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Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models

arXiv:2610.08420v1 Announce Type: new Abstract: We establish a polynomial sample complexity separation between symmetry-aware and symmetry-agnostic feature learning. We study growing-rank multi-index

researcharxiv-cs-lg

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Symphony for Text Generation: Benchmarking Clinical Note Generation

DGX agent

arXiv:2610.08161v1 Announce Type: cross Abstract: Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized. We introduce MedCo

model-releasesarxiv-cs-ai

7 Oct 2026

Systemization of Knowledge (SoK): Human-Centered AI Safety for Youth

arXiv:2610.08554v1 Announce Type: cross Abstract: While HCI increasingly examines AI-safety for youth, the literature lacks a comprehensive view of what risks have been identified, how they are addres

safetyarxiv-cs-ai

Hardware

T-CCL: Resource Efficient and Performant Collective Communication using Tensor Memory Accelerator

arXiv:2610.07098v1 Announce Type: cross Abstract: Large transformer-based models increasingly depend on multi-GPU execution, which requires frequent collective communication among GPUs. Existing commu

hardwarearxiv-cs-ai

TabiBERT: A Large-Scale ModernBERT Foundation Model and A Unified Benchmark for Turkish

model-releasesarxiv-cs-cl

arXiv:2512.23065v4 Announce Type: replace Abstract: The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models

Applications

TacZero: Training-Free Peg Insertion Using a General-Purpose Vision-Language Model with Tactile Feedback

arXiv:2610.07621v1 Announce Type: new Abstract: Robots that autonomously determine their actions from language instructions and sensory observations could perform new contact-rich manipulation tasks w

applicationsarxiv-cs-ro

TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity

model-releasesarxiv-cs-lg

arXiv:2610.07559v1 Announce Type: new Abstract: Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with

TARE: Weigh a Never-Poisoned Twin Before Reading Backdoor-Defense Costs

arXiv:2610.06994v1 Announce Type: cross Abstract: Backdoor-defense leaderboards print a clean-accuracy drop and read it as removal cost. Measured on the poisoned victim alone, the drop cannot separate

Targeted search shows that random-device testing underestimates worst-case error in a simulated wave-based neural operator

arXiv:2610.07529v1 Announce Type: new Abstract: Wave-based processors promise fast, energy-efficient Fourier layers for neural operators. They are usually validated on randomly sampled devices, but us

Task-Space Imitation Guidance for Efficient Reinforcement Learning

arXiv:2610.07527v1 Announce Type: new Abstract: We introduce Task-Space Imitation Guidance for Efficient Reinforcement Learning (TIGER), a reward-construction and pretraining framework for sparse-rewa

safetyarxiv-cs-ro

TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Experts

arXiv:2610.06824v2 Announce Type: replace Abstract: We introduce TasteVal, a benchmark to evaluate the experimental research taste of frontier models. We define research taste as the ability to pick i

TeleHairing: A Teleoperation Baseline for Robotic Haircutting

arXiv:2610.07096v1 Announce Type: new Abstract: Robotic haircutting requires controlled tool motion near the head while simultaneously accounting for communication, visual feedback, tool actuation, an

safetyarxiv-cs-ro

TeleTune: Evolving Agent Skills From Offline Telemetry

arXiv:2610.05437v2 Announce Type: replace Abstract: Computer-use agents need to capture procedural knowledge of how people use software. User telemetry offers a scalable source of this knowledge. Howe

TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning

arXiv:2610.06855v1 Announce Type: cross Abstract: Scalable driver identification requires embedding models that maintain discriminative performance as fleet size grows, yet existing triplet-loss formu

researcharxiv-cs-cv

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

arXiv:2610.08358v1 Announce Type: cross Abstract: Post-training quantization is a standard route to fitting vision transformers (ViTs) into edge compute and memory budgets, yet quantized models become

Test-Time Agent Evolution for Long-Horizon Legal Reasoning

arXiv:2610.08138v1 Announce Type: new Abstract: Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. Howev

Text2Dashboard: A Governed Agent Architecture for Natural-Language Dashboard Generation over Enterprise DataBrain

arXiv:2610.06914v1 Announce Type: new Abstract: Text2Dashboard is a DataBrain-specific prototype that turns natural-language analytic requests into inspectable dashboards. An installable Codex plugin

