All papers

Introduces a multi-agent framework where simulated scientists formulate, test, and revise scientific conjectures alongside mathematical equations.

The paper presents an agentic reinforcement learning framework that navigates organized chemical spaces for cross-scaffold molecular discovery.

The authors present an interactive environment to evaluate how language models integrate exploration, hypothesis generation, and reasoning.

The paper proposes an error-correction method to prioritize and transmit key-value cache tokens across edge-deployed multi-agent systems under latency deadlines.

The authors introduce a benchmark to audit mathematical claims and repair errors using an automated witness verification checker.

Introduces an AND/OR graph proof search algorithm optimizing customizable objectives like length and compute cost during automated formal theorem proving.

Models repeated games with endogenous delegation to analyze trade-offs between economies of scale and institutional accountability.

Presents an end-to-end joint-embedding predictive architecture that combines world modeling and action flow matching for robot planning and control.

The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback.

The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks.

JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets.

The paper proposes an iterative self-improvement method combining trajectory rewriting and policy refinement for terminal-based coding agents.

The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks.

Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace.

The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors.

Presents a textbook providing an overview of the concepts, architectures, and interactions of agentic large language models.

Introduces a multimodal embodied agent policy learning framework using trajectory construction, distillation, and token-level evidence routing.

The paper introduces an autonomous multi-agent framework that translates natural language prompts into playable browser games with automated verification.

Introduces HOA, a multi-session LLM agent framework coordinating review, wiki knowledge, and verification for complex Olympiad problem solving.

Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines.

Combines genetic programming and diagnostic evaluation loops to discover quantitative financial trading factors without backtest overfitting.

The paper introduces an agent-based approach that adapts search spaces to optimize post-training recipes and configurations for pre-trained language models.

This paper presents a robotic manipulation framework combining high-level language planning with vision-language verification to enforce physical and kinematic constraints.

This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning.

This paper proposes a progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models.

The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies.

The authors propose a memory-driven vision-language navigation system using temporal compression and geometric-semantic representations to support long-horizon trajectory tasks.

The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation.

Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use.

Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing.

Presents a multi-agent swing trading framework separating long-term strategy formulation from fast-timescale portfolio risk governance.

This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead.

Introduces Agentic-MatriBO, a modular Bayesian optimization framework using LLM agents and deterministic compilation for material and chemical experiment design.

The paper presents an instrumented predator-prey simulation framework to evaluate communication, scheduling, and collective dynamics in remote decision-making agents.

The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation.

Constructs context-matched preference comparisons from sibling rollouts to improve credit assignment during language model agent training.

Decomposes joint temporal logic constraint violations to allocate credit among agents in cooperative reinforcement learning.

Presents RegulaTutor, a personalized tutoring framework that grounds instructional policies in explicit cognitive and self-regulated learning theories.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

Presents a local agentic framework using smaller language models to automatically synthesize edge-case testing scenarios for autonomous systems.

Introduces an offline goal-conditioned policy optimization method to help vision-language-action models recover from execution deviations.

Introduces a recursive self-improvement agent framework that refines symbolic graphics programs using execution traces and runtime state changes.

Proposes a multi-agent framework that evolves the data generation space using learner feedback to synthesize agent training trajectories.

Introduces a benchmark and environment for evaluating coding agents on text-conditioned LEGO design with physical and discrete assembly constraints.

Presents a top-down skill refinement system for robot self-improvement through structured execution feedback and reusable modular skills.

Introduces an evidence-grounded multi-agent framework combining multimodal medical data and clinical literature for knee osteoarthritis progression prediction.

Introduces a hierarchical memory architecture that consolidates nested agent task intents into reusable knowledge across long horizons.

Proposes a self-evolving agent that allocates evaluation resources dynamically across candidate programs during iterative code evolution.

Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet.

Presents a program-search method using a curated elite solution table to guide language models in synthesizing executable control policies.

The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute.

The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents.

The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates.

The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas.

This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems.

The paper presents an automated framework that jointly designs reinforcement learning observation spaces, reward programs, algorithms, and training configurations.

The paper refines a high-level spatial AI orchestration framework into an implementation-focused low-level architecture for governed agent execution.

The paper introduces a sparse interaction diffusion method that coordinates multi-robot trajectories using a frozen single-robot prior and modular corrections.

The paper introduces an agentic framework that removes peripheral visual clues to protect geolocation privacy in images without adding visible artifacts.

The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning.