oculomix: Hierarchical Sampling for Retinal-Based Systemic Disease Prediction
arXiv:2601.19939v1 Announce Type: new
Abstract: Oculomics - the concept of predicting systemic diseases, such as cardiovascular disease and dementia, through retinal imaging - has advanced rapidly due to the data efficiency of transformer-based foundation models like RETFound. Image-level mixed sam...
Continuous-Flow Data-Rate-Aware CNN Inference on FPGA
arXiv:2601.19940v1 Announce Type: new
Abstract: Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each neuron is mapped to a dedicated hardware unit, making them well-suited for field-progra...
Latent Object Permanence: Topological Phase Transitions, Free-Energy Principles, and Renormalization Group Flows in Deep Transformer Manifolds
arXiv:2601.19942v1 Announce Type: new
Abstract: We study the emergence of multi-step reasoning in deep Transformer language models through a geometric and statistical-physics lens. Treating the hidden-state trajectory as a flow on an implicit Riemannian manifold, we analyze the layerwise covariance...
Teaching LLMs to Ask: Self-Querying Category-Theoretic Planning for Under-Specified Reasoning
arXiv:2601.20014v1 Announce Type: new
Abstract: Inference-time planning with large language models frequently breaks under partial observability: when task-critical preconditions are not specified at query time, models tend to hallucinate missing facts or produce plans that violate hard constraints...
Insight Agents: An LLM-Based Multi-Agent System for Data Insights
arXiv:2601.20048v1 Announce Type: new
Abstract: Today, E-commerce sellers face several key challenges, including difficulties in discovering and effectively utilizing available programs and tools, and struggling to understand and utilize rich data from various tools. We therefore aim to develop Ins...
Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control
arXiv:2601.20090v1 Announce Type: new
Abstract: Large language model (LLM)-powered agents can translate high-level user intents into plans and actions in an environment. Yet after observing an outcome, users may wonder: What if I had phrased my intent differently? We introduce a framework that enab...
40 companies shaping Silicon Valley’s AI landscape in 2026
Silicon Valley still sits at the center of the AI conversation, not because it has a monopoly on ideas, but because so many of the forces shaping AI’s future collide here.
Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
arXiv:2601.18811v1 Announce Type: new
Abstract: This paper presents a Quantum Reinforcement Learning (QRL) solution to the dynamic portfolio optimization problem based on Variational Quantum Circuits. The implemented QRL approaches are quantum analogues of the classical neural-network-based Deep De...
IPBC: An Interactive Projection-Based Framework for Human-in-the-Loop Semi-Supervised Clustering of High-Dimensional Data
arXiv:2601.18828v1 Announce Type: new
Abstract: High-dimensional datasets are increasingly common across scientific and industrial domains, yet they remain difficult to cluster effectively due to the diminishing usefulness of distance metrics and the tendency of clusters to collapse or overlap when...
arXiv:2601.18833v1 Announce Type: new
Abstract: Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orch...
LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties
arXiv:2601.18846v1 Announce Type: new
Abstract: Benchmarking in continuous black-box optimisation is hindered by the limited structural diversity of existing test suites such as BBOB. We explore whether large language models embedded in an evolutionary loop can be used to design optimisation proble...
Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System
arXiv:2601.18897v1 Announce Type: new
Abstract: Wastewater treatment plants consume 1-3% of global electricity, making accurate energy forecasting critical for operational optimization and sustainability. While machine learning models provide point predictions, they lack explainable uncertainty qua...
RIFT: Reordered Instruction Following Testbed To Evaluate Instruction Following in Singular Multistep Prompt Structures
arXiv:2601.18924v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly relied upon for complex workflows, yet their ability to maintain flow of instructions remains underexplored. Existing benchmarks conflate task complexity with structural ordering, making it difficult to is...
Neural Theorem Proving for Verification Conditions: A Real-World Benchmark
arXiv:2601.18944v1 Announce Type: new
Abstract: Theorem proving is fundamental to program verification, where the automated proof of Verification Conditions (VCs) remains a primary bottleneck. Real-world program verification frequently encounters hard VCs that existing Automated Theorem Provers (AT...
arXiv:2601.16984v1 Announce Type: new
Abstract: The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them difficult to process. While Large Lan...
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
arXiv:2601.16991v1 Announce Type: new
Abstract: Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in resource-constrained environments. Low-rank Adaptation (LoRA) reduces ...
MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning
arXiv:2601.17006v1 Announce Type: new
Abstract: In mathematical reasoning tasks, the advancement of Large Language Models (LLMs) relies heavily on high-quality training data with clearly defined and well-graded difficulty levels. However, existing data synthesis methods often suffer from limited di...
Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability
arXiv:2601.17168v1 Announce Type: new
Abstract: Agentic systems have transformed how Large Language Models (LLMs) can be leveraged to create autonomous systems with goal-directed behaviors, consisting of multi-step planning and the ability to interact with different environments. These systems diff...
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
arXiv:2601.17310v1 Announce Type: new
Abstract: Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of ...
arXiv:2601.17311v1 Announce Type: new
Abstract: Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse. We develop a minimal and calibratable theory that predicts these regimes from three binding constraints of modern agent stacks...
Principled Coarse-Grained Acceptance for Speculative Decoding in Speech
Speculative decoding accelerates autoregressive speech generation by letting a fast draft model propose tokens that a larger target model verifies. However, for speech LLMs that generate acoustic tokens, exact token matching is overly restrictive: many discrete tokens are acoustically or semanticall...
SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
The common approach to communicate a large language model’s (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of generating a single answer and then hedging it, an LLM that is fully transparent to the user needs to be able to reflec...
Learning to Reason as Action Abstractions with Scalable Mid-Training RL
Large language models excel with reinforcement learning (RL), but fully unlocking this potential requires a mid-training stage. An effective mid-training phase should identify a compact set of useful actions and enable fast selection among them through online RL. We formalize this intuition by prese...