🦾 LLM Agent

🤖 AAAI2026 · 33 paper notes

📌 Same area in other venues: 📷 CVPR2026 (39) · 🔬 ICLR2026 (162) · 💬 ACL2026 (82) · 🧪 ICML2026 (59) · 🧠 NeurIPS2025 (39) · 📹 ICCV2025 (4)

🔥 Top topics: Agents ×10 · LLM ×10 · Reasoning ×4 · Adversarial Robustness ×2 · Alignment/RLHF ×2

A2Flow: Automating Agentic Workflow Generation via Self-Adaptive Abstraction Operators

This paper proposes A2Flow, a framework that automatically extracts reusable abstract execution operators from expert data via a three-stage pipeline (case generation → functional clustering → deep extraction), replacing manually predefined operators. Combined with an operator memory mechanism that accumulates intermediate outputs to assist node decision-making, A2Flow outperforms AFLOW and other state-of-the-art methods across 8 benchmarks while reducing resource consumption by 37%.

Agent-SAMA: State-Aware Mobile Assistant

This paper proposes Agent-SAMA, which for the first time introduces a finite state machine (FSM) into mobile GUI agents, modeling UI screens as states and user actions as transitions. Four specialized agents collaborate to achieve state-aware task planning, execution verification, and error recovery, improving success rate by up to 12% and recovery rate by 13.8% on cross-app benchmarks.

AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search

This paper proposes AgentSwift, a framework that automatically discovers high-performance LLM agent designs through a hierarchical search space (jointly optimizing agentic workflows and functional components), a lightweight value model for predicting agent performance, and an uncertainty-guided MCTS search strategy, achieving an average improvement of 8.34% across 7 benchmarks.

AMS-IO-Bench and AMS-IO-Agent: Benchmarking and Structured Reasoning for Analog and Mixed-Signal Integrated Circuit Input/Output Design

This paper proposes AMS-IO-Agent, a domain-specific LLM-based agent that transforms natural language design intent into production-ready analog and mixed-signal IC I/O ring designs via a structured Intent Graph and a domain knowledge base. It also introduces AMS-IO-Bench, the first benchmark for AMS I/O ring automation. The agent-generated I/O ring is validated in a 28nm CMOS tape-out and demonstrated to be directly applicable to real chip fabrication.

AutoGLM: Autonomous Foundation Agents for GUIs

AutoGLM builds a GUI foundation agent for web browsers and Android devices on top of ChatGLM. By introducing an intermediate interface design that decouples planning from grounding, and proposing a self-evolving online curriculum reinforcement learning framework, the system achieves a 55.2% success rate on VAB-WebArena-Lite, substantially surpassing GPT-4o's 18.2%.

Automating Complex Document Workflows via Stepwise and Rollback-Enabled Operations

This paper proposes AutoDW, a framework that automates complex document workflows through stepwise planning (generating one API call at a time) combined with adaptive rollback (parameter-level and API-level). On DWBench—a benchmark of 250 sessions and 1,708 instructions—AutoDW achieves 90% instruction-level and 62% session-level completion rates, surpassing the strongest baseline by 40% and 76%, respectively.

AutoTool: Efficient Tool Selection for Large Language Model Agents

This paper proposes AutoTool, a graph-based tool selection framework that exploits tool usage inertia to construct a Tool Inertia Graph (TIG). By leveraging statistical structure, AutoTool bypasses redundant LLM inference for tool selection and parameter filling, reducing inference overhead by up to 30% while maintaining task completion rates.

BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling

This paper proposes the vPGM framework, which guides LLM agents via natural language to simulate Bayesian reasoning over probabilistic graphical models (PGMs)—discovering latent variables and inferring posterior distributions—and further applies numerical Bayesian calibration with a Dirichlet prior (BayesVPGM), achieving simultaneous improvements in accuracy and confidence calibration across multiple reasoning tasks.

CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing

This paper proposes CausalTrace — a neurosymbolic causal analysis agent integrated into an industrial CoPilot (SmartPilot) that combines data-driven causal discovery with industrial ontologies and knowledge graphs, enabling real-time root cause analysis, counterfactual reasoning, and interpretable decision support.

Co-EPG: A Framework for Co-Evolution of Planning and Grounding in Autonomous GUI Agents

This paper proposes Co-EPG, a framework that decouples a GUI Agent into separate Planning and Grounding models, establishes a positive feedback loop via GRPO co-training and a Confidence-based Dynamic Reward Ensemble Mechanism (C-DREM), enabling both models to co-evolve through self-iteration. Using only benchmark datasets (no external data), Co-EPG achieves state-of-the-art results on Multimodal-Mind2Web (58.4%) and AndroidControl (83.1%).

