From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
arXiv:2603.00021v1 Announce Type: new Abstract: This paper proposes a data-driven method to automatically construct graph-based document representations. Building upon the recent work of Bugue\~no and de Melo (2025), we leverage the dynamic sliding-window attention module to effectively capture local and...
Stepwise Penalization for Length-Efficient Chain-of-Thought Reasoning
arXiv:2603.00296v1 Announce Type: new Abstract: Large reasoning models improve with more test-time computation, but often overthink, producing unnecessarily long chains-of-thought that raise cost without improving accuracy. Prior reinforcement learning approaches typically rely on a single outcome reward with trajectory-level length...
Engineering Reasoning and Instruction (ERI) Benchmark: A Large Taxonomy-driven Dataset for Foundation Models and Agents
arXiv:2603.02239v1 Announce Type: new Abstract: The Engineering Reasoning and Instruction (ERI) benchmark is a taxonomy-driven instruction dataset designed to train and evaluate engineering-capable large language models (LLMs) and agents. This dataset spans nine engineering fields (namely: civil, mechanical, electrical, chemical,...
LiveAgentBench: Comprehensive Benchmarking of Agentic Systems Across 104 Real-World Challenges
arXiv:2603.02586v1 Announce Type: new Abstract: As large language models grow more capable, general AI agents have become increasingly prevalent in practical applications. However, existing benchmarks face significant limitations, failing to represent real-world user tasks accurately. To address this gap, we...
AgentAssay: Token-Efficient Regression Testing for Non-Deterministic AI Agent Workflows
arXiv:2603.02601v1 Announce Type: new Abstract: Autonomous AI agents are deployed at unprecedented scale, yet no principled methodology exists for verifying that an agent has not regressed after changes to its prompts, tools, models, or orchestration logic. We present AgentAssay, the...
See and Remember: A Multimodal Agent for Web Traversal
arXiv:2603.02626v1 Announce Type: new Abstract: Autonomous web navigation requires agents to perceive complex visual environments and maintain long-term context, yet current Large Language Model (LLM) based agents often struggle with spatial disorientation and navigation loops. In this paper, we propose...
Retrieval-Augmented Robots via Retrieve-Reason-Act
arXiv:2603.02688v1 Announce Type: new Abstract: To achieve general-purpose utility, we argue that robots must evolve from passive executors into active Information Retrieval users. In strictly zero-shot settings where no prior demonstrations exist, robots face a critical information gap, such as...
Architecting Trust in Artificial Epistemic Agents
arXiv:2603.02960v1 Announce Type: new Abstract: Large language models increasingly function as epistemic agents -- entities that can 1) autonomously pursue epistemic goals and 2) actively shape our shared knowledge environment. They curate the information we receive, often supplanting traditional search-based...
RO-N3WS: Enhancing Generalization in Low-Resource ASR with Diverse Romanian Speech Benchmarks
arXiv:2603.02368v1 Announce Type: new Abstract: We introduce RO-N3WS, a benchmark Romanian speech dataset designed to improve generalization in automatic speech recognition (ASR), particularly in low-resource and out-of-distribution (OOD) conditions. RO-N3WS comprises over 126 hours of transcribed audio collected from broadcast...
Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches
arXiv:2603.02655v1 Announce Type: new Abstract: Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in domains such as sports, esports, and livestreaming. Commentary generation involves two essential decisions: what to say and...
CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics
arXiv:2603.04741v1 Announce Type: new Abstract: Large pre-trained models (LMs) and Large Language Models (LLMs) are typically effective at capturing language semantics and contextual relationships. However, these models encounter challenges in maintaining optimal performance on tasks involving numbers. Blindly treating numerical...
Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
arXiv:2603.04791v1 Announce Type: new Abstract: We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K. To overcome the scalability bottleneck in existing pre-trained...
Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs
arXiv:2603.04896v1 Announce Type: new Abstract: The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value pretrained models. Effective IP protection should proactively confine model deployment within authorized domains and prevent...
EvoTool: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection
arXiv:2603.04900v1 Announce Type: new Abstract: LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging due to delayed supervision and the difficulty of credit assignment in long-horizon trajectories. Existing optimization approaches tend to...
The Trilingual Triad Framework: Integrating Design, AI, and Domain Knowledge in No-code AI Smart City Course
arXiv:2603.05036v1 Announce Type: new Abstract: This paper introduces the "Trilingual Triad" framework, a model that explains how students learn to design with generative artificial intelligence (AI) through the integration of Design, AI, and Domain Knowledge. As generative AI rapidly enters...
Semantic Containment as a Fundamental Property of Emergent Misalignment
arXiv:2603.04407v1 Announce Type: new Abstract: Fine-tuning language models on narrowly harmful data causes emergent misalignment (EM) -- behavioral failures extending far beyond training distributions. Recent work demonstrates compartmentalization of misalignment behind contextual triggers, but these experiments mixed 97% benign data...
Context-Dependent Affordance Computation in Vision-Language Models
arXiv:2603.04419v1 Announce Type: new Abstract: We characterize the phenomenon of context-dependent affordance computation in vision-language models (VLMs). Through a large-scale computational study (n=3,213 scene-context pairs from COCO-2017) using Qwen-VL 30B and LLaVA-1.5-13B subject to systematic context priming across 7 agentic...
Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Exam
arXiv:2603.04454v1 Announce Type: new Abstract: How carefully and unambiguously a question is phrased has a profound impact on the quality of the response, for Language Models (LMs) as well as people. While model capabilities continue to advance, the interplay between...
iAgentBench: Benchmarking Sensemaking Capabilities of Information-Seeking Agents on High-Traffic Topics
arXiv:2603.04656v1 Announce Type: new Abstract: With the emergence of search-enabled generative QA systems, users are increasingly turning to tools that browse, aggregate, and reconcile evidence across multiple sources on their behalf. Yet many widely used QA benchmarks remain answerable by...
Optimizing Language Models for Crosslingual Knowledge Consistency
arXiv:2603.04678v1 Announce Type: new Abstract: Large language models are known to often exhibit inconsistent knowledge. This is particularly problematic in multilingual scenarios, where models are likely to be asked similar questions in different languages, and inconsistent responses can undermine their...
ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts
arXiv:2603.04992v1 Announce Type: new Abstract: The safety evaluation of large language models (LLMs) remains largely centered on English, leaving non-English languages and culturally grounded risks underexplored. In this work, we investigate LLM safety in the context of the Thai language...
Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
arXiv:2603.04431v1 Announce Type: new Abstract: Physical fields are typically observed only at sparse, time-varying sensor locations, making forecasting and reconstruction ill-posed and uncertainty-critical. We present SOLID, a mask-conditioned diffusion framework that learns spatiotemporal dynamics from sparse observations alone: training and...
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
arXiv:2603.04437v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption...
PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion
arXiv:2603.04606v1 Announce Type: new Abstract: PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstream evaluations focus on forward problems, such as autoregressive...
Probabilistic Dreaming for World Models
arXiv:2603.04715v1 Announce Type: new Abstract: "Dreaming" enables agents to learn from imagined experiences, enabling more robust and sample-efficient learning of world models. In this work, we consider innovations to the state-of-the-art Dreamer model using probabilistic methods that enable: (1) the...
Why Is RLHF Alignment Shallow? A Gradient Analysis
arXiv:2603.04851v1 Announce Type: new Abstract: Why is safety alignment in LLMs shallow? We prove that gradient-based alignment inherently concentrates on positions where harm is decided and vanishes beyond. Using a martingale decomposition of sequence-level harm, we derive an exact characterization...
Meta sued over AI smart glasses’ privacy concerns, after workers reviewed nudity, sex, and other footage
Lawyers say Meta's marketing materials promised privacy and user control over sharing footage. But an investigation found that subcontractors are reviewing footage from customers' glasses.
AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents
arXiv:2603.03290v1 Announce Type: cross Abstract: Long-horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long-term dialogue: (i) \textbf{disconnected evidence}, where multi-hop answers require linking facts distributed across...
TATRA: Training-Free Instance-Adaptive Prompting Through Rephrasing and Aggregation
arXiv:2603.03298v1 Announce Type: cross Abstract: Large Language Models (LLMs) have improved substantially alignment, yet their behavior remains highly sensitive to prompt phrasing. This brittleness has motivated automated prompt engineering, but most existing methods (i) require a task-specific training set, (ii)...