Question: Please help me find research papers on algorithmic design for agent context management that must involve tool calling, and exclude any approaches that introduce or rely on Vision-Language Models (VLM).

Answer: AgentFold: Long-Horizon Web Agents with Proactive Context Management

Answer: Scaling Long-Horizon LLM Agent via Context-Folding

Answer: Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management

Answer: BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair

Answer: A2FM: An Adaptive Agent Foundation Model for Tool-Aware Hybrid Reasoning

Artificial Intelligence and Computing


Question: Find research papers about training an LLM agent and involving websailor as a baseline

Answer: BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair

Answer: ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization

Answer: WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents

Answer: AGENTFOLD: Long-Horizon Web Agents with Proactive Context Folding

Answer: WEBSAILOR‑V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning

Answer: Scaling Agents via Continual Pre-training

Answer: DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL

Answer: Repurposing Synthetic Data for Fine-grained Search Agent Supervision

Answer: WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling Info-Rich Seeking

Answer: AgentFrontier: Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis

Engineering and Technology


Question: I am researching humanoid whole-body manipulation (combining locomotion and dexterous hand control). Currently, I have only found some papers related to whole-body control, but I know nothing about dexterous hand manipulation or the combination of the two. I hope you can find me some papers to help me understand. My current robot is a G1 equipped with an Inspire dexterous hand. The hand must perform specific manipulation tasks; simply grasping or clamping with the wrist or palm is not sufficient.

Answer: Humanoids in Hospitals: A Technical Study of Humanoid Robot Surrogates for Dexterous Medical Interventions

Answer: TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning

Answer: HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit

Answer: Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation

Answer: Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

Answer: Humanoid Policy ~ Human Policy

Answer: TACT: Humanoid Whole-body Contact Manipulation through Deep Imitation Learning with Tactile Modality

Answer: Generalizable Humanoid Manipulation with 3D Diffusion Policies

Answer: ULC: A Unified and Fine-Grained Controller for Humanoid Loco-Manipulation

Answer: Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning

Answer: DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation

Answer: DEXPLORE: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration

Answer: $\mathcal{D(R,O)}$ Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping

Answer: AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control

Answer: ArtVIP: Articulated Digital Assets of Visual Realism, Modular Interaction, and Physical Fidelity for Robot Learning

Answer: Behavior Generation with Latent Actions

Answer: BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark

Answer: Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards

Answer: CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks

Answer: DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects

Answer: DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation

Answer: DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation

Answer: DexGarmentLab: Dexterous Garment Manipulation Environment with Generalizable Policy

Answer: DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos

Answer: DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning

Answer: DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation

Answer: DexSinGrasp: Learning a Unified Policy for Dexterous Object Singulation and Grasping in Cluttered Environments

Answer: EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video

Answer: EgoMimic: Scaling Imitation Learning via Egocentric Video

Answer: Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning

Answer: Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning

Answer: H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies

Answer: Hand-Eye Autonomous Delivery: Learning Humanoid Navigation, Locomotion and Reaching

Answer: HDMI: Learning Interactive Humanoid Whole-Body Control from Human Videos

Answer: InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions

Answer: KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills

Answer: ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning

Answer: MaskedManipulator: Versatile Whole-Body Control for Loco-Manipulation

Answer: MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos

Answer: OakInk2 : A Dataset of Bimanual Hands-Object Manipulation in Complex Task Completion

Answer: OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction

Answer: RealDex: Towards Human-like Grasping for Robotic Dexterous Hand

Answer: Residual Off-Policy RL for Finetuning Behavior Cloning Policies

Answer: ResMimic: From General Motion Tracking to Humanoid Whole-Body Loco-Manipulation via Residual Learning

Answer: RobustDexGrasp: Robust Dexterous Grasping of General Objects

Answer: Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos

Answer: TWIST: Teleoperated Whole-Body Imitation System

Answer: VisualMimic: Visual Humanoid Loco-Manipulation via Motion Tracking and Generation

Answer: XRoboToolkit: A Cross-Platform Framework for Robot Teleoperation

Answer: X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

Engineering and Technology


Question: I’m working on an end-to-end autonomous driving system trained directly from raw sensor inputs to control outputs (steering, throttle, etc.). However, during real-world deployment, the model fails catastrophically in rare but critical scenarios like sudden pedestrian darts or occluded intersections—yet it performs well on standard benchmarks like nuScenes or CARLA.I need papers that propose an end-to-end driving architecture (no explicit modular pipeline) that explicitly incorporates a mechanism to detect or recover from out-of-distribution (OOD) driving states at inference time, and this mechanism must be integrated into the control policy itself—not just as a separate monitor or alarm. Additionally, the paper must evaluate safety-critical failure modes (e.g., collision avoidance in unseen scenarios) on a real-world or high-fidelity closed-loop simulator, and report quantitative metrics on both nominal performance and OOD robustness (e.g., intervention rate, collision rate under perturbation).

Answer: End-to-End Urban Autonomous Driving With Safety Constraints

Answer: Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Answer: Centaur: Robust End-to-End Autonomous Driving with Test-Time Training

Answer: DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving

Answer: InterFuser: Safety-Enhanced Autonomous Driving using Sequential Sensor Fusion for Urban Driving

Answer: ReasonNet: End-to-End Driving with Temporal and Global Reasoning

Answer: Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models

Engineering and Technology


Question: Please help me find papers on autonomous machine learning systems implemented as LLM agent frameworks that are evaluated on MLE-bench Lite and achieve a score higher than 50. I care specifically about works where the agentic framework (planning, tool-use, or self-improvement loop) is clearly described in the main text.

Answer: The FM Agent

Answer: KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems

Answer: R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science

Answer: AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents

Answer: MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement

Answer: Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering

Engineering and Technology


Question: I am building a unified safety evaluation framework for Vision-Language Models (VLMs) and would like help finding relevant papers that propose a multimodal safety evaluation benchmark including at least one explicit jailbreak attack category, with dataset statistics or evaluation protocols described in the full text. The papers should also introduce a reproducible safety enhancement method with implementation details provided. Additionally, the method should be quantitatively evaluated on the proposed benchmark, reporting at least one safety metric such as attack success rate, harmful-response rate, or safety score.

Answer: MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

Answer: Can Language Models be Instructed to Protect Personal Information?

Answer: Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

Answer: Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models

Answer: FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts

Answer: LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models

Answer: SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model

Answer: SafeMT: Multi-turn Safety for Multimodal Language Models

Answer: Red Teaming Visual Language Models

Answer: HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model

Medicine and Life Sciences


Question: I'm working with Vision-Language-Action models. I aim to develop a generalized architecture or pipeline that improves zero-shot performance in robotic manipulation tasks. Find 5-10 high-quality papers that propose a novel architecture integrating explicit planning representations into Vision-Language-Action (VLA) models.

Answer: Robotic Control via Embodied Chain-of-Thought Reasoning

Answer: GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions

Answer: Compositional Foundation Models for Hierarchical Planning

Answer: DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping

Answer: ZERO-SHOT ROBOTIC MANIPULATION WITH PRETRAINED IMAGE-EDITING DIFFUSION MODELS

Answer: PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies

Answer: Hume: Introducing System-2 Thinking in Visual-Language-Action Model

Answer: CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models

Answer: F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions

Answer: RoBridge: A Hierarchical Architecture Bridging Cognition and Execution for General Robotic Manipulation

Answer: OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints

Answer: HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation

Answer: Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models

Answer: VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models

Answer: Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation

Answer: RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation

Answer: DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies

Answer: RobustDexGrasp: Robust Dexterous Grasping of General Objects

Answer: Dexterous Non-Prehensile Manipulation for Ungraspable Object via Extrinsic Dexterity

Answer: Learning Diverse Bimanual Dexterous Manipulation Skills from Human Demonstrations

Answer: Object-Focus Actor for Data-efficient Robot Generalization Dexterous Manipulation

Answer: Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans

Answer: DemoGrasp: Universal Dexterous Grasping from a Single Demonstration

Answer: Dexterous Functional Pre-Grasp Manipulation with Diffusion Policy

Answer: ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

Answer: VAMOS: A Hierarchical Vision-Language-Action Model for Capability-Modulated Navigation

Answer: CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordance

Answer: π0.5: a Vision-Language-Action Model with Open-World Generalization

Answer: TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics

Artificial Intelligence and Computing


Question: Please provide papers that investigate the alignment of heterogeneous modalities in collaborative perception for autonomous driving. The selected papers should be under an open collaborative perception setting, where collaborative modalities are extensible. The papers must include experiments involving collaboration between LiDAR and camera modalities.

Answer: Communication-Efficient Collaborative Perception via Information Filling with Codebook

Answer: RG-Attn: Radian Glue Attention for Multi-modality Multi-agent Cooperative Perception

Answer: You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception

Answer: CoDS: Enhancing Collaborative Perception in Heterogeneous Scenarios via Domain Separation

Answer: Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism

Answer: STAMP: Scalable Task And Model-agnostic Collaborative Perception

Answer: An Extensible Framework for Open Heterogeneous Collaborative Perception

Fundamental Sciences


Question: I'm working on humanoid robot control systems and I'm specifically looking for research where the proposed methods are validated in the real world rather than only in simulation. I’m interested in papers where the training data comes from human motion retargeting or from directly collected robot demonstrations. Also, I want to avoid VLA‑style models that use vision as input — instead, the control should be driven by reference motions or teleoperation signals. Can you recommend papers that meet these requirements?

Answer: BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion

Answer: HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit

Answer: GMT: General Motion Tracking for Humanoid Whole-Body Control

Answer: Think on your feet: Seamless Transition between Human-like Locomotion in Response to Changing Commands

Answer: KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills

Answer: ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning

Answer: Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

Answer: A Whole-Body Motion Imitation Framework from Human Data for Full-Size Humanoid Robot

Answer: Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots

Answer: Whole-body Humanoid Robot Locomotion with Human Reference

Answer: OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning

Answer: Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering

Answer: Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation

Answer: Learning Getting-Up Policies for Real-World Humanoid Robots

Answer: Implicit Kinodynamic Motion Retargeting for Human-to-humanoid Imitation Learning

Answer: WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts

Answer: ExBody2: Advanced Expressive Humanoid Whole-Body Control

Answer: HumanPlus: Humanoid Shadowing and Imitation from Humans

Answer: ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills

Answer: Implicit Kinodynamic Motion Retargeting (IKMR) for Human-to-Humanoid Imitation

Answer: TWIST: Teleoperated Whole-Body Imitation System

Answer: Expressive Whole-Body Control for Humanoid Robots

Answer: CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks

Answer: SoftMimic: Learning Compliant Whole-Body Control from Examples

Fundamental Sciences


Question: Please help me find some benchmarks to measure the ability of network search agents to solve complex network research tasks or search problems in real network environments. My requirement is that the questions must have standard answers that can clearly verify whether they are right or wrong since 2023.

Answer: SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

Answer: DeepWideSearch: Benchmarking Depth and Width in Agentic Information Seeking

Answer: BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese

Answer: MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents

Answer: MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents

Answer: WebWalker: Benchmarking LLMs in Web Traversal

Answer: WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent

Answer: BEARCUBS: A benchmark for computer-using web agents

Answer: BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Answer: WideSearch: Benchmarking Agentic Broad Info-Seeking

Answer: GAIA: a benchmark for General AI Assistants

Answer: WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models

Answer: EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments

Answer: AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?

Fundamental Sciences


Question:
I am looking for research papers published in 2024 or 2025 regarding Autonomous Agents that utilize an Evolving Skill Library (or Procedural Memory). Specifically, the agent must have the capability to generate new executable code, functions, or tools, verify their correctness, and store them in a memory module for future reuse in similar tasks.
Exclusion Constraints:
Strictly exclude papers where the "memory" is purely Episodic (i.e., just retrieving past natural language conversation logs or text summaries) without executable code.
Exclude papers where the agent relies solely on a fixed/static set of pre-defined tools (APIs) provided by humans, without the ability to create and save new tools during the interaction.

Answer: SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills

Answer: Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

Answer: Rethinking Agent Design: From Top-Down Workflows to Bottom-Up Skill Evolution

Answer: Agents of Change: Self-Evolving LLM Agents for Strategic Planning

Answer: Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution

Answer: Automated Design of Agentic Systems

Answer: AlphaEvolve: A coding agent for scientific and algorithmic discovery

Answer: G\"odel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement

Answer: The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Answer: AFlow: Automating Agentic Workflow Generation

Answer: Empowering Large Language Model Agents through Action Learning

Answer: Inducing Programmatic Skills for Agentic Tasks

Answer: DynaSaur: Large Language Agents Beyond Predefined Actions

Answer: ToolLibGen: Scalable Automatic Tool Creation and Aggregation for LLM Reasoning

Answer: ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models

Answer: AnalogCoder: Analog Circuit Design via Training-Free Code Generation

Answer: SEW: Self-Evolving Agentic Workflows for Automated Code Generation

Answer: AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation

Answer: A Self-Improving Coding Agent

Answer: Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents

Answer: HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model

Answer: CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging

Answer: ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning

Answer: Autonomous Code Evolution Meets NP-Completeness

Answer: MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations

Answer: MetaAgent: Toward Self-Evolving Agent via Tool Meta-Learning

Answer: Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation

Answer: AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning

Answer: Democratizing AI scientists using ToolUniverse

Artificial Intelligence and Computing


Question:
I am looking for research papers published in 2024 or 2025 that propose a Multi-modal Agentic Framework specifically for 3D Scene Generation or 3D Scene Synthesis. The method must involve an agent that utilizes Large Language Models (LLMs) (for planning or code generation) and Vision-Language Models (VLMs) (for visual feedback/verification) to iteratively arrange, layout, or generate complex indoor 3D scenes from natural language descriptions.
Exclusion Constraints:
Strictly exclude papers that focus solely on Single Object Generation (e.g., text-to-mesh diffusion models like DreamFusion, Point-E) without scene-level layout planning.
Exclude papers about agents that perform only Navigation or Exploration in 3D environments (e.g., Habitat-based agents) without the capability to generate or modify the scene structure.

Answer: Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation

Answer: SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and Self-Reflective Agent

Answer: Agentic 3D Scene Generation with Spatially Contextualized VLMs

Answer: MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse

Answer: 3D-Generalist: Self-Improving Vision-Language-Action Models for Crafting 3D Worlds

Answer: WonderJourney: Going from Anywhere to Everywhere

Answer: HOLODECK 2.0: Vision-Language-Guided 3D World Generation with Editing

Answer: Global-Local Tree Search in VLMs for 3D Indoor Scene Generation

Answer: Holodeck: Language Guided Generation of 3D Embodied AI Environments

Answer: WorldCraft: Photo-Realistic 3D World Creation and Customization via LLM Agents

Answer: DisCo-Layout: Disentangling and Coordinating Semantic and Physical Refinement in a Multi-Agent Framework for 3D Indoor Layout Synthesis

Answer: Latticeworld: A multimodal large language model-empowered framework for interactive complex world generation

Answer: Scenecraft: An llm agent for synthesizing 3d scenes as blender code

Answer: An agentic vision-action framework for generative 3D architectural modeling from sketches

Answer: Designing LLM based agents to interact with the embodied world

Answer: FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement

Answer: 3D-VLA: A 3D Vision-Language-Action Generative World Model

Answer: OptiScene: LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization

Answer: Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning

Artificial Intelligence and Computing


Question: I need to search for papers that were presented at the NeurIPS 2024, IROS 2024 or ICRA 2024 conferences, and which deal with the use of a behavior foundation model for the full-body control of humanoid robots for locomotion manipulation.

Answer: Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models

Answer: Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model

Answer: Humanoid Locomotion as Next Token Prediction

Answer: OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning

Answer: Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation

Artificial Intelligence and Computing


Question: I need to search for papers that were presented at NeurIPS, ICRA, IROS, CoRL, RSS , CVPR, ICCV, and AAAI after 2023, and which deal with the use of active perception for object search (i.e., the robot actively chooses viewpoints/motions to find a target object in a realistic indoor environment).

