All papers

The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation.

The paper presents a framework using vision-language models to generate reusable visual planning code from a single practice scene.

The paper examines communicating calibrated action probabilities rather than direct actions to improve multi-agent collaboration under partial information.

The paper develops an analytical framework to interpret and reverse-engineer incentives and cooperative behaviors in multi-agent artificial intelligence systems.

This paper introduces MolLean, a verifiable language and tool interface that grounds chemical reasoning in formal state transitions and explicit checks.

This paper proposes Claim Cards to make agent interpretations explicit when verifying ambiguous natural-language scientific claims.

The paper investigates how team composition and organizational interdependence affect performance in self-evolving multi-agent systems.

The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching.

The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models.

The paper introduces a unified routing framework that learns both execution mode selection and model assignment across diverse tasks.

The paper introduces a localized Japanese benchmark for evaluating tool-using conversational agents in customer-support scenarios.

The authors propose a learned steering agent that monitors execution context and intervenes to prevent goal drift in tool-using language model systems.

The paper builds state graphs across multiple agent rollouts to evaluate behavioral consistency and predict execution failures.

The paper guides knowledge-graph retrieval agents by evaluating the causal impact of evidence on answering questions rather than text similarity.

The paper pairs language models with symbolic verifiers to test and update reusable hypotheses for environment diagnostics and repair.

The authors introduce a framework to benchmark language model agent robustness under interaction perturbations across diverse tasks.

Uses large language models to construct prior trade-off preferences to address cold-start challenges in choice-learning systems.

Presents a training framework that teaches language model agents to transfer reusable problem-solving experience across structured task families.

Creates a structured schema that converts research papers into connected scientific entities to improve retrieval-augmented generation for autonomous agents.

The paper proposes a federated learning framework that uses version control operations instead of full document aggregation to evolve agent skills.

The paper introduces an agenda-driven neuro-symbolic framework to improve the reliability of user simulation when testing interactive conversational agents.

The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents.

The paper proposes a test-time aggregation method using bias control and injection to resolve disagreements among reasoning language model agents.

Analyzes trade-offs between aggressive and conservative planning horizons in language models operating under partial observability.

Presents a multi-turn benchmark measuring language agent capabilities on complex, long-horizon bioinformatics workflows.

Presents an evaluation system that tracks state and evidence to ensure autonomous agents fulfill obligations across long-horizon executions.

Introduces a localized code repair framework that evolves language model memory architectures using fine-grained execution feedback.

The paper introduces an action and feedback contract separating agent decisions from deterministic tool execution for constraint-governed engineering tasks.

The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback.

The paper trains reasoning models to perform sequential experimental design by optimizing information-gain rewards during action selection.

The paper presents a framework and dataset for learning to detect and repair errors during multi-agent system execution.

The paper introduces an adversarial self-play framework modeling user mental states to improve proactive dialogue agent planning.

The authors present an active spatial search framework enabling vision-language agents to ground 3D objects in city-scale environments via interactive tool use.

Introduces AAArena, a benchmark evaluating how AI agents revise executable code policies across 12 adversarial games under fixed budgets.

This paper introduces an agentic post-training framework that coordinates multiple agents to evaluate and adaptively train text-to-image models.

The paper introduces a watermarking method to verify ownership and trace unauthorized copying of agent skill instructions.

Equips multi-agent systems with latent mental models to infer counterpart beliefs and intentions for better decision-making under partial observability.

The paper proposes an agentic reinforcement learning method that uses executable oracle continuations to guide exploration during long-horizon tasks.

Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research.

Introduces a benchmark evaluating how language model agents adapt to latent environmental dynamics and social dilemmas over repeated interactions.

The paper introduces an open-source agent framework that improves code-rendered explainer videos through program-level debugging, static rules, and unit testing.

This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation.

The paper proposes an agent framework that plans multi-slide exploration and visual tool use for patient-level pathology visual question answering.

The authors introduce a calibration harness using structured domain knowledge to safely delegate Earth science model parameterization to language model agents.

The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility.

Tests language models on graph-based information retrieval and navigation using the Wikipedia Game across multiple languages and citation datasets.

Pre-trains embodied robot policies on human egocentric videos using learned continuous latent action representations.

Studies how fine-tuning helps tool-using agents learn to withdraw and repair actions when environment assumptions change.

Introduces MAGMA-Bench to evaluate long-horizon embodied agent reasoning during dynamic task execution failures and multi-robot coordination.

Proposes a symbolic world modeling method using functional role abstractions to transfer interaction dynamics across environments for LLM agents.

Proposes ARGUS, an agentic framework separating deterministic genomic models from LLM reasoning to interpret noncoding single nucleotide variants reliably.

This paper investigates an agent framework that selectively queries expert judgments for borderline emotion-cause pairs to improve extraction accuracy.

This paper establishes mathematical conditions to guarantee long-term performance and low counterfactual regret for reflective language agents under resource constraints.

Proposes a latent space model that summarizes historical interactions to guide decentralized multi-agent self-organization.

Uses task dependency graphs to help multi-agent teams maintain and evolve shared procedural memories across long tasks.

Introduces a latency-aware workflow learning method to reduce execution delays in recurring multi-agent queries.

The paper introduces a federated multi-agent framework that guides agent selection and query refinement under dynamic privacy constraints.

The paper introduces a framework to model and evaluate how extremist agents affect collective behavior in multi-agent language model societies.

Proposes a graph-guided synthetic data generation framework with controlled difficulty levels for training multi-hop research agents.

SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles.