All papers

The paper introduces a compiler framework that transforms defective natural-language protocols into verifiable laboratory execution workflows.

The authors propose KnowGPO, a reinforcement learning framework that assigns credit to deep search agents based on acquired knowledge conditions rather than trajectories.

The paper proposes FGSS-BO, adding structured hypothesis management to language-model-guided Bayesian optimization.

The paper examines decentralized multi-agent coordination through sequential market auctions under conditions of imperfect competition.

Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose.

Combines language models with multi-agent auctions to manage resource contention in 5G edge network slicing.

The authors introduce an embodied reasoning foundation model that unifies perception, planning, and execution using task-conditioned adaptive adapters.

Orchestra decomposes multimodal instructions into subtasks, tracks step progress, and maintains execution context to improve long-horizon robotic manipulation.

TrueMAD introduces one-to-one peer feedback between debate rounds to help language model agents resolve conflicting reasoning before final revision.

This paper introduces an agent coordination framework for scaling recursive self-improvement research teams without updating model weights.

This paper introduces a framework that enables language models to optimize physical structural designs through simulated fracture experience and hypothesis revision.

This paper presents a coupled evolution framework that co-adapts an executable tool library and a model's policy for scientific equation discovery.

This paper proposes a training framework that triggers supervised fine-tuning specifically on failure states during reinforcement learning to prevent agent forgetting.

Introduces an adaptive-group process reward framework that partitions GUI trajectories into subgoals to provide intermediate credit assignment for reinforcement learning.

The paper proposes a multi-agent framework to coordinate analytical tools and evaluate multi-factor supply chain risks across diverse locations.

The paper introduces an agentic system that reconstructs multi-layer 3D garment sewing patterns from a single image using simulation feedback.

Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options.

The paper argues that uncertainty estimation in smart assistants is necessary to prevent unsafe actions when user preferences remain ambiguous.

Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models.

This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines.

The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation.

The paper introduces a conflict-clustering solver with lightweight learning to coordinate distributed agents solving constraint satisfaction problems.

The paper presents an ontology-guided retrieval framework combining population and personalized memory for wearable sensor human activity recognition.

Proposes a hierarchical evolution framework that allows individual agent software harnesses in multi-agent systems to adapt using feedback.

Uses multi-objective evolutionary algorithms to optimize agent execution harnesses across competing metrics like accuracy and operating cost.

This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature.

This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement.

This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules.

This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management.

The paper introduces a reinforcement learning approach for multi-turn agents that identifies recurring decision patterns to improve intermediate step credit assignment.

The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation.

This paper presents a multi-agent framework that translates natural language into verified, executable data analytical programs using schema-grounded refinement.

This paper proposes a critic-free reinforcement learning algorithm using prefix trees to assign step-level credit for interactive multi-step tasks.

This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks.

The paper proposes a fine-tuning method that trains language model agents using textual expert critiques alongside corrected actions.

Presents an automated framework using language models to manage storytelling, rules, and combat in tabletop role-playing games.

The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints.

The paper compares three textual interface designs for optimizing prompts in multimodal audio-visual question answering systems.

The paper proposes a vision-language-action model that dynamically crops high-resolution regions to manipulate objects without using a wrist camera.

The paper proposes a progressive validation framework for zero-shot tool selection that evaluates functional compatibility and contextual relevance.

The paper introduces a brain-inspired reflection framework that diagnoses failure points to guide self-evolving agents in embodied spatial tasks.

Proposes an interactive agent framework that utilizes physics simulation and spatial exploration for complex 3D object assembly and composition.

Presents a graph-based framework with decision certificates to determine when multimodal agents should acquire visual evidence under cost constraints.

The paper introduces a method that selectively adjusts positive credit for undesirable agent actions using the policy's confidence during multi-turn reinforcement learning.

The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents.

The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change.

The paper presents a multi-agent system designed to automate advertising operations and coordinate tasks like bidding and budget allocation.

The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains.

The paper presents an autonomous agent platform designed to monitor surveillance data and generate early warnings for infectious disease outbreaks.

The paper introduces an operation-level graph framework that helps interactive agents retrieve and compose reusable fragments from past task experience.

The paper introduces an experience-graph framework enabling heterogeneous agents to share and reuse execution history across diverse tasks.

The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation.

The paper proposes an in-context reinforcement learning method enabling vision-language-action policies to adapt during robot deployment.

The paper introduces a distillation framework that triggers teacher takeovers when student policies diverge during long-horizon reasoning.

The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes.

Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists.

The paper presents a graph-based framework using control nodes and behavior trees to coordinate heterogeneous artificial intelligence models.

The paper introduces a hierarchical task network framework that separates multi-agent planning from execution scheduling.

The paper uses language model subtask decomposition and dense progress tracking to enhance reinforcement learning on long-horizon tasks.

The authors present an agent loop that closes functional test coverage in hardware design using independent simulation scoreboards.