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arXiv:2609.27903v1 Announce Type: new Abstract: Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors, including aviati

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'AI Is Turning Too Human': How Teenagers Experience and Negotiate AI in Everyday Life

DGX agent

arXiv:2609.27824v1 Announce Type: new Abstract: Generative AI is rapidly entering adolescents' everyday lives during a critical period of cognitive, social and emotional development. Yet its adoption

safetyarxiv-cs-cl

24 Sept 2026

Alignment Inertia: Auditing the Durability of Training Data Influence Through Policy Override Resistance

arXiv:2609.27333v1 Announce Type: new Abstract: Platform operators increasingly rely on system prompts and fine-tuning to govern model behavior, yet it remains unclear how reliably these interventions

An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice

arXiv:2609.28335v1 Announce Type: new Abstract: Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is

ASCIIBench: Evaluating Language-Model-Based Understanding of Visually-Oriented Text

model-releasesarxiv-cs-lg

arXiv:2512.04125v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated several emergent behaviors with scale, including reasoning and fluency in long-form text generation.

Automated Extraction of Records of Processing Activities (RoPA) Using Hybrid RAG and Locally Deployed Large Language Models

arXiv:2609.27359v1 Announce Type: new Abstract: Vietnam's Personal Data Protection Law (Law No. 91/2025/QH15) and Decree No. 356/2025/ND-CP, effective January 1, 2026, require organizations to establi

CART: Closed-Loop Adaptive Red Teaming for Large Language Models

arXiv:2609.27336v1 Announce Type: new Abstract: Automated red teaming often replays a fixed set of prompts, which measures known risks but cannot learn from failures found during testing. We present C

Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers

arXiv:2609.28247v1 Announce Type: cross Abstract: Large Language Models (LLMs) introduce an exciting new paradigm for planning and navigation in robotics, but fail on even simple multi-robot tasks as

local-aiarxiv-cs-ai

Cross-Scale Transfer Learning for Depression Severity Prediction: From PHQ-8 to HAMD-17 Across Languages and Clinical Paradigms

arXiv:2609.28430v1 Announce Type: new Abstract: This work addresses continuous depression-severity score prediction from clinical interview transcripts under data scarcity. We propose a sequential low

EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues

arXiv:2609.27043v1 Announce Type: new Abstract: Large language models have allowed the rapid deployment of pedagogical annotations corresponding to constructs of interest, allowing a natural language

EidosDoc: Implicit Structure Encoding for Cost-Effective Semi-Structured Document QA

arXiv:2609.27784v1 Announce Type: cross Abstract: Semi-structured documents are ubiquitous in scientific reports, financial statements, and technical manuals. Question answering over such documents re

EMA: Elastic and Performance Transparent Memory Across GPUs

arXiv:2609.27040v1 Announce Type: cross Abstract: Multi-GPU servers have become the standard building block of modern data centers, providing aggregated capacity through high-bandwidth interconnects.

local-aiarxiv-cs-ai

EnComp: Lightweight Encoder-Only Context Compression for Retrieval-Augmented Question Answering

arXiv:2603.09222v2 Announce Type: replace Abstract: Efficient context compression is critical for retrieval-augmented question answering in resource-constrained settings, where long retrieved contexts

FLEET: From Logits Entropy to Enhanced Trajectories in Text Generation

arXiv:2609.27657v1 Announce Type: cross Abstract: Solutions based on large language models (LLMs) often rely on temperature sampling to improve accuracy and stability by aggregating multiple samples f

From Sentiment Classification to Actionable and Responsible Feedback: A Scoping Review and Evidence Map of NLP in Student Evaluation of Teaching, 2015-2026

arXiv:2609.27939v1 Announce Type: new Abstract: Natural language processing (NLP) applied to open-ended teaching-evaluation comments (Student Evaluation of Teaching, SET) has tracked the field's techn

safetyarxiv-cs-cl

Giving Credit Where It's Due: Redundancy-Aware Learning for Efficient Reasoning

arXiv:2609.27156v1 Announce Type: new Abstract: Large reasoning models can produce correct yet unnecessarily long reasoning traces. Existing methods improve reasoning efficiency with trajectory-level

local-aiarxiv-cs-cl

Hidden not Deleted: How Networks Suppress Entangled Features

arXiv:2609.27593v1 Announce Type: cross Abstract: Concept erasure methods that operate via linear projection assume that features occupy separable subspaces. We show this assumption fails under dense

Hunyuan-A13B Technical Report

arXiv:2609.27284v1 Announce Type: new Abstract: We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but act

Tutorials

Meet, Compare, or Abstain: LatWeave for Deterministic Multi-Hop Question Answering on Knowledge Lattices

arXiv:2609.27225v1 Announce Type: cross Abstract: Probabilistic question-answering systems -- whether large language models (LLMs) themselves, retrieval-augmented generation (RAG), or trained multi-ho

tutorialsarxiv-cs-ai

Quantifying the Occult: A Comparative Study of Hindu and Buddhist Deities Using Machine Learning Methods

model-releasesarxiv-cs-lg

arXiv:2609.27074v1 Announce Type: cross Abstract: This study introduces a dual-matrix computational architecture to mathematically quantify the morphological and theological divergence of 196 Hindu an

SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems

arXiv:2603.17373v2 Announce Type: replace Abstract: Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety i

Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer

arXiv:2609.28372v1 Announce Type: cross Abstract: Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using 'Tool-Lab,' an adaptation of

StudentBench: AI and human tutoring yield equivalent GRE learning gains

arXiv:2609.28470v1 Announce Type: new Abstract: Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advanci

BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

model-releasesarxiv-cs-cv

arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simila

CoEvo: Oracle-Grounded Self-Evolution of a Single Model for Multi-Step Causal Reasoning

arXiv:2609.26094v1 Announce Type: new Abstract: Multi-step causal reasoning requires chaining inferences where each step constrains the next. An early error propagates silently, and a correct answer r

safetyarxiv-cs-lg

Conduct Under Pressure: What Sixty Language Models Do When a User Pushes

arXiv:2609.25447v1 Announce Type: new Abstract: We study what LLMs do when a user applies pressure in an uncomfortable situation: a user insists, begs, flatters or grieves, and the model gives up a co

CraftBench-UE: Deterministic Evaluation for Coding Agents in Unreal Engine

arXiv:2609.23142v1 Announce Type: new Abstract: Building gameplay features in a game engine requires more than code, as code that compiles and runs does not necessarily implement the requested gamepla

Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection

arXiv:2609.24801v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation throug

safetyarxiv-cs-ai

Differentiable Fuzzy Inference Layer: A Monotone, Compositional Ordinal Reasoning Head for Large Language Models

arXiv:2609.26113v1 Announce Type: new Abstract: A state-of-the-art language model asked to interpret 'most of most students passed' typically answers 'most,' though composing two instances of 'most' y

Discovery-Driven Integration of Disjoint Tables via Text

arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit attr

local-aiarxiv-cs-cl

Discovery of Interpretable Surrogates via Agentic AI: Application to Gravitational Waves

arXiv:2605.11280v2 Announce Type: replace-cross Abstract: Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present e

Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning

arXiv:2505.13994v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy tra

Do Synthetic Personas Predict Real Audience Response? A Sim-to-Real Study Where a No-Persona Baseline Beats Persona-Based Copy Simulation

arXiv:2609.25010v1 Announce Type: cross Abstract: Marketers increasingly use large language models (LLMs) as 'synthetic personas' to predict how an audience will react to a piece of copy before it shi

DTOC: Dynamic Tool Output Compression for Adaptive Context Management in AI Agents

arXiv:2609.26121v1 Announce Type: cross Abstract: As agent capabilities have grown, practical limitations increasingly stem from constrained context windows rather than model capacity. Common strategi

From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models

arXiv:2609.24084v1 Announce Type: cross Abstract: Open-weight large language models (LLMs) can be copied, modified, and redeployed behind black-box APIs, making post-release ownership verification dif

Tutorials

Identifying Intelligent Processes via Online Sequential Testing

arXiv:2609.26193v1 Announce Type: cross Abstract: Active sequential hypothesis testing studies how to identify an unknown hypothesis with a given set of sensing actions. We study this in the setting o

tutorialsarxiv-cs-lg

Incremental Consistency Execution for Autonomous Intelligent Systems

arXiv:2609.24090v1 Announce Type: new Abstract: Long-horizon autonomous intelligent systems rely on heterogeneous components such as large language models, databases, external APIs, and rule engines,

safetyarxiv-cs-ai

LIMIT: Less Is More for Instruction Tuning in Text-to-SQL

arXiv:2609.24186v1 Announce Type: new Abstract: Large language models have achieved remarkable progress on Text-to-SQL through reasoning-enhanced fine-tuning, yet existing approaches predominantly rel

MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning

arXiv:2609.22724v1 Announce Type: cross Abstract: Mobile agents powered by foundation models now automate complex, multi-step workflows on real devices, but their trajectories can violate app-specific

Matryoshka attribution: Learning to attribute language model outputs to representations and weights

arXiv:2609.25518v1 Announce Type: new Abstract: Attributing language model outputs to their internal computations is an open problem in interpretability. Existing methods, which use causal interventio

Optimal Sequential Annotations for Off-Policy Evaluation

arXiv:2609.26707v1 Announce Type: cross Abstract: Offline reinforcement learning and off-policy evaluation evaluates dynamic treatment rules based on retrospectively collected data prior to deployment

safetyarxiv-cs-lg

Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models

arXiv:2609.24881v2 Announce Type: new Abstract: In high-stakes decision-making applications of large language models (LLMs), practitioners require not only accurate LLMs but also uncertainty estimates

PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning

arXiv:2606.18473v2 Announce Type: replace Abstract: Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. Howeve

SambaGraph: Action-Reaction Spatio-Temporal Graphs for Soccer Tactical Response Modeling

model-releasesarxiv-cs-lg

arXiv:2609.25569v1 Announce Type: new Abstract: Soccer tactics are interactive: an attacking action changes the opponent's defensive problem, and the observed response depends on the multi-agent match

Same Quantity, Different Answer: Numerical Representation Invariance in Language Models

arXiv:2609.25009v1 Announce Type: new Abstract: Numerically equivalent word problems should yield the same canonical answer whether a quantity is written as a decimal, fraction, percentage, number wor

SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue

arXiv:2609.26780v1 Announce Type: new Abstract: Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish

local-aiarxiv-cs-cl

StepTrigger: Contact-State-Triggered Backdoor Attacks on VLM-Powered Legged Robots

arXiv:2609.26131v1 Announce Type: new Abstract: Large language models and vision-language models are increasingly used as high-level planners in robotic systems, using task goals and sensor summaries

safetyarxiv-cs-ro

Applications

The Challenge of Identifying the Origin of Black-Box Large Language Models

arXiv:2503.04332v2 Announce Type: replace-cross Abstract: The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we f

applicationsarxiv-cs-lg