arXiv AI
Aug 11
How sensitive do we want AI to be? Socio-communicative competencies of large language models in healthcare
arXiv:2608. 07511v1 Announce Type: cross Abstract: Background.
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
Aug 11
From token probabilities to calibrated confidence: An empirical study of mathematical question answering
arXiv:2608. 07827v1 Announce Type: new Abstract: Confidence estimation for large language models (LLMs) aims to estimate the probability that a generated answer is correct, while calibration aligns these estimates with empirical accuracy.
arXiv AI
Aug 11
GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
arXiv:2608. 08968v1 Announce Type: cross Abstract: Microservice root cause analysis (RCA) requires correlating failures across heterogeneous telemetry within complex service dependency graphs.
arXiv AI
Aug 11
Compositional Threat Analysis of Latent Compromise in LLM Agent Systems: The Order 66 Scenario
arXiv:2608. 08131v1 Announce Type: cross Abstract: In the fictional Order 66, catastrophe does not arise from a powerful command alone: a trusted population is preconditioned, a short directive activates the concealed condition, and protective authority turns against the system.
arXiv AI
Aug 11
Towards Realistic Guarantees: A Probabilistic Certificate for SmoothLLM
arXiv:2511. 18721v4 Announce Type: replace-cross Abstract: The SmoothLLM defense provides a certification guarantee against jailbreaking attacks, but it relies on a strict "k-unstable" assumption that rarely holds in practice.
arXiv AI
Aug 11
Coupled Graph--Policy Distillation for Personalized Medication Safety in Older Adults with Multimorbidity
arXiv:2608. 09443v1 Announce Type: new Abstract: Large language model (LLM) agents can support medication review between clinical visits, but safe choices for older adults with multimorbidity depend on conditions, medications, and geriatric risks that users may omit.
arXiv AI
Aug 11
Hallucination-Free GUI Grounding via Regression-Free Layout-Aware Matching
arXiv:2608. 09654v1 Announce Type: new Abstract: GUI agents are shifting from metadata-dependent large language models to purely visual multimodal large language models (MLLMs) that operate directly on screenshots.
arXiv Machine Learning
Aug 11
Mechanistic Interpretability-Guided Selective Fine-Tuning of Vision-Language Models for Centimeter-Level Flood Depth Estimation
arXiv:2608. 07562v1 Announce Type: cross Abstract: Urban flooding poses an escalating threat to transportation infrastructure, yet no operational system provides real-time, street-level flood-depth estimates at centimeter resolution.
arXiv AI
Aug 11
Contextual Value Alignment via Multilayer Combinatorial Fusion
arXiv:2608. 07642v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human values remains a major challenge, especially for trustworthy AI.
arXiv AI
Aug 11
Determinization in Structure Theories: A Unified Framework via Closure, Comparability, and Joint Admissibility
arXiv:2608. 07476v1 Announce Type: new Abstract: We develop a formal framework for constructing canonical interpretations from plural structure theories.
arXiv AI
Aug 11
EmoPatient: An Emotion-Directed Patient Simulator for Realistic Palliative Care Communication Training
arXiv:2608. 07495v1 Announce Type: cross Abstract: Effective communication during palliative care discussions is a critical clinical skill, yet training clinicians to manage complex patient emotions remains challenging.
arXiv AI
Aug 11
Unified Hallucination Fuzzing for Multimodal Large Language Models
arXiv:2608. 07525v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applications.
arXiv AI
Aug 11
Learning an Interior Layout Policy in a Domain Specific Language Action Space
arXiv:2608. 07547v1 Announce Type: cross Abstract: Indoor scene layout generation is a challenging task in interior design.
arXiv AI
Aug 11
Weather- and Location-Aware Agentic Dining Recommendation: Leveraging LLM World Knowledge for Region-Sensitive Contextual Reasoning
arXiv:2608. 07593v1 Announce Type: cross Abstract: Context-aware recommender systems have long recognized that factors such as location, time, and weather shape where and what people choose to eat.
arXiv AI
Aug 11
Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders
arXiv:2608. 08284v1 Announce Type: new Abstract: Fairness audits for LLM-based recommenders have largely focused on observable outputs, implicitly assuming that stable recommendations reflect stable internal processing.
arXiv AI
Aug 11
Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration
arXiv:2608. 08881v1 Announce Type: new Abstract: The current work developed seven Retrieval-Augmented Generation (RAG) models based on leading deception theories and compared how deception judgments were made relative to baseline models.
arXiv AI
Aug 11
LAUDE: LLM-Assisted Unit Test Generation and Debugging of Hardware DEsigns
arXiv:2601. 08856v3 Announce Type: replace-cross Abstract: Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level.
arXiv AI
Aug 11
Who Verifies the Benchmark? Decentralizing Trust in Large Language Model Evaluation
arXiv:2608. 07762v1 Announce Type: new Abstract: LLM benchmarks can build an organization's reputation and attract customers, but only when results are transparent and verifiable.
arXiv AI
Aug 11
Deferred Audio Pruning with Local Audio-Visual Dynamics for Omni-LLMs
arXiv:2608. 08794v1 Announce Type: new Abstract: Omni-modal LLMs jointly process audio, video, and text, but long multimodal sequences incur substantial prefill and KV-cache costs.
arXiv AI
Aug 11
One Adapter Pair per Model: A Universal Activation Interface for Language Models
arXiv:2608. 09521v1 Announce Type: new Abstract: Activation-based tools are usually tied to one model's native hidden space, requiring probes, sparse autoencoders, and natural-language interpreters to be rebuilt or rediscovered for each new language model.