Answer: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation

Answer: PixelNav: Bridging Zero-Shot Object Navigation and Foundation Models through Pixel-Guided Navigation Skill

Answer: Aligning Knowledge Graph with Visual Perception for Object-Goal Navigation

Answer: Commonsense-Aware Object Value Graph for Object Goal Navigation

Answer: SEEK: Semantic Reasoning for Object Goal Navigation in Real World Inspection Tasks

Answer: Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks

Answer: Context-Aware Replanning with Pre-Explored Semantic Map for Object Navigation

Answer: TriHelper: Zero-Shot Object Navigation with Dynamic Assistance

Answer: Advancing Object Goal Navigation Through LLM-enhanced Object Affinities Transfer

Answer: SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation

Answer: Trajectory Diffusion for ObjectGoal Navigation

Answer: GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-part and Affordance Maps

Answer: Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments

Answer: EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval

Answer: GOAL: Distilling LLM Prior to Flow Model for Generalizable Agent’s Imagination in Object Goal Navigation

Answer: Imagine Before Go: Self-Supervised Generative Map for Object Goal Navigation

Answer: Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation

Answer: GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation

Answer: UniGoal: Towards Universal Zero-shot Goal-oriented Navigation

Answer: CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs

Answer: Function-centric Bayesian Network for Zero-Shot Object Goal Navigation

Answer: RoboTrom-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning

Answer: Learning on the Go: A Meta-learning Object Navigation Model

Answer: Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues

Answer: Bridging Zero-shot Object Navigation and Foundation Models through Pixel-Guided Navigation Skill (PixNav)

Answer: Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation (HIMOS)

Answer: VLPG-Nav: Object Navigation Using Visual Language Pose Graph and Object Localization Probability Maps

Answer: Commonsense Scene Graph-based Target Localization for Object Search

Answer: WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation

Answer: VLFM: Vision-Language Frontier Maps for Semantic Navigation

Answer: Vision in Action: Learning Active Perception from Human Demonstrations

Answer: Active Perception Meets Rule-Guided RL: A Two-Phase Approach for Precise Object Navigation in Complex Environments

Answer: GOAT: GO to Any Thing

Answer: RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction

Answer: LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Video

Answer: MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-Object Demand-driven Navigation

Artificial Intelligence and Computing


Question:
Enhancing LLMs’ agent capabilities currently relies mostly on RL, but scaling RL for agents faces several challenges:

  1. Rollouts are expensive, especially for complex tasks that require extensive thinking and tool use.
  2. Rewards are sparse, so on difficult tasks the model doesn’t get positive rewards and training stalls.
  3. Scaled environments carry high engineering costs—for example, software engineering requires bespoke test cases for each repo.

  However, we have abundant high-quality data such as repos, papers, and books. We treat these data as the terminal states of agent trajectories, with the intermediate actions and states unknown. In this work, we try to reconstruct
  those intermediate states and actions. Because this approach scales well for constructing agent trajectories, we use the reconstructed trajectories for continued pretraining.

  Could you help me find two mid-2025 papers related to constructing these states?

Answer: SimuRA: A World-Model-Driven Simulative Reasoning Architecture for General Goal-Oriented Agents

Answer: Adaptive Tool Generation with Models as Tools and Reinforcement Learning

Answer: TongUI: Building Generalized GUI Agents by Learning from Multimodal Web Tutorials

Artificial Intelligence and Computing


Question: I want to find some 2025 papers focused on repository-level code generation that are not about SWE-Bench-style tasks. Please identify the five most well-known, highly cited works whose code is open-sourced.

Answer: RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation

Answer: EpiCoder: Encompassing Diversity and Complexity in Code Generation

Answer: aiXcoder-7B-v2: Training LLMs to Fully Utilize the Long Context in Repository-level Code Completion

Answer: GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion

Answer: AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers

Answer: Fea-bench: A benchmark for evaluating repository-level code generation for feature implementation

Answer: ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation

Answer: SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development

Artificial Intelligence and Computing


Question: I am looking for benchmarks published in 2024 or 2025, that specifically evaluate the ability of Large Language Models (LLMs) to generate comprehensive academic survey papers or literature reviews. The paper may not be a pure benchmark, but it does mention a benchmark the authors conducted to evaluate ai+survey.

Answer: Are LLMs Good Literature Review Writers? Evaluating the Literature Review Writing Ability of Large Language Models

Answer: SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing

Answer: SurveyX: Academic Survey Automation via Large Language Models

Answer: SurveyBench: How Well Can LLM(-Agents) Write Academic Surveys?

Answer: SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation

Answer: AutoSurvey: Large Language Models Can Automatically Write Surveys

Answer: SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems

Answer: SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation

Answer: LLMMapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System

Answer: Large language models for automated literature review: An evaluation of reference generation, abstract writing, and review composition

Answer: SurveyBench: Can LLM(-Agents) Write Academic Surveys that Align with Reader Needs?

Artificial Intelligence and Computing


Question: I want some mcp benchmarks that focu on tool-using LLM/Agent struggle in mcp tools. Papers published in 2025 are preferred.

Answer: MCPToolBench++: A Large Scale AI Agent Model Context Protocol MCP Tool Use Benchmark

Answer: LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Answer: MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Answer: MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Answer: LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

Answer: MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools

Answer: MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use

Answer: MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Models

Answer: TheMCPCompany: Creating General-purpose Agents with Task-specific Tools

Answer: OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents

Answer: MCPVerse: An Expansive, Real-World Benchmark for Agentic Tool Use

Answer: MCPWorld: A Unified Benchmarking Testbed for API, GUI, and Hybrid Computer Use Agents

Answer: The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution

Answer: MSC-Bench: A Rigorous Benchmark for Multi-Server Tool Orchestration

Answer: InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents

Answer: MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models

Artificial Intelligence and Computing


Question: I’m looking for different regularization methods for self-supervised learning models or JEPA models. The regularization method needs to provide theoretical bounds.

Answer: T-REGS: Minimum Spanning Tree Regularization for Self-Supervised Learning

Answer: Modulate Your Spectrum in Self-Supervised Learning

Answer: An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization

Answer: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Answer: WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization

Answer: Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Answer: An Investigation into Whitening Loss for Self-supervised Learning

Answer: Failure-Proof Non-Contrastive Self-Supervised Learning

Answer: From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Answer: Self-Supervised Learning with an Information Maximization Criterion

Answer: Implicit variance regularization in non-contrastive SSL

Answer: Probabilistic self-supervised learning via scoring rules minimization

Answer: Deep nonlocal regularizer: A self-supervised learning method for 3-d seismic denoising

Answer: Self-supervised representation learning via latent graph prediction

Answer: A Bayesian Unification of Self-Supervised Clustering and Energy-Based Models

Answer: Predicting what you already know helps: Provable self-supervised learning

Answer: SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures

Answer: Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations

Answer: TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning

Answer: Towards Demystifying Representation Learning with Non-contrastive Self-supervision

Answer: Projection Head is Secretly an Information Bottleneck

Answer: Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods

Answer: Harnessing small projectors and multiple views for efficient vision pretraining

Answer: Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective

Answer: What Do We Maximize in Self-Supervised Learning?

Answer: Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations

Answer: AdaDim: Dimensionality Adaptation for SSL Representational Dynamics

Answer: Joint Embedding Self-Supervised Learning in the Kernel Regime

Answer: Video Representation Learning with Joint-Embedding Predictive Architectures

Answer: Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics

Artificial Intelligence and Computing


Question: I am also looking for the practical paper about how JEPA helps to build and direct embodied AI system, the simulation works also count

Answer: DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Answer: V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Answer: MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features

Answer: DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive Architecture

Answer: EmbodiSwap for Zero-Shot Robot Imitation Learning

Answer: Label-Efficient Grasp Joint Prediction with Point-JEPA

Answer: AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data

Answer: Time-series jepa for predictive remote control under capacity-limited networks

Answer: ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning

Answer: Utilizing V-JEPA for action recognition on the lunar surface: advancing human-robot collaboration

Answer: A Survey on Joint Embedding Predictive Architectures and World Models

Answer: seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models

Artificial Intelligence and Computing


Question: I need to find papers published in ASE 2024 (Automated Software Engineering), ICSE 2024, or NeurIPS 2024 that utilize Multi-Agent Systems (MAS) based on LLMs to improve complex code generation.

Answer: PairCoder: A Pair Programming Framework for Code Generation via Multi-Plan Exploration

Answer: MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue ReSolution

Answer: INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness

Answer: Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

Answer: A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement

Answer: SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

Answer: CoqPilot: A Plugin for LLM-Based Generation of Proofs (Multi-Agent Debate)

Answer: CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs

Artificial Intelligence and Computing


Question: Find academic papers or technical reports published by OpenAI between January 1, 2024, and November 1, 2025, that introduce new benchmarks or evaluation frameworks for AI agents.

Answer: Introducing SWE-bench Verified

Answer: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Answer: Introducing the SWE-Lancer benchmark

Answer: PaperBench: Evaluating AI's Ability to Replicate AI Research

Answer: BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Answer: GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

Answer: ON REAL-WORLD ECONOMICALLY VALUABLE TASKS

Answer: CUA eval extra information

Answer: SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?

Answer: Our updated Preparedness Framework

Answer: Measuring Short-Form Factuality in Large Language Models (SimpleQA)

Artificial Intelligence and Computing


Question: Find research papers concerning Jailbreak attacks specifically targeting Large Vision-Language Models, not text-only LLMs. The papers must be published in top-tier AI or Security conferences from 2023 to November 2025. Crucially, I only want papers that propose a new visual jailbreak benchmark dataset to evaluate safety. Theoretical attack methods without a comprehensive benchmark dataset release are not required.

Answer: MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

Answer: Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models

Answer: MMJ-Bench: A Comprehensive Study on Jailbreak Attacks and Defenses for Vision Language Models

Answer: Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models (HADES)

Answer: IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves (VLJailbreakBench)

Answer: Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models (VLGuard)

Answer: MMJ-Bench: A Comprehensive Study on Jailbreak Attacks and Defenses for Multimodal Large Language Models

Answer: IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves

Answer: ImgTrojan: Jailbreaking Vision-Language Models with ONE Image

Artificial Intelligence and Computing


Question: Please help me identify papers published in CCF-A recommended conferences in 2024 or 2025 (before Nov 1st). The topic should be about KV Cache compression or optimization for Large Language Models. My specific requirement is that the methods must be designed for streaming applications or infinite context length scenarios. Papers merely discussing quantization without addressing the long-context/streaming memory bottleneck should be ignored.

Answer: CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Inference

Answer: InfiniGen: Efficient Generative Inference for Large Language Models with Dynamic KV Cache Management

Answer: NACL: A General and Effective KV Cache Eviction Framework for LLM at Inference Time

Answer: SnapKV: LLM Knows What You are Looking for Before Generation

Answer: MiniCache: KV Cache Compression in Depth Dimension for Large Language Models

Answer: Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

Answer: TreeKV: Reducing KV Cache with Tree Structures for Efficient Long-Sequence LLM Inference

Answer: NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time

Answer: LoCoCo: Dropping In Convolutions for Long Context Compression

Answer: InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management

Answer: Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention

Answer: Mooncake: Trading More Storage for Less Computation — A KVCache-centric Disaggregated Architecture for Serving LLM Chatbot

Answer: KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

Answer: TreeKV: Smooth Key-Value Cache Compression with Tree Structures

Answer: SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers

Answer: Training Free Exponential Context Extension via Cascading KV Cache

Answer: KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Answer: CAKE: Cascading and Adaptive KV Cache Eviction with Layer Preferences

Answer: Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning

Answer: MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection

Answer: ClusterAttn: KV Cache Compression under Intrinsic Attention Clustering

Answer: CacheGen: Fast Context Loading for Language Model Applications via KV Cache Streaming

Answer: ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Artificial Intelligence and Computing


Question: Please help me find papers on reasoning capabilities of vision-language models (VLMs) in visual question answering (VQA) tasks requiring commonsense knowledge, where the models explicitly employ chain-of-thought (CoT) prompting or similar step-by-step reasoning mechanisms adapted for multimodal inputs. The studies must evaluate performance primarily on the Visual Commonsense Reasoning (VCR) dataset, include ablation studies on the impact of CoT, and provide comparisons with non-CoT baselines. Limit to papers published after January 2023 but before November 2025.

Answer: IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

Answer: Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models

Answer: Coherent Multimodal Reasoning with Iterative Self-Evaluation for Vision-Language Models

Answer: Vicor: Bridging visual understanding and commonsense reasoning with large language models

Answer: VIKSER: Visual Knowledge-Driven Self-Reinforcing Reasoning Framework

Answer: Do vision-language transformers exhibit visual commonsense? an empirical study of VCR

Answer: Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing

Answer: Infimm-eval: Complex open-ended reasoning evaluation for multi-modal large language models

Answer: Improve Vision Language Model Chain-of-thought Reasoning

Answer: Enhancing Advanced Visual Reasoning Ability of Large Language Models

Answer: Cognitive Chain-of-Thought: Structured Multimodal Reasoning about Social Situations

Artificial Intelligence and Computing


Question: Please help me find papers on safety alignment techniques for vision-language models (VLMs) that focus on mitigating biases in multimodal data, specifically addressing jailbreak attacks or adversarial prompts involving both text and images. The methods should incorporate reinforcement learning from human feedback (RLHF) or similar preference optimization approaches, and include empirical evaluations on benchmarks like MM-SafetyBench or similar datasets. Papers should be published between 2023 and October 2025.

Answer: Aligning Large Multimodal Models with Factually Augmented RLHF

Answer: Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback

Answer: Towards Harmless Multimodal Assistants with Blind Preference Optimization

Answer: MM-RLHF: The Next Step Forward in Multimodal LLM Alignment

Answer: PRISM: Robust VLM Alignment with Principled Reasoning for Integrated Safety in Multimodality

Answer: AdPO: Enhancing the adversarial robustness of large vision-language models with preference optimization

Answer: SaFeR-VLM: Toward Safety-aware Fine-grained Reasoning in Multimodal Models

Answer: DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback

Answer: BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks

Answer: VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization

Artificial Intelligence and Computing


Question: I find that pure End-to-End VLAs lack long-horizon reliability in general Embodied AI (excluding Autonomous Driving), while off-the-shelf MLLMs lack specific physical common sense.  Please search for recent papers (2024-2025) proposing an explicit hierarchical architecture. I am specifically looking for a general-purpose MLLM that serves as a 'System 2' brain (reasoning/planning) and has undergone targeted alignment via SFT (Supervised Fine-Tuning) or RL (Reinforcement Learning) to inject extensive embodied knowledge and physical reasoning capabilities.

Answer: Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning

Answer: EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence

Answer: Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models

Answer: Hume: Introducing System-2 Thinking in Visual-Language-Action Model

Answer: Gemini Robotics: Bringing AI into the Physical World

Answer: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Answer: RoboGPT-R1: Enhancing Robot Planning with Reinforcement Learning

Answer: Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation

Answer: Structured Preference Optimization for Vision-Language Long-Horizon Task Planning

Answer: RoboBrain 2.0 Technical Report

Answer: TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics

Artificial Intelligence and Computing


Question: I am struggling to collect mobile manipulation trajectories for a humanoid robot due to the high cost of motion capture suits. I only have a consumer VR headset (e.g., Quest 3/Vision Pro) and a low budget (<$1000). Please search for recent papers that provide an open-source VR teleoperation framework specifically for humanoids.

Answer: BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots

Answer: HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit

Answer: Holo-Dex: Teaching Dexterity with Immersive Mixed Reality

Answer: OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation

Answer: TWIST: Teleoperated Whole-Body Imitation System

Answer: Exploiting virtual reality and the robot operating system to remote-control a humanoid robot

Answer: Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots

Artificial Intelligence and Computing


Question: I'm building a multi-agent system specifically using Large Language Models (not traditional RL agents) for complex task automation. As I scale beyond 10 LLM agents, the quadratic growth in communication overhead is becoming prohibitive. I need papers published after 2023 that propose novel architectures or protocols explicitly designed to handle 50+ LLM agents efficiently—particularly interested in hierarchical, graph-based, or selective communication patterns that reduce the O(n²) complexity while maintaining task performance.

Answer: Scaling Large-Language-Model-based Multi-Agent Collaboration

Answer: Language Agents as Optimizable Graphs

Answer: Scaling Large Language Model-based Multi-Agent Collaboration

Answer: MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs

Answer: Agentnet: Decentralized evolutionary coordination for llm-based multi-agent systems

Answer: Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development

Answer: A scalable communication protocol for networks of large language models

Answer: Self-organizing agent network for llm-based workflow automation

Answer: Gptswarm: Language agents as optimizable graphs

Answer: Multi-Agent Collaboration via Evolving Orchestration

Answer: Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models

Answer: G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Answer: Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

Answer: Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence

Answer: DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems

Answer: Multi-agent Architecture Search via Agentic Supernet

Answer: HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution

Answer: HALO: Hierarchical Autonomous Logic-Oriented Orchestration for Multi-Agent LLM Systems

Answer: GraphTrafficGPT: Enhancing Traffic Management Through Graph-Based AI Agent Coordination

Answer: SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication

Answer: Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems

Answer: Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications

Answer: G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems

Answer: Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy

Answer: Agents meet OKR: An Object and Key Results Driven Agent System with Hierarchical Self-Collaboration and Self-Evaluation

Answer: AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration

Answer: OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

Answer: A Survey of AI Agent Protocols

Answer: AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management

Answer: Embodied LLM Agents Learn to Cooperate in Organized Teams

Answer: KVComm: Enabling Efficient LLM Communication through Selective KV Sharing

Answer: Mixture-of-Agents Enhances Large Language Model Capabilities

Answer: Problem-Solving in Language Model Networks

Answer: AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors

Answer: MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Artificial Intelligence and Computing


Question: Our team needs to align custom LLMs but RLHF's reward model training keeps failing due to noisy preference data and training instability. Looking for alignment methods published after 2023 that completely bypass reward model training—specifically direct optimization methods that work with preference pairs or even single-point feedback. The method must be theoretically grounded (not just heuristic), work with standard preference datasets like HH-RLHF or UltraFeedback, and show comparable or better performance than PPO-based RLHF.

