LegoNet: Memory Footprint Reduction Through Block Weight Clustering
arXiv:2603.06606v1 Announce Type: new Abstract: As the need for neural network-based applications to become more accurate and powerful grows, so too does their size and memory footprint. With embedded devices, whose cache and RAM are limited, this growth hinders their...
CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training
arXiv:2603.06610v1 Announce Type: new Abstract: Large language model (LLM) post-training enhances latent skills, unlocks value alignment, improves performance, and enables domain adaptation. Unfortunately, post-training is known to induce forgetting, especially in the ubiquitous use-case of leveraging third-party pre-trained models, which...
OptiRoulette Optimizer: A New Stochastic Meta-Optimizer for up to 5.3x Faster Convergence
arXiv:2603.06613v1 Announce Type: new Abstract: This paper presents OptiRoulette, a stochastic meta-optimizer that selects update rules during training instead of fixing a single optimizer. The method combines warmup optimizer locking, random sampling from an active optimizer pool, compatibility-aware learning-rate scaling...
Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models
arXiv:2603.06621v1 Announce Type: new Abstract: Process Reward Models (PRMs) are rapidly becoming the backbone of LLM reasoning pipelines, yet we demonstrate that state-of-the-art PRMs are systematically exploitable under adversarial optimization pressure. To address this, we introduce a three-tiered diagnostic framework...
Grouter: Decoupling Routing from Representation for Accelerated MoE Training
arXiv:2603.06626v1 Announce Type: new Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads...
Leakage Safe Graph Features for Interpretable Fraud Detection in Temporal Transaction Networks
arXiv:2603.06632v1 Announce Type: new Abstract: Illicit transaction detection is often driven by transaction level attributes however, fraudulent behavior may also manifest through network structure such as central hubs, high flow intermediaries, and coordinated neighborhoods. This paper presents a time respecting,...
SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts
arXiv:2603.06636v1 Announce Type: new Abstract: Due to the strong context-awareness capabilities demonstrated by large language models (LLMs), recent research has begun exploring their integration into smart home assistants to help users manage and adjust their living environments. While LLMs have...
SR-TTT: Surprisal-Aware Residual Test-Time Training
arXiv:2603.06642v1 Announce Type: new Abstract: Test-Time Training (TTT) language models achieve theoretically infinite context windows with an O(1) memory footprint by replacing the standard exact-attention KV-cache with hidden state ``fast weights'' W_fast updated via self-supervised learning during inference. However, pure...
Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Health
arXiv:2603.06646v1 Announce Type: new Abstract: This paper presents a trust aware federated learning (FL) framework for interpreting bone healing stages using spectral features derived from frequency response data. The primary objective is to address the challenge posed by either unreliable...
HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks
arXiv:2603.06649v1 Announce Type: new Abstract: The coastal regions of the eastern and southern United States are impacted by severe storm events, leading to significant loss of life and properties. Accurately forecasting storm surge and wind impacts from hurricanes is essential...
Safe Transformer: An Explicit Safety Bit For Interpretable And Controllable Alignment
arXiv:2603.06727v1 Announce Type: new Abstract: Current safety alignment methods encode safe behavior implicitly within model parameters, creating a fundamental opacity: we cannot easily inspect why a model refuses a request, nor intervene when its safety judgments fail. We propose Safe...
Orion: Characterizing and Programming Apple's Neural Engine for LLM Training and Inference
arXiv:2603.06728v1 Announce Type: new Abstract: Over two billion Apple devices ship with a Neural Processing Unit (NPU) - the Apple Neural Engine (ANE) - yet this accelerator remains largely unused for large language model workloads. CoreML, Apple's public ML framework,...
Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
arXiv:2603.06729v1 Announce Type: new Abstract: Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-of-distribution crowd sizes due to density-sensitive observation normalization and social-cost scaling, while analytical...
Improved Constrained Generation by Bridging Pretrained Generative Models
arXiv:2603.06742v1 Announce Type: new Abstract: Constrained generative modeling is fundamental to applications such as robotic control and autonomous driving, where models must respect physical laws and safety-critical constraints. In real-world settings, these constraints rarely take the form of simple linear...
CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain
arXiv:2603.05569v1 Announce Type: cross Abstract: Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for healthcare decision-making and research. While a promising approach is to use Large Language Models (LLMs) to translate natural language...
DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality
arXiv:2603.05912v1 Announce Type: new Abstract: Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers are primarily designed for general-domain, factoid-style atomic claims, and there is no benchmark to test whether such verifiers...
Aggregative Semantics for Quantitative Bipolar Argumentation Frameworks
arXiv:2603.06067v1 Announce Type: new Abstract: Formal argumentation is being used increasingly in artificial intelligence as an effective and understandable way to model potentially conflicting pieces of information, called arguments, and identify so-called acceptable arguments depending on a chosen semantics. This...
Offline Materials Optimization with CliqueFlowmer
arXiv:2603.06082v1 Announce Type: new Abstract: Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling...
Towards Neural Graph Data Management
arXiv:2603.05529v1 Announce Type: cross Abstract: While AI systems have made remarkable progress in processing unstructured text, structured data such as graphs stored in databases, continues to grow rapidly yet remains difficult for neural models to effectively utilize. We introduce NGDBench,...
From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews
arXiv:2603.05514v1 Announce Type: cross Abstract: Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that imposes a high cognitive load. This friction suppresses the iterative, exploratory nature of scholarly work....
Omni-C: Compressing Heterogeneous Modalities into a Single Dense Encoder
arXiv:2603.05528v1 Announce Type: cross Abstract: Recent multimodal systems often rely on separate expert modality encoders which cause linearly scaling complexity and computational overhead with added modalities. While unified Omni-models address this via Mixture-of-Expert (MoE) architectures with specialized experts and routing,...
Post Fusion Bird's Eye View Feature Stabilization for Robust Multimodal 3D Detection
arXiv:2603.05623v1 Announce Type: cross Abstract: Camera-LiDAR fusion is widely used in autonomous driving to enable accurate 3D object detection. However, bird's-eye view (BEV) fusion detectors can degrade significantly under domain shift and sensor failures, limiting reliability in real-world deployment. Existing...
On the Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction
arXiv:2603.05532v1 Announce Type: cross Abstract: Despite continuing hype about the role of AI in drug discovery, no "AI-discovered drugs" have so far received regulatory approval. Here we assess one of the latest AI based tools in this domain. The ability...
Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
arXiv:2603.06278v1 Announce Type: new Abstract: Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Designing effective adaptation strategies is challenging due to the long-term, sequential nature...
Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective
arXiv:2603.05525v1 Announce Type: cross Abstract: Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need...
Tool-Genesis: A Task-Driven Tool Creation Benchmark for Self-Evolving Language Agent
arXiv:2603.05578v1 Announce Type: cross Abstract: Research on self-evolving language agents has accelerated, drawing increasing attention to their ability to create, adapt, and maintain tools from task requirements. However, existing benchmarks predominantly rely on predefined specifications, which limits scalability and hinders...
RACAS: Controlling Diverse Robots With a Single Agentic System
arXiv:2603.05621v1 Announce Type: cross Abstract: Many robotic platforms expose an API through which external software can command their actuators and read their sensors. However, transitioning from these low-level interfaces to high-level autonomous behaviour requires a complicated pipeline, whose components demand...
Model Change for Description Logic Concepts
arXiv:2603.05562v1 Announce Type: cross Abstract: We consider the problem of modifying a description logic concept in light of models represented as pointed interpretations. We call this setting model change, and distinguish three main kinds of changes: eviction, which consists of...
EigenData: A Self-Evolving Multi-Agent Platform for Function-Calling Data Synthesis, Auditing, and Repair
arXiv:2603.05553v1 Announce Type: cross Abstract: Function-calling agents -- large language models that invoke tools and APIs -- require high-quality, domain-specific training data spanning executable environments, backing databases, and diverse multi-turn trajectories. We introduce EigenData, an integrated, self-evolving platform that automates...
VDCook:DIY video data cook your MLLMs
arXiv:2603.05539v1 Announce Type: cross 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, retrieval-synthesis ratio,...