VLA) and world-action (WAM) models map observations and instructions directly to robot actions. This directness ties a policy to training: minor layout or v...
The Low-Rank Structure of VLA Reinforcement Learning
Reinforcement learning (RL) is increasingly used to post-train vision-language-action (VLA) models, yet how RL reshapes these policies remains poorly understood. We find that RL ac...
Narrow Multimodal Fine-Tuning Can Induce Emergent Misalignment
Modern AI models are aligned through post-training to adapt them to downstream tasks. Recent work shows that fine-tuning language models on narrow tasks can induce emergent misalig...
Rethinking Latent Visual Reasoning: Grounding Latent Reasoning in Visual Evidence
Latent visual reasoning (LVR) enables multimodal large language models (MLLMs) to perform intermediate computation in continuous latent tokens rather than expressing every reasonin...
LLMs are General Asynchronous Agents
Modern LLMs are increasingly capable as autonomous agents, but they follow sequential interaction cycles: read, think, reply or call tools, repeat. Many real-world use cases are no...
Persistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion
Pixel-space diffusion Transformers (DiTs) directly operate on high-dimensional visual data, yet their hidden representations typically undergo uniform refinement across depth. Natu...
SEAD: A State-Based Perspective on Attack and Defense in Tool-Using Agents
Language-model agents increasingly use tools to act on external systems. Earlier actions can alter files, permissions, database records, or other state, making a later routine-look...
Multilinguality in Hybrid Attention LLMs
In response to the growing demand for long sequences in agentic and reasoning use cases, many state-of-the-art LLMs combine multiple variants of attention to mitigate the quadratic...
Jev thinks "I don't know'', but doesn't say it: Introducing Sys1Cal-v1 Dataset for Probability Calibration
The appearance of Jev marked the era of System One Models, foundation models that return structured decisions with probability distributions rather than text. Aside from low cost a...
StructRL: Online Structured Reinforcement Learning for Long-Horizon Vision-Language-Action Tasks
Vision-language-action (VLA) models perform well on shorter-horizon manipulation tasks but still struggle with long-horizon tasks that require multiple dependent manipulations from...
EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold
Interactive 3D head generation requires coordinated speaking and listening motion that responds to an evolving conversation. Existing generators use incoming observations as contex...
Knowing When Thinking Is Not Enough: Teaching Small Reasoning Models to Reason Beyond Their Parametric Knowledge
Scaling test-time computation is a powerful way to improve language-model reasoning, and is particularly appealing for small reasoning models (sRMs) that are cheap to serve. Howeve...