Answer: Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Answer: Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Answer: Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization

Answer: Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts

Answer: Token-level Direct Preference Optimization

Answer: KTO: Model Alignment as Prospect Theoretic Optimization

Answer: Direct Preference Optimization with an Offset

Answer: SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Answer: Binary Classifier Optimization for Large Language Model Alignment

Answer: ORPO: Monolithic Preference Optimization without Reference Model

Answer: Zephyr: Direct Distillation of LM Alignment

Answer: RRHF: Rank Responses to Align Language Models with Human Feedback without tears

Answer: Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

Answer: Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both

Answer: SimPO: Simple Preference Optimization with a Reference-Free Reward

Answer: Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization

Answer: Towards Efficient Exact Optimization of Language Model Alignment

Answer: Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier

Answer: Beyond reverse kl: Generalizing direct preference optimization with diverse divergence constraints

Answer: UNA: unifying alignments of RLHF/PPO, DPO and KTO by a generalized implicit reward function

Answer: Contrastive preference learning: learning from human feedback without rl

Answer: Alignment as Distribution Learning: Your Preference Model is Explicitly a Language Model

Answer: DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization

Answer: Triple Preference Optimization: Achieving Better Alignment using a Single Step Optimization

Answer: ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization

Answer: ASFT: Aligned Supervised Fine-Tuning through Absolute Likelihood

Answer: Stable Preference Optimization for LLMs: A Bilevel Approach Beyond Direct Preference Optimization

Answer: Learn Your Reference Model for Real Good Alignment

Answer: Soft Preference Optimization: Aligning Language Models to Expert Distributions

Answer: Curry-DPO: Enhancing Alignment using Curriculum Learning & Ranked Preferences

Answer: Active Preference Learning for Large Language Models

Answer: REBEL: Reinforcement Learning via Regressing Relative Rewards

Answer: Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization

Answer: Explicit Preference Optimization: No Need for an Implicit Reward Model

Artificial Intelligence and Computing


Question: I'm preparing to construct a computational prediction system for regioselectivity of transition metal catalyzed cross-coupling on mutihalogen reactants, but do not know which prediciton model/descriptors should I include in our method. Can you recommend papers with methods to predict such regioselectivity? Do a thorough search for these papers, provide as many papers as you can find.

Answer: Site-selective Suzuki–Miyaura coupling of heteroaryl halides – understanding the trends for pharmaceutically important classes

Answer: A Dichotomy in Cross-Coupling Site Selectivity in a Dihalogenated Heteroarene: Influence of Mononuclear Pd, Pd Clusters, and Pd Nanoparticles—the Case for Exploiting Pd Catalyst Speciation

Answer: A General Reactivity Map for Predicting Outcomes in Palladium-Catalyzed Cross-Coupling

Answer: Mechanisms and Site Selectivity of (Het)Ar–X Oxidative Addition to Pd(0) Are Controlled by Frontier Molecular Orbital Symmetry

Answer: Regio-MPNN: Predicting Regioselectivity for General Metal-Catalyzed Cross-Coupling Reactions

Answer: The Road Less Traveled: Unconventional Site Selectivity in Palladium-Catalyzed Cross-Couplings of Dihalogenated N-Heteroarenes

Answer: Ligand-Controlled Regioselectivity in Palladium-Catalyzed Cross Coupling Reactions

Answer: Origin of Regioselectivity in Palladium-Catalyzed Cross-Coupling Reactions of Polyhalogenated Heterocycles

Answer: Theoretical Bond Dissociation Energies of Halo-Heterocycles: Trends and Relationships to Regioselectiviy in Palladium-Catalyzed Cross-Coupling Reactions

Answer: A simple guide for predicting regioselectivity in the coupling of polyhaloheteroaromatics

Fundamental Sciences


Question: Could you help check whether there are any papers that studied the photoisomerization of retinol? These papers should include computational investigation of the isomerization process. These papers should locate the reaction potential energy surface in excited states and perform non-adibatic dynamic simulations. Summarize the absorption wavelength reported in these papers. Do a thorough search for these papers, provide as many papers as you can find.

Answer: Nonadiabatic ab initio dynamics of a model protonated Schiff base of 9-cis retinal

Answer: Quantum molecular dynamics simulations of the effect of secondary modes on the photoisomerization of a retinal chromophore model

Answer: Theory and Simulation of the Ultrafast Double-Bond Isomerization of Biological Chromophores

Answer: Molecular dynamics simulation of bacteriorhodopsin's photoisomerization using ab initio forces for the excited chromophore

Answer: Nonadiabatic Photodynamics of a Retinal Model in Polar and Nonpolar Environment

Answer: Photochemical Reaction Dynamics of the Primary Event of Vision Studied by Means of a Hybrid Molecular Simulation

Answer: Trajectory surface hopping molecular dynamics simulations for retinal protonated Schiff-base photoisomerization

Answer: Comparison of the isomerization mechanisms of human melanopsin and invertebrate and vertebrate rhodopsins

Answer: Benchmarking Density Functional Approximations in Nonadiabatic Dynamics: Trans–Cis Isomerization in Retinal Model

Answer: Insight into the Isomerization Mechanism of Retin  Hybrid QM/MM Simulations

Fundamental Sciences


Question: Please retrieve peer-reviewed, original research articles (excluding reviews) published from 2010 onward that investigate electrochemical deuteration reactions. Each article must:(1) explicitly report or discuss Faradaic efficiency (also referred to as Faraday efficiency) in the context of deuterium incorporation, and (2) employ spectroscopic techniques to study or elucidate the reaction mechanism.For each qualifying article, provide the full title, publication year, journal name, and a valid, verifiable DOI. Prioritize studies published in reputable journals with clear experimental evidence and reproducible methodology.

Answer: Highly  efficient  electrocatalytic  deuteration  of  acetylene  to  deuterated  ethylene  using  deuterium  oxide

Answer: Electrocatalytic Deuteration of Halides with D2O as the DeuteriumSource over a Copper Nanowire Arrays Cathode

Answer: Selective Transfer Semihydrogenation of Alkynes with H₂O (D₂O) as the H (D) Source over a Pd–P Cathode

Answer: Converting copper sulfide to copper with surface  sulfur for electrocatalytic alkyne semi hydrogenation with water

Answer: Microenvironment regulation breaks the  Faradaic efficiency-current density trade-off  for electrocatalytic deuteration using D2O

Answer: Boosted high-throughput D⁺ transfer from  D₂O to unsaturated bonds via Pdδ+ cathode  for solvent-free deuteration

Answer: Electrocatalytic Alkene Hydrogenation/Deuteration

Answer: Plasma-Electrocatalysis Cascade Synthesis of Deuterated Ammonia

Answer: Pd Nanoparticles Supported on N-Doped Porous Carbon Cathodes for the Catalytic Semideuteration of Alkynes

Answer: Electrocatalytic Hydrogenation and Deuteration of Unsaturated C−N Bonds to Amines with Vacancy-Rich Cu3P Nanowires as Catalysts in Aqueous Solution

Answer: Cuδ+ Site-Enhanced Adsorption and Crown Ether-Reconfigured Interfacial D2O Promote Electrocatalytic Dehalogenative Deuteration

Answer: Defect-Induced Electron Localization Promotes D(2)O Dissociation and Nitrile Adsorption for Deuterated Amines

Fundamental Sciences


Question: 帮我尽可能多的查找有关氯析出反应机理的文献，给我文章的题目或DOI。要求机理介绍中至少要有Tafel斜率这项动力学参数值，不要2010年以前的文章，不要综述，注意文献真实性

Answer: Selectivity Trends Between Oxygen Evolution and Chlorine  Evolution on Iridium-Based Double Perovskites in Acidic Media

Answer: Microscopic Insights into the Chlorine Evolution Reaction  on RuO2(110): a Mechanistic Ab Initio Atomistic  Thermodynamics Study

Answer: Enhancing the activity of oxygen-evolution  and chlorine-evolution electrocatalysts by  atomic layer deposition of TiO2†

Answer: Decoupling Oxygen and Chlorine Evolution Reactions in Seawater using Iridium-based Electrocatalysts

Answer: Ti2O3 film electrode for water treatment via  electrochemical chlorine evolution

Answer: Preparation of the Ordered Spherical Ru-RuO2 and  Electrocatalysis toward Chlorine Evolution Reaction

Answer: Stable and efficient chlorine evolution reaction with atomically dispersed Ru on surface tensile strained TiO₂

Answer: Electrosynthesis of chlorine from seawater like solution through single-atom catalysts

Answer: Efficient electrocatalytic chlorine evolution under neutral seawater  conditions enabled by highly dispersed Co3O4 catalysts on porous carbon

Answer: Probing the effects of electrode composition and morphology on the effectiveness of silicon oxide overlayers to enhance selective oxygen evolution in the presence of chloride ions

Answer: Atomically dispersed Pt–N₄ sites as efficient and selective electrocatalysts for the chlorine evolution reaction

Answer: Chlorine evolution reaction electrocatalysis on RuO₂(110) and IrO₂(110) grown using molecular-beam epitaxy

Answer: Full Kinetics from First Principles of the Chlorine Evolution Reaction over a RuO₂(110) Model Electrode

