Search: “context”

AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs

arXiv:2610.16042v1 Announce Type: cross Abstract: The increasing use of artificial intelligence (AI) in mental health applications raises concerns about its potential to internalize and perpetuate soc

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Alignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria

DGX agent

arXiv:2511.17937v2 Announce Type: replace Abstract: Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in

model-releasesarxiv-cs-ai

15 Oct 2026

Asymptotic Behavior of Transformers

arXiv:2610.19358v1 Announce Type: new Abstract: In this paper we study the behavior of transformer networks generating text over long periods of time. We show that even a three-token, single-layer att

researcharxiv-cs-lg

Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models

arXiv:2610.20157v1 Announce Type: new Abstract: World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level ins

researcharxiv-cs-ro

AuthGateBench: Benchmarking LLMs as Authorization Gate

arXiv:2610.16426v1 Announce Type: new Abstract: Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a classi

Bayesian Learning to Select Fair Classifiers

arXiv:2610.20514v1 Announce Type: new Abstract: Evaluations of classifiers (AI or human or agents) often assume a batch setting in which every agent produces labels on every example in a test set, whi

Benchmarking Quantum Kernel SVMs for Credit Card Fraud Detection: Performance Realities Under NISQ Constraints

arXiv:2610.17027v1 Announce Type: new Abstract: Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning (

Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents

arXiv:2610.18052v1 Announce Type: new Abstract: Long-term memory has become a core component of large language model (LLM) agents. As agents accumulate personal information over long-term use, users i

ChemArgus: Automated Scientific Diagnostics for Lab-Grade Multimodal Chemistry Reasoning: A Frontier Benchmark

arXiv:2610.20012v1 Announce Type: new Abstract: Chemistry evaluation has moved past multiple-choice probes toward open-ended expert problems, yet the benchmarks that define the current frontier still

Claim Framing as a Driver of LLM Sycophancy

arXiv:2610.18159v1 Announce Type: cross Abstract: Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using questions

researcharxiv-cs-ai

Collective Learning for Unified Robotic Manipulation via Predictive Dynamics Adaptation

arXiv:2610.19141v1 Announce Type: new Abstract: Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodimen

safetyarxiv-cs-ro

CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting

arXiv:2610.19831v1 Announce Type: new Abstract: Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging. Prevai

CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation

arXiv:2610.14763v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must

safetyarxiv-cs-ai

Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents

arXiv:2606.18947v2 Announce Type: replace Abstract: Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injec

safetyarxiv-cs-ai

Local Ai

Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing

arXiv:2610.17831v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for

local-aiarxiv-cs-ai

Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility

arXiv:2606.15251v2 Announce Type: replace-cross Abstract: Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential

DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression

arXiv:2610.19206v1 Announce Type: new Abstract: While work on LLM-based regression has largely focused on scalar prediction, multi-output text regression remains less explored. We present DualView, a

Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression

arXiv:2610.20108v1 Announce Type: new Abstract: Neural symbolic regression (SR) models require diverse datasets of mathematical expressions for robust training. However, existing datasets rely predomi

FlowWise: Stateful Fast-Slow Model Serving for Streaming Traffic Intelligence

arXiv:2402.03694v3 Announce Type: replace-cross Abstract: Network traffic analysis in cloud and internet service provider (ISP) networks increasingly relies on machine learning (ML) models, as encrypt

Framework for an AI-Ready ARPES Community Database

arXiv:2610.15236v1 Announce Type: cross Abstract: Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization i

researcharxiv-cs-ai

From Brewing to Resolution: Tracing the Internal Lifecycle of Code Reasoning in LLMs

arXiv:2606.17648v3 Announce Type: replace Abstract: Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle

Applications

From Chasing Ghosts to Missed Attacks: Perspectives and Perceptions of SOC Practitioners on LLM Integration, Risks, and Readiness

arXiv:2608.00672v2 Announce Type: replace-cross Abstract: Security Operations Centers (SOCs) process large volumes of security events, requiring analysts to accurately detect and assess ongoing cybera

applicationsarxiv-cs-ai

From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort

arXiv:2610.20396v1 Announce Type: new Abstract: When does a human correction become useful learning, and what establishes better segmentation at matched human effort? This critical narrative review fo

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship

arXiv:2506.23101v2 Announce Type: replace-cross Abstract: Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially

Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records

arXiv:2610.20502v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery,

researcharxiv-cs-lg

Graph-free Retrieval with Graph-Modified Embeddings

arXiv:2610.18410v1 Announce Type: cross Abstract: Dense vector retrieval can miss evidence connected through intermediate entities, while direct graph retrieval requires access to the graph at query t

HGRP: Heterogeneous Graph Neural Network for Routability Prediction

arXiv:2610.20209v1 Announce Type: new Abstract: Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement opti

researcharxiv-cs-lg

Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play

arXiv:2610.15449v2 Announce Type: new Abstract: Despite significant advances in language understanding, Large Language Models (LLMs) can still miss meaning hidden in plain sight, failing to identify h

safetyarxiv-cs-ai

Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling

arXiv:2610.20497v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual r

How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking

arXiv:2610.19185v1 Announce Type: new Abstract: Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them.

