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GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
arXiv:2602.12316v1 Announce Type: new Abstract: Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and conflict poorly understood. We introduce GT-HarmBench,...
Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting
arXiv:2602.12389v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity representations at each timestamp...
Scaling Web Agent Training through Automatic Data Generation and Fine-grained Evaluation
arXiv:2602.12544v1 Announce Type: new Abstract: We present a scalable pipeline for automatically generating high-quality training data for web agents. In particular, a major challenge in identifying high-quality training instances is trajectory evaluation - quantifying how much progress was made towards...
WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning
arXiv:2602.12852v1 Announce Type: new Abstract: Deep Research systems based on web agents have shown strong potential in solving complex information-seeking tasks, yet their search efficiency remains underexplored. We observe that many state-of-the-art open-source web agents rely on long tool-call trajectories...
Information-theoretic analysis of world models in optimal reward maximizers
arXiv:2602.12963v1 Announce Type: new Abstract: An important question in the field of AI is the extent to which successful behaviour requires an internal representation of the world. In this work, we quantify the amount of information an optimal policy provides...
Language-Guided Invariance Probing of Vision-Language Models
arXiv:2511.13494v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) such as CLIP, OpenCLIP, EVA02-CLIP and SigLIP achieve strong zero-shot performance, but it is unclear how reliably they respond to controlled linguistic perturbations. We introduce Language-Guided Invariance Probing (LGIP), a benchmark...
Energy-Aware Reinforcement Learning for Robotic Manipulation of Articulated Components in Infrastructure Operation and Maintenance
arXiv:2602.12288v1 Announce Type: cross Abstract: With the growth of intelligent civil infrastructure and smart cities, operation and maintenance (O&M) increasingly requires safe, efficient, and energy-conscious robotic manipulation of articulated components, including access doors, service drawers, and pipeline valves. However, existing...
Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method
arXiv:2602.12296v1 Announce Type: cross Abstract: This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi-channel state representation. A road...
Quantum walk inspired JPEG compression of images
arXiv:2602.12306v1 Announce Type: cross Abstract: This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a...
Perceptual Self-Reflection in Agentic Physics Simulation Code Generation
arXiv:2602.12311v1 Announce Type: cross Abstract: We present a multi-agent framework for generating physics simulation code from natural language descriptions, featuring a novel perceptual self-reflection mechanism for validation. The system employs four specialized agents: a natural language interpreter that converts user...
AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping
arXiv:2602.12315v1 Announce Type: cross Abstract: The proliferation of e-commerce has made web shopping platforms key gateways for customers navigating the vast digital marketplace. Yet this rapid expansion has led to a noisy and fragmented information environment, increasing cognitive burden as...
Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement
arXiv:2602.12317v1 Announce Type: cross Abstract: Medical image foundation models (MIFMs) have demonstrated remarkable potential for a wide range of clinical tasks, yet their development is constrained by the scarcity, heterogeneity, and high cost of large-scale annotated datasets. Here, we propose...
ForeAct: Steering Your VLA with Efficient Visual Foresight Planning
arXiv:2602.12322v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models convert high-level language instructions into concrete, executable actions, a task that is especially challenging in open-world environments. We present Visual Foresight Planning (ForeAct), a general and efficient planner that guides a VLA...
Intrinsic Credit Assignment for Long Horizon Interaction
arXiv:2602.12342v1 Announce Type: cross Abstract: How can we train agents to navigate uncertainty over long horizons? In this work, we propose {\Delta}Belief-RL, which leverages a language model's own intrinsic beliefs to reward intermediate progress. Our method utilizes the change in...
Reproducing DragDiffusion: Interactive Point-Based Editing with Diffusion Models
arXiv:2602.12393v1 Announce Type: cross Abstract: DragDiffusion is a diffusion-based method for interactive point-based image editing that enables users to manipulate images by directly dragging selected points. The method claims that accurate spatial control can be achieved by optimizing a single...
What does RL improve for Visual Reasoning? A Frankenstein-Style Analysis
arXiv:2602.12395v1 Announce Type: cross Abstract: Reinforcement learning (RL) with verifiable rewards has become a standard post-training stage for boosting visual reasoning in vision-language models, yet it remains unclear what capabilities RL actually improves compared with supervised fine-tuning as cold-start initialization...
Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
arXiv:2602.12444v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications. In this paper, we introduce a novel recovery-based shielding framework that enables safe RL with...
Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification
arXiv:2602.12575v1 Announce Type: new Abstract: Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data...
$\mathcal{X}$-KD: General Experiential Knowledge Distillation for Large Language Models
arXiv:2602.12674v1 Announce Type: new Abstract: Knowledge Distillation (KD) for Large Language Models (LLMs) has become increasingly important as models grow in size and complexity. While existing distillation approaches focus on imitating teacher behavior, they often overlook the original learning environment...
Lamer-SSL: Layer-aware Mixture of LoRA Experts for Continual Multilingual Expansion of Self-supervised Models without Forgetting
arXiv:2602.12746v1 Announce Type: new Abstract: Despite their impressive performance, self-supervised speech models often struggle to generalize to new languages and tend to forget previously acquired knowledge during continual training. To address this, we propose Lamer-SSL, a parameter-efficient framework that integrates...
Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews
arXiv:2602.12778v1 Announce Type: new Abstract: This study advances aspect-based sentiment analysis (ABSA) for Persian-language user reviews in the tourism domain, addressing challenges of low-resource languages. We propose a hybrid BERT-based model with Top-K routing and auxiliary losses to mitigate routing...
MentalBench: A Benchmark for Evaluating Psychiatric Diagnostic Capability of Large Language Models
arXiv:2602.12871v1 Announce Type: new Abstract: We introduce MentalBench, a benchmark for evaluating psychiatric diagnostic decision-making in large language models (LLMs). Existing mental health benchmarks largely rely on social media data, limiting their ability to assess DSM-grounded diagnostic judgments. At the...