Answer: Temperature-dependent kinetic studies of the chlorine evolution reaction over RuO₂(110) model electrodes

Answer: Ultrafast Synthesis of IrB  1.15  Nanocrystals for Efficient Chlorine and  Hydrogen Evolution Reactions in Saline Water

Answer: Understanding electrochemical chlorine evolution process of ultrafine spinel cobalt oxide nanoparticle

Answer: Highly Selective Active Chlorine Generation Electrocatalyzed by  Co3O4 Nanoparticles: Mechanistic Investigation through in Situ  Electrokinetic and Spectroscopic Analyses

Answer: General Efficacy of Atomically Dispersed Pt Catalysts for the Chlorine Evolution Reaction: Potential-Dependent Switching of the Kinetics and Mechanism

Answer: Atomically dispersed dinuclear iridium active sites for efficient and stable electrocatalytic chlorine  evolution reaction

Answer: Sea urchin-like nickel cobaltite for enhanced chlorine evolution  reaction activity

Answer: Engineering antibonding orbital occupancy of NiMoO4-supported Ru  nanoparticles for enhanced chlorine evolution reaction

Answer: Mechanistic Insights of Electrochemical Cl2 and O2 Generation from Lanthanum Cobalt Manganese Oxide

Fundamental Sciences


Question: I'm looking for research on self-evolving AI agent architectures that examine human-AI coevolution through longitudinal studies. The papers must propose novel agent frameworks with explicit memory mechanisms and demonstrate behavioral adaptation over multiple interaction sessions. Studies should include quantitative analysis of how both human behavior and agent strategies change over time, measured across at least three time points with statistical validation of coevolutionary dynamics. The research must address human-centered design principles in the agent architecture itself, not just in the evaluation.

Answer: Memory-Based Personalization for Fostering a Long-Term Child-Robot Relationship

Answer: Engagement in longitudinal child-robot language learning interactions: Disentangling robot and task engagement

Answer: Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders

Answer: AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation

Answer: ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery

Answer: AI Agent Behavioral Science

Answer: Incentivized Symbiosis: A Paradigm for Human-Agent Coevolution

Answer: Human-AI Coevolution

Artificial Intelligence and Computing


Question: I need papers on identifying RNA modifications using mass spectrometry that employ machine learning algorithms for spectrum annotation. The mass spectrometry approach should use tandem MS with fragmentation patterns.

Answer: Pytheas: a software package for the automated analysis of RNA sequences and modifications via tandem mass spectrometry

Answer: Improved Application of RNAModMapper – an RNA modification mapping software tool – for analysis of liquid chromatography tandem mass spectrometry (LC-MS/MS) data

Answer: Mass Spectrometry-Based Pipeline for Identifying RNA Modifications Involved in a Functional Process: Application to Cancer Cell Adaptation

Answer: Detection technologies for RNA modifications

Answer: A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry

Answer: RoboOligo: software for mass spectrometry data to support manual and de novo sequencing of post-transcriptionally modified ribonucleic acids

Answer: RNAModMapper: RNA modification mapping software for analysis of liquid chromatography tandem mass spectrometry data

Answer: Ariadne: a database search engine for identification and chemical analysis of RNA using tandem mass spectrometry data

Answer: RNAModMapper (Original Paper): An RNA modification mapping software tool

Answer: Mass Spectrometry of Modified RNAs: Recent Developments

Answer: Mass spectrometry to study the primary structure of mRNA

Answer: Instrumental analysis of RNA modifications

Answer: Phosphorothioate RNA Analysis by NETD Tandem Mass Spectrometry

Answer: Analysis of RNA and its Modifications

Answer: Identifying Modifications in RNA by MALDI Mass Spectrometry

Answer: Review of fragmentation of synthetic single‐stranded oligonucleotides

Medicine and Life Sciences


Question: Could you help determine whether there exist research papers that investigate exchange bias (EB) phenomena in amorphous or partially amorphous magnetic alloy systems prepared specifically by magnetron sputtering, where the origin of EB is attributed to intrinsic structural disorder, nanoscale FM–AFM phase coexistence, or local exchange coupling within the amorphous matrix rather than engineered interfaces? The studies must include magnetic measurements such as hysteresis loop analysis and at least one structural characterization technique capable of confirming amorphous features, such as TEM, XRD, or PDF analysis. Papers involving intentionally synthesized FM/AFM bilayers, multilayers, epitaxial interfaces, core–shell nanoparticles, or any artificially designed crystalline interfaces should be excluded. Only papers where the true origin of EB and the amorphous structural characteristics can be determined solely through detailed reading of the full text, including microstructural analysis, deposition parameters, and magnetic characterization, should be considered.

Answer: Observation of an atomic exchange bias effect in DyCo(4)film

Answer: Exchange Bias and Coercivity in Vicinity of Magnetic Compensation Point in GdFeCo Amorphous Film

Answer: Exchange bias and bistable magneto-resistance states in amorphous TbFeCo thin films

Answer: Magnetic behavior of amorphous Fe–Zr thin films sputtered at different Ar pressures

Answer: Positive surface and perpendicular magnetic anisotropy in natural nanomorphous Ni/NiO multilayers

Answer: Preparation of amorphous magnetic thin film by two source r.f. magnetron sputtering

Answer: Magnetic properties of dc magnetron sputtered and evaporated amorphous films

Answer: Structural and magnetic properties of micropolycrystalline cobalt thin films fabricated by direct current magnetron sputtering

Engineering and Technology


Question: Could you help determine whether there exist research papers in which the performance of oxygen evolution reaction (OER) catalysts is improved specifically through quenching-based thermal modification, including cases where new elements are introduced or redistributed as a consequence of the quenching process? The quenching treatment must be the primary and defining modification strategy, and the study must provide structural evidence such as TEM and XRD to verify the structural or compositional changes induced by quenching. Papers that rely on additional modification strategies, such as hydrothermal structural tuning, solvothermal growth, templating, interface engineering, or multi-step chemical treatments, should be excluded.

Answer: Quenching-induced surface engineering of ZnCo(2)O(4) spinel oxide for enhanced oxygen evolution reaction

Answer: Quench-induced surface engineering of NiO/NiSe(x) hybrid for highly enhanced electrochemical oxygen evolution reaction

Answer: Laser Fragmentation-Induced Defect-Rich Cobalt Oxide Nanoparticles for Electrochemical Oxygen Evolution Reaction

Answer: Stabilization of Brownmillerite-Type SrCoO(2.5) by a Cost-Effective Quenching Method for Oxygen-Scavenging Applications

Answer: Mo-modified Co₃O₄ nanosheets array by a rapid quenching strategy for efficient OER

Answer: Quenching-induced surface reconstruction of FeMn(2)O(4) for promoted oxygen evolution reaction

Answer: Quenching-Induced Three-Phase Heterostructured Catalysts for Oxygen Electrocatalysis with Lattice Oxygen Participation

Answer: Activating Metal Oxides Nanocatalysts for Electrocatalytic Water Oxidation by Quenching-Induced Near-Surface Metal Atom Functionality

Answer: Quench-Induced Surface Engineering Boosts Alkaline Freshwater and Seawater Oxygen Evolution Reaction of Porous NiCo(2)O(4) Nanowires

Answer: Quenching modification of NiFe layered double hydroxides as efficient and highly stable electrocatalysts for the oxygen evolution reaction

Engineering and Technology


Question: Could you help me find any research paper about calculating properties of Frequency Selective Surface based on First Principle, rather than equivalent model? The theory should be instrumental for fast FSS inverse design.

Answer: Characteristic mode analysis of resistor-loaded frequency selective surfaces: Theoretical research and experimental verification

Answer: An Improved Model for Full Wave Analysis of Multilayered Frequency Selective Surface with Gridded Square Element

Answer: Full-Wave Analysis of Stable Cross Fractal Frequency Selective Surfaces Using an Iterative Procedure Based on Wave Concept

Answer: A Machine Learning Approach for Design of Frequency Selective Surface based Radar Absorbing Material via Image Prediction

Answer: Deep Electromagnetic Structure Design Under Limited Evaluation Budgets

Answer: Inverse design in nanophotonics

Answer: Controlling angular dispersions in optical metasurfaces

Answer: Tailoring the lineshapes of coupled plasmonic systems based on a theory derived from first principles

Engineering and Technology


Question: Could you help me search any research papers that build neural operator especially for predicting performance of EM metasurface? The neural operator must combine some pre-informed physical theories (such as RCWA algorithm) to improve accuracy and robustness.

