Harness-Model Collaboration for Long-Horizon Continual Learning
Long-horizon agents continuously encounter reusable procedural knowledge, yet existing systems typically rely either on model adaptation or external memory, trading persistent lear...
Research Papers
Latest AI/ML research from ArXiv and Papers with Code
Cross-modal Steering Vectors Exist Across Both Image and Text Generation
Are concepts and mechanisms unified across modalities in current AI models, or instead implemented separately for each one? We operationalize this broad question of modality unific...
On the SPOT: Enhancing Speculative Decoding for Diffusion Language Models via Commit-Order Prediction
Diffusion language models (DLMs) promise parallel text generation, but in practice face an unresolved speed-quality trade-off. Exact decoding fills in only one masked position per ...
Rollback: Experience Backtracking in World Models for Policy Improvement
Action-conditioned world models offer a scalable alternative to costly real-world interaction for improving robot policies. However, existing world-model-based methods largely trea...
Rethinking Verification in Agent Harnesses
Agent harnesses enable large language models to act through tools, observe the results, and iteratively work toward a task goal. Verification checks and revises agents' work to imp...
Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila
Animals have a remarkable capacity for autonomy---driven by intrinsic motivation---without external sensory cues or extrinsic objectives, yet the computational mechanisms behind th...
RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions
Scaling vision-language-action (VLA) models is constrained by scarce action-labeled demonstrations and incompatible control spaces across robot embodiments. We present RynnVLA-Late...
DynaFlow: Learning Free-Form Multi-Agent Orchestration
Multi-agent systems often rely on predefined collaboration structures, limiting their ability to adapt to diverse task requirements. We introduce DynaFlow, a framework that integra...
Δτ-Bench: Pushing Agents Toward a Moving Capability
In this paper, we introduce Δτ-Bench, an executable agent benchmark that models the structural evolution of large tool ecosystems. In Δτ-Bench, independently evolving services cont...
AgentProofBench: Benchmarking Reusable Verifiers for Web Agents
Reusable Web-agent verification requires a rule to follow changing requirements and support subsequent execution. We introduce AgentProofBench, which connects paired tests of a fro...
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO) suffer from a fu...
h1: A Taste of the Bitter Lesson in Automated Theorem Proving
While progress in informal mathematics has come largely from scaling the chain-of-thought and agentic reasoning of frontier language models, formal theorem proving has increasingly...
Memory Tax: Dissecting the Net Value of Agent Memory
Memory is increasingly used in long-horizon agents, yet the net value of building memory over direct retrieval from raw history remains largely unclear. Through a large-scale evalu...
HuGe: Scaling Humanoid Generalist Vision-Language-Action Models with Egocentric Experience
Extending Vision-Language-Action models (VLAs) to humanoid robots requires coordinated whole-body control and diverse demonstrations that are costly to collect in real-world scenar...
Identically Trained Language Models Break on Different Jailbreaks
Jailbreak benchmarks test one checkpoint and treat which jailbreaks break it as a property of its pretraining data and safety training. We ask whether another pretraining run of th...
InteractiveWorld: A Verifiable Human-Centric Environment for Building Better Interactive MLLM Assistants
People have long dreamed of an assistant that is always there to guide them and remind what they forget, from new dishes to lost keys. Powered by multimodal large language models (...
Continual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents
Continual learning is the ability of LLM agents to improve over time by acquiring, refining, and applying knowledge from experience while retaining useful prior knowledge. It is es...
ParetoRSI-Gym: Can AI Agents Improve Language Models Across Multiple Domains?
Recursive self-improvement (RSI) requires an agentic system to design, train, and evaluate its own successor. Data-driven post-training is a central part of that loop, yet existing...
Spatial Audio Generation and Editing with Executable Plans and Self-Distilling Transfusion
Spatial audio authoring coordinates source decisions with soundfield synthesis. For editing, plans specify what to change and retain, while reference audio supplies further acousti...
Humans in Space: Can MLLMs Construct Human-Centric Spatial Understanding via Active Exocentric Exploration?
While Multimodal Large Language Models (MLLMs) excel at zero-shot reasoning, their spatial capabilities are primarily evaluated through static, passive observation. In complex, hum...
Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language Agents
Language agents increasingly act through external tools and interactive environments, making the interface between the model and its executable capabilities part of the decision pr...
Broken on Arrival: Non-Functional Quantized LLM Artifacts in Public Model Registries and How to Catch Them
Developers download quantized large language models (LLMs) from public registries to run them locally. None of the registries we examined documented functional testing of their fil...
LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends
Visual agents need to coordinate reasoning and action while their environment evolves during inference. We introduce LOLPlay, a benchmark in native League of Legends with 36 champi...
AgentBaton: Efficient Model Collaboration in Agentic Tasks
Solving long-horizon agentic tasks requires language agents to interact with their environments over multiple steps, and reliable performance often comes from the expensive capable...