A multi-agent pipeline that reads a PDE problem description in plain text and writes, debugs, and validates a classical numerical solver end-to-end. Generates spectral and finite-dβ¦
Evaluates recommender systems via agent-RS interactions.
Introduces long-running multi-agent systems that self-evolve via shared persistent memory, asynchronous execution, and heartbeat-based interventions; 3β10Γ higher improvement ratesβ¦
Investigates dynamically rewiring agent-to-agent connections at each reasoning round via semantic matching instead of fixed communication topologies.
Explores automated game balancing by combining multi-agent LLM self-play with Bayesian optimization on a civ-style game.
Examines how conformal prediction can filter noisy inter-agent messages to improve multi-robot coordination.
Introduces a 110+ task benchmark to evaluate how well multi-agent LLM systems handle buyer-seller negotiation through natural language.
Analyzes social network formation among 70K+ autonomous LLM agents on Chirper.ai to study emergent group behavior and bias.
Proposes breaking large tasks into subtask trees that run in parallel across multiple agents to handle long-horizon workflows without exceeding context windows.
Proposes a deterministic multi-agent orchestrator where multiple LLMs analyze a problem independently and a merge agent selects the best answer without any training.
Simulates end-to-end hospital administrative workflows with multi-agent LLMs and FHIR integration to test LLM-driven automation in healthcare settings.
Proposes a multi-agent system for autonomous software engineering that assigns specialized agents to roles like coordination, research, implementation, and review.
Examines whether self-organizing LLM agent teams can match or beat their best member's performance across collaborative benchmarks.
Explores using LLM-driven genetic programming to automatically discover behavioral norms for multi-agent coordination in a survival-pressure grid-world simulation.
Proposes per-action process rewards from AI feedback to improve credit assignment and sample efficiency when finetuning multi-agent LLM systems.
Proposes a framework for safely growing multi-agent pools by generating familiarization tasks and building routing memory, with a guaranteed non-decreasing performance across onboaβ¦
Proposes a task-adaptive multi-agent framework that routes control to the most suitable LLM at each decision step using semantic matching against each model's success history.
Explores using a pool of different LLM agents within MCTS planning to increase rollout diversity and improve multi-step reasoning.
Proposes a recommendation framework that uses historical calling trees to select the best agents or agent teams for each subtask in multi-agent orchestration.
Investigates actor-critic reinforcement learning methods for training decentralized LLM agent collaboration across writing, coding, and game-playing tasks.
Proposes a role-structured multi-agent courtroom debate framework with defined agent roles, interaction protocols, and private reasoning strategies for auditable high-stakes decisiβ¦
Introduces a reasoning framework that builds peer reliability profiles from interaction history so agents in multi-agent systems learn which peers to trust when uncertain.
Explores structured multi-agent debate with three role-based agents and adaptive confidence gating to improve small language model code generation.
Proposes a lightweight router for dynamic model selection in graph-based multi-agent systems that combines semantic embeddings with structural meta-features and self-optimizes throβ¦
Develops a theory for predicting when budgeted multi-agent LLM systems improve, saturate, or collapse based on context windows, communication fidelity, and shared-error correlationβ¦
Proposes a meta-debate framework that dynamically assigns roles in multi-agent systems by matching model capabilities to positions through proposal and peer review stages.
Introduces orchestrated decentralized peer-to-peer LLM collaboration that uses contextual bandits to learn optimal matchmaking between heterogeneous agents via secure distillation.
Explores a runtime Mixture-of-Models architecture with a dynamic expertise broker and quadratic voting consensus that enables small model ensembles to match frontier performance.
Formalizes through operator theory why multi-agent LLM systems access invariant solutions that a single agent applying all constraints simultaneously cannot reach.
Proposes a training-time framework that formulates multi-agent orchestration as function-calling reinforcement learning with holistic system-level reasoning and introduces MASBENCHβ¦
Proposes a bi-level optimization framework for multi-agent companions that aligns individual personas via RLAIF and optimizes collaborative dialogue through group-level meta-policyβ¦
Explores a team-of-rivals multi-agent architecture with specialized roles and a remote code executor that separates reasoning from data execution to maintain clean context windows.
Formalizes a unified architectural framework for orchestrated multi-agent systems integrating MCP for tool access and Agent2Agent protocol for peer coordination, delegation, and poβ¦
Proposes Multi-Agent Reward Optimization, a method that decomposes multi-agent social interaction outcomes into per-behavior learning signals to improve LLM reasoning through simulβ¦
Introduces an LSTM-inspired multi-agent architecture with worker, filter, judge, and manager agents that emulate gated memory mechanisms to control information flow for long-contexβ¦
Examines whether query-level workflow generation is always necessary in multi-agent systems and proposes a low-cost task-level framework that uses self-prediction with few-shot calβ¦
Proposes a latency-aware multi-agent orchestration framework that explicitly optimizes the critical execution path under parallel execution to reduce end-to-end latency while maintβ¦
Proposes a one-shot topology generation framework with diverse interaction modes that enables decentralized agents to autonomously construct heterogeneous communication topologies β¦
Replaces predefined multi-agent workflows with a dynamic information-flow orchestrator that coordinates agents through natural-language A2A communication.
Reviews LLM-based multi-agent systems across the software development lifecycle, covering frameworks, communication protocols, and orchestration challenges from requirements to debβ¦
Explores injecting structured textual experience into multi-agent deliberation at test time to improve reasoning accuracy without any model tuning.
Argues that LLMs can replace hand-crafted numerical reward functions with language-based objective specifications for multi-agent coordination, drawing on EUREKA and RLVR as evidenβ¦
Analyzes over 42K commits and 4.7K resolved issues across eight leading multi-agent AI systems (LangChain, CrewAI, AutoGen, etc.) to study development patterns, maintenance practicβ¦
Proposes a hierarchical multi-agent framework that decouples high-level coordination from subtask execution with active task-level memory control and reinforcement-learning-driven β¦
Proposes a constrained temporal hierarchical architecture for multi-agent LLM systems that projects inter-layer communication onto structured manifolds with typed message contractsβ¦
Introduces dynamic path generation for multi-agent debate that allocates diverse solution paths to agents, shifts focus to step-by-step logic critique, and uses a trigger-based verβ¦
Investigates how diversity-aware initialization and confidence-modulated updates improve multi-agent debate, connecting findings from human deliberation research to LLM-based debatβ¦
Proposes a multi-agent framework with confidence-aware routing that dynamically selects agent roles and model scales across heterogeneous LLMs based on task complexity.
Analyzes role-based authority bias in multi-agent evaluation frameworks using French and Raven's power-based theory across legitimate, referent, and expert power types.
