MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind
arXiv:2603.00808v1 Announce Type: new Abstract: A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and plan over long horizons with collective awareness, without centralized supervision or explicit communication. In this...
Tracking Capabilities for Safer Agents
arXiv:2603.00991v1 Announce Type: new Abstract: AI agents that interact with the real world through tool calls pose fundamental safety challenges: agents might leak private information, cause unintended side effects, or be manipulated through prompt injection. To address these challenges, we...
MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Contextual Reasoning
arXiv:2603.01055v1 Announce Type: new Abstract: We present MMCOMET, the first multimodal commonsense knowledge graph (MMKG) that integrates physical, social, and eventive knowledge. MMCOMET extends the ATOMIC2020 knowledge graph to include a visual dimension, through an efficient image retrieval process, resulting...
Semantic XPath: Structured Agentic Memory Access for Conversational AI
arXiv:2603.01160v1 Announce Type: new Abstract: Conversational AI (ConvAI) agents increasingly maintain structured memory to support long-term, task-oriented interactions. In-context memory approaches append the growing history to the model input, which scales poorly under context-window limits. RAG-based methods retrieve request-relevant information,...
Incremental LTLf Synthesis
arXiv:2603.01201v1 Announce Type: new Abstract: In this paper, we study incremental LTLf synthesis -- a form of reactive synthesis where the goals are given incrementally while in execution. In other words, the protagonist agent is already executing a strategy for...
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...
Noise reduction in BERT NER models for clinical entity extraction
arXiv:2603.00022v1 Announce Type: new Abstract: Precision is of utmost importance in the realm of clinical entity extraction from clinical notes and reports. Encoder Models fine-tuned for Named Entity Recognition (NER) are an efficient choice for this purpose, as they don't...
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...
Can machines be uncertain?
arXiv:2603.02365v1 Announce Type: new Abstract: The paper investigates whether and how AI systems can realize states of uncertainty. By adopting a functionalist and behavioral perspective, it examines how symbolic, connectionist and hybrid architectures make room for uncertainty. The paper distinguishes...
VL-KGE: Vision-Language Models Meet Knowledge Graph Embeddings
arXiv:2603.02435v1 Announce Type: new Abstract: Real-world multimodal knowledge graphs (MKGs) are inherently heterogeneous, modeling entities that are associated with diverse modalities. Traditional knowledge graph embedding (KGE) methods excel at learning continuous representations of entities and relations, yet they are typically...
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...
SorryDB: Can AI Provers Complete Real-World Lean Theorems?
arXiv:2603.02668v1 Announce Type: new Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub. Unlike existing static benchmarks, often composed of competition problems, hillclimbing the SorryDB benchmark will yield tools...
No Memorization, No Detection: Output Distribution-Based Contamination Detection in Small Language Models
arXiv:2603.03203v1 Announce Type: new Abstract: CDD, or Contamination Detection via output Distribution, identifies data contamination by measuring the peakedness of a model's sampled outputs. We study the conditions under which this approach succeeds and fails on small language models ranging...
Density-Guided Response Optimization: Community-Grounded Alignment via Implicit Acceptance Signals
arXiv:2603.03242v1 Announce Type: new Abstract: Language models deployed in online communities must adapt to norms that vary across social, cultural, and domain-specific contexts. Prior alignment approaches rely on explicit preference supervision or predefined principles, which are effective for well-resourced settings...
A Zipf-preserving, long-range correlated surrogate for written language and other symbolic sequences
arXiv:2603.02213v1 Announce Type: new Abstract: Symbolic sequences such as written language and genomic DNA display characteristic frequency distributions and long-range correlations extending over many symbols. In language, this takes the form of Zipf's law for word frequencies together with persistent...
Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effects
arXiv:2603.02333v1 Announce Type: new Abstract: Autoregressive language models (ARMs) have been shown to memorize and occasionally reproduce training data verbatim, raising concerns about privacy and copyright liability. Diffusion language models (DLMs) have recently emerged as a competitive alternative, yet their...
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...
GLoRIA: Gated Low-Rank Interpretable Adaptation for Dialectal ASR
arXiv:2603.02464v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) in dialect-heavy settings remains challenging due to strong regional variation and limited labeled data. We propose GLoRIA, a parameter-efficient adaptation framework that leverages metadata (e.g., coordinates) to modulate low-rank updates in...
CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think
arXiv:2603.02547v1 Announce Type: new Abstract: We study why continuous diffusion language models (DLMs) have lagged behind discrete diffusion approaches despite their appealing continuous generative dynamics. Under a controlled token--recovery study, we identify token rounding, the final projection from denoised embeddings...
GPUTOK: GPU Accelerated Byte Level BPE Tokenization
arXiv:2603.02597v1 Announce Type: new Abstract: As large language models move toward million-token context windows, CPU tokenizers become a major slowdown because they process text one step at a time while powerful GPUs sit unused. We built a GPU-based byte-level BPE...
Evaluating Cross-Modal Reasoning Ability and Problem Characteristics with Multimodal Item Response Theory
arXiv:2603.02663v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently emerged as general architectures capable of reasoning over diverse modalities. Benchmarks for MLLMs should measure their ability for cross-modal integration. However, current benchmarks are filled with shortcut questions,...
RAGNav: A Retrieval-Augmented Topological Reasoning Framework for Multi-Goal Visual-Language Navigation
arXiv:2603.03745v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) is evolving from single-point pathfinding toward the more challenging Multi-Goal VLN. This task requires agents to accurately identify multiple entities while collaboratively reasoning over their spatial-physical constraints and sequential execution order. However,...
In-Context Environments Induce Evaluation-Awareness in Language Models
arXiv:2603.03824v1 Announce Type: new Abstract: Humans often become more self-aware under threat, yet can lose self-awareness when absorbed in a task; we hypothesize that language models exhibit environment-dependent \textit{evaluation awareness}. This raises concerns that models could strategically underperform, or \textit{sandbag},...
Phi-4-reasoning-vision-15B Technical Report
arXiv:2603.03975v1 Announce Type: new Abstract: We present Phi-4-reasoning-vision-15B, a compact open-weight multimodal reasoning model, and share the motivations, design choices, experiments, and learnings that informed its development. Our goal is to contribute practical insight to the research community on building...
$\tau$-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge
arXiv:2603.04370v1 Announce Type: new Abstract: Conversational agents are increasingly deployed in knowledge-intensive settings, where correct behavior depends on retrieving and applying domain-specific knowledge from large, proprietary, and unstructured corpora during live interactions with users. Yet most existing benchmarks evaluate retrieval...
Capability Thresholds and Manufacturing Topology: How Embodied Intelligence Triggers Phase Transitions in Economic Geography
arXiv:2603.04457v1 Announce Type: new Abstract: The fundamental topology of manufacturing has not undergone a paradigm-level transformation since Henry Ford's moving assembly line in 1913. Every major innovation of the past century, from the Toyota Production System to Industry 4.0, has...
Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding
arXiv:2603.04514v1 Announce Type: new Abstract: Diffusion language models generate text through iterative denoising under a uniform refinement rule applied to all tokens. However, tokens stabilize at different rates in practice, leading to substantial redundant refinement and motivating refinement control over...
Interactive Benchmarks
arXiv:2603.04737v1 Announce Type: new Abstract: Standard benchmarks have become increasingly unreliable due to saturation, subjectivity, and poor generalization. We argue that evaluating model's ability to acquire information actively is important to assess model's intelligence. We propose Interactive Benchmarks, a unified...
Memory as Ontology: A Constitutional Memory Architecture for Persistent Digital Citizens
arXiv:2603.04740v1 Announce Type: new Abstract: Current research and product development in AI agent memory systems almost universally treat memory as a functional module -- a technical problem of "how to store" and "how to retrieve." This paper poses a fundamental...
Bounded State in an Infinite Horizon: Proactive Hierarchical Memory for Ad-Hoc Recall over Streaming Dialogues
arXiv:2603.04885v1 Announce Type: new Abstract: Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. However, existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc...