Search: “llm”

Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation

arXiv:2607.04907v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over

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Silicon Sampling via Cross-Survey Transfer

DGX agent

arXiv:2607.03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional

safetyarxiv-cs-ai

7 Jul 2026

Structured Prompting and Automated Evaluation in Fixed Synthetic Japanese-Language Counseling Dialogues

arXiv:2507.02950v3 Announce Type: replace-cross Abstract: Large language models (LLMs) may support counseling training, yet evidence from Japanese-language interactions and automated quality ratings r

Toward Trustworthy Large Language Model Agents in Healthcare

arXiv:2607.05055v1 Announce Type: new Abstract: Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high admini

Local Ai

WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection

arXiv:2607.04350v1 Announce Type: new Abstract: Online social media posts provide scalable signals for early depression screening, and recent studies mainly improve pre-classification evidence through

local-aiarxiv-cs-cl

does your agent need a wiki? should humans and agent use the same wiki? should you have one wiki or many wikis? if you're wiki-curious, join…

does your agent need a wiki? should humans and agent use the same wiki? should you have one wiki or many wikis? if you're wiki-curious, join @BraceSproul @hwchase17 and I on Thursday! you don't want t

agentsharrison-chase--x

6 Jul 2026

AgenticDataBench: A Comprehensive Benchmark for Data Agents

arXiv:2607.01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern so

3 Jul 2026

Prompt Coverage Adequacy

arXiv:2607.02057v1 Announce Type: cross Abstract: In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software

3 Jul 2026

AGC-Bench: Measuring Artificial General Creativity

model-releasesarxiv-cs-cl

arXiv:2607.01152v1 Announce Type: new Abstract: Creativity research has debated whether creativity is domain-specific (e.g., visual, writing, science), and if it is psychometrically separable from gen

AGI Maze as a Benchmark Framework for World-Modeling Agents

arXiv:2607.00627v1 Announce Type: new Abstract: Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context

LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data

model-releasesarxiv-cs-cl

arXiv:2510.24434v3 Announce Type: replace Abstract: The effectiveness of instruction-tuned Large Language Models (LLMs) is often limited in low-resource linguistic settings due to a lack of high-quali

Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection

arXiv:2607.00035v1 Announce Type: new Abstract: LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable because of dependency errors, bro

Message Passing Enables Efficient Reasoning

arXiv:2607.01077v1 Announce Type: new Abstract: While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is

researcharxiv-cs-cl

Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework

arXiv:2607.00010v1 Announce Type: cross Abstract: Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to activel

Towards an automated AI-based framework for floor plan compliance checks for residential buildings

arXiv:2607.00015v1 Announce Type: cross Abstract: To improve residents' well-being in Australia's urban areas, governments have introduced policy reforms such as SEPP65, BADS, and SPP7.3 to enhance ap

ZO-Act: Efficient Zeroth-Order Fine-Tuning via One-Shot Activation-Informed Low-Rank Subspaces

model-releasesarxiv-cs-lg

arXiv:2607.01125v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables fine-tuning large language models when backpropagation is unavailable or memory-prohibitive, but existing methods

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems

arXiv:2606.31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environ

A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization

arXiv:2606.30775v1 Announce Type: cross Abstract: Enterprise AI agents route user queries to specialized skills by matching queries against natural language skill descriptions. When two skills share o

ACE: Pluggable Adaptive Context Elasticizer across Agents

arXiv:2606.31564v1 Announce Type: new Abstract: The increasing complexity of agentic tasks has led to rapidly growing trajectory lengths, which poses significant challenges for large language model (L

Adaptive Cluster-First Route-Second Decomposition for Industrial-Scale Vehicle Routing

arXiv:2606.31820v1 Announce Type: new Abstract: Large-scale capacitated vehicle routing problems (CVRPs) are commonly addressed using cluster-first route-second (CFRS) approaches that split a routing

Applications

AI-Generated PowerShell Malware: An Experimental Framework and Dataset

arXiv:2606.30819v1 Announce Type: cross Abstract: Generative AI has emerged as a significant cybersecurity threat, with several recent attack campaigns leveraging LLMs to generate code for malicious p

applicationsarxiv-cs-ai

An Empirical Study of Security Calibration in Large Language Models for Code

model-releasesarxiv-cs-lg

arXiv:2606.31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do mode

Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models

arXiv:2606.31614v1 Announce Type: cross Abstract: Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and

Beyond Static Prompts: Building Scale-Proof, Polymorphic Multi-Agent Systems with Google's ADK

model-releasesgoogle-cloud-ai

As enterprise generative AI transitions from simple, conversational chatbots to autonomous multi-agent workflows, developers face a critical bottleneck: scale. In a production environment, an enterpri

Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models

model-releasesarxiv-cs-lg

arXiv:2606.31630v1 Announce Type: new Abstract: Language models increasingly write probabilistic programs (in NumPyro, Stan, or Pyro), but a program that compiles, runs, and passes every unit test can

Certified Speculative Execution for Untrusted AI Agents

arXiv:2606.31023v1 Announce Type: cross Abstract: Hard-constrained sequential decision systems have no certified way to spend the test-time compute of modern AI: executing the multi-step drafts of a l

safetyarxiv-cs-lg

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

arXiv:2606.31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross

CSTrader: A Testbed for Language-Grounded Trading in a Community-Driven Virtual Asset Market

arXiv:2606.31461v1 Announce Type: new Abstract: Niche asset markets, such as Counter-Strike 2 (CS2) weapon skins, are small, volatile, and heavily driven by community discussions and platform rules. T

From Propositional to Perceptual Asymmetry: Extending Frictive Policy Optimization to Asymmetric Partial Information Dialogue

arXiv:2606.30973v1 Announce Type: new Abstract: Frictive Policy Optimization (FPO; Pustejovsky et al., 2025) treats friction in collaborative dialogue -- misalignment, misunderstanding, repair -- as a

safetyarxiv-cs-cl

JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering

arXiv:2606.31745v1 Announce Type: cross Abstract: Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what n

Local Ai

PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding

arXiv:2606.31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform rea

local-aiarxiv-cs-ai

SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA

arXiv:2504.07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluati

Spatial Reasoning via Modality Switching Between Language and Symbolic Representation

arXiv:2606.31285v1 Announce Type: new Abstract: Human reasoning is inherently multimodal: when problems become difficult, we rarely think in words alone. We often externalize our reasoning by sketchin

researcharxiv-cs-ai

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

arXiv:2601.18778v3 Announce Type: replace-cross Abstract: RL methods for scaling large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigat

researcharxiv-cs-cl

When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models

arXiv:2604.03316v2 Announce Type: replace Abstract: Attention sinks are defined as tokens that attract disproportionate attention. While these have been studied in single modality transformers, their

researcharxiv-cs-cv

A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training

arXiv:2606.28526v1 Announce Type: new Abstract: The clinical and communication skills of medical students are commonly assessed through Objective Structured Clinical Examinations (OSCEs), which consis

researcharxiv-cs-cl

Local Ai

AI Trading's Alpha Singularity: Emergent Market Reasoning through Agent-to-Agent Self-Evolution

arXiv:2606.29194v1 Announce Type: new Abstract: Automated alpha mining holds the scoring function fixed and varies the search algorithm over it. A search that converges against a fixed scorer overfits

local-aiarxiv-cs-ai

AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills

arXiv:2606.29999v1 Announce Type: new Abstract: Designing an algorithm from a natural-language problem statement requires identifying the problem structure, reading constraints, choosing a suitable pa

researcharxiv-cs-ai

AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control

arXiv:2603.26005v2 Announce Type: replace Abstract: Grid-interactive building control has emerged as a promising approach for improving demand-side flexibility in modern power systems. Realistic studi

Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation

arXiv:2602.12089v3 Announce Type: replace-cross Abstract: As AI usage becomes more prevalent in social contexts, understanding agent-user interaction is critical to designing systems that imp rove bot

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems

arXiv:2606.28533v1 Announce Type: cross Abstract: Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) b

researcharxiv-cs-ai

Cognitive World Models for Process-Level Social Influence Evaluation

arXiv:2606.29495v1 Announce Type: new Abstract: Social influence dialogue changes user behavior by altering internal cognitive states. The central evaluation question is whether the user's beliefs, de

Digitizing Coaching Intelligence: An Agentic Framework for Holistic Athlete Profiling using VLM and RAG

arXiv:2606.28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent. However, during mass recruitment drives, traditi

Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration

arXiv:2509.21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. W

safetyarxiv-cs-lg

Insidious by Design: Implications of Large Language Model algorithmic bias for the Global South

arXiv:2606.28333v1 Announce Type: cross Abstract: egin{quote} The biases in Large Language Models' (LLMs) outputs remain inadequately theorised, particularly from the perspective of the Global South.

Labeling Training Data for Entity Matching Using Large Language Models

model-releasesarxiv-cs-cl

arXiv:2606.28823v1 Announce Type: new Abstract: Recent large language models (LLMs) achieve strong performance on entity matching without requiring task-specific training data. However, applying these

Tutorials

LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features

arXiv:2606.28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modal

tutorialsarxiv-cs-ai

LUMEN: Cost-Transparent Multi-Agent Pipeline for Automated Systematic Review and Meta-Analysis

arXiv:2606.28362v1 Announce Type: cross Abstract: Systematic reviews and meta-analyses (SR/MA) remain the gold standard for evidence synthesis, yet completing one typically requires 67 weeks and subst