Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding
arXiv:2605.06679v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free infer...
GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning
arXiv:2605.06671v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph algorithmic tasks is still unsatisfying, since graphs are naturally more complex in topology and often require systemat...
More Thinking, More Bias: Length-Driven Position Bias in Reasoning Models
arXiv:2605.06672v1 Announce Type: new
Abstract: Chain-of-thought (CoT) reasoning and reasoning-tuned models such as DeepSeek-R1 are commonly assumed to reduce shallow heuristic biases by thinking carefully. We test this on position bias in multiple-choice QA and find a different story: within any r...
Fast and Effective Redistricting Optimization via Composite-Move Tabu Search
arXiv:2605.06682v1 Announce Type: new
Abstract: Spatial redistricting is a practical combinatorial optimization problem that demands high-quality solutions, rapid turnaround, and flexibility to accommodate multi-criteria objectives and interactive refinement. A central challenge is the contiguity c...
State Representation and Termination for Recursive Reasoning Systems
arXiv:2605.06690v1 Announce Type: new
Abstract: Recursive reasoning systems alternate between acquiring new evidence and refining an accumulated understanding. Two design choices are typically left implicit: how to represent the evolving reasoning state, and when to stop iterating. This paper addre...
Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representations
arXiv:2605.06696v1 Announce Type: new
Abstract: Collections of interacting AI agents can form coalitions, creating emergent group-level organization that is critical for AI safety and alignment. However, observing agent behavior alone is often insufficient to distinguish genuine informational coupl...
BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning
Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large language models (MLLMs). In pursuit of ever more detailed and accurate captions, recent work has increasingly turned to reinf...
Understanding Annotator Safety Policy with Interpretability
arXiv:2605.05329v1 Announce Type: new
Abstract: Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from multiple sources such as operational failures (annotators misunderstand ...
Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems
arXiv:2605.05379v1 Announce Type: new
Abstract: Enterprise agents increasingly operate inside scoped retrieval systems, delegated workflows, and policy-constrained evidence environments. In these settings, access control can be enforced correctly while the system still produces an answer that appea...
BALAR : A Bayesian Agentic Loop for Active Reasoning
arXiv:2605.05386v1 Announce Type: new
Abstract: Large language models increasingly operate in interactive settings where solving a task requires multiple rounds of information exchange with a user. However, most current systems treat dialogue reactively and lack a principled mechanism to reason abo...
Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections
arXiv:2605.05402v1 Announce Type: new
Abstract: Artificial intelligence (AI) and computer vision are transforming transportation data collection. This study introduces an AI-enabled analytics framework leveraging existing CCTV infrastructure to evaluate the impact of soft interventions, such as tem...
Building realistic electric transmission grid dataset at scale: a pipeline from open dataset
Microsoft Research is excited to release an open dataset of approximate transmission topology of the U.S. power grid derived from publicly available data. The ability to study transmission-level power grid behavior is essential for modern power systems research. Analyses of congestion, transmission ...
Becoming AI ready: Building a company with 12 AI agents as my first hires
I left Google ten days ago to found my own company. It's been quite a journey figuring out how things work outside of the mothership, and I'm genuinely excited to share what I've learned from both sides of the house...
7 signs your AI agent system needs to start building its own tools
Most AI agents are stuck in their ways. Built once, they repeat the same patterns regardless of the task at hand. But new research suggests a smarter path forward: agents that get sharper with every challenge they face...
Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
Overview of adaptive parallel reasoning.
What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the...
Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models
arXiv:2605.05213v1 Announce Type: new
Abstract: Chronic rhinosinusitis (CRS) is a common heterogeneous inflammatory disorder that causes substantial morbidity and healthcare costs. CRS is difficult to identify early from routine encounters, as symptom presentations overlap with common conditions su...
SAT: Sequential Agent Tuning for Coordinator Free Plug and Play Multi-LLM Training with Monotonic Improvement Guarantees
arXiv:2605.05216v1 Announce Type: new
Abstract: Large language models (LLMs) with a large number of parameters achieve strong performance but are often prohibitively expensive to deploy. Recent work explores using teams of smaller, more efficient LLMs that collectively match or even outperform a si...
Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning
arXiv:2605.05217v1 Announce Type: new
Abstract: We propose a self-supervised physics-informed neural network (PINN) framework that adaptively balances physics-based and data-driven supervision for scientific machine learning under data scarcity. Unlike prior PINNs that rely on fixed or heuristic we...
RVPO: Risk-Sensitive Alignment via Variance Regularization
Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset critical failures in others (e.g., safety or formatting), masking low-performing “bottleneck” rewar...
Velox: Learning Representations of 4D Geometry and Appearance
We introduce a framework for learning latent representations of 4D objects which are descriptive, faithfully capturing object geometry and appearance; compressive, aiding in downstream efficiency; and accessible, requiring minimal input, i.e., an unstructured dynamic point cloud, to construct. Speci...
Endogenous Regime Switching Driven by Scalar-Irreducible Learning Dynamics
arXiv:2605.04054v1 Announce Type: new
Abstract: Achieving endogenous regime switching is crucial for the emergence of autonomous intelligence, yet remains a central challenge for existing machine learning frameworks, where such transitions are typically externally imposed. In this work, we introduc...
Transformation Categorization Based on Group Decomposition Theory Using Parameter Division
arXiv:2605.04056v1 Announce Type: new
Abstract: Representation learning seeks meaningful sensory representations without supervision and can model aspects of human development. Although many neural networks empirically learn useful features, a principled account of what makes a representation "good...
Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
arXiv:2605.04057v1 Announce Type: new
Abstract: This paper focuses on a key challenge in Neural Architecture Search (NAS): integrating established architectural knowledge while exploring new designs under expensive evaluations. Large language models (LLMs) are a promising assistant for NAS because ...
MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning
arXiv:2605.04058v1 Announce Type: new
Abstract: Parameter-efficient transfer learning (PETL) has emerged as a pivotal paradigm for adapting pre-trained foundation models to downstream tasks, significantly reducing trainable parameters yet suffering from substantial memory overhead caused by gradien...