Investigates when a single agent with a skill library can replace multi-agent systems, studying scaling limits and phase transitions in skill selection as libraries grow.
Proposes a two-stage framework for enhancing multi-agent system resilience through RL-based topology generation and topology-aware prompt optimization under perturbations.
Proposes an adaptive reasoning router for multi-agent systems that generates natural-language reasoning chains before predicting candidate agents, with a collaborative execution piβ¦
Investigates covert communication in LLM multi-agent systems through game-theoretic analysis of implicit coordination signals across different communication regimes.
Proposes a Bayesian, cost-aware multi-LLM orchestration framework that treats LLMs as approximate likelihood models and aggregates across diverse models for sequential decision-makβ¦
Turns natural-language optimization problems into working solver code with a four-agent pipeline (Formulator, Planner, Coder, Critic) and UCB bandit scheduling over candidate formuβ¦
Compiles a corpus offline into a hierarchical tree of Agent Skills that the LLM agent navigates at query time, replacing retrieval with skill-tree traversal.
Investigates routing agent memory queries to different processing tiers based on query difficulty to control the cost-accuracy trade-off at runtime.
Proposes a shared memory bank with a learned controller that decides what information is worth passing between parallel agent teams to reduce redundant work.
Explores converting a corpus into atomic QA pairs offline to resolve multi-hop questions with just two LLM calls regardless of hop count.
Examines breaking financial RAG answers into atomic facts and verifying each against retrieved documents using reinforcement learning rewards.
Surveys graph-based memory architectures for agents, covering extraction, storage, retrieval, and how memory evolves over time.
Proposes a multi-agent system for inventory management that retrieves similar past decisions to adapt ordering across various supply chain scenarios.
Explores replacing flat chunk-based RAG with graph experts that understand entity relationships, causality, and process flows for structured documents like SOPs.
Investigates letting agents save step-by-step procedural skills from past runs and reuse them later without retraining to reduce repeated computation.
Proposes an agentic method for aggregation queries over unstructured text that tries to find all matching evidence, breaking the task into disambiguation, filtering, and aggregatioβ¦
Proposes an agentic RAG framework that uses reflection and memory-based refinement to generate diverse answers for open-ended questions.
Proposes joint optimization of planning and execution in agentic RAG by modeling the system as a cooperative multi-agent team with shared backbone and outcome-based rewards.
Proposes process-supervised reinforcement learning for RAG that uses MCTS-based step-level rewards to identify and fix flawed reasoning steps in multi-hop retrieval.
Introduces an episodic memory framework where assistant agents maintain uncompressed memory contexts while a master agent orchestrates global planning, replacing destructive memoryβ¦
Proposes a tiered memory service for agentic LLM systems that uses masked mixture-of-experts routing to probe only eligible memory shards under a fixed budget.
Explores adaptive query optimization in RAG using reinforcement learning to dynamically decide when to split complex queries into sub-queries and fuse the retrieved results.
Introduces an adaptive agentic Graph-RAG framework that verifies evidence sufficiency and progressively escalates retrieval effort, mapping graph signals back to source text to hanβ¦
Investigates augmenting multimodal LLMs with a trainable memory gate that decides which observations to retain, update, or discard during online embodied agent exploration.
Proposes a multi-agent memory framework with hierarchical granularity, adaptive query routing, consistency verification, and targeted memory refresh for long-term agent interactionβ¦
Examines when iterative retrieval-reasoning loops outperform static gold-context RAG in scientific multi-hop QA, diagnosing failure modes across retrieval coverage, hypothesis drifβ¦
Introduces a dependency-aware search framework that uses GRPO reinforcement learning to teach LLMs to decompose questions with dependency relationships and store intermediate resulβ¦
Proposes a biologically-inspired agent memory architecture with adaptive exponential decay, LLM-guided conflict resolution, and intelligent memory fusion across a dual-layer hierarβ¦
Explores two fusion operators for Graph RAG that combine graph-aware reranking with semantic-topological expansion to improve retrieval accuracy and generation quality.
Proposes a generator-aligned reranking and pruning module for RAG that selects evidence using utility signals and filters weak or harmful passages before context truncation.
Introduces a step-by-step reasoning reranking agent for RAG that distinguishes semantically similar but logically irrelevant passages in retrieval-augmented question answering.
Introduces a multi-agent RAG framework that coordinates sequential and parallel inference-time scaling under unified context management to prevent contamination and improve multi-hβ¦
Proposes a nugget-augmented generation system that constructs a bank of Q&A nuggets from retrieved documents to guide extraction, selection, and report generation with citation proβ¦
Introduces a hybrid RAG architecture combining query augmentation, agentic routing, and structured retrieval that merges vector and graph-based techniques with context unification β¦
Presents a systematic study of metadata-aware retrieval strategies for RAG, comparing prefix, suffix, unified embedding, and late-fusion approaches with field-level ablations on emβ¦
Proposes a hierarchical global-to-local retrieval strategy for GraphRAG with beam search-optimized re-ranking and a compact LLM integration module trained via dynamic-weighting reiβ¦
Introduces an agentic memory system that indexes trajectory steps with structured contextual intent cues and retrieves history by intent compatibility to reduce interference in lonβ¦
Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented Systems
Proposes a structure-informed and diversity-constrained context bubble construction framework for RAG that preserves document structure and balances relevance, coverage, and redundβ¦
Introduces a dual-architecture RAG framework that routes narrative through dense retrievers and tabular data through a cell-aware late interaction mechanism to preserve spatial relβ¦
Defines a class of memory systems for long-horizon agents that maintain persistent, temporally chained internal state instead of stateless RAG lookups, specifying the architecturalβ¦
Surveys foundation agent memory organized by substrate (internal/external), cognitive mechanism (episodic, semantic, working, procedural), and subject (agent- vs user-centric).
Surveys memory in LLMs and multimodal LLMs across implicit, explicit, and agentic paradigms, covering cross-modal integration and challenges like capacity, alignment, and factual cβ¦
Decomposes memory management into atomic CRUD operations and learns an autonomous policy via SFT + RL to study whether learnable memory outperforms static-workflow methods on long-β¦
Feeds explicit document quality signals (relevance score, ranking, QPP) into RAG generation to study whether exposing retrieval metadata makes the model more robust to noisy contexβ¦
Benchmarks vector-only, LLM-extracted KG, and AST-derived graph pipelines for code RAG, comparing correctness and indexing cost across deterministic and LLM-based graph constructioβ¦
Proposes an agentic RAG framework that dynamically decides whether to retrieve new evidence or reason over existing context at each step, aiming to eliminate redundant retrieval.
Proposes a training-free RAG decoding method that treats retrieved documents as isolated "experts" and aggregates their logits via retrieval-aware contrastive decoding to recover cβ¦
Proposes a query-aware agentic memory system that achieves sub-linear retrieval through temporal and semantic DAG-Tag indexing with an embedding-tag co-consolidation mechanism for β¦
Proposes treating memory abstraction as a learnable cognitive skill, training a memory copilot via DPO to determine how memories should be structured, abstracted, and reused acrossβ¦
Introduces a temporal semantic memory framework that organizes memories by actual occurrence time rather than dialogue time and consolidates temporally continuous information into β¦
Proposes an agent-centric architecture inspired by Physarum polycephalum where the agent autonomously decides when to consolidate learnings and prune raw interaction history to manβ¦
Proposes a reason-and-construct paradigm for GraphRAG that dynamically builds query-specific evidence graphs by instantiating facts from a latent relation pool and discarding distrβ¦
Introduces a plug-and-play RAG framework that disentangles semantic match from factual consistency and estimates self-answerability to make the conflict-resolution decision processβ¦
Proposes a construction-integration approach for multi-hop RAG that preserves multiple evidence chains via iterative triple construction and adaptively expands context granularity β¦
Proposes a working memory framework that constructs structured episodic narratives from conversational fragments, consolidates memories with momentum, and semanticizes peripheral fβ¦
Proposes an adaptive RAG framework that uses entropy-based gating to bypass vector database retrieval when model uncertainty is low, triggering expensive chunk retrieval only when β¦
Proposes a decoupled multi-agent RAG framework for multi-hop QA with a Plan-Retrieve-Inspect-Solve-Memoize architecture and two-stage GRPO optimization to address retrieval collapsβ¦
Proposes a framework for user-controllable memory reliance in long-term agent interactions, modeling memory dependence as an explicit and steerable dimension.
Proposes proactive memory extraction using self-questioning feedback loops instead of one-off static summarization to recover missing information and correct errors iteratively.
Proposes a hierarchical memory architecture with a Topic Loom that groups consecutive same-topic dialogue turns into coherent memory boxes and links them via long-range event-timelβ¦
Proposes a multi-graph agentic memory architecture that represents memories across orthogonal semantic, temporal, causal, and entity graphs with policy-guided traversal for retrievβ¦
Proposes a hippocampus-inspired memory architecture for AI assistants that fuses RL-trained short-term memory extraction with partitioned long-term memory for personalization.
Proposes a three-stage memory framework based on semantic lossless compression with structured compression, online semantic synthesis, and intent-aware retrieval planning.
Benchmarks browser agents on 283 everyday tasks (V1 153 + V2 130) across 163 live production sites, with a Chrome-extension plus CDP layer that blocks only the final write request β¦
Compares attribution-based explanations with trace-based diagnostics across static and agentic settings to study how explainability methods translate to multi-step agent trajectoriβ¦
Investigates whether agents can accurately predict their own success rates in agentic tasks.
Introduces 20 research tasks from real ML papers covering idea generation, experiments, and refinement for benchmarking science agents.
Proposes evaluating agent outputs by decomposing responses into individual claims and checking each against expert knowledge.
Explores using multi-agent debate to fill missing labels in information retrieval benchmarks.
Proposes a specialized verifier that detects and locates errors in agent execution trajectories at runtime to enable precise rollback-and-retry.
Examines whether GPT-4/5 agents can reproduce aggregate human cognitive biases in interactive decision-making scenarios.
Proposes generating families of equivalent CTF challenges through code transformations to test whether agents truly understand exploits or just memorize patterns.
Introduces a negotiation benchmark where frontier LLM agents are evaluated against MBA students to reveal cross-model differences in deception, accuracy, and trustworthiness.
Benchmarks how well conversational agents retain and use personal information over long emotional support conversations.
Introduces a benchmark that replays published human-subject experiments with LLM agents to test how well they simulate real participants.
Proposes an expert-designed multi-turn insurance underwriting benchmark to evaluate agent performance under real-world enterprise conditions with noisy tools and proprietary knowleβ¦
Proposes automated state abstraction from agent execution traces using predicate trees and counterexample refinement for probabilistic runtime verification of agent behavior.
Compares three LLM agent frameworks (Aider, OpenHands, SWE-agent) on vulnerability false positive filtering to study how agent design and backbone model affect triage performance.
Analyzes 8,106 fix-related pull requests from five AI coding agents to catalog the reasons agent-generated contributions are closed without merging.
Proposes a judge agent framework that evaluates query-response pairs jointly across a cohort rather than in isolation, using in-context neighborhoods for cross-instance pattern detβ¦
Evaluates LLM reasoning under ReAct and Plan-and-Execute agentic workflows across 48,000 simulated failure scenarios, producing a taxonomy of 16 common reasoning failures.
CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty
Introduces a benchmark for evaluating LLM agent consistency, uncertainty handling, and capability awareness in multi-turn tool-using scenarios with incomplete or ambiguous user reqβ¦
Examines code quality, maintainability, and reviewer sentiment toward AI-agent-generated pull requests compared to human-authored contributions.
Analyzes silent (no-comment) AI-generated pull requests to examine their impact on code complexity, quality issues, and security vulnerabilities.
Analyzes over 1,300 agent benchmarks against public-sector requirements including process-based evaluation, realism, and domain-specific metrics.
Applies Shapley values to attribute emergent extreme events in LLM multi-agent systems to specific agent actions across time, agent, and behavior dimensions.
Analyzes AI agent contributions to documentation pull requests and examines how human developers review and intervene in agent-authored documentation changes.
Examines how core and peripheral developers differ in their use, review, modification, and verification of coding-agent-generated pull requests.
Introduces an end-to-end benchmark with 700+ real-world tasks across build, monitoring, issue resolving, and test generation for evaluating AI agents in full software DevOps workflβ¦
Proposes an architecture-informed evaluation approach that links agent components like planners, memory, and tool routers to observable behaviors and diagnostic metrics.
Investigates whether smaller-scale language models can reduce energy consumption in multi-agent agentic AI systems without compromising task quality.
Analyzes 19,450 inline review comments on agent-authored pull requests and derives a taxonomy of 12 review themes to understand how reviewers respond to AI-generated code.
Analyzes 40,214 developer and agentic pull requests to compare merge outcomes and identify how submitter attributes and review features differ between human and AI agent contributiβ¦
Presents structural testing methods for LLM-based agents using OpenTelemetry traces, mocking for reproducible behavior, and automated assertions for component-level verification.
Analyzes how five AI coding agents interact with CI/CD configurations across 8,031 pull requests, examining modification frequency, merge rates, and build success.
Identifies behavioral signatures of five AI coding agents from 33,580 pull requests using commit, PR structure, and code features for agent attribution.
