arXiv:2510.13302v3 Announce Type: replace-cross Abstract: Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, identity linking, and plagiarism detection. Existing supervised and contrastive approaches often rely on datasets with spurious correlations, conflating style with topic. Despite the relevance of language modeling to these tasks, the pre-training of modern large […]
EnviSAgE: A Survey of Environment Scaling for Qualitative Agentic Experience Collection
arXiv:2511.09586v2 Announce Type: replace-cross Abstract: LLM-based agents can autonomously accomplish complex tasks across various domains. However, to further cultivate capabilities such as adaptive behavior and long-term decision-making, training on static datasets built from human-level knowledge is insufficient. These datasets are costly to construct and lack both dynamism and realism. A growing consensus is that agents […]
Towards Closing the Domain Gap with Event Cameras
arXiv:2512.16178v1 Announce Type: cross Abstract: Although traditional cameras are the primary sensor for end-to-end driving, their performance suffers greatly when the conditions of the data they were trained on does not match the deployment environment, a problem known as the domain gap. In this work, we consider the day-night lighting difference domain gap. Instead of […]
Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
arXiv:2512.16310v1 Announce Type: cross Abstract: Driven by Large Language Models, the single-agent, multi-tool architecture has become a popular paradigm for autonomous agents due to its simplicity and effectiveness. However, this architecture also introduces a new and severe privacy risk, which we term Tools Orchestration Privacy Risk (TOP-R), where an agent, to achieve a benign user […]
Reverse Supervision at Scale: Exponential Search Meets the Economics of Annotation
arXiv:2510.10446v2 Announce Type: replace-cross Abstract: We analyze a reversed-supervision strategy that searches over labelings of a large unlabeled set (B) to minimize error on a small labeled set (A). The search space is (2^n), and the resulting complexity remains exponential even under large constant-factor speedups (e.g., quantum or massively parallel hardware). Consequently, arbitrarily fast — […]
INTELLECT-3: Technical Report
arXiv:2512.16144v1 Announce Type: cross Abstract: We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-3 achieves state of the art performance for its size across math, code, science and reasoning benchmarks, outperforming many larger frontier models. We open-source the model together with the full […]
Ev-Trust: A Strategy Equilibrium Trust Mechanism for Evolutionary Games in LLM-Based Multi-Agent Services
arXiv:2512.16167v1 Announce Type: cross Abstract: The rapid evolution of the Web toward an agent-centric paradigm, driven by large language models (LLMs), has enabled autonomous agents to reason, plan, and interact in complex decentralized environments. However, the openness and heterogeneity of LLM-based multi-agent systems also amplify the risks of deception, fraud, and misinformation, posing severe challenges […]
KineST: A Kinematics-guided Spatiotemporal State Space Model for Human Motion Tracking from Sparse Signals
arXiv:2512.16791v1 Announce Type: cross Abstract: Full-body motion tracking plays an essential role in AR/VR applications, bridging physical and virtual interactions. However, it is challenging to reconstruct realistic and diverse full-body poses based on sparse signals obtained by head-mounted displays, which are the main devices in AR/VR scenarios. Existing methods for pose reconstruction often incur high […]
TACOS: Task Agnostic COordinator of a multi-drone System
arXiv:2510.01869v2 Announce Type: replace-cross Abstract: When a single pilot is responsible for managing a multi-drone system, the task may demand varying levels of autonomy, from direct control of individual UAVs, to group-level coordination, to fully autonomous swarm behaviors for accomplishing high-level tasks. Enabling such flexible interaction requires a framework that supports multiple modes of shared […]
LinkedOut: Linking World Knowledge Representation Out of Video LLM for Next-Generation Video Recommendation
arXiv:2512.16891v1 Announce Type: cross Abstract: Video Large Language Models (VLLMs) unlock world-knowledge-aware video understanding through pretraining on internet-scale data and have already shown promise on tasks such as movie analysis and video question answering. However, deploying VLLMs for downstream tasks such as video recommendation remains challenging, since real systems require multi-video inputs, lightweight backbones, low-latency […]