Cook and Clean Together: Teaching Embodied Agents for Parallel Task Execution

This paper introduces ORS3D, a novel task that incorporates operations research (OR) knowledge into embodied AI task scheduling. Agents are required to exploit the waiting time of parallelizable sub-tasks to execute other tasks, thereby minimizing total completion time, while simultaneously localizing target objects in 3D scenes. The authors construct a 60K-scale dataset ORS3D-60K and propose the GRANT model, which connects to an external dynamic programming solver via a scheduling token mechanism, achieving a 30.53% improvement in time efficiency over baselines.

COVR: Collaborative Optimization of VLMs and RL Agent for Visual-Based Control

This paper proposes COVR, a bidirectional collaborative optimization framework for VLMs and RL agents: high-quality interaction data generated by RL is used to fine-tune the VLM, while the enhanced VLM in turn guides RL policy learning via action priors, achieving SOTA performance on CARLA and DMControl.

D-GARA: A Dynamic Benchmarking Framework for GUI Agent Robustness in Real-World Anomalies

This paper proposes D-GARA, a dynamic robustness evaluation framework for Android GUI Agents. By injecting real-world anomalies—such as permission dialogs, low-battery warnings, and app crashes—during live interactions, D-GARA reveals that existing SOTA agents (including UI-TARS-72B and GPT-4o) suffer an average success rate drop of over 17.5%, with a maximum degradation of 33%, under interruption scenarios.

DEPO: Dual-Efficiency Preference Optimization for LLM Agents

This paper proposes the concept of dual-efficiency, decomposing LLM agent efficiency into step-level (reducing tokens per step) and trajectory-level (reducing total number of steps) dimensions. Building on KTO, the authors introduce DEPO, which jointly optimizes efficiency and task performance by incorporating an efficiency bonus into the reward for desirable samples.

From Biased Chatbots to Biased Agents: Examining Role Assignment Effects on LLM Agent Robustness

The first systematic case study demonstrating that demographically grounded persona assignment causes up to 26.2% performance degradation in LLM agent task execution across 5 operational domains, establishing that persona-induced bias extends beyond text generation into action decision-making.

History-Aware Reasoning for GUI Agents

This paper proposes the HAR framework, which transforms the reasoning paradigm of GUI agents from "history-unaware" to "history-aware" by constructing reflective learning scenarios, synthesizing error-correction guidelines, and designing a hybrid RL reward function incorporating a Memory-Augmented Reward (MAR). A 3B model trained under this framework surpasses larger models on multiple benchmarks including AITW, Mind2Web, and GUI-Odyssey.

LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs

This paper proposes LLMTM — the first comprehensive benchmark for evaluating LLMs on temporal motif analysis in dynamic graphs. It covers 6 task categories across 9 temporal motif types and evaluates 9 models, finding that LLM performance on temporal motif recognition degrades rapidly with increasing motif complexity. A Structure-Aware Dispatcher is further proposed to intelligently route queries to either standard LLM prompting or tool-augmented agents based on graph structural properties and cognitive load, achieving near-peak accuracy while reducing computational cost.

Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNets

This paper proposes LGGFN (Loss-Guided GFlowNets), in which the exploration of an auxiliary GFlowNet is directly driven by the training loss of the primary GFlowNet. The auxiliary agent's reward is defined as \(R_{aux}(x) = R(x) + \lambda \cdot L_{main}(x)\), prioritizing regions where the primary model is least well-understood. On grid, sequence generation, and Bayesian structure learning tasks, LGGFN discovers 40× more unique modes and reduces exploration error by 99%.

MoralReason: Generalizable Moral Decision Alignment For LLM Agents Using Reasoning-Level Reinforcement Learning

This work employs Group Relative Policy Optimization (GRPO) to train LLMs at the reasoning level for ethical framework alignment, achieving out-of-distribution generalization on the Moral-Reason-QA dataset (680 high-ambiguity scenarios) with utilitarian alignment scores improving from 0.207 to 0.964.

PerTouch: VLM-Driven Agent for Personalized and Semantic Image Retouching

This paper proposes PerTouch, a framework that integrates a semantic region-level retouching model based on Stable Diffusion + ControlNet with a VLM-driven Agent (incorporating feedback-driven rethinking and scene-aware memory) to achieve fine-grained, personalized image retouching.

Physics-Informed Autonomous LLM Agents for Explainable Power Electronics Modulation Design

This paper proposes PHIA, a system in which an LLM planner collects design requirements via a chat interface and autonomously coordinates a physics-informed neural network surrogate model (hierarchical PINN) with optimization algorithms to iteratively generate power converter modulation designs, achieving a 63.2% reduction in MAE, a 33× speedup in design time, and usability validated by 20 domain experts.

ProBench: Benchmarking GUI Agents with Accurate Process Information

ProBench is proposed as the first mobile GUI Agent benchmark that evaluates both final state and operational process: 200+ challenging tasks cover 34 mainstream Chinese and English apps. A Process Provider (Structure Description Converter + MLLM Summarizer) automatically captures accurate intermediate process information. Evaluation reveals that even the strongest model, Gemini 2.5 Pro, completes only 40.1% of tasks, exposing three prevalent issues: insufficient grounding, poor awareness of action history, and oversimplified task planning.

Promoting Sustainable Web Agents: Benchmarking and Estimating Energy Consumption Through Empirical and Theoretical Analysis

This paper presents the first systematic quantification of energy consumption and carbon emissions of Web Agents from both empirical benchmarking and theoretical estimation perspectives, finding that higher energy consumption does not equate to better performance, and advocating for the inclusion of energy efficiency metrics in evaluation protocols.

Prune4Web: DOM Tree Pruning Programming for Web Agent

This paper proposes Prune4Web, a programmatic DOM pruning approach that achieves 25–50× candidate element reduction via "LLM-generated scoring function parameters + fixed heuristic template execution." The three-stage pipeline (Planner decomposes subtasks → Programmatic Filter generates scoring functions to prune DOM → Grounder executes actions) enables a 3B model to achieve 52.4% Step SR on Multimodal-Mind2Web, surpassing all baselines of the same parameter scale and even some 9.6B/32B models, while improving low-level grounding accuracy from 46.8% to 88.28%.

Reflection-Driven Control for Trustworthy Code Agents

This paper proposes a Reflection-Driven Control module that elevates "self-reflection" from a post-hoc patch to a first-class control loop within the agent reasoning process. Through three components—a lightweight self-checker, evidence-driven repair, and a reflective memory repository—the approach significantly improves code security rates on secure code generation tasks.

SoMe: A Realistic Benchmark for LLM-based Social Media Agents

This paper introduces SoMe, the first comprehensive benchmark for social media agents, comprising 8 tasks, over 9 million real-world posts, and 17,869 annotated queries. It evaluates 13 mainstream LLMs on social media agent capabilities and reveals substantial performance gaps on complex social tasks.

Structured Personalization: Modeling Constraints as Matroids for Data-Minimal LLM Agents

This paper formalizes structured constraints in LLM agent personalization—comprising logical dependencies and hierarchical quotas—as laminar matroids, proves that greedy algorithms retain constant-factor approximation guarantees under such constraints, and addresses the data-minimization selection problem with dependency relations and hierarchical limits.

Time, Identity and Consciousness in Language Model Agents

This paper applies the temporal gap concept from Stack Theory to LLM agent evaluation, proposing a conservative evaluation toolkit that distinguishes between "talking like a stable self" and "being organized like a stable self." It reveals identity trade-offs across different scaffold structures via persistence scores and an identity morphospace.

TongUI: Internet-Scale Trajectories from Multimodal Web Tutorials for Generalized GUI Agents

TongUI proposes a framework that automatically converts multimodal web tutorials (videos and illustrated articles) into GUI operation trajectories, constructing the million-scale GUI-Net-1M dataset for fine-tuning Qwen2.5-VL. The resulting models surpass or approach state-of-the-art methods such as UI-TARS across multiple grounding and navigation benchmarks.

Towards Trustworthy Multi-Turn LLM Agents via Behavioral Guidance

This paper proposes a task completion framework in which a Task Profiler, a Reasoning Module, and a Generation Module co-evolve to enable verifiable and reliable behavioral guidance for LLM agents in multi-turn interactive environments.

Verification-Guided Context Optimization for Tool Calling via Hierarchical LLMs-as-editors

This paper proposes the VGCO framework, which employs LLMs as hierarchical editors to iteratively optimize tool documentation and knowledge base context through verification-guided signals, achieving significant improvements in retrieval recall, tool selection, and parameter filling accuracy in large-scale tool calling scenarios.

When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agents

This paper systematically investigates how long-context padding affects the safety behavior of LLM agents. Models claiming support for 1M–2M token windows exhibit performance collapse exceeding 50% at 100K tokens. Refusal rates fluctuate in unpredictable directions (GPT-4.1-nano rises from 5% to 40%; Grok 4 Fast drops from 80% to 10%), revealing critical safety vulnerabilities in long-context agent systems.

With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems

This paper proposes the Agentic Risk & Capability (ARC) framework, which systematically identifies, assesses, and mitigates safety and security risks in agentic AI systems from a capability perspective, providing organizations with an actionable and structured methodology for governance.