Assesses existing interpretability methods for agentic systems and identifies gaps in explaining temporal dynamics, compounding decisions, and context-dependent behaviors.
Investigates maintainability and security-related build code smells in AI-agent-generated pull requests across 364 identified quality issues.
Examines long-term survival of AI-agent-generated code through survival analysis of 200,000+ code units across 201 open-source projects.
Develops an oracle counterfactual framework for multi-turn agentic tasks that measures the criticality of individual capabilities like planning and state tracking.
Presents a 12-category error taxonomy and diagnostic framework for evaluating tool-use reliability across open-weight and proprietary LLMs in multi-agent systems on edge hardware.
Introduces the problem of agentic confidence calibration and proposes Holistic Trajectory Calibration, extracting process-level features across an agent's entire trajectory to diagβ¦
Examines methodological challenges in evaluating AI agents across sensitive information leakage, fraud, and cybersecurity threats through a multi-national collaborative benchmarkinβ¦
Introduces a multi-agent framework that generates verified, domain-specific, multimodal, multi-hop question-answer datasets for benchmarking retrieval-augmented generation systems.
Analyzes 1,187 bug reports from LLM agent software across seven frameworks to categorize bug types, root causes, effects, and tests automated bug labeling with a ReAct agent.
Proposes a hierarchical framework for general agentic attribution that identifies internal factors driving agent actions through temporal likelihood dynamics and perturbation-basedβ¦
Analyzes token consumption patterns across software development lifecycle stages in a multi-agent system to identify where tokens are consumed and which stages drive cost.
Introduces a benchmark of 480 long-horizon, cross-application productivity tasks created by investment banking analysts, consultants, and lawyers for evaluating AI agent capabilitiβ¦
Introduces a benchmark of 600+ collaborative coding tasks to evaluate whether coding agents can coordinate as effective teammates under various coordination structures.
Investigates how RAG systems can game nugget-based LLM judge evaluations through metric overfitting, demonstrating near-perfect scores when evaluation elements are leaked or predicβ¦
Introduces the Determinism-Faithfulness Assurance Harness for measuring trajectory determinism and evidence-conditioned faithfulness in tool-using LLM agents across 74 configuratioβ¦
Presents a process-aware and auditable multi-agent evaluation framework that plans, executes, and aggregates multi-step evaluations across heterogeneous agentic workflows under humβ¦
Introduces a curated benchmark of 89 hard tasks in computer terminal environments with unique environments, human-written solutions, and comprehensive tests for evaluating frontierβ¦
Introduces a benchmark and evaluation framework for agentic task-oriented dialogue systems covering multi-goal coordination, dependency management, memory, adaptability, and proactβ¦
Decouples task execution from environment understanding with a deterministic QA paradigm to study whether task success is actually a good proxy for how well agents understand theirβ¦
Evaluates frontier models on 150 workplace tasks to identify an empirical hierarchy of agentic capabilities spanning tool use, planning, adaptability, groundedness, and common-sensβ¦
Introduces a multimodal RAG benchmark with 26K pages and 3,099 queries in 6 languages to evaluate retrieval across non-textual elements and open-ended queries.
Evaluates LLM agent social behaviors in mixed-motive games using process-aware analysis of both reasoning and communication rather than outcome-only metrics.
Benchmarks whether agents can proactively use long-term memory to execute tool-based actions, rather than just passively retrieving facts on demand.
Proposes a formal framework for actively evaluating general-purpose agents across multiple tasks, selecting which tasks and agents to sample next to minimize ranking error over timβ¦
Introduces an Unreal Engine 5 simulation platform for benchmarking LLM-driven agents on embodied tasks including navigation, object manipulation, and multi-agent coordination in prβ¦
Presents an open-source platform combining visual workflow orchestration with LLM-as-a-Judge evaluation for prototyping and validating RAG-based agent pipelines without infrastructβ¦
Benchmarks model robustness across 11 RAG, reasoning, alignment, and tool-use tasks against diverse contextual noise types including random documents, irrelevant histories, and harβ¦
Introduces a project-oriented memory benchmark with 2,000+ cross-session dialogues across eleven scenarios to evaluate how well agents track evolving goals and dynamic context depeβ¦
Introduces the first benchmark for interactive deep research combining a modular multi-agent framework with on-demand user interaction, a scalable user simulator, and interaction-aβ¦
Introduces an open-world tool-using environment with 5,571 tools across 204 apps, a task engine for multi-tool workflows with wild constraints, and a state controller that injects β¦
Introduces a tower defense environment for evaluating LLM agent planning and decision-making with low computational demands, multimodal observation, and hallucination assessment suβ¦
Introduces a user-authored benchmark for memory-aware LLM agents in Minecraft with parametric task templates, machine-checkable validators, and bounded-knowledge evaluation under aβ¦
Proposes a framework for detecting tool-calling hallucinations in LLM agents by analyzing internal representations during a single forward pass, targeting incorrect tool selection,β¦
Surveys the evolution from LLM-as-a-Judge to Agent-as-a-Judge, where agentic judges employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory fβ¦
Introduces the first benchmark for evaluating tool-calling and agentic capabilities of LLMs in Arabic, measuring functional accuracy and robustness in Arabic agentic workflows.
Examines how Big Five personality steering affects cooperative behavior in LLM agents using repeated Prisoner's Dilemma games across multiple model generations.
Analyzes message-code inconsistency in pull requests authored by AI coding agents across five agent systems to study trustworthiness of agent-generated PR descriptions.
Proposes a multi-agent framework for autonomous exploratory GUI testing that decouples navigation from verification via planning-execution and hierarchical reflection modules.
Introduces the concept of agent drift and a composite metric framework for quantifying semantic, coordination, and behavioral degradation in multi-agent LLM systems over extended iβ¦
Introduces a unified benchmark for evaluating Multi-Agent Debate methods across multiple domains, modalities, and efficiency metrics including token consumption and inference time.
Documents six recurring failure modes across four end-to-end attempts at autonomous ML research using a pipeline of LLM agents mapped to stages of the scientific workflow.
Introduces a data analysis benchmark for evaluating LLM agents under documentation-intensive analytical workflows requiring long document navigation and multi-step computation.
Proposes a closed-loop multi-agent testing framework with generation, execution analysis, and review optimization agents for autonomous software test refinement.
Proposes a causal framework using structural causal models and counterfactual interventions to audit whether reasoning traces in LLM agents are faithful generative drivers or post-β¦
Introduces a benchmark for evaluating agent reliability across consistency, robustness to perturbations, and fault tolerance under chaos-engineering-style tool failure injection.
