arXiv AI
Jul 22
FinRAG-12B: A Production-Validated Recipe for Grounded Question Answering in Banking
arXiv:2605. 05482v2 Announce Type: replace Abstract: Large language models (LLMs) are rapidly being adopted across various domains.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv Machine Learning
Jul 22
ATLAS: A Foundation Neural Sampler for Amorphous Materials
arXiv:2607. 19198v1 Announce Type: cross Abstract: Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample.
arXiv AI
Jul 22
Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes
arXiv:2605. 28965v2 Announce Type: replace Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration of comparative morphological data.
arXiv Machine Learning
Jul 22
ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling
arXiv:2607. 19332v1 Announce Type: new Abstract: Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching.
arXiv Machine Learning
Jul 22
Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction
arXiv:2607. 18997v1 Announce Type: cross Abstract: Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detection, a vital 'middleware' organizing graphical and textual hierarchies, is underexplored.
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 Machine Learning
Jul 22
CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
arXiv:2603. 21014v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information.
arXiv AI
Jul 22
Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks
arXiv:2607. 18767v1 Announce Type: cross Abstract: The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalability.
arXiv AI
Jul 22
Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning
arXiv:2607. 18979v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at multi-step reasoning, yet current parallel reasoning approaches often fail to distinguish the contributions of individual reasoning paths.
arXiv Machine Learning
Jul 22
SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks
arXiv:2510. 14812v2 Announce Type: replace Abstract: Structured weight sparsity accelerates training and inference on modern GPUs, but it trails unstructured dynamic sparse training (DST) in accuracy especially at extreme sparsity.
arXiv AI
Jul 22
Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains
arXiv:2607. 18438v1 Announce Type: cross Abstract: Introducing Relay-Bench, an unsaturated, holistic, text-only benchmark that measures LLMs' ability to complete an assortment of tasks from distinct domains in a single prompt.
arXiv AI
Jul 22
Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models
arXiv:2607. 18280v1 Announce Type: cross Abstract: Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance degradation beyond an essential sparsity boundary.
arXiv AI
Jul 22
An Analysis of Residual-Stream Geometry Across Transformer Depth
arXiv:2607. 18348v1 Announce Type: cross Abstract: We propose a transition-centred geometric analysis of transformer residual streams.
arXiv AI
Jul 22
AI Tour Meeting: Group Travel Planning by LLM Agents
arXiv:2607. 18806v1 Announce Type: new Abstract: This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents.
arXiv AI
Jul 22
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation
arXiv:2601. 06471v2 Announce Type: replace-cross Abstract: Large language model (LLM) personalization aims to adapt general-purpose models to individual users.
arXiv AI
Jul 22
Quality Action Assurance: Multimodal Verification of Examiner Claims in VR OSCEs
arXiv:2607. 19063v1 Announce Type: new Abstract: Objective Structured Clinical Examinations (OSCEs) are the gold standard for assessing clinical competence, yet scoring remains vulnerable to examiner subjectivity, fatigue, and cognitive bias.
arXiv AI
Jul 22
Censoring-Aware In-Context Learning for Generalized Supplier Lead Time Estimation in Supply Chain Planning
arXiv:2607. 18530v1 Announce Type: cross Abstract: Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk management.
arXiv Machine Learning
Jul 22
Pain in 3D: Generating Controllable Synthetic Faces for Automated Pain Assessment
arXiv:2509. 16727v5 Announce Type: replace-cross Abstract: Automated pain assessment from facial expressions is crucial for non-communicative patient.
arXiv AI
Jul 22
Appearance Pointers -- Multimodal Region Control of Diffusion Transformers
arXiv:2607. 19344v1 Announce Type: cross Abstract: Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identities, and spatial arrangements that cannot be reliably achieved through text prompting alone.
arXiv AI
Jul 22
MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications
arXiv:2409. 07314v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows.