Search: “llm”

Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers

arXiv:2608.19124v1 Announce Type: cross Abstract: Astrolinguistics -- communication with minds that categorize reality differently from ours -- has been purely speculative since Freudenthal's Lincos (

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Model Releases

Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy

DGX agent

arXiv:2608.19006v1 Announce Type: new Abstract: Hate speech is a real and timely threat that affects a large portion of online users, especially youth and minority groups. While building reliable and

model-releasesarxiv-cs-cl

20 Aug 2026

Applications

Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges

arXiv:2608.18080v1 Announce Type: new Abstract: We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy

applicationsarxiv-cs-ai

Learning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation

arXiv:2608.18681v1 Announce Type: new Abstract: We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting

LT-Mem: Volatility-Aware Spatio-Temporal Memory for Lifelong Scene Understanding

arXiv:2608.19059v1 Announce Type: cross Abstract: Long-term robot operation in evolving environments requires object-level understanding that persists across repeated revisits. Existing systems either

safetyarxiv-cs-cv

Metrics That Write Themselves: Evolving an Evaluator from Its Own Blind Spots

arXiv:2608.18744v1 Announce Type: new Abstract: Agents improve quickly against a reliable automatic metric and stall without one, and the applications that need them most, report generation among them

Mise-en-Scene: Implicit Layout Emergence in Diffusion Transformers for Human-AI Design Co-Creation

model-releasesarxiv-cs-cv

arXiv:2608.19000v1 Announce Type: new Abstract: Automating graphic design synthesis from user-provided elements requires both a coherent overall composition and the exact preservation of each asset. E

NE-BERT: A Multilingual Language Model for Nine Northeast Indian Languages

arXiv:2608.18094v1 Announce Type: cross Abstract: Large pretrained language models have demonstrated remarkable capabilities across diverse languages, yet critically underrepresented low-resource lang

ORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery

arXiv:2608.18846v1 Announce Type: new Abstract: Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recent learning-ba

local-aiarxiv-cs-ai

Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions

arXiv:2608.18078v1 Announce Type: new Abstract: This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be requi

Revisiting the 'Push-T' Robot Manipulation Task with Agentic Robotics

model-releasesarxiv-cs-ro

arXiv:2608.18227v1 Announce Type: new Abstract: Push-T is an iconic benchmark for learning manipulation policies from human demonstrations. The robot must use a single point of contact to push a T-sha

Shared Circuits for Shared Grammar: Tracing Subject-Verb Agreement Across Languages

arXiv:2608.18545v1 Announce Type: new Abstract: Multilingual large language models often generalize across languages, and prior work suggests that their internal mechanisms can overlap cross-lingually

SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation

arXiv:2608.18779v1 Announce Type: cross Abstract: Semantic-ID mappings are reusable interfaces between item tokenizers and generative recommenders, yet released mappings rarely state whether they are

SPADE: Self-Play in Adaptive Synthetic Executable Environments

arXiv:2608.19197v1 Announce Type: cross Abstract: Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environ

Think Shallow, Solve Deep: Controlling Recurrent Dynamics for Reliable Test-Time Depth

arXiv:2608.18222v1 Announce Type: cross Abstract: Recurrent-depth reasoners aim to solve harder problems by iterating their update longer at test time, but additional iterations can improve, preserve,

Verifiable abstention makes AI leak diagnosis accountable in water distribution networks

arXiv:2608.18836v1 Announce Type: new Abstract: Utilities lose a substantial share of treated water to leakage, yet rarely trust artificial-intelligence localizers to dispatch crews: guessing everywhe

When Readability and Source Retention Diverge: An Evaluability Gap in AI Translation

arXiv:2608.19083v1 Announce Type: cross Abstract: Readable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves.

You Are What You Prompt: Prompt Quality, Domain Shift, and Uncertainty in Agrifood Vision-Language Models

arXiv:2608.18116v1 Announce Type: new Abstract: Vision-language models enable zero-shot classification through natural language prompts, but performance is sensitive to prompt formulation, especially

aDSL: Agentic 3D Creation via Joint Agent-Program Design

arXiv:2608.17975v1 Announce Type: cross Abstract: Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit struct

agentsarxiv-cs-cv

AISA: AI Safety Assistant Framework for Continuous Improvement of Highway Construction

arXiv:2608.17184v1 Announce Type: new Abstract: Job Safety Analysis (JSA) and pre-task planning can benefit from prior incident records, yet historical accident data is often stored as unstructured na

local-aiarxiv-cs-cl

ARASH: Adaptive Retrieval And Shot Selection for Tabular Prediction

arXiv:2608.17856v1 Announce Type: new Abstract: Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approaches for adapt

Auditing Self-Evolution in Financial Agents: Capability Gains, Security Drift, and Execution-Interface Mismatch

arXiv:2608.17684v1 Announce Type: new Abstract: Self-evolving agents turn experience into reusable skills, workflows, or memories, but post-evolution accuracy alone does not show whether learned behav

ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing

arXiv:2606.15315v2 Announce Type: replace Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user pr

Collective Counterfactual Planning: Coordination, Consent, and Verification under Representational Constraints

arXiv:2608.17932v1 Announce Type: cross Abstract: Groups routinely complete projects that no single member can plan, execute, or verify alone. We propose a formal model of this phenomenon, Collective

agentsarxiv-cs-ai

Cross-Model Memory Transfer via Target-Side Reader Adaptation

arXiv:2608.17050v1 Announce Type: cross Abstract: Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to exter

safetyarxiv-cs-ai

DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval

arXiv:2608.17632v1 Announce Type: cross Abstract: Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model for query exp

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as

Do Large Language Models Play Six Degrees of Separation? Measuring Topological Compression in Long-Context Manifolds

arXiv:2608.17950v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable multi-hop reasoning capabilities over long contexts, yet the internal mechanisms enabling these dist

Does Unification Come at a Cost? Uni-SafeBench: A Safety Benchmark for Unified Multimodal Large Models

arXiv:2604.00547v2 Announce Type: replace Abstract: Unified Multimodal Large Models (UMLMs) integrate understanding and generation capabilities within a single architecture. While unified architecture

Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries

arXiv:2608.17567v1 Announce Type: cross Abstract: Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practic

Eval4Sim: An Evaluation Framework for Persona Simulation

arXiv:2603.02876v2 Announce Type: replace Abstract: Large Language Model personas, explicit profiles specifying a user's attributes, preferences, and behavioural tendencies, are increasingly used to s

Explicit State Elicitation Is Not Enough: A Controlled Audit of Memory-Policy Classification

arXiv:2608.17247v1 Announce Type: new Abstract: Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task. We use this

Large Language Models: A Mathematical Formulation

arXiv:2601.22170v2 Announce Type: replace-cross Abstract: Large language models (LLMs) process and predict sequences containing text to answer questions, and address tasks including document summariza

researcharxiv-cs-lg

MANIGUARD: A Benchmark and Data Suite for Specification-Grounded Safety Evaluation and Improvement of Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.17386v1 Announce Type: new Abstract: Foundation-model policies for robotic manipulation are advancing rapidly on task success, but rigorous evaluation of whether they succeed safely is stil

Memory Tree Guided Key Frame Querying for Efficient 3D Question Answering

arXiv:2608.18009v1 Announce Type: new Abstract: Answering questions accurately and efficiently in embodied scenarios presents significant challenges due to limited computational and memory resources f

researcharxiv-cs-cv

MITRE-SAGE: A Multi-Agent Cybersecurity Question-Answering Model

model-releasesarxiv-cs-lg

arXiv:2608.16921v2 Announce Type: cross Abstract: Effective cybersecurity operations require timely and accurate analysis of large-scale heterogeneous security information; however, analysts increasin

Mixture-of-Expert Blocks Contain Strong Hallucination Detection Signals

arXiv:2608.17687v1 Announce Type: new Abstract: Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known

local-aiarxiv-cs-ai

MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding

arXiv:2608.17402v1 Announce Type: new Abstract: Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling

researcharxiv-cs-cv

MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale

arXiv:2506.01297v5 Announce Type: replace Abstract: Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly diverging p

MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure

arXiv:2608.17823v1 Announce Type: cross Abstract: Powered two-wheeler riders face critical safety challenges in low- and middle-income countries, yet limited studies exist on how cognitive stressors s

local-aiarxiv-cs-ai

Procedural Content Metageneration via Program Search and Continual Abstraction Discovery

arXiv:2608.17947v1 Announce Type: new Abstract: Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than indivi

Quantifying Memorization and Privacy Risks in Genomic Language Models

arXiv:2603.08913v2 Announce Type: replace Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction

researcharxiv-cs-lg

Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making

arXiv:2608.17574v1 Announce Type: new Abstract: How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties cautio

safetyarxiv-cs-ai

QuantumNovelty: A Skill-Orchestrating Language Agent for Referee-Style Review and Patentability Screening of Quantum Papers and Patents

arXiv:2608.16900v1 Announce Type: cross Abstract: Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable,

agentsarxiv-cs-ai

SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning

arXiv:2510.01832v2 Announce Type: replace Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complic

SeqFeed: Improving Agentic RTL Code Generation with Sequential Behavior Feedback

arXiv:2608.16934v1 Announce Type: cross Abstract: RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To

agentsarxiv-cs-cl

Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection

arXiv:2608.17170v1 Announce Type: new Abstract: Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing feature extract

agentsarxiv-cs-ai

Applications

TabularQGAN: A quantum generative model for tabular data synthesis

arXiv:2505.22533v2 Announce Type: replace-cross Abstract: In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real

applicationsarxiv-cs-ai