Unlocking Feature Learning in Gated Delta Networks at Scale
Training and scaling Large Language Models demand enormous computational resources, motivating both efficient sub-quadratic architectures and principled hyperparameter tuning metho...
Papers
Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large...
Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrai...
Benchmarking Visual State Tracking in Multimodal Video Understanding
Understanding a video requires more than recognizing isolated moments, as humans continuously track entities, states, and events over time. This capacity for visual state tracking ...
AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
We present AAD-1, an Asymmetric Adversarial Distillation framework for One-step autoregressive image-to-video generation. State-of-the-art methods adopt adversarial distillation bu...
PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training
We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. Although PaddleOCR-VL-1.5 establishes a strong 0.9B baseline, its remaining e...
Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recent advances in LLMs' agentic ...
BA-T: An Iterative Transformer for Two-View Bundle Adjustment
Feed-forward models for 3D reconstruction have achieved strong performance using deep cross-view attention to exchange information across images. However, these approaches often de...
Neural Networks Provably Learn Spectral Representations for Group Composition
Understanding how structured internal structure emerges during neural network training is central to the study of deep learning. We investigate this phenomenon through the group co...
Self-Distilled Policy Gradient
On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense supervision for sparse-reward ...
RobotValues: Evaluating Household Robots When Human Values Conflict
While household robots are often evaluated based on task completion, everyday domestic environments involve value-conflicting situations in which robots are expected to choose acti...
AgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes. Continual learning ...
Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents
Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for e...
SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation
Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRE...
Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting
Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles. However...
Large Language Models Are Overconfident in Their Own Responses
Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the frequ...
AURA: Action-Gated Memory for Robot Policies at Constant VRAM
The KV-cache is the right memory for datacenters but the wrong memory for robots. Datacenter inference batches many short requests and resets them, amortizing an attention cache ac...
Ψ-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues
Personalization is a crucial capability of modern language agents. However, current research primarily positions personalized agents as passive responders to user preferences, limi...
MMG2Skill: Can Agents Distill In-the-Wild Guides into Self-Evolving Skills?
Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimodal, heterogeneous, noisy, and i...
HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems
LLM agents are increasingly expected to operate across heterogeneous task regimes that require distinct execution paradigms. This challenges fixed agent systems and motivates syste...
Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams
Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infrastructure from execution feedba...
TRON: Targeted Rule-Verifiable Online Environments for Visual Reasoning RL
Reinforcement learning (RL) for visual reasoning needs scalable, verifiable, and controllable training signals. Existing visual RL post-training trains on static curated datasets, ...
Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models
Inverse graphics is a longstanding and highly underconstrained problem that seeks to reconstruct images as editable 3D scenes which can be rendered, relit, and manipulated. In this...
Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025
Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and ho...