All papers

WorkRover introduces a framework to train long-horizon cowork agents by recursively evolving contexts, tasks, and verifiers based on execution feedback.

Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks.

The paper introduces a hierarchical reinforcement learning method designed for agentic tasks that span multiple interaction sessions.

The paper introduces a domain-general framework using predictive world models to guide action selection and policy improvement in recursive self-improvement settings.

Introduces SwitchMem, a routing system that dynamically selects between text and visual memory representations for vision-language GUI agents.

The paper introduces a multi-agent system to automate data curation and dataset editing for supervised fine-tuning.

This paper proposes an agentic video framework combining change-aware memory and targeted replay for streaming video understanding.

Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning.

This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models.

The authors present a training framework and dataset that enables terminal coding agents to self-improve by recovering from execution errors.

The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access.

The paper introduces a multi-domain benchmark to evaluate language model agents on identifying, understanding, and applying domain-specific rules across complex files.

The authors present an engine to synthesize environments, tasks, and rubrics for training and evaluating data analysis agents.

The paper presents a latent world action model that predicts action-induced state transitions rather than full-screen pixel reconstructions for graphical agents.

Proposes a recurrent harness framework that uses streaming feedback to revise textual memory for continual agent learning.

The paper proposes a training-free interface for heterogeneous language models to communicate facts using shared symbolic word indices.

This paper presents a training framework that teaches language models to orchestrate tools through executable code instead of sequential prompt accumulation.

The paper investigates an asynchronous training recipe combining the Muon optimizer, token-averaged loss, and penalties for long-horizon agent tasks.

Presents a large graphical user interface grounding dataset and a combined distillation and reinforcement learning training method for agents.

The paper presents AutoEnvScaling, using terminal agents to automatically generate reinforcement learning environments from model rollouts.

The authors present a market-based post-training framework that allows decentralized agent swarms to learn both action policies and coordination mechanisms without central orchestration.

The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning.

Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication.

Introduces Cowork-Gym, a framework synthesizing multi-tool collaborative work environments and user simulations to train enterprise agents.

RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience.

The paper introduces a framework to model evolving social dynamics and update personas in multi-agent language model teams.

The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history.

The authors formulate a meta-game framework to dynamically route and select language models during strategic multi-agent interactions.

The paper introduces an adversarial co-evolution reinforcement learning framework to strengthen language model agents against tool-use manipulation attacks.

Proposes Web$hell, a unified interface combining broad information gathering, browser interactions, and evidence management for web-based agents.

Presents an environment scaling framework that alters dependencies to scale agent benchmark difficulty while preserving task objectives.

The authors develop a system that systematically tests, evolves, and prunes skill and rule files used by coding agents.

SHELDON introduces a dual-search reasoning framework that independently explores hypotheses and observation spaces to avoid confirmation bias.

Equips multimodal vision-language models with video world model operations as tools to support dynamic spatiotemporal visual reasoning.

Proposes a policy mechanism enabling language models to decide when, how, and which test-time training procedures to execute.

The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments.

Presents a benchmark evaluating agentic large language models on long-horizon strategic reasoning in the game Slay the Spire.

Presents speculative recursive self-improvement to draft successor agents before full evaluation and optimize verification budgets.

Presents a framework allowing language model agents to evolve environment dynamics beliefs through falsifiable hypothesis testing.

Introduces AgenticBBO-Bench, a benchmark across multiple domains to evaluate LLM agents on black-box optimization under unified finite budgets.

Introduces POrchestra, an agentic orchestration framework enabling sub-agents to proactively communicate with the orchestrator during dynamic execution.

Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models.

The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes.

The paper audits historical evaluation tasks to see whether an agent's internal post-cutoff memory skews its point-in-time evidence gathering.

The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks.

The paper presents a reinforcement learning framework that trains language models to solve complex logical reasoning tasks using interactive symbolic solvers.

This paper updates a long-horizon terminal benchmark to measure sustained agent progress across complex engineering workflows using dense process rewards.

This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses.

The paper presents a runtime framework that tracks evolving repository state and dependencies to assist long-horizon coding agents.

This paper proposes a system where language model agents write code to create and refine tools directly over raw knowledge-graph files.

The paper introduces an optimization method that jointly refines communication instructions between communicating agents to improve multi-agent collaboration.

Introduces a benchmark evaluating how reliably software coding agents extract and digitize complex unstructured clinical trial protocol documents.

This paper introduces a benchmark dataset to evaluate hallucination detection in language model responses after external tool execution.

This paper introduces an environment benchmark and exploration framework to help graphical user interface agents learn unfamiliar interaction dynamics.

This paper introduces a multi-agent sandbox benchmark to evaluate language models on state inference, forecasting, and intervention prediction.

RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps.

The paper re-evaluates coding-agent benchmark results to measure sensitivity to test execution environments and grading configurations.

The paper presents SciCurateBench to evaluate language models on verifying complex experimental conditions from unprocessed scientific literature.

The paper presents OpenMobile-2, combining open mobile environments, trajectory datasets, and hybrid GUI-tool actions for mobile agents.

Introduces Sauron, an evidence-first agentic verifier that checks both environment state and execution traces against original sources to validate task rubrics.