arXiv:2505.13903v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently achieved remarkable progress in mathematical reasoning. To enable such capabilities, many existing works distill strong reasoning models into long chains of thought or design algorithms to construct high-quality math question-answer (QA) data for training. However, these efforts primarily focus on generating correct reasoning paths […]
KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System
arXiv:2512.20299v2 Announce Type: replace-cross Abstract: Visual-language reasoning, driving knowledge, and value alignment are essential for advanced autonomous driving systems. However, existing approaches largely rely on data-driven learning, making it difficult to capture the complex logic underlying decision-making through imitation or limited reinforcement rewards. To address this, we propose KnowVal, a new autonomous driving system that […]
Few-for-Many Personalized Federated Learning
arXiv:2603.11992v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) aims to train customized models for clients with highly heterogeneous data distributions while preserving data privacy. Existing approaches often rely on heuristics like clustering or model interpolation, which lack principled mechanisms for balancing heterogeneous client objectives. Serving $M$ clients with distinct data distributions is inherently a […]
Realizing Common Random Numbers: Event-Keyed Hashing for Causally Valid Stochastic Models
arXiv:2603.11084v1 Announce Type: cross Abstract: Agent-based models (ABMs) are widely used to estimate causal treatment effects via paired counterfactual simulation. A standard variance reduction technique is common random numbers (CRNs), which couples replicates across intervention scenarios by sharing the same random inputs. In practice, CRNs are implemented by reusing the same base seed, but this […]
Security Considerations for Artificial Intelligence Agents
arXiv:2603.12230v1 Announce Type: cross Abstract: This article, a lightly adapted version of Perplexity’s response to NIST/CAISI Request for Information 2025-0035, details our observations and recommendations concerning the security of frontier AI agents. These insights are informed by Perplexity’s experience operating general-purpose agentic systems used by millions of users and thousands of enterprises in both controlled […]
Probabilistic Verification of Voice Anti-Spoofing Models
arXiv:2603.10713v2 Announce Type: replace-cross Abstract: Recent advances in generative models have amplified the risk of malicious misuse of speech synthesis technologies, enabling adversaries to impersonate target speakers and access sensitive resources. Although speech deepfake detection has progressed rapidly, most existing countermeasures lack formal robustness guarantees or fail to generalize to unseen generation techniques. We propose […]
Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL
arXiv:2512.17053v3 Announce Type: replace-cross Abstract: Deploying accurate Text-to-SQL systems at the enterprise level faces a difficult trilemma involving cost, security and performance. Current solutions force enterprises to choose between expensive, proprietary Large Language Models (LLMs) and low-performing Small Language Models (SLMs). Efforts to improve SLMs often rely on distilling reasoning from large LLMs using unstructured […]
SDUM: A Scalable Deep Unrolled Model for Universal MRI Reconstruction
arXiv:2512.17137v2 Announce Type: replace-cross Abstract: Clinical MRI encompasses diverse imaging protocols–spanning anatomical targets (cardiac, brain, knee), contrasts (T1, T2, mapping), sampling patterns (Cartesian, radial, spiral, kt-space), and acceleration factors–yet current deep learning reconstructions are typically protocol-specific, hindering generalization and deployment. We introduce Scalable Deep Unrolled Model (SDUM), a universal framework combining a Restormer-based reconstructor, a […]
HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification
arXiv:2603.11783v1 Announce Type: cross Abstract: Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing. Existing methods, however, struggle with multi-path hierarchies where instances belong to multiple branches, and they rarely exploit unlabeled data. We introduce HELM (textitHierarchical and Explicit Label Modeling), a novel framework that overcomes these limitations. HELM: (i) […]
Structured Agent Distillation for Large Language Model
arXiv:2505.13820v3 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks. Yet, their practical deployment is constrained by high inference costs and large model sizes. We propose Structured Agent Distillation, a framework that compresses large LLM-based agents into smaller student models […]
Locating Demographic Bias at the Attention-Head Level in CLIP’s Vision Encoder
arXiv:2603.11793v1 Announce Type: cross Abstract: Standard fairness audits of foundation models quantify that a model is biased, but not where inside the network the bias resides. We propose a mechanistic fairness audit that combines projected residual-stream decomposition, zero-shot Concept Activation Vectors, and bias-augmented TextSpan analysis to locate demographic bias at the level of individual attention […]
RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset
arXiv:2603.11811v1 Announce Type: cross Abstract: The acquisition of large-scale physical interaction data, a critical prerequisite for modern robot learning, is severely bottlenecked by the prohibitive cost and scalability limits of human-in-the-loop collection paradigms. To break this barrier, we introduce Robust Autonomous Data Acquisition for Robotics (RADAR), a fully autonomous, closed-loop data generation engine that completely […]