Introduces an evaluation suite that standardizes MAS configuration and execution, exports framework-agnostic execution traces, and enables systematic reliability assessment across β¦
Introduces a benchmark for evaluating LLM agent function-calling under realistic API complexity including noisy outputs, detailed specifications, and runtime challenges.
Proposes a multi-agent observe-analyze-repair loop that uses runtime traces to find and fix bugs in LLM-generated code.
Explores constraining LLM generation with executable schemas and multi-agent roles to produce structurally valid yet creative outputs.
Tests how context format (YAML, JSON, Markdown) affects agent accuracy across 9,649 experiments in file-native agentic systems.
Explores training agents to think ahead by distilling environment search into causal reasoning chains in interactive environments.
Investigates teaching agents to ask themselves the right questions before acting to adapt to new situations autonomously.
Surveys the connection between agentic architectures and spatial tasks like robotics and navigation, covering memory, planning, and world models in embodied agents.
Argues for using world models as a bridge between agents and high-cost real-world environments to provide richer learning signals across domains like robotics and ML engineering.
Presents a reference architecture for production AI agents integrating Clean Architecture, event-driven design, per-agent MLOps lifecycles, and human-in-the-loop governance.
Proposes a meta-agent framework that builds, runs, and keeps refining data processing pipelines through hierarchical agent orchestration.
Proposes a multi-agent framework for automatically building executable test environments across ten programming languages using planning-execution-verification with environment reuβ¦
Proposes an adaptive data generation framework for training mobile GUI agents that matches task difficulty to the agent's current capability level.
Proposes extracting dual-form reusable expertise from agent execution histories β specialized subagents for procedural tasks and skill patterns for static knowledge β with continuoβ¦
Proposes modeling GUI agent operations as sequences of learnable tool tokens with semantic anchoring and curriculum-based training instead of coordinate-based visual grounding.
Proposes a framework combining a self-evolving multi-agent data engine with verifier-based reinforcement learning to train multi-turn interactive tool-using agents.
Investigates why step-wise reasoning struggles with long-horizon planning in LLM agents and proposes future-aware lookahead with reward estimation to let early actions account for β¦
Proposes a test-time scaling method for software engineering agents that recycles prior trajectories and branches at critical intermediate steps instead of resampling from scratch.
Proposes bundling recurring sequences of agent tool calls into deterministic meta-tools to skip unnecessary intermediate LLM reasoning steps and cut failures.
Explores integrating LLM capabilities into the ASTRA agent programming language to study how traditional agent toolkits and modern LLM-based agentic platforms can inform each otherβ¦
Introduces a bi-level framework where a meta-agent evolves context engineering skills via agentic crossover while a base agent executes them to optimize context as files and code.
Proposes a multi-agent framework and benchmark for cross-modal data analysis that coordinates specialized sub-agents via a divide-and-conquer workflow across structured and unstrucβ¦
Explores agentic AI for Android app testing that uses code inspection and dynamic instrumentation to reach activities that standard GUI fuzzers cannot access.
Introduces a large-scale computer-using agent skill library with parameterized execution, composition graphs, dynamic retrieval, and memory-aware failure recovery for desktop appliβ¦
Explores local equilibrium propagation for optimizing deep compound AI systems that avoids signal degradation in long-horizon agentic workflows by replacing global textual backpropβ¦
Investigates counterfactual reasoning in agentic LLM control scenarios using structural causal models and conformal prediction for formal reliability guarantees.
Introduces a hierarchical multi-agent system with out-of-domain detection and BERT-based agent routing for delivering personalized data insights at production scale.
Introduces a system-theoretic framework that decomposes agentic AI into five functional subsystems and derives 12 reusable design patterns for building robust agent architectures.
Explores a pragmatic framework for transitioning organizational processes to agentic AI, covering domain-driven use case identification, task delegation, and human-in-the-loop operβ¦
Proposes a training-free continual learning framework for LLM agents that retrieves relevant past experiences and modulates output logits at test time without gradient updates.
Proposes embedding explicit reasoning at both function and parameter levels during agent tool calls, with dynamic complexity scoring to trigger granular justification for critical β¦
Investigates which RL training environment properties and modeling choices most influence cross-domain generalization for LLM agents deployed beyond their training domains.
Proposes disaggregating LLM investigation into bounded local evidence mining with deterministic graph traversal and belief propagation for reliable open-ended agent reasoning.
Presents a LangGraph-based AI agent framework combining GraphRAG, multi-stage retrieval, and RL-inspired adaptive feedback for reverse-engineering legacy scientific code.
Proposes a continuous vulnerability repair system that orchestrates a diverse LLM agent ensemble with two-phase deduplication for integration with continuous fuzzing pipelines.
Introduces a declarative architectural layer for agentic workflows with formalized capabilities, declarative discovery protocol, and deterministic task graph construction.
Presents a task-aware context pruning framework for coding agents that trains a lightweight neural skimmer to selectively retain relevant code lines based on explicit goals.
Proposes a multi-agent prompt optimization framework guided by requirements engineering principles for system and user prompts in agent-based software development.
Introduces a self-evolving multi-agent framework for automated environment configuration with expert diagnosis and dynamic error-fixing priority adjustment.
Proposes a pipeline-aware caching architecture for agentic systems that elevates structured intermediate reasoning representations to first-class cacheable artifacts to reduce reduβ¦
Investigates imposing explicit dynamical structure on an external affective state to induce temporal coherence and controlled recovery in multi-turn dialogue agents.
Proposes a Dual-Process framework that transforms verbalized uncertainty into bi-directional control signals for agent memory and reflection to prevent cascading hallucination erroβ¦
Presents a Unified Agent Lifecycle Management blueprint with five control-plane layers for governing agent fleets including identity registry, orchestration, and runtime policy enfβ¦
Introduces a neuro-symbolic architecture that integrates LLM agents with predicate-logic programming and knowledge graphs to orchestrate end-to-end business initiatives through tasβ¦
Proposes a software engineering framework for capturing and embedding codified human domain knowledge into LLM-based agents through request classification, RAG, and expert rule intβ¦
Defines the agent:// URI scheme that decouples agent identity from network location through trust roots, hierarchical capability paths, and cryptographic attestation for multi-agenβ¦
Surveys efficiency in agent systems across memory, tool learning, and planning, comparing approaches under fixed cost budgets and analyzing the Pareto frontier between effectivenesβ¦
Proposes self-coding information systems that use agentic AI to dynamically generate, test, and redeploy their own source code at runtime to reduce feature delivery time.
Presents a lightweight open-source Python framework for building LLM-driven agents with composable skill abstractions, a unified LLM backend interface, and declarative YAML-based cβ¦
Introduces a multi-agent reward model system for GUI agents that combines domain-specific and general-purpose reward models with automated data reflux for self-evolving agent trainβ¦
Investigates three deployment architectures for integrating LLM-based agentic AI with edge computing in UAV swarms, covering standalone, edge-enabled, and edge-cloud hybrid configuβ¦
Proposes a unified taxonomy decomposing AI agents into Perception, Brain, Planning, Action, Tool Use, and Collaboration subsystems, covering MCP, native computer use, and evaluatioβ¦
Surveys agentic reasoning across foundational, self-evolving, and collective multi-agent dimensions, distinguishing in-context reasoning from post-training approaches across planniβ¦
Introduces a governed orchestration framework that treats agentic automation as typed plan synthesis with DAG-based planning, rubric-guided selection, validator-gated execution, anβ¦
Explores how the Unix 'everything is a file' principle informs agentic AI design through file-like abstractions and code-based specifications for composable, auditable agent interfβ¦
Introduces a hierarchical self-evolving multi-agent framework that integrates curriculum learning, reward-based learning, and genetic algorithm evolution for continuous autonomous β¦
Proposes a self-evolving agent framework that evolves an explicit finite state machine instead of free-form code rewriting, constraining flow and skill optimization to a structuredβ¦
Identifies and studies a conflict type where a tool-augmented LLM's internal knowledge contradicts external tool outputs, evaluating whether existing resolution techniques like proβ¦
Uses lookahead planning to estimate the value of tool usage at each step and selects stable, high-value reasoning paths, with a convergence mechanism that halts rollouts once consiβ¦
Trains history-aware routers for large-scale MCP tool ecosystems using dependency graphs and multi-turn trajectory synthesis to generalize across multi-agent collaboration and massβ¦
Proposes iterative query planning for tool retrieval that decomposes instructions into sub-tasks and dynamically generates queries, trained via synthetic trajectories and reinforceβ¦
Introduces a Computer-Using Agent framework with milestone-driven long-term memory for trajectory-level self-correction and a multimodal searcher that synthesizes live, visually alβ¦
Presents a conversational AI interface for dynamic tool discovery and execution via the OPACA framework, comparing multiple task-solving strategies across different agent setups anβ¦
Proposes test-time tool evolution where agents synthesize, verify, and evolve executable tools during inference instead of relying on static pre-defined tool libraries.
Introduces a large-scale distributed orchestration system that decouples agent training into independent Model, Agent, and Environment services for scheduling tens of thousands of β¦
Proposes an evaluation-judge-optimization pipeline that assigns block-level responsibility scores to failing logic blocks in agentic workflows, focusing modifications on the most pβ¦
Introduces a reproducibility-constrained framework for Large Action Models with structured action schemas, deterministic execution policies, and provenance tracking to ensure auditβ¦
Proposes a composable RL infrastructure for LLM agents that separates algorithm design, execution, and agent-environment interaction with a centralized scheduler for managing shareβ¦
Introduces activation-guided, role-conditioned neuron transplantation for training-free merging of environment-specific LLM agent experts into a single generalist model.
Proposes a dynamics-aware framework grounded in Schema Theory that routes agent training data to SFT or RL based on gradient concentration, using cognitive conflict as the allocatiβ¦
Introduces a training framework for calibrating agent tool-use behavior through a self-evolving data flywheel and two-phase behavior calibration to reduce redundant and insufficienβ¦
Proposes a co-evolutionary framework that jointly optimizes the agent policy and its natural-language critic through synchronized GRPO updates, preventing the critic from becoming β¦
Introduces context engineering techniques for agentic workflows including structured DS-specific prompting, separate plan and code agents, and smart history rendering for fault tolβ¦
Proposes a reinforcement learning paradigm that replaces pointwise scalar scoring with intra-group relative ranking via tournament-based schemes to address discrimination collapse β¦
Introduces a conceptual framework with six capabilities (Contextualize, Harmonize, Anticipate, Negotiate, Generate, Evolve) for architecting AgentOps platforms that manage the lifeβ¦
Proposes internalizing execution priors to predict agent outcomes before physical execution, using a Predict-then-Verify loop to accelerate ML agent workflows without running expenβ¦
Proposes an automated framework for generating scalable tool-interaction environments via programmatic synthesis, constructing diverse environment skeletons and task scenarios for β¦
Proposes a multi-agent framework for localizing integration defects in LLM-integrated software using code knowledge graphs enriched with LLM-aware annotations and counterfactual reβ¦
Proposes a unified framework for multi-turn agentic RL that uses a turn-level tree structure for entropy-guided exploration, turn-wise credit assignment, and turn-based policy optiβ¦
Proposes a framework that decouples agentic search into Search Behavior Agents and Knowledge Management Agents with turn-level rewards for multi-hop QA.
Reframes agent self-improvement as a release engineering pipeline with implementation-blind quality signals, symptom-level diagnosis, and flip-centered regression gating.
Proposes an attribution-driven requirements engineering methodology for specifying what domain knowledge LLM agents need at design time, organized along four causal dimensions.
Proposes a structured generation engine for agentic LLMs with dynamic tag dispatching, JIT compilation, and cross-grammar caching for tool calling and conditional structured generaβ¦
Formalizes transitive expert error in AI routing architectures including MoE, multi-model orchestration, and tool-using agents, proposing boundary-aware calibration and coverage gaβ¦
Introduces a multi-agent workflow for synthesizing research-grade training data with a two-stage SFT plus agentic RL strategy for open-source deep research models.
Proposes design patterns for architecting agentic communities derived from enterprise distributed systems standards, covering coordination, governance, and formal collaboration agrβ¦
Proposes a skill-conditioned RL framework for tool-using agents that grounds reward modeling in a library of skill prototypes for mid-level credit assignment.
Proposes a Context-Aware MCP architecture with a Shared Context Store that enables MCP servers to coordinate autonomously by reading from and writing to shared context memory.
Proposes a multi-agentic workflow that decouples optimization of primary task descriptions from constraint optimization using quantitative feedback for iterative prompt refinement.
Proposes a general-purpose agent framework that keeps reasoning context bounded regardless of task duration by externalizing persistent state into a file-centric state abstraction.
Surveys agentic AI architectures covering planning, memory, tool use, and iterative reasoning with a critical assessment of safety, alignment, and reliability challenges.
Proposes an agentic memory enhanced recursive reasoning framework for root cause localization with cross-alert memory reuse and multi-agent recursive refinement.
Introduces a lightweight Python framework providing a unified, type-safe interface for building LLM agents across multiple providers with tool calling, memory management, and MCP iβ¦
Surveys AI agent architectures spanning reasoning, planning, tool calling, orchestration patterns, and deployment settings with a unified taxonomy of agent components and design trβ¦
Proposes a dual-stream architecture that elevates the persistent Python runtime as the central locus of agent state, with stateful runtime management and skill injection for long-hβ¦
Proposes an active feedback model where AI agents proactively interact with the environment to discover and verify feedback without relying on predefined measurements.
