All papers

The authors formulate colon cancer treatment planning as an interactive reinforcement learning task in settings where actions are only partially verifiable.

Introduces HelpMeOut, an embodied AI benchmark for diagnosing human execution errors across household activity trajectories motivated by neuropsychology research.

The paper proposes a budgeted KV-cache repair technique to restore model performance efficiently after deleting obsolete context tokens.

The paper introduces a diagnostic framework to test whether language model agents genuinely infer opponent beliefs rather than following superficial behavioral patterns.

The paper proposes a reinforcement learning method that assigns distinct credit to initial code generation and subsequent post-execution correction steps.

Designs an explicit digital-twin world model where robotic agents inspect, simulate, and calibrate physical parameters before acting.

The paper introduces Trace-to-Architecture, a framework that diagnoses LLM agent execution failures to automatically revise agent harness components.

Introduces a benchmark and tool-augmented agent for evaluating multi-hop reasoning in cryptocurrency fund tracing.

The paper uses language models to generate semantically diverse agent policies in code to train reinforcement learning cooperators for zero-shot collaboration.

This paper presents an imitation framework that enables agents to identify violated contracts, extract skills, and repair execution failures.

This paper proposes a coevolution framework where language model agents and domain foundation models iteratively train and improve each other.

The paper presents an in-context learning method that extracts compact concept vectors from demonstrations to guide sequential action choices for agents.

The paper analyzes implicit communication conventions between humans and AI agents in the cooperative card game Hanabi.

The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets.

Analyzes the price of anarchy and economic efficiency of first-price auctions where AI agents bid to serve user prompts.

Combines language model review with mechanical numerical checks to verify mathematical formalizations and catch errors missed by automated judges.

The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes.

This paper introduces a multi-agent mathematical discovery system that organizes proofs, assumptions, and failed attempts into structured research graphs.

The paper examines failure modes in recursively self-improving execution harnesses for agentic video understanding models.

This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures.

This paper measures semantic drift and stability across AI agent communities, finding higher drift and lower temporal continuity than human forums.

This paper investigates whether gossip discourse naturally emerges in autonomous AI agent online communities compared to human discussion boards.

The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models.

The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively.

The authors evaluate how developer scaffolding and general prompt instructions affect the performance of environment-agnostic coding agents across diverse benchmarks.

Introduces a benchmark evaluating whether language model agents can independently direct and validate goal-specified physics research investigations.

The paper introduces an authorization system that reuses validated consequence evidence across agent execution branches while maintaining separate live authority.

Introduces a reinforcement learning framework that evaluates memory operations in agents using a shared state and process rewards.

Proposes a decentralized coordination mechanism using commitment tracking to preserve clear accountability across multi-agent delegation chains.

Presents an autonomous supervisor agent that iteratively tunes an agent's code, harness, and prompt on few examples.

The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering.

The authors introduce 3DCodeVerse, a dataset and multi-agent framework enabling coding agents to generate, reason over, and verify 3D executable code.

Proposes a rolling-denoising framework for vision-language-action models to improve robot control reactivity without sacrificing long-horizon execution consistency.

Introduces a controlled Minecraft benchmark to evaluate whether embodied agents acquire persistent, transferrable skills across environment resets.

Introduces ChemDiffAgent, applying masked diffusion to plan tool use and reasoning trajectories bidirectionally for chemical problem-solving.

Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems.

Investigates whether LLM scientific agents rely on task priors or experimental feedback when configuring neural operator adaptations.

Presents ME-World, an egocentric world model generating synchronized multi-agent first-person video streams with fine-grained physical interactions.

Introduces Compo and a spatial canvas interface enabling users or agents to guide poster layout and image generation.

Surveys benchmarks, metrics, and experimental practices used to evaluate automated research systems across ideation, coding, writing, and peer review.

Proposes a three-dimensional framework evaluating decision-making artificial intelligence methods across sequential formalisms, autonomy levels, and computational cost.

This paper proposes MMLA-RSI, a recursive self-improvement framework using reasoning-time policy training and authoritative memory adaptation.

Orchestrates persistent multi-robot missions with language models by offloading ongoing tasks to classical protocols.

The paper introduces a dataset and framework pairing synchronized point-of-view and panoramic video to evaluate multiplayer action-conditioned world models.

The paper introduces an agentic reasoning controller framework and an evaluation benchmark for optimizing open radio access networks.

This paper introduces a framework combining language model agents with deterministic software controls for reliable sensor-based industrial fault diagnosis.

This paper introduces a framework that quantifies delegation flexibility to analyze the spectrum between centralized and decentralized multi-agent coordination.

The paper proposes predicting 3D bounding box trajectories instead of dense video frames as compact representations for world action models.

The paper introduces a multi-agent framework comprising an orchestrator and four specialized agents to automate iterative drug discovery workflows.

The paper introduces a benchmark to evaluate coding agents building executable physical simulations from video observations using Blender.

Introduces a benchmark and agent framework for embodied navigation tasks requiring physical environment interactions like opening doors.

The paper develops a dataset and framework to simulate realistic user social media actions using psychological profiles and retrieved interaction histories.

Develops a multimodal search framework that learns to turn intermediate visual discoveries into guidance for subsequent search actions.

Introduces a benchmark of 29 open algebraic combinatorics problems to evaluate algorithm discovery by frontier AI models.

The paper introduces a navigation framework and benchmark using depth and spatial audio for obstacle avoidance in unlit industrial environments.

This paper introduces a persistent survival sandbox benchmark to evaluate sustained goal execution and program synthesis in autonomous agents.

The paper presents a unified taxonomy and design space organizing recursive self-improvement methods for language model agents.

The paper proposes definition grounding to continuously evaluate and revise formal mathematical definitions generated by language model agents.

The paper introduces a neurosymbolic world model that learns interpretable concepts from temporal logic constraints for agent planning.

The paper introduces a horse-racing decision benchmark to evaluate whether agents can extract profitable signals from noisy historical data.