Autonomous AI Agents for Option Hedging: Enhancing Financial Stability through Shortfall Aware Reinforcement Learning
arXiv:2603.06587v1 Announce Type: new
Abstract: The deployment of autonomous AI agents in derivatives markets has widened a practical gap between static model calibration and realized hedging outcomes. We introduce two reinforcement learning frameworks, a novel Replication Learning of Option Pricin...
arXiv:2603.06601v1 Announce Type: new
Abstract: Deep neural networks, and more recently large-scale generative models such as large language models (LLMs) and large vision-action models (LVAs), achieve remarkable performance across diverse domains, yet their prohibitive computational cost hinders d...
FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures
arXiv:2603.06600v1 Announce Type: new
Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions designed to del...
How Attention Sinks Emerge in Large Language Models: An Interpretability Perspective
arXiv:2603.06591v1 Announce Type: new
Abstract: Large Language Models (LLMs) often allocate disproportionate attention to specific tokens, a phenomenon commonly referred to as the attention sink. While such sinks are generally considered detrimental, prior studies have identified a notable exceptio...
vLLM Hook v0: A Plug-in for Programming Model Internals on vLLM
arXiv:2603.06588v1 Announce Type: new
Abstract: Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for transformer-based large language models (LLMs). The vLLM project is a major open-source library to su...
Scaling Strategy, Not Compute: A Stand-Alone, Open-Source StarCraft II Benchmark for Accessible Reinforcement Learning Research
arXiv:2603.06608v1 Announce Type: new
Abstract: The research community lacks a middle ground between StarCraft IIs full game and its mini-games. The full-games sprawling state-action space renders reward signals sparse and noisy, but in mini-games simple agents saturate performance. This complexity...
Breaking the Martingale Curse: Multi-Agent Debate via Asymmetric Cognitive Potential Energy
arXiv:2603.06801v1 Announce Type: new
Abstract: Multi-Agent Debate (MAD) has emerged as a promising paradigm for enhancing large language model reasoning. However, recent work reveals a limitation:standard MAD cannot improve belief correctness beyond majority voting; we refer to this as the Marting...
MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines
arXiv:2603.06679v1 Announce Type: new
Abstract: Video world models have shown immense promise for interactive simulation and entertainment, but current systems still struggle with two important aspects of interactivity: user control over the environment for reproducible, editable experiences, and s...
How AstraZeneca is quietly rewiring Boston’s AI ecosystem
For AI professionals tired of hype decks and stalled pilots, AstraZeneca’s Boston strategy offers a practical blueprint for making AI work in complex, regulated environments.
The World Won't Stay Still: Programmable Evolution for Agent Benchmarks
arXiv:2603.05910v1 Announce Type: new
Abstract: LLM-powered agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Yet, most existing benchmarks assume static environments with fixed schemas and toolsets, neglecting the evolutionary...
Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery
arXiv:2603.05860v1 Announce Type: new
Abstract: Clinical image interpretation is inherently multi-step and tool-centric: clinicians iteratively combine visual evidence with patient context, quantify findings, and refine their decisions through a sequence of specialized procedures. While LLM-based a...
Reasoning Models Struggle to Control their Chains of Thought
arXiv:2603.05706v1 Announce Type: new
Abstract: Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if models can control what they verbalize in their CoT, it could undermine CoT monitorability. To m...
Real-Time AI Service Economy: A Framework for Agentic Computing Across the Continuum
arXiv:2603.05614v1 Announce Type: new
Abstract: Real-time AI services increasingly operate across the device-edge-cloud continuum, where autonomous AI agents generate latency-sensitive workloads, orchestrate multi-stage processing pipelines, and compete for shared resources under policy and governa...
RoboLayout: Differentiable 3D Scene Generation for Embodied Agents
arXiv:2603.05522v1 Announce Type: new
Abstract: Recent advances in vision language models (VLMs) have shown strong potential for spatial reasoning and 3D scene layout generation from open-ended language instructions. However, generating layouts that are not only semantically coherent but also feasi...
Autocorrelation effects in a stochastic-process model for decision making via time series
arXiv:2603.05559v1 Announce Type: new
Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optical signal as the driving source for sequential decisions. In such syste...
IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddings
arXiv:2603.05556v1 Announce Type: new
Abstract: Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic s...
arXiv:2603.05539v1 Announce Type: new
Abstract: We introduce VDCook: a self-evolving video data operating system, a configurable video data construction platform for researchers and vertical domain teams. Users initiate data requests via natural language queries and adjustable parameters (scale, re...
JAWS: Enhancing Long-term Rollout of Neural Operators via Spatially-Adaptive Jacobian Regularization
arXiv:2603.05538v1 Announce Type: new
Abstract: Data-driven surrogate models improve the efficiency of simulating continuous dynamical systems, yet their autoregressive rollouts are often limited by instability and spectral blow-up. While global regularization techniques can enforce contractive dyn...
Traversal-as-Policy: Log-Distilled Gated Behavior Trees as Externalized, Verifiable Policies for Safe, Robust, and Efficient Agents
arXiv:2603.05517v1 Announce Type: new
Abstract: Autonomous LLM agents fail because long-horizon policy remains implicit in model weights and transcripts, while safety is retrofitted post hoc. We propose Traversal-as-Policy: distill sandboxed OpenHands execution logs into a single executable Gated B...
Operational stability for mission-critical ML systems
If observability tools can capture everything happening in modern infrastructure, why can’t AI systems clearly explain the decisions they recommend? This tension lies at the heart of the growing explainability crisis in applied AI.
arXiv:2603.04549v1 Announce Type: new
Abstract: LLM-based agents increasingly rely on long-term memory to support multi-session reasoning and interaction, yet current systems provide little control over what information is retained. In practice, agents either accumulate large volumes of conversatio...
Discovering mathematical concepts through a multi-agent system
arXiv:2603.04528v1 Announce Type: new
Abstract: Mathematical concepts emerge through an interplay of processes, including experimentation, efforts at proof, and counterexamples. In this paper, we present a new multi-agent model for computational mathematical discovery based on this observation. Our...
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 ...