🦾 LLM Agent

🎞️ ECCV2026 · 23 paper notes

📌 Same area in other venues: 📷 CVPR2026 (42) · 🔬 ICLR2026 (162) · 💬 ACL2026 (82) · 🧪 ICML2026 (59) · 🤖 AAAI2026 (33) · 🧠 NeurIPS2025 (39)

🔥 Top topics: Agents ×8 · Multimodal/VLM ×6 · Reasoning ×3 · Layout & Composition ×2

Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection

The paper distracts GUI agents with real, content-harmless UI icons subject to spatial and semantic constraints, reaching a five-depth miss rate of 32.99% on UI-TARS-1.5-7B through cumulative multi-candidate search and using injected-icon click statistics to expose grounding weaknesses that content safety alignment alone does not address.

ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search

ARGOS turns person search into budgeted evidence gathering: a four-module agent asks questions and invokes camera-topology tools to isolate a target, with best spatial and temporal Turn-Weighted Success scores of 0.383 and 0.590 across a benchmark of 2,691 tasks.

CiQi-Agent: Aligning Vision, Tools and Aesthetics in Multimodal Agent for Cultural Reasoning on Chinese Porcelains

CiQi-Agent integrates porcelain expertise, local image zoom, and image/text retrieval through two phases of SFT and GRPO, bringing Qwen2.5-VL-7B to 81.5% average multiple-choice accuracy and a 66.7% free-form attribute score on CiQi-Bench; these results measure connoisseurship attributes, not authenticity detection.

Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement

EyeControl interprets clicks or coarse strokes as coordinated global and local retouching plans, then trains a diffusion executor with pseudo-intent attention alignment and operation consistency to emphasize the intended region, achieving 21.8845 dB PSNR and an overall score of 8.7054 on ControlArt-Bench without simply maximizing editing strength.

Exploratory, Communicative, and Deployable: Vision-Driven Embodied Agents for Open-World Mobile Manipulation

REAL trains Qwen3-VL-8B to explore, ask when necessary, and then manipulate through a tool interface that withholds privileged object state; SFT followed by online GSPO reaches 56.9% success on the interactive REAL-Bench split, and the high-level policy transfers zero-shot to a physical robot with 78.3% success over 60 episodes.

From Gaze to Meaning: An AI Agent for Unified Zero-Shot Grounding and Explanation

Gaze Target Agent (GTA) renders predicted gaze as an arrow for a VLM, uses first-token confidence to trigger retrieval of labeled examples, and selects an object instance using the gaze heatmap, raising GazeFollow Acc@1 from GGVL's 0.450 to 0.493 without parameter updates, although its memory still uses training-set labels.

GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine

GEAR-Seg converts an image into detailed descriptions of indexed regions, then lets a text-only LLM select masks according to the user's intent, achieving 57.5 ReasonSeg gIoU without task-specific fine-tuning and extending the same pipeline into an explanation-producing data engine for end-to-end student training.

GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation

GUIDE is a training-free, plug-and-play framework that automatically retrieves and extracts domain-specific planning and grounding knowledge from YouTube tutorial videos. By injecting this knowledge into corresponding modules of GUI agents to eliminate domain bias, it brings performance improvements of +4.47 to +7.48 percentage points for three agents with different architectures on OSWorld.

Guiding the Blind: Generalizing GUI Agents to Unseen Websites via Multimodal Tutorials

On WebOne's website-disjoint training and test split, WebLearner uses hierarchical memory and step-level GRPO rewards to learn to understand the current page, identify a relevant tutorial subtask, and execute an action, raising unseen-website task success from the Qwen3-VL-8B RAG baseline's 36.8% to 56.9%, a gain of 20.1 percentage points.

KATANA: Knowledge-Aligned Topology-Aware Neural Agents for RL-Driven Vision-Language Model Compression

KATANA treats the pruning algorithm itself as a learnable program, trains a code agent with rewards measured in a sandbox, and discovers KIRI's combination of cubic sparsity scheduling, Dual-Norm Activation scoring, and in-mask reconstruction; at 70% sparsity, LLaVA-1.5-7B reaches 84.6 MSCOCO CIDEr versus GSOP's 83.3, a 1.3-point gain, with approximately 2.8x decode acceleration on the specified sparse runtime.

Knowledge-Centric Agents for Workflow Generation in ComfyUI

The paper recovers strategies, reasoning explanations, and skeleton pseudocode from real ComfyUI workflows, then uses hierarchical SFT and self-refinement to reconstruct executable graphs from user instructions, improving average Pass / Resolve over ComfyAgent from 36.4% / 29.4% to 86.9% / 61.1% on its curated test set, although Resolve remains only 25.0% on the external ComfyBench subset.

Learning from Reliable Negatives: Confidence-Anchored Test-Time Adaptation for GUI Grounding

The paper selects a pseudo-label from multiple predictions using coordinate-token confidence, then uses CANL to penalize only predictions far from that anchor; after independent label-free test-time training on each benchmark, Qwen-2.5-VL-7B improves from 88.1% to 92.1% on ScreenSpot-V2 and from 24.9% to 33.8% on ScreenSpot-Pro.

MolmoWeb: Open Visual Web Agent and Open Data for the Open Web

MolmoWeb combines trajectories from structured-web teachers, human demonstrations, and GUI perception supervision into screenshot-conditioned action learning, reaching 78.2% single-run success on WebVoyager with an 8B model and 94.7% pass@4 through parallel attempts; its main contribution is a reproducible data and training recipe rather than a new planning architecture.

NaLA: A 3D Native LLM Layout Agent for High-quality 3D Scene Generation

NaLA proposes a large language model layout agent that directly encodes 3D point clouds. Through a coarse-to-fine pose prediction mechanism, it achieves high-quality and highly efficient 3D scene generation, comprehensively outperforming existing methods in physical rationality, semantic consistency, and aesthetics.

OCTOPUS: Multi-Agentic Universal Compositional Visual Retrieval

OCTOPUS uses three task-training-free agents for perception, creation, and retrieval to turn a reference image or video and an editing instruction into visual and textual proxies, then improve ranking through bidirectional cross-modal retrieval and sequential candidate verification, reaching 67.15% WebVid R@1 and 37.00% average FashionIQ R@10 with ViT-L/14.

ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering

ProMSA turns knowledge-based visual question answering into a budgeted loop of retrieval, evidence checking, and either further search or answering, using rejection-sampling SFT followed by TN-GSPO to bring Qwen3-VL-8B to overall scores of 52.6 on E-VQA and 53.4 on InfoSeek.

Self-Evolving MCP-GUI Agents via Automated Environment Generation and Experience Learning

The paper improves MCP-GUI agents through a loop of diagnosing weaknesses, generating executable tasks, collecting trajectories, and updating either the model or its experience, raising Chrome pass rate from 60.0% to 77.8% while finding that GUI-intensive VS Code benefits more from training-free experience augmentation than from directly imitating expert trajectories.

SpatiO: Adaptive Test-Time Orchestration of Vision-Language Agents for Spatial Reasoning

Rather than updating vision-language model weights, SpatiO uses a small labeled set to calibrate how reliable each model is under a particular role and spatial category, then selects specialists and integrates geometric evidence, reporting overall results of 72.4% on 3DSRBench, 88.2% on STVQA-7k, and 86.9% on CV-Bench in the paper's tables.

Think While You Map: Asynchronous Vision-Language Agents for Incremental 3D Scene Graphs

ThinkGraphs proposes an asynchronous architecture that decouples lightweight incremental 3D mapping from heavy VLM inference. By running a background Critic Agent (semantic loop closure detection to merge fragmented trajectories) and a Description Agent (multi-target frame scheduling to inject fine-grained attributes) without blocking online mapping, it continuously enriches scene graph semantics. It outperforms previous state-of-the-art by 15.3-18.8 [email protected] on three visual grounding benchmarks: Sr3D+, Nr3D, and ScanRefer.

TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration

TIR-Agent trains task ordering and tool selection as a closed-loop Qwen3-VL-8B policy, using perturbed demonstrations to broaden exploration and adaptive multi-metric rewards to coordinate fidelity and perceptual quality, reaching 22.80 dB PSNR on MiO100 Group B and at least 2.5x and 5x inference speedups over AgenticIR and 4KAgent, respectively.

VisCritic: Visual State Comparison as Process Reward for GUI Agents

VisCritic proposes a process reward framework for GUI agents based on visual state comparison. Using a Siamese ViT to compare pre- and post-action screenshots in semantic feature space, it fuses action context to jointly predict action success probability, task progress, and error types. Serving as a plug-and-play inference-time verification module, it consistently improves the task success rate of various GUI agents across five benchmarks.

WebRetriever: A Large-Scale Comprehensive Benchmark for Efficient Web Agent Evaluation

WebRetriever combines 1,550 tasks across 800 websites, the interaction-aware NavEval judge, and three evaluation protocols, achieving 91.2% human agreement on its benchmark while revealing that six agents average only 11.8% human-assessed end-to-end task success.

WorldAgents: Can Foundation Image Models be Agents for 3D World Models?

WorldAgents closes the loop among a VLM director, a 2D image generator, and a two-stage 2D/3D verifier, using 3D-reprojection-constrained sequential inpainting to reduce reprojection error from the previous best of 0.39 to 0.36 without updating the generator's weights.