DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
arXiv:2607.16203v1 Announce Type: new
Abstract: Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and ...
Fully-sensorized smart-eyewear platform for on-device Machine Learning
arXiv:2607.16222v1 Announce Type: new
Abstract: This paper presents ARGO, a smart eyewear platform designed to bridge ergonomic comfort, high computational throughput, and energy efficiency. Unlike cloud-dependent solutions, ARGO leverages the STM32N6 microcontroller and its integrated Neural Proce...
LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats
arXiv:2607.16227v1 Announce Type: new
Abstract: LLMs are increasingly deployed in security-critical systems across healthcare, finance, education, and decision support, yet their inability to forget creates serious cybersecurity, privacy, and safety risks. Sensitive personal information, copyrighte...
Rater State Bias in RLHF Preference Data: An Audit Framework
arXiv:2607.16195v1 Announce Type: new
Abstract: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF). Pairwise preference labels are intended to reflect the compared outputs, but they may also reflect the rater's state during annotation. Under sustained stressful o...
Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions
arXiv:2607.16196v1 Announce Type: new
Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect. This study pres...
PlanFlip: Attacking Multi-Agent LLM Systems via Planning-Phase Prompt Injection
arXiv:2607.16199v1 Announce Type: new
Abstract: Multi-agent LLM systems increasingly rely on a Planner to decompose goals into sub-task sequences that downstream Executor and Critic agents execute and audit. We identify the planning phase as a critical attack surface: a single injection into the Pl...
Environment-free Synthetic Data Generation for API-Calling Agents
Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for s...
Accelerating Text-to-Video Generation with Calibrated Sparse Attention
Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiotemporal attention. In this paper, we identify that a significant fraction of token-to-token connections consistently yiel...
Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling
arXiv:2607.15313v1 Announce Type: new
Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities. This position paper argues that applying this probabilistic paradigm to generic quantum circuit synthesis is a directional error. Unlike natural la...
Structure of the Circular-Dyadic Convolution Error
arXiv:2607.15293v1 Announce Type: new
Abstract: Dyadic and circular convolution can both be computed in $O(N\log N)$ time using the Hadamard transform and the FFT-computed discrete Fourier transform (DFT), respectively. The Hadamard transform is preferable for its real-valued sign flips, yet its su...
A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data
arXiv:2607.15394v1 Announce Type: new
Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility. We introduce a statistically grounded framework that provides fully interpretable, rule-based clinical classification...
AI Trading: Evaluating Large Language Models for Technical Market Analysis
arXiv:2607.15414v1 Announce Type: new
Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets. This paper presents a systematic, comparative evaluation of five prominent LLMs: GPT-4 Turbo, Claude 3 O...
GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis
arXiv:2607.15280v1 Announce Type: new
Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering. Existing Large Language Model (LLM) approaches exhibit a critical knowledge-reasoning gap: despite encoding extensive medical k...
Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
arXiv:2607.15281v1 Announce Type: new
Abstract: Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms. However, existing LLM-based methods often rely...
AnovaX: A Local, Multi-Agent Voice Assistant with LLM Planning, Typed Executors, and Adaptive Recovery
arXiv:2607.15367v1 Announce Type: new
Abstract: Desktop voice assistants are still dominated by cloud pipelines that ship raw audio off the machine and expose a fixed set of skills. We describe AnovaX, a small local-first assistant that runs entirely on the user's computer and treats the desktop it...
arXiv:2607.15314v1 Announce Type: new
Abstract: Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized LLMs that cover these use cases together remain limited. A healthcare model must handle patient consultation, clinical reasoning over text and images...
Increasing reliance on language models for legal tasks have led to calls for more information about the likelihood and severity of potential errors. Realizing useful and robust transparency requires an institutional perspective, say the editors of a new special section in PNAS.
Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling
Token serves as the fundamental unit of computation in modern autoregressive models, and generation length directly influences both inference cost and reasoning performance. Despite its importance, existing approaches lack fine-grained length modeling, operating primarily at the coarse-grained seque...
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
arXiv:2607.14125v1 Announce Type: new
Abstract: Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.g., "cat") into a prompt (e.g., "a photo of a")...
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
arXiv:2607.14123v1 Announce Type: new
Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful acti...
QFireNet: A Quantum-Enhanced U-Net for Wildfire Segmentation from Sentinel-2 Imagery
arXiv:2607.14160v1 Announce Type: new
Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference. In this paper, we build on the foundational...
Show Me Examples: Inferring Visual Concepts from Image Sets
Vision-language models (VLMs) can follow complex textual instructions, yet they struggle to reason from purely visual context. In particular, current models fail to infer shared concepts from sets of example images and apply them to new inputs. We introduce Visual Concept Inference from Sets (VICIS)...
When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs
As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained models becomes increasingly important. While state-of-the-art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this w...