arXiv:2603.06903v1 Announce Type: new Abstract: Genetic interactions and protein co-dependencies shape cellular fitness, buffering capacity, and disease vulnerability. However, systematic integration of co-dependency relationships across heterogeneous datasets remains limited. Here, we present HIDDENdb (Harnessing Intelligent Data Discovery to Explore Gene Networks), a comprehensive database that captures genetic and protein co-dependencies inferred from large-scale perturbation screens, […]
Synthetic Homes: An Accessible Multimodal Pipeline for Producing Residential Building Data with Generative AI
arXiv:2509.09794v3 Announce Type: replace Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building level as well as urban scale. However, these models require a plethora of data on building parameters, some of which can be inaccessible, expensive, or can raise privacy concerns. We introduce a modular multimodal framework […]
How Private Are DNA Embeddings? Inverting Foundation Model Representations of Genomic Sequences
arXiv:2603.06950v1 Announce Type: new Abstract: DNA foundation models have become transformative tools in bioinformatics and healthcare applications. Trained on vast genomic datasets, these models can be used to generate sequence embeddings, dense vector representations that capture complex genomic information. These embeddings are increasingly being shared via Embeddings-as-a-Service (EaaS) frameworks to facilitate downstream tasks, while supposedly […]
Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making
arXiv:2511.12876v3 Announce Type: replace Abstract: Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While multi-agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose LAMP […]
Is continuous CoT better suited for multi-lingual reasoning?
arXiv:2603.08177v1 Announce Type: cross Abstract: We investigate whether performing reasoning in a continuous latent space leads to more robust multilingual capabilities. We compare Continuous Chain-of-Thought (using the CODI framework) against standard supervised fine-tuning across five typologically diverse languages: English, Chinese, German, French, and Urdu. Our experiments on GSM8k and CommonsenseQA demonstrate that continuous reasoning significantly […]
Minor First, Major Last: A Depth-Induced Implicit Bias of Sharpness-Aware Minimization
arXiv:2603.08290v1 Announce Type: cross Abstract: We study the implicit bias of Sharpness-Aware Minimization (SAM) when training $L$-layer linear diagonal networks on linearly separable binary classification. For linear models ($L=1$), both $ell_infty$- and $ell_2$-SAM recover the $ell_2$ max-margin classifier, matching gradient descent (GD). However, for depth $L = 2$, the behavior changes drastically — even on […]
Mapping Overlaps in Benchmarks through Perplexity in the Wild
arXiv:2509.23488v4 Announce Type: replace Abstract: We introduce benchmark signatures to characterize the capacity demands of LLM benchmarks and their overlaps. Signatures are sets of salient tokens from in-the-wild corpora whose model token perplexity, reflecting training exposure, predicts benchmark performance. We extract them via stepwise forward selection with linear regression in a meta-evaluation spanning 32 LLMs […]
X-SYS: A Reference Architecture for Interactive Explanation Systems
arXiv:2602.12748v2 Announce Type: replace Abstract: The explainable AI (XAI) research community has proposed numerous technical methods, yet deploying explainability as systems remains challenging: Interactive explanation systems require both suitable algorithms and system capabilities that maintain explanation usability across repeated queries, evolving models and data, and governance constraints. We argue that operationalizing XAI requires treating explainability […]
Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
arXiv:2603.08304v1 Announce Type: cross Abstract: This work addresses the accurate and efficient simulation of physical phenomena governed by parametric Partial Differential Equations (PDEs) characterized by varying boundary conditions, where parametric instances modify not only the physics of the problem but also the imposition of boundary constraints on the computational domain. In such scenarios, classical Galerkin […]
Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
arXiv:2603.08588v1 Announce Type: cross Abstract: State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible with the constraints of resource-limited hardware, due to their reliance on replay buffers, batch updates, and target networks. The emerging paradigm of streaming deep RL addresses this limitation through […]
Thickening-to-Thinning: Reward Shaping via Human-Inspired Learning Dynamics for LLM Reasoning
arXiv:2602.04265v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for enhancing reasoning in Large Language Models (LLMs). However, it frequently encounters challenges such as entropy collapse, excessive verbosity, and insufficient exploration for hard problems. Crucially, existing reward schemes fail to distinguish between the need for extensive search […]
Human-Certified Module Repositories for the AI Age
arXiv:2603.02512v3 Announce Type: replace-cross Abstract: Human-Certified Module Repositories (HCMRs) are introduced in this work as a new architectural model for constructing trustworthy software in the era of AI-assisted development. As large language models increasingly participate in code generation, configuration synthesis, and multi-component integration, the reliability of AI-assembled systems will depend critically on the trustworthiness of […]