Answer: Physics-Informed Neural Networks in Electromagnetic and Nanophotonic Design

Answer: Meent: Differentiable Electromagnetic Simulator for Machine Learning

Answer: Physics-informed learning in artificial electromagnetic materials

Answer: Physics-Guided Hierarchical Neural Networks for Maxwell’s Equations in Plasmonic Metamaterials

Answer: Generalizing Deep Surrogate Solvers for Broadband Electromagnetic Field Prediction at Unseen Wavelengths

Answer: FE-PIRBN: Feature-enhanced physics-informed radial basis neural networks for solving high-frequency electromagnetic scattering problems

Answer: NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation

Answer: Physics-informed neural networks for inverse problems in nano-optics and metamaterials

Engineering and Technology


Question: Please find papers that propose data selection methods for reinforcement learning post-training of LLMs (such as GRPO or RLVR). The methods should select training samples based on criteria like difficulty, learnability, gradient alignment, or influence functions, and report experiments on mathematical reasoning benchmarks such as GSM8K, MATH, or AIME.

Answer: LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment

Answer: Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay

Answer: Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning

Answer: How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs’ Reasoning Capabilities: A Preliminary Experimental Study

Answer: DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-training

Answer: Data-Efficient RLVR via Off-Policy Influence Guidance

Answer: UFO-RL: Uncertainty-Focused Optimization for Efficient Reinforcement Learning Data Selection

Answer: SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models

Answer: Scheduling Your LLM Reinforcement Learning with Reasoning Trees

Answer: Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward

Answer: CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning

Answer: LIMR: Less is More for RL Scaling

Answer: Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

Answer: CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models

Answer: Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning

Answer: Prompt Curriculum Learning for Efficient LLM Post-Training

Answer: Unlocking the Potential of Difficulty Prior in RL-based Multimodal Reasoning

Answer: Synthetic Data RL: Task Definition Is All You Need

Answer: Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts

Answer: Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding

Answer: From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization

Answer: Hard Examples Are All You Need: Maximizing GRPO Post-Training Under Annotation Budgets

Answer: DARO: Difficulty-Aware Reweighting Policy Optimization

Answer: LSPO: Length-aware Dynamic Sampling for Policy Optimization in LLM Reasoning

Answer: Enhancing Math Reasoning in Small-sized LLMs via Preview Difficulty-Aware Intervention

Answer: SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM

Answer: RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning

Answer: EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework

Answer: Stabilizing Policy Gradients for Sample-Efficient Reinforcement Learning in LLM Reasoning

Answer: Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Answer: Learning to Reason at the Frontier of Learnability

Artificial Intelligence and Computing


Question: What are the recent papers that combine tree-based search algorithms (e.g., Monte Carlo Tree Search or tree-structured rollouts) with reinforcement learning for LLM post-training? I need works that specifically integrate tree search into policy optimization frameworks such as GRPO or PPO, with experimental validation on math reasoning or code generation tasks.

Answer: Tree search for llm agent reinforcement learning

Answer: Exploiting Tree Structure for Credit Assignment in RL Training of LLMs

Answer: Segment policy optimization: Effective segment-level credit assignment in rl for large language models

Answer: ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL

Answer: TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling

Answer: Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers

Answer: Policy Guided Tree Search for Enhanced LLM Reasoning

Answer: TreeRL: LLM Reinforcement Learning with On-Policy Tree Search

Answer: TreeRPO: Tree Relative Policy Optimization

Answer: Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning

Answer: Lookahead Tree-Based Rollouts for Enhanced Trajectory-Level Exploration in Reinforcement Learning with Verifiable Rewards

Answer: VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment

Answer: DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

Answer: ReST-RL: Achieving Accurate Code Reasoning of LLMs with Optimized Self-Training and Decoding

Answer: MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision

Answer: ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context

Answer: Preference-Guided Reflective Sampling for Aligning Language Models

Answer: Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding

Answer: Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning

Answer: DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Answer: Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Answer: Your Reward Function for RL is Your Best PRM for Search: Unifying RL and Search-Based TTS

Answer: Making ppo even better: Value-guided monte-carlo tree search decoding

Answer: rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Answer: Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Answer: SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement

Answer: SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents

Artificial Intelligence and Computing


Question: Search for papers about tree search techniques that enhance LLM reasoning capabilities. Exclude papers that only use linear chain-of-thought prompting without any tree exploration mechanism. The target papers must implement explicit tree-structured search with branching strategies to explore multiple candidate reasoning paths simultaneously.

Answer: Tree search for llm agent reinforcement learning

Answer: Enhancing llm reasoning with reward-guided tree search

Answer: Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Answer: Policy Guided Tree Search for Enhanced LLM Reasoning

Answer: Dynamic Parallel Tree Search for Efficient LLM Reasoning

Answer: Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls

Answer: Tree Search for Language Model Agents

Answer: Reasoning with Language Model is Planning with World Model

Answer: TreeRPO: Tree Relative Policy Optimization

Answer: Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Answer: Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Answer: Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning

Answer: ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search

Answer: Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

Answer: rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Answer: Interpretable Contrastive Monte Carlo Tree Search Reasoning

Answer: Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning

Answer: Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning

Answer: No Train Still Gain. Unleash Mathematical Reasoning of Large Language Models with Monte Carlo Tree Search Guided by Energy Function

Answer: PathFinder: Guided Search over Multi-Step Reasoning Paths

Answer: LLM-First Search: Self-Guided Exploration of the Solution Space

Answer: ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning

Answer: Leveraging Constrained Monte Carlo Tree Search to Generate Reliable Long Chain-of-Thought for Mathematical Reasoning

Answer: Accelerating Large Language Model Reasoning via Speculative Search

Answer: SIGMA: Refining Large Language Model Reasoning via Sibling-Guided Monte Carlo Augmentation

Answer: SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement

Answer: Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search

Answer: MC-NEST -- Enhancing Mathematical Reasoning in Large Language Models with a Monte Carlo Nash Equilibrium Self-Refine Tree

Answer: MCTSr-Zero: Self-Reflective Psychological Counseling Dialogues Generation via Principles and Adaptive Exploration

Answer: RoT: Enhancing Large Language Models with Reflection on Search Trees

Answer: ReST-RL: Achieving Accurate Code Reasoning of LLMs with Optimized Self-Training and Decoding

Answer: Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search

Answer: MASPRM: Multi-Agent System Process Reward Model

Answer: Autonomous Tree-search Ability of Large Language Models

Answer: Improving Autonomous AI Agents with Reflective Tree Search and Self-Learning

Answer: LiteSearch: Efficacious Tree Search for LLM

Answer: MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search

Answer: ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search

Answer: MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision

Answer: AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search

Answer: ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving

Answer: ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context

Answer: Unifying Tree Search Algorithm and Reward Design for LLM Reasoning: A Survey

Answer: Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding

Answer: MIRAGE: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains

Answer: SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL

Answer: Tree of Uncertain Thoughts Reasoning for Large Language Models

Artificial Intelligence and Computing


Question: Search for SCI papers related to PDE4 published in the past 20 years. The papers should be in the fields of biology and pharmacology. The first author's institution should be from China. The papers should be review or basic research type, and clinical research papers should be excluded.

Answer: The multifaceted role of phosphodiesterase 4 in tumor: from tumorigenesis to immunotherapy

Answer: Targeting Phosphodiesterase 4 in Gastrointestinal and Liver Diseases: From Isoform-Specific Mechanisms to Precision Therapeutics

Answer: Design, Synthesis, and Evaluation of Selective PDE4 Inhibitors for the Therapy of Pulmonary Injury

Answer: A novel PDE4 inhibitor ZX21011 alleviates neuronal apoptosis by decreasing GSK3β-mediated Drp1 Ser616 phosphorylation in cerebral ischemia reperfusion

Answer: A novel phosphodiesterase 4/5 dual-target inhibitor ameliorates neuroinflammation via the NF-κB and JNK pathway

Answer: PDE4 inhibitor apremilast ameliorates TNBS-induced irritable bowel syndrome in mice by activating the Nrf-2 signaling pathway in enteric glial cells

Answer: Inhibition of PDE4B ameliorates cognitive defects in the model of alcoholic dementia in 3xTg-AD mice via PDE4B/cAMP/PKA signaling

Answer: Phosphodiesterase 4B (PDE4B) inhibitors and their applications in recent years (2014 to early 2025)

Answer: Uncovering the selectivity mechanism of PDE4B and PDE4D through computational investigations

Answer: PDE4D inhibition ameliorates cardiac hypertrophy and heart failure by activating mitophagy

Answer: Phosphodiesterase 4B inhibitors and their efficacy in autoimmune diseases: a comprehensive review of therapeutic innovations and future prospects

Answer: Microglial circDlg1 modulates neuroinflammation by blocking PDE4B degradation to exacerbate tau pathology and cognitive impairment in Alzheimer's disease

Answer: Inhibition of GRK2-PDE4D Axis Suppresses Fibroblast-Like Synoviocytes Activation in Rheumatoid Arthritis and Alleviates Arthritis In Vivo

Answer: PDE4B Modulates Phosphorylation of p65 (Ser468) via cAMP/PKA in Acute Lung Injury

Medicine and Life Sciences


Question: Can you help check if there is a paper that has designed a gel electrolyte for batteries, where this gel electrolyte is not for lithium-ion batteries but for lithium-sulfur batteries? This gel electrolyte is not for other systems, but specifically for the PDOL system.