Imaging-anchored Multiomics in Cardiovascular Disease: Integrating Cardiac Imaging, Bulk, Single-cell, and Spatial Transcriptomics

arXiv:2601.07871v2 Announce Type: replace-cross Abstract: Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed c

safetyarxiv-cs-cv

In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B

arXiv:2610.15373v1 Announce Type: cross Abstract: We adapted Nemotron-3-Nano-30B-A3B for eight Indian languages without changing its backbone architecture. We expanded the vocabulary in place from 131

Into the Unknown: Benchmarking AI agents on open scientific research problems

arXiv:2610.19159v1 Announce Type: cross Abstract: Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper a

KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms?

arXiv:2610.19399v1 Announce Type: new Abstract: Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization. To enable systematic evaluation, we

Agents

Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer

arXiv:2610.16140v1 Announce Type: new Abstract: Transportation planning has traditionally required substantial expertise in network modeling, traffic assignment, and optimization. As a result, strateg

agentsarxiv-cs-ai

Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers

arXiv:2610.19768v1 Announce Type: new Abstract: Looped transformers compute by applying one block repeatedly, so they can run for more loops than they were trained with. Recurrent reasoners are known

LLM Alignment--Utility Asymmetry under Semantic-Preserving Transformations

arXiv:2609.32717v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) alignment is intended to ensure that models remain helpful and safe, but its stability under input distributional s

Tutorials

LLMs program using Reusable and Composable Abstract Representations

arXiv:2610.19238v1 Announce Type: new Abstract: When writing code, the same computation can be expressed in different programming languages and through different implementation strategies. We show tha

tutorialsarxiv-cs-lg

Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading

arXiv:2607.08964v3 Announce Type: replace Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple

Agents

LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement

arXiv:2610.14777v1 Announce Type: new Abstract: LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data. However, existing a

agentsarxiv-cs-ai

MedHarnessBench: A Unified Benchmark for Healthcare Agent Evaluation

arXiv:2610.15714v1 Announce Type: new Abstract: Healthcare agents must transform longitudinal clinical evidence into reliable actions through heterogeneous tools and stateful workflows, conditions und

Local Ai

MobiGuide: Vocabulary-Guided Online Recovery for Mobile Manipulation

arXiv:2604.01567v2 Announce Type: replace Abstract: A central challenge in mobile manipulation is preserving multiple plausible action modes while remaining reactive during execution. For example, gra

local-aiarxiv-cs-ro

MoMaTune: Model, Match, Then Tune Language Models for Numerical Records

arXiv:2610.15235v1 Announce Type: new Abstract: Despite remarkable progress in language understanding and generation, decoder language models continue to exhibit striking failures on numerical data. N

researcharxiv-cs-ai

Agents

Narrowing the Collaboration Gap, Probably

arXiv:2610.19215v1 Announce Type: new Abstract: Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents c

agentsarxiv-cs-lg

Tutorials

NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection

arXiv:2610.20349v1 Announce Type: new Abstract: Asynchronous EEG event detection supports practical brain-computer interfaces by identifying event classes and temporal boundaries directly from continu

tutorialsarxiv-cs-lg

One-shot Human Video-Guided Adaptation for Vision-Language-Action Models

arXiv:2610.19746v1 Announce Type: new Abstract: Vision-language-action (VLA) models have recently achieved remarkable progress by leveraging large-scale robot datasets and foundation models for genera

safetyarxiv-cs-ro

OneLA: Learning Many Worlds from Latent Actions

arXiv:2610.20160v1 Announce Type: new Abstract: Learning world models across diverse domains calls for a unified control space, yet ac- tion interfaces vary across domains and action annotations are o

safetyarxiv-cs-cv

Local Ai

PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration

arXiv:2610.14855v1 Announce Type: new Abstract: The growing deployment of compact large language models on smartphones is enabling autonomous on-device decision-making, such as emerging mobile GUI age

local-aiarxiv-cs-ai