Proposes an asynchronous architecture for million-agent scaling that reduces memory complexity via singleton weight sharing and topological synapse-inspired KV-cache sparsificationβ¦
Reveals that AI agents produce harmful content (toxic text, exploits, dangerous data) as a side effect of completing normal professional tasks β no adversarial prompting needed. Atβ¦
Trains an LLM to generate RAG poison that survives real-world content processing and query variation for stress-testing RAG defenses.
Analyzes 98K agent skills from community registries to study the prevalence and nature of malicious third-party agent plugins.
Investigates whether attackers can reconstruct knowledge graphs from Graph RAG outputs through multi-turn probing.
Proposes consume-once mandate semantics for AI agent payment protocols to prevent replay and redirect attacks in autonomous transactions.
Explores using an LLM agent to identify attack techniques in stripped malware binaries through incremental context retrieval.
Maps attack paths in agent-to-agent communication protocols for automotive LLM assistants, from driver distraction to unauthorized vehicle control.
Explores using reinforcement learning to auto-generate prompt injection attacks that transfer across multiple frontier LLM models.
Proposes an LLM agent with dual feedback loops for strategy and code to automate vulnerability reproduction from CVE descriptions.
Organizes agentic security risks into four layers (Core, Connection, Cognition, Compliance) to address trust and governance issues beyond prompt injection.
Proposes a co-evolving RL game between an attacker and defender agent to stress-test safety alignment against novel attack patterns.
Introduces an LLM agentic system that reconstructs blockchain exploit lifecycles from limited evidence and generates runnable proof-of-concept reproductions.
Argues that AI-agent-driven cyber attacks are inevitable and proposes building frontier offensive AI capabilities responsibly as essential defensive infrastructure.
Proposes protocol-level security improvements for the Model Context Protocol including unified identity management, mutual authentication, and fine-grained policy enforcement.
Investigates how user persuasion during conversation can carry over and change how autonomous AI agents perform later tasks.
Explores how collective false memories form in LLM-based multi-agent systems and proposes defenses including cognitive anchoring and alignment-based approaches.
Proposes a black-box attack method that generates transferable adversarial tokens to manipulate LLM-based retrieval systems without needing access to the target's queries or model.
Introduces CacheAttack, a black-box framework that exploits the trade-off between locality and collision resistance in semantic caching to hijack LLM responses and manipulate agentβ¦
Proposes a verify-then-pay infrastructure for agent transactions that locks funds in escrow, requires cryptographic proof of task execution, and releases payment only after verificβ¦
Red-teams Google's Agent Payments Protocol via prompt injection attacks that manipulate product ranking and extract sensitive user data in agent-led purchase flows.
Introduces a benchmark for evaluating when agent violations are detected during execution rather than just whether, with temporal metrics for early intervention and tokens saved.
Argues that static compliance-based governance is insufficient for agentic AI at machine speed and proposes runtime governance to preserve human relevance in agent-driven decision-β¦
Introduces an adversarial attack that poisons retrieval contexts in RAG-based code generation to force longer outputs, increasing GPU latency and energy consumption.
Surveys security threats targeting AI agents in cyber-physical systems, covering deepfake attacks, MCP-mediated vulnerabilities, and defense-in-depth architectures.
Explores AutoGen-based multi-agent coordination with specialized agents for static, dynamic, and network-level ransomware family classification using confidence-aware decisions.
Introduces a multi-agent auto-healing defense framework with semantic similarity retrieval, pattern matching, and an evolving knowledgebase for defending LLMs against resource exhaβ¦
Explores agentic AI for pre-commit secure code review that uses autonomous decision-making, tool invocation, and security-focused semantic memories to detect immature vulnerabilitiβ¦
Introduces a three-dimensional taxonomy for agentic risks and a diagnostic guardrail framework that monitors agent trajectories with fine-grained root cause analysis beyond binary β¦
Examines how benign personal memories in personalized agents can bias intent inference and cause models to legitimize harmful queries through a previously unexplored safety vector.
Proposes a multi-agent collaborative framework with specialized LLM-enhanced agents for intelligent data processing and adaptive intrusion classification in aerial IoT networks.
Introduces a protocol-agnostic execution control plane for autonomous agents that enforces authorization boundaries with canonical action representation and deterministic policy evβ¦
Examines privacy risks in multimodal RAG pipelines through inclusion inference and metadata leakage attacks during standard model prompting.
Presents the first security analysis of the Model Context Protocol specification, identifying three protocol-level vulnerabilities and proposing backward-compatible security extensβ¦
Surveys 78 studies to systematize prompt injection attacks on agentic coding assistants with a three-dimensional taxonomy across delivery vectors, modalities, and propagation.
Introduces RAGCrawler, a knowledge graph-guided attack that adaptively steals RAG corpus content through targeted queries to maximize coverage under a query budget.
Presents a multi-tenant chatbot deployment platform with container-based isolation and platform-level defenses against prompt injection attacks in RAG-based systems.
Interoperable Architecture for Digital Identity Delegation for AI Agents with Blockchain Integration
Introduces delegation grants and a canonical verification context for bounded, auditable identity delegation across human users and AI agents in heterogeneous identity ecosystems.
Proposes an infection-aware defense framework for multi-agent systems that distinguishes infected agents from attackers and applies topological constraints to halt malicious propagβ¦
Proposes AGEA, an agentic framework using novelty-guided exploration and graph memory to steal latent entity-relation graphs from GraphRAG systems under strict query budgets.
Introduces activation-space guardrails that detect privacy-violating intent in LLM agents through linear separation of internal representations, including drift detection across muβ¦
Proposes a three-agent sandbox simulation framework with 40 crime tasks across 13 objectives to evaluate the criminal capabilities of LLM agents in realistic scenarios.
Introduces an adaptive prompt injection framework targeting navigation agents under black-box, long-context, and action-executable constraints across indoor and outdoor environmentβ¦
Explores a multi-agent defense pipeline combining semantic similarity caching, nested learning, and observability-aware evaluation to mitigate prompt injection attacks while reduciβ¦
Introduces an overthinking attack framework for RAG systems with reasoning models, using multi-agent-constructed poisoning samples that cause excessive reasoning token consumption β¦
Introduces a framework for detecting and mitigating tool-driven agency risks through offline interface verification and runtime per-step least-privilege tool access with adaptive fβ¦
Proposes a privacy-preserving RAG framework using conditional approximate distance-comparison-preserving encryption that enables similarity computation on encrypted embeddings in uβ¦
Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework
Proposes a mandatory access control framework for LLM agent systems that monitors agent-tool interactions via information flow graphs and enforces attribute-based policies against β¦
Introduces governance graphs as public, immutable manifests with enforceable sanctions and restorative paths to govern multi-agent LLM coordination and prevent harmful collusion.
