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Stress-Testing LLM Lie Detectors: Role-Play Failures and Spurious Correlations

arXiv:2609.39807v1 Announce Type: cross Abstract: Lie detection probes aim to predict from a language model's internal states whether its output is truthful or dishonest. However, role-play complicate

safetyarxiv-cs-ai

Strike a Chord! Modal Kinetic Typography

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DGX agent

arXiv:2609.38325v1 Announce Type: new Abstract: We introduce modal kinetic typography, which animates a vector glyph to express a semantic concept while keeping it legible. Our key idea is to build mo

safetyarxiv-cs-cv

1 Oct 2026

Strong Multilingual Privacy Tagging at Encoder Speed

arXiv:2609.38630v1 Announce Type: new Abstract: Privacy redaction must remove personal information while preserving relationships expressed in text. We develop a multilingual named-entity tagger with

local-aiarxiv-cs-cl

Structural Limits of the Information-Theoretic Uncertainty Decomposition

arXiv:2609.39591v1 Announce Type: new Abstract: Uncertainty estimation in machine learning typically decomposes uncertainty into aleatoric uncertainty (AU) and epistemic uncertainty (EU) using the sta

researcharxiv-cs-cv

Structure-aware Reinforcement Learning for Protein Directed Evolution

arXiv:2609.39048v1 Announce Type: cross Abstract: Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely

Structure of Basic Human Values in Russian Social Media

arXiv:2603.18822v2 Announce Type: replace Abstract: What is known about the values of national populations rests almost entirely on questionnaires, which prompt respondents to rate researcher-supplied

Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity

arXiv:2609.39049v1 Announce Type: cross Abstract: A large language model (LLM) can rate depression severity directly from a social media post or mark which clinical criteria the post shows and let cod

Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operators

model-releasesarxiv-cs-cv

arXiv:2609.39926v1 Announce Type: new Abstract: Achieving cross-sensor generalization and arbitrary-scale reconstruction with a single model remains challenging in hyperspectral super-resolution (HSR)

Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models

arXiv:2512.00729v2 Announce Type: replace Abstract: Motivated by the observed human-like behaviours in Large Reasoning Models (LRMs), this paper introduces a comprehensive taxonomy to characterise ato

Agents

Survival is the Only Reward: Sustainable Self-Training Through Environment-Mediated Selection

arXiv:2601.12310v2 Announce Type: replace Abstract: Self-training systems often degenerate due to the lack of an external criterion for judging data quality, leading to reward hacking and semantic dri

agentsarxiv-cs-ai

Switching Linear Attention

arXiv:2609.39034v1 Announce Type: cross Abstract: Designing expressive sequence layers with efficient inference remains a central challenge in modern machine learning. Standard softmax attention achie

Tutorials

Synchronous Multi-view Neural Diffusion

arXiv:2609.39019v1 Announce Type: new Abstract: Multi-view learning seeks to learn more comprehensive representations by exploiting the complementarity and consistency across diverse modalities or vie

tutorialsarxiv-cs-lg

SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets

arXiv:2609.38225v1 Announce Type: cross Abstract: Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when d

Agents

Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data

arXiv:2609.38414v1 Announce Type: new Abstract: Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despit

agentsarxiv-cs-lg

Synthetic Data Characterization via Training Dynamics

arXiv:2609.39447v1 Announce Type: new Abstract: Interpreting properties of LLM-generated data is important for understanding its utility and limitations across learning tasks. In this work, we charact

Synthetic Pre-pretraining Survives Scale, but Not as a Grammatical Prior

arXiv:2609.39827v1 Announce Type: cross Abstract: Pre-pretraining (PPT) on synthetic non-natural language data improves token efficiency during language model pre-training (PT). Prior work attributes

Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies

arXiv:2609.38537v1 Announce Type: new Abstract: GPT-6 Astra exhibits a remarkable ability to generate numerical robot actions, extending its role beyond high-level planning. To assess Astra's capabili

local-aiarxiv-cs-ro

T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models

model-releasesarxiv-cs-lg

arXiv:2609.39466v1 Announce Type: new Abstract: In this work, we introduce a new clustering method, namely T-ARC (Topology-Aware Randomized Clustering), that corrects the geometric bias of K-means by

T-Router: Learning Thalamic Routing for Reasoning with Parameter-Efficient Reinforcement Learning

arXiv:2609.39109v1 Announce Type: cross Abstract: Parameter-efficient reinforcement learning aims to improve reasoning with a compact trainable interface to a pretrained model. We introduce the Thalam

Tutorials

TACIT: Optimization Models that Learn from Their Mistakes

arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,

tutorialsarxiv-cs-ai

Tacit-TTS: From Autoregressive Decoding to Masked Prediction for Efficient Transcript-Free Voice Cloning

arXiv:2609.38658v1 Announce Type: cross Abstract: TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but thei

TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks

arXiv:2609.39969v1 Announce Type: cross Abstract: Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding tra

local-aiarxiv-cs-ai

Tactile Curiosity Drives Robot Interaction

arXiv:2609.40134v1 Announce Type: cross Abstract: Mastering robot manipulation skills via reinforcement learning (RL) remains largely sample-inefficient. The most common RL algorithms rely on random a

Agents

TAGGRAPH: Tag-Augmented Graphs for Graph Retrieval of Agent Persistent Histories

arXiv:2609.38353v1 Announce Type: cross Abstract: Long-term memory lets LLM agents recall past interactions and remain consistent across sessions, but memory systems are hard to compare because they o

agentsarxiv-cs-ai

Applications

Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphs

arXiv:2602.06700v2 Announce Type: replace-cross Abstract: Graph-structured data underpin a wide spectrum of modern applications, yet their multiple sensitive attributes are not isolated but deeply cou

applicationsarxiv-cs-lg

TALK-Dem: Benchmarking Embodied Task Planning under Dementia-Associated Communication Patterns

arXiv:2609.38371v1 Announce Type: cross Abstract: Existing LLM-driven robot task planners rely on a taken-for-granted assumption of an ideal user whose instructions are clear, complete, and task-focus

Talk2Agent: Benchmarking Voice Interfaces for Text Agents

arXiv:2609.38867v1 Announce Type: new Abstract: Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular int

Taming Speculative Search for Test-Time Scaling in LLM Serving

arXiv:2609.39334v1 Announce Type: cross Abstract: Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, subst

Targeted Retrieval, Compact Representations: How CoT Reasoning Improves Long-Context Counting

arXiv:2609.38958v1 Announce Type: new Abstract: Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mecha

Team MSU GenText-Forensics Challenge 2026 Technical Report

arXiv:2609.38391v1 Announce Type: new Abstract: Document text forgery has evolved beyond simple pixel-level manipulation: modern attacks alter not only the appearance of a document but also its meanin

local-aiarxiv-cs-cv

TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization

arXiv:2609.39033v1 Announce Type: cross Abstract: CLIP is a powerful vision-language model, but it was not designed for fine-grained defect localization; CLIP-based anomaly detectors therefore adapt i

Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking

arXiv:2609.38637v1 Announce Type: cross Abstract: Visual object tracking typically assumes that the initial template and subsequent search frames share the same sensing modality. In practice, sensor a

local-aiarxiv-cs-ai

TERRA: Terrain-Aware Reconstruction, Retargeting and Control for Musculoskeletal Locomotion

arXiv:2609.38653v1 Announce Type: cross Abstract: Recent advances in musculoskeletal modeling and reinforcement learning have enabled muscle-actuated agents to reproduce increasingly complex human mot

safetyarxiv-cs-ai

Text-to-3D Policy: Fine-Grained Language-Behavior Alignment for Unseen Specification Generalization

arXiv:2609.39599v1 Announce Type: cross Abstract: 3D visuomotor policies provide a strong foundation for spatially precise manipulation, yet current text-to-3D policies struggle to follow unseen fine-

safetyarxiv-cs-ai

TexTailor: Texture-Preserving Video Virtual Try-On via Adaptive Garment Conditioning

arXiv:2609.39335v1 Announce Type: new Abstract: Video virtual try-on has attracted increasing attention due to its broad potential in digital fashion and intelligent e-commerce. However, existing meth

researcharxiv-cs-cv

The Advantages of Fresh Sketching for Ridge Regression

arXiv:2609.38565v1 Announce Type: new Abstract: Over the past 25 years, sketching and sampling have become widely used tools for accelerating large-scale regression. In iterative randomized solvers, a

researcharxiv-cs-lg

The Backdrop Exposes What the World Around an Agent Costs It

arXiv:2609.38469v1 Announce Type: cross Abstract: Agent benchmarks test agents in worlds that stay still. Deployed agents work in worlds that other people also change. Someone texts the agent to send

The Concrete-Arbitrary Gap: Kinship Reasoning in LLMs Is Not Indifferent to Presentation

arXiv:2609.39913v1 Announce Type: new Abstract: We test whether large language models solve formally matched kinship problems equally well when relations are expressed in familiar vocabulary or by exp

The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends

arXiv:2609.39661v1 Announce Type: new Abstract: Self-attention gives LLMs fine-grained, query-dependent access to context, but dense token interactions incur quadratic prefill cost and a key--value ca

The Geometry of Harmfulness in Multi-Turn Attacks

arXiv:2609.38389v1 Announce Type: cross Abstract: Large language models (LLMs) remain vulnerable to adversarial attacks that circumvent safety alignment to elicit harmful outputs. It remains unclear h

The Geometry of Randomized Smoothing on Feasible Sets

model-releasesarxiv-cs-lg

arXiv:2609.39497v1 Announce Type: new Abstract: Randomized smoothing certifies the probability of a fixed output event as the center of Gaussian noise moves. Feasibility or confidence filtering report

The Golden Path Hypothesis: Reusable Schedules in Diffusion Caching

arXiv:2609.39343v1 Announce Type: new Abstract: Diffusion caching accelerates generation by replacing transformer computation with cached or predicted features at selected denoising steps. We introduc

The Invisible Language Tax: Token Premiums of French and Regional Languages in 2026 LLM Tokenizers, and a French-Optimized Prototype

arXiv:2609.39001v1 Announce Type: cross Abstract: LLM services are billed per token and context windows are measured in tokens, yet the number of tokens needed for the same content varies across langu

The Missing Coefficients: Bayesian Pairwise Merging for Model Personalization

arXiv:2609.39055v1 Announce Type: new Abstract: How can we personalize a shared expert library from a user's pairwise choices? Prior work can realize different reward trade-offs by merging reward-spec

safetyarxiv-cs-lg

The Nixtlaverse: An Open-Source Ecosystem for Forecasting

model-releasesarxiv-cs-lg

arXiv:2609.39741v1 Announce Type: new Abstract: Large forecasting applications often combine statistical, machine-learning, and neural models. These families solve the same problem but differ in fitte

The Planning Limits of Latent World Models

arXiv:2609.39235v1 Announce Type: cross Abstract: World models offer a promising way to help robots understand how the physical world evolves and plan complex behaviours through imagination. Yet exist

safetyarxiv-cs-cv

The Row Normalization Puzzle in Muon

arXiv:2609.39114v1 Announce Type: new Abstract: This paper examines how row-wise renormalization affects Muon, focusing on the gap between NorMuon's worst-case guarantees and its practical performance

researcharxiv-cs-lg

The System Prompt Illusion: How Instruction Preambles Modify Computation in Language Models

arXiv:2609.38205v1 Announce Type: new Abstract: System prompts are the primary lever practitioners use to control language model behavior, yet what they actually do to the computation inside the trans

safetyarxiv-cs-cl