arXiv AI
Jul 28
Kalypso: Relational LLM Serving
arXiv:2607. 23815v1 Announce Type: cross Abstract: Large language models are increasingly used as semantic operators for filtering, extracting, ranking, joining, and transforming unstructured data.
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 AI
Jul 28
The Half-Lives of Generative-AI Evidence: A 40-Record Audit, a Claim-Currency Framework, and a Reflexive Case of Frontier-Model-Assisted Research
arXiv:2607. 24032v1 Announce Type: new Abstract: Generative-AI evaluations can become historical before publication, yet calendar age does not affect every conclusion equally.
arXiv AI
Jul 28
MedLoCoMo: A Long-Context Multi-Session Medical Dialogue Benchmark for Large Language Models
arXiv:2607. 22566v1 Announce Type: new Abstract: MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue.
arXiv AI
Jul 28
SyRuP: Enhancing System-Prompt Following via Reward-Guided Prediction in LLM Decoding
arXiv:2607. 23991v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly controlled through system prompts that specify roles, styles, formats, and safety requirements.
arXiv AI
Jul 28
Reference Feature Atlases for Mechanistic Auditing of Language Models
arXiv:2607. 22570v1 Announce Type: new Abstract: Auditing a new language model usually means relearning and reinterpreting its internal features from scratch.
arXiv AI
Jul 28
HELIOS: An LLM-Driven Autonomous Indirect Trajectory Optimization Agent
arXiv:2607. 24051v1 Announce Type: cross Abstract: Low-thrust trajectory optimization is a core technology in deep-space mission design.
arXiv AI
Jul 28
Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning
arXiv:2607. 22697v1 Announce Type: new Abstract: Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution.
arXiv Machine Learning
Jul 28
Visual Token Compression Enhances Robustness of MLLMs
arXiv:2607. 22716v1 Announce Type: cross Abstract: In this paper, we show for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vulnerabilities such as jailbreak attacks and hallucinations.
arXiv Machine Learning
Jul 28
Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions
arXiv:2506. 05678v3 Announce Type: replace Abstract: The evolution of sequence modeling architectures, from recurrent neural networks and convolutional models to Transformers and structured state-space models, reflects ongoing efforts to address the diverse temporal dependencies inherent in sequential data.
arXiv AI
Jul 28
Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
arXiv:2602. 17634v2 Announce Type: replace-cross Abstract: Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains.
arXiv AI
Jul 28
DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs
arXiv:2607. 22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with explanations.
arXiv AI
Jul 28
DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.
arXiv Machine Learning
Jul 28
IndicTalk: A Large-Scale Persona-Based Multilingual Conversational Corpus for Indic Languages
arXiv:2607. 23242v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed conversational AI, yet high-quality multilingual code-mixed dialogue resources remain scarce, particularly for Indic languages where speakers naturally alternate between English and their native language in both native-script and Romanized forms.
arXiv Machine Learning
Jul 28
Hallucination Rates in Language Generation
arXiv:2607. 23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algorithm that learns solely based on example strings.
arXiv Machine Learning
Jul 28
HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows
arXiv:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.
arXiv Machine Learning
Jul 28
Accelerating Frequency Domain Diffusion Models with Error-Feedback Event-Driven Caching
arXiv:2604. 22901v2 Announce Type: replace Abstract: Diffusion models achieve remarkable success in time series generation.
arXiv AI
Jul 28
LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish
arXiv:2510. 07074v2 Announce Type: replace-cross Abstract: Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts.
arXiv Machine Learning
Jul 28
WorldDiT: A Unified Diffusion Architecture for World and Action Modeling
arXiv:2607. 23909v1 Announce Type: new Abstract: Many recent robot policies pursue stronger control by using large pretrained vision-language models (VLMs) as the action backbone.
arXiv AI
Jul 28
The Lattice Representation Hypothesis of Large Language Models
arXiv:2603. 01227v3 Announce Type: replace Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations in embedding geometry.
arXiv AI
Jul 28
Moral Hazard in Multi-Agent Language Models
arXiv:2607. 23982v1 Announce Type: cross Abstract: Cooperation can fail when socially valuable effort is costly, weakly observable, and mainly benefits others.