arXiv Machine Learning
Jul 22
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
arXiv:2607. 18639v1 Announce Type: new Abstract: Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv AI
Jul 22
Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary
arXiv:2607. 18553v1 Announce Type: cross Abstract: Can a language model read the quality of ongoing computation, and can an external intervention turn that readout into better outcomes?
arXiv AI
Jul 22
Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs
arXiv:2607. 18638v1 Announce Type: cross Abstract: Purpose: To develop and validate a deep learning ensemble for estimating adult sex, age, height, and weight from coronal digitally reconstructed radiographs (DRRs) generated from diagnostic CT.
arXiv AI
Jul 22
Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization
arXiv:2607. 18306v1 Announce Type: cross Abstract: Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss in a local parameter neighborhood.
arXiv AI
Jul 22
The Economics of Autonomy: Real-Time Risk Indexing for Insurable AI-Driven 6G Systems
arXiv:2607. 18267v1 Announce Type: cross Abstract: The transition to sixth-generation (6G) networks transforms wireless infrastructure into a cognitive substrate supporting Vehicle-to-Everything (V2X), Industrial IoT (IIoT), and Integrated Sensing and Communication (ISAC).
arXiv AI
Jul 22
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
arXiv:2510. 22087v2 Announce Type: replace-cross Abstract: The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) evaluations.
arXiv AI
Jul 22
ABOPD: Antibody CDR Design via On-Policy Distillation
arXiv:2607. 18835v1 Announce Type: cross Abstract: Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface.
arXiv AI
Jul 22
LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation
arXiv:2602. 05493v2 Announce Type: replace-cross Abstract: Data annotation remains a significant bottleneck in the field of humanities and social sciences, particularly for complex linguistic tasks such as metaphor identification.
arXiv Machine Learning
Jul 22
NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift
arXiv:2607. 18845v1 Announce Type: cross Abstract: For decades, ABR has kept two kinds of intelligence apart.
arXiv AI
Jul 22
Using LLMs for Explainable, Data-Driven Insight Generation from Time Series
arXiv:2607. 18271v1 Announce Type: new Abstract: Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations.
arXiv AI
Jul 22
GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
arXiv AI
Jul 22
Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT
arXiv:2607. 18429v1 Announce Type: cross Abstract: Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them.
arXiv AI
Jul 22
Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA
arXiv:2607. 18725v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation, remains difficult.
Hugging Face Trending Papers
Jul 21
ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis
Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation.
Hugging Face Trending Papers
Jul 21
OmniReasoner: Thinking with Long Audio-Video via Native Tool Use
Long audio-video reasoning is difficult for omnimodal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs. We introduce OmniReasoner, a tool-use post-training framework for Thinking with Long Audio-Video: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering.
Hugging Face Trending Papers
Jul 21
GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
Hugging Face Trending Papers
Jul 21
Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards
Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited.
Towards Data Science
Jul 21
I Tried Fine-Tuning a Robot AI Model on Colab. Here Is What Worked
A reproducible 100-step LoRA fine-tuning run for OpenVLA, with dataset checks, Colab setup, training metrics, and W&B evidence. The post I Tried Fine-Tuning a Robot AI Model on Colab.
Hugging Face Trending Papers
Jul 21
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing.
Hugging Face Trending Papers
Jul 21
DobicVLM: Aligning Chest X-Ray Report Generation with Clinically-Grounded Programmatic Rewards via Group Relative Policy Optimization
Medical imaging is a cornerstone of diagnostics, yet automated chest X-ray report generation struggles with structural adherence, anatomical completeness, and semantic faithfulness. We introduce DobicVLM, a vision-language model combining supervised fine-tuning on MedGemma-4B with Group Relative Policy Optimization (GRPO) and clinically-grounded programmatic rewards.