Proposes a prompt-injection-resilient RAG framework that decouples security enforcement from generation by applying sanitization and policy-aware disclosure controls during the retβ¦
Introduces a stealthy multi-turn economic DoS attack exploiting the agent-tool communication loop through MCP-compatible tool server modifications that inflate costs by up to 658x.
Introduces a benchmark and harness for evaluating web-facing RAG systems under indirect prompt injection and retrieval poisoning attacks with standardized end-to-end evaluation froβ¦
Introduces a neuro-symbolic containment architecture that decouples normative reasoning from instrumental decision-making through a Moral Module, Decision-Making Module, and compliβ¦
Presents a security framework that learns context-aware access-control policies from monitored execution traces to govern AI agent operations and detect malicious inputs while presβ¦
Analyzes 42,447 agent skills from two major marketplaces to study the prevalence and types of security vulnerabilities spanning prompt injection, data exfiltration, privilege escalβ¦
Proposes single-shot planning for Computer Use Agents that provides provable control flow integrity against prompt injection while preserving agent capability.
Tests open-source function-calling LLMs against multiple attack types with various defenses to study the readiness of current models and mitigations for production deployment.
Examines how commercial planning and web-use agents handle user-mediated attacks where the user themselves provides adversarial instructions without explicit safety requests.
Formalizes how propositions gain unwarranted trust by crossing architecturally trusted interfaces in agent systems, studying whether circular epistemic justification is inevitable β¦
Proposes applying System-Theoretic Process Analysis to identify hazards in agent tool-use workflows, deriving formal safety specifications enforced through a capability-enhanced Moβ¦
Introduces an automated framework for implicit tool poisoning in MCP where a poisoned tool remains uninvoked but its metadata manipulates the agent into performing malicious operatβ¦
Proposes a black-box attack that decomposes indirect prompt injection into trigger and attack fragments to study end-to-end IPI exploits under natural queries across RAG and agentiβ¦
Proposes a hardware-backed zero-trust architecture for AI memory systems that applies TEE protection across five functional layers with a cross-application sharing protocol for ageβ¦
Introduces a benchmark for evaluating safety alignment of AI agents performing professional-level tasks across diverse domains, uncovering new unsafe behaviors in complex professioβ¦
Demonstrates that off-the-shelf LLM agents with web search can re-identify participants in anonymized qualitative datasets using only natural-language prompts, lowering the technicβ¦
Proposes a conceptual and operational framework for safe AI agent development grounded in transparency, accountability, and trustworthiness, with progressive validation analogous tβ¦
Proposes a verify-before-commit protocol for defending LLM agents against tool stream injection, using speculative hypothesis generation and intent-grounded verification to balanceβ¦
Evaluates memory poisoning attacks on memory-augmented LLM agents and proposes two defense mechanisms: input/output moderation with composite trust scoring and memory sanitization β¦
Proposes a secure transpiler and executor for LLM-generated code that detects vulnerabilities and safely executes code snippets in autonomous production AI systems without relying β¦
Investigates conformity bias in AI agents under social pressure using adapted visual experiments from social psychology, studying sensitivity to group size, unanimity, task difficuβ¦
Proposes a tool result parsing method for defending LLM agents against indirect prompt injection by providing precise data while filtering out injected malicious code.
Surveys agent-blockchain interoperability patterns and threat models for agent-driven transaction pipelines, covering custody models, policy enforcement, and multi-agent workflows.
Proposes a stage-aware framework for analyzing backdoor attacks across planning, memory, and tool-use stages of LLM agent workflows with cross-stage trigger propagation.
Proposes a deceptive defense framework using collaborative defender agents to counter multi-turn jailbreak attacks by strategically wasting attacker resources.
Systematizes privacy risks, mitigation techniques, and evaluation strategies in RAG systems through a comprehensive literature review with a taxonomy and process diagram.
Proposes a behavioral watermarking framework that embeds multi-bit identifiers into agent planning decisions for IP protection and regulatory provenance while preserving utility.
Proposes structural tokenization that encodes execution-flow patterns instead of conversational content to improve cross-attack generalization in AI agent threat detection.
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage
Introduces a cognitive collusion attack where colluding agents steer victim beliefs using only truthful evidence fragments distributed through public channels without covert communβ¦
Proposes a lightweight framework that safely executes untrusted MCP tools inside a WebAssembly sandbox and produces auditable reports of external-to-sink exposures.
Analyzes toxicity adoption dynamics among LLM-driven agents on a fully AI-driven social platform, studying how cumulative toxic exposure affects the probability of toxic responses.
Proposes a Siamese Recurrent Autoencoder with hybrid contrastive-reconstruction loss for real-time anomaly detection in agent action trajectories.
Maps human anti-collusion mechanisms including sanctions, leniency, monitoring, and market design to potential interventions for multi-agent AI systems.
Proposes a data adulteration framework that pre-emptively injects plausible but false entries into knowledge graphs to make stolen GraphRAG KGs unusable to adversaries.
Examines intergroup bias in LLM agents under minimal group cues and formalizes a Belief Poisoning Attack that manipulates agent identity beliefs to induce outgroup bias toward humaβ¦
Despite enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains on popular benchmarks are often minimal. This gap highlights a critical need for a principled understaβ¦
LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environments. This paper provides the fβ¦
Existing benchmarks measure capability -- whether a model succeeds on a single attempt -- but production deployments
require reliability -- consistent success across repeated attemβ¦
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The emergence of writable, cross-session persistent memory in LLM agents introduces a qualitatively different threat landscape from conventional input-centric security concerns, chβ¦
AI agents -- systems that plan, reason, and act using large language models -- produce non-deterministic, path-dependent behavior that cannot be fully governed at design time, wherβ¦
Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instrucβ¦
Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existinβ¦
Tool-using multi-agent large language model (LLM) systems spend computation through model tokens, tool calls, retries, and code execution before producing an answer. When a run faiβ¦
In this paper, a mathematical model is developed to describe the evolution of the concentration of compounds through a gas chromatography column. The model couples mass balances anβ¦
Large Language Models (LLMs) have demonstrated near-human performance in summarization tasks based on traditional metrics such as ROUGE and BERTScore. However, these metrics do notβ¦
In this manuscript, we study a special class of correspondences on $\mathbb{P}^{1} \times \mathbb{P}^{1}$ given by a polynomial relation, say $P(z, w)$. We focus on what we call reβ¦