Answer: An in situ fabricated multifunctional gel electrolyte for lithium–sulfur batteries

Answer: Improving lithium–sulfur battery performance under lean electrolyte through nanoscale confinement in soft swellable gels

Answer: Novel gel polymer electrolyte for high-performance lithium–sulfur batteries

Answer: Accelerating polysulfide conversion kinetics via in situ ring-opening gelation electrolyte-electrode dual catalysis for advanced quasi-solid-state lithium-sulfur batteries

Answer: How a gel polymer electrolyte affects performance of lithium/sulfur batteries

Answer: In Situ Polymerization Bi-Functional Gel Polymer Electrolyte for High Performance Quasi-Solid-State Lithium-Sulfur Batteries

Answer: Ion-selective gel polymer electrolyte and cathode binder derived from a shared polyether to synergistically mitigate polysulfides shuttling in lithium sulfur batteries

Answer: In situ forming asymmetric bi-functional gel polymer electrolyte in lithium–sulfur batteries

Answer: PEDOT:PSS Dual-Function Film Initiated 1,3-Dioxolane Polymerization in Li/S Cells

Answer: InF3 initiated ionic/electronic conductive SEI for highly stable solid-state lithium sulfur batteries

Answer: Quasi-Solid-State Electrolyte Induced by Metallic MoS2 for Lithium-Sulfur Batteries

Answer: A high ion conductive solid electrolyte film and interface stabilization strategy for solid-state Li-S battery

Answer: Temperature-dependent viscoelastic liquid MOFs based cellulose gel electrolyte for advanced lithium-sulfur batteries over an extensive temperature range

Answer: Gel electrolyte with flame retardant polymer stabilizing lithium metal towards lithium-sulfur battery

Answer: An in situ gel electrolyte for the facile preparation of high‐safety Li‐S battery

Answer: In Situ Polymerization Bi‐Functional Gel Polymer Electrolyte for High Performance Quasi‐Solid‐State Lithium−Sulfur Batteries

Answer: InPc-modified gel electrolyte based on in situ polymerization in practical high-loading lithium-sulfur batteries

Answer: Electrochemical studies on composite gel polymer electrolytes for lithium sulfur-batteries

Answer: A gel-ceramic multi-layer electrolyte for long-life lithium sulfur batteries

Answer: A highly stretchable gel-polymer electrolyte for lithium-sulfur batteries

Answer: A Rational Reconfiguration of Electrolyte for High‐Energy and Long‐Life Lithium–Chalcogen Batteries

Answer: A Concept for Making Poly(ethylene oxide) Based Composite Gel Polymer Electrolyte Lithium/Sulfur Battery

Answer: Superstretchable, thermostable and ultrahigh-loading lithium–sulfur batteries based on nanostructural gel cathodes and gel electrolytes

Answer: Low concentration salt triggered in-situ asymmetric gel electrolyte for Li-S battery

Engineering and Technology


Question: Please help me find papers on lithium-sulfur battery gel electrolytes. These papers should include gel electrolytes used in the field of lithium-sulfur batteries, including research papers and reviews. These papers should have been published after 2023.

Answer: Solvent Dynamics in Gel Polymer Electrolytes for Lithium–Sulfur Batteries

Answer: Simultaneously Suppressing Shuttle Effect and Dendrite Growth in Lithium–Sulfur Batteries via Building Dual‐Functional Asymmetric‐Cellulose Gel Electrolyte

Answer: 2,5-Dimercapto-1,3,4-thiadiazole-modified gel electrolyte for reduced shuttle effect and enhanced redox kinetics of lithium–sulfur batteries

Answer: Ion-selective gel polymer electrolyte and cathode binder derived from a shared polyether to synergistically mitigate polysulfides shuttling in lithium sulfur batteries

Answer: In Situ Polymerization Bi-Functional Gel Polymer Electrolyte for High Performance Quasi-Solid-State Lithium−Sulfur Batteries

Answer: In Situ Polymerization Bi-Functional Gel Polymer Electrolyte for High Performance Quasi-Solid-State Lithium-Sulfur Batteries

Answer: Quasi-Solid-State Electrolyte Induced by Metallic MoS2 for Lithium–Sulfur Batteries

Answer: A gel polymer electrolyte functionalized separator for high-performance lithium–sulfur batteries

Answer: Quasi-solid-state sulfur cathode with ultralean electrolyte via in situ polymerization

Answer: A universal dual modified strategy of gel polymer electrolyte for efficient sulfur conversion

Answer: Gel Polymer Electrolyte Selection for Cathode of Solid-State Lithium–Sulfur Batteries

Answer: Stabilising graphite anode with quasi-solid-state electrolyte for long-life lithium–sulfur batteries

Answer: Polymer-Based Solid-State Electrolytes For Lithium–Sulfur Batteries

Answer: Powering the future: advances, challenges, and sustainability of polymer electrolytes in lithium–sulfur batteries

Answer: Comprehensive Study on Solvent Dynamics in Gel Polymer Electrolytes for Lithium-Sulfur Battery

Answer: Temperature-dependent viscoelastic liquid MOFs based cellulose gel electrolyte for advanced lithium-sulfur batteries over an extensive temperature range

Answer: A 3D network-structured gel polymer electrolyte with soluble starch for enhanced quasi-solid-state lithium-sulfur batteries

Answer: Polymer Electrolytes for Lithium-Sulfur Batteries: Progress and Challenges

Answer: Gel electrolyte with flame retardant polymer stabilizing lithium metal towards lithium-sulfur battery

Answer: Powering the Future: Advances, Challenges, and Sustainability of Polymer Electrolytes in Lithium-Sulfur Batteries

Answer: Construction of High-Strength Flame-Retardant Li-SPEEK-Modified PEG Gel Polymer Electrolytes for Lithium Batteries

Answer: InPc-modified gel electrolyte based on in situ polymerization in practical high-loading lithium-sulfur batteries

Engineering and Technology


Question: Please help me find papers that utilize Knowledge Graphs (KGs) to mitigate hallucinations or improve factual accuracy in Retrieval-Augmented Generation (RAG) systems. The methods should specifically focus on incorporating KG reasoning paths or subgraph retrieval during the inference phase, rather than just using KGs for pre-training or simple entity masking.

Answer: FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs

Answer: Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction

Answer: Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation

Answer: Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation

Answer: KG-RAG: Bridging the Gap Between Knowledge and Creativity

Answer: SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation

Answer: Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation

Answer: G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

Answer: GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

Answer: HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

Answer: Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation

Answer: Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph

Answer: KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs

Answer: Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning

Answer: GraphRunner: A Multi-Stage Framework for Efficient and Accurate Graph-Based Retrieval

Answer: Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

Answer: GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases

Answer: GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models

Answer: KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering

Answer: Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation

Answer: Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework

Answer: GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation

Answer: GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoning

Answer: NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes

Answer: HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge Representation

Answer: GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

Answer: GRAG: Graph Retrieval-Augmented Generation

Answer: Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning

Answer: ROGRAG: A Robustly Optimized GraphRAG Framework

Answer: EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory

Answer: PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation

Answer: Query-Specific GNN: A Comprehensive Graph Representation Learning Method for Retrieval Augmented Generation

Answer: Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation

Answer: PropRAG: Guiding Retrieval with Beam Search over Proposition Paths

Answer: SG-RAG MOT: SubGraph Retrieval Augmented Generation with Merging and Ordering Triplets for Knowledge Graph Multi-Hop Question Answering

Answer: HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning

Answer: MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

Answer: DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTS

Answer: SUBQRAG: Sub-Question Driven Dynamic Graph RAG

Answer: Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

Answer: DF-graph: Structured and explainable analysis of communication data for digital forensics

Answer: KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations

Answer: Graph Retrieval-Augmented Generation: A Survey

Answer: Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

Answer: In-depth Analysis of Graph-based RAG in a Unified Framework

Answer: Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization

Answer: Knowledge Graph Based Retrieval-Augmented Generation for Multi-Hop Question Answering Enhancement

Answer: GraphTrace: A Modular Retrieval Framework Combining Knowledge Graphs and Large Language Models for Multi-Hop Question Answering

Answer: PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths

Answer: Empowering GraphRAG with Knowledge Filtering and Integration

Answer: KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation

Answer: Paths-over-Graph: Knowledge Graph Enpowered Large Language Model Reasoning

Artificial Intelligence and Computing