Local Ai

Textual Environmental Context and Spatial Graphs for LLM-Based Regional SST Forecasting

arXiv:2610.07895v1 Announce Type: new Abstract: Sea surface temperature (SST) forecasting depends on local temporal persistence, regional spatial dependence, and environmental conditions that evolve w

local-aiarxiv-cs-ai

TF-PRVR: Training-Free Partially Relevant Video Retrieval

arXiv:2610.07925v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos containing moments relevant to a given text query. Despite recent progress,

safetyarxiv-cs-cv

The Failure Is in the Readout: Fine-Grained Emotion Recognition Benchmarks Measure Elicitation, Not Perception

model-releasesarxiv-cs-cl

arXiv:2610.08162v1 Announce Type: cross Abstract: Fine-grained emotion recognition supports therapy tools and social robots, but it needs facial data, which raises privacy and data-protection concerns

The Geometry of Empowerment

arXiv:2610.07796v1 Announce Type: cross Abstract: Empowerment captures the capacity for an agent to actively control its environment. While conceptually appealing as an information-theoretic quantity,

The Geometry of Statistical Feature Learning in Mean-Field Langevin Dynamics

model-releasesarxiv-cs-lg

arXiv:2606.31429v2 Announce Type: cross Abstract: We introduce a geometric formulation of statistical feature learning for supervised regression. Feature learning is defined through a base--fiber deco

The Implicit Bias of Hyperbolic Representation Learning for Multiclass Data: A Busemann Risk Perspective

arXiv:2610.07131v1 Announce Type: new Abstract: We study the implicit bias of Riemannian gradient flow for hyperbolic multiclass classification with fixed class prototypes in hyperbolic space H^n. Our

The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation

model-releasesarxiv-cs-cl

arXiv:2610.08026v1 Announce Type: new Abstract: In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. These methods are often benchmarked wi

The Missing Minimal Pair: Stereotype Evaluation in LLMs

arXiv:2610.08747v1 Announce Type: new Abstract: A common approach to measuring bias in Large Language Models is to compare the log-likelihoods of two contrastive stereotype sentences. We argue that su

safetyarxiv-cs-cl

The Model Plants the Trigger: Answer-Side Backdoor Attacks in Multi-Turn Large Language Models

arXiv:2610.07723v1 Announce Type: cross Abstract: Safety alignment in Large Language Models (LLMs) remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric: activati

Tutorials

The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptation

arXiv:2610.07038v1 Announce Type: new Abstract: Modern forecasting models are often updated after deployment so they can respond to changing data. These updates can also make predictions worse, so pra

tutorialsarxiv-cs-lg

The Right Memory in the Wrong Context: Verifying Retrieval Admissibility in Long-Term Agent Memory

arXiv:2610.07309v1 Announce Type: new Abstract: Long-term-memory agents can retrieve relevant information that is inadmissible for the current request because it belongs to another principal, violates

The Robot Is Not Its Description: GaugeBench for Representation Robustness in Morphology-Aware Policies

arXiv:2610.07597v1 Announce Type: cross Abstract: A robot description does more than specify a physical mechanism: it also encodes arbitrary conventions, such as joint-axis direction, joint-angle zero

The Standardization Trap: Certifying Joint Label Processing in Tabular Foundation Models

arXiv:2610.08314v1 Announce Type: new Abstract: Linear regression and kernel smoothing offer tractable explanations of in-context learning: in both, the features determine the weight assigned to each

researcharxiv-cs-ai

Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Facts

arXiv:2610.07798v1 Announce Type: new Abstract: People increasingly ask large language models what to do with their money, yet seldom describe their finances in full. This paper asks what a model does

Local Ai

ThinkFuse: Trajectory-Aware Test-Time Fusion for Small Reasoning Models

arXiv:2610.07803v1 Announce Type: new Abstract: Small reasoning models (SRMs) have shown strong performance on complex reasoning tasks by generating extended chain-of-thought trajectories, but they of

local-aiarxiv-cs-ai

TICDA: Tabular In-Context Data Attribution

arXiv:2610.07996v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) achieve strong predictive performance by conditioning on labeled demonstrations provided in context, without any para

TIDE 2.0: an open, model-agnostic engine for keyed de-identification of clinical notes

arXiv:2610.07224v1 Announce Type: cross Abstract: Clinical notes capture most of what is documented about a patient's care, but they cannot be used for research until protected health information (PHI

researcharxiv-cs-ai

Time-o1: Time-Series Forecasting Needs Transformed Label Alignment

arXiv:2505.17847v3 Announce Type: replace-cross Abstract: Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared

safetyarxiv-cs-ai

Token-Efficient Multi-Agent Collaboration via System One-Guided Computational Division of Labor

arXiv:2610.08155v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) have become a promising paradigm for complex information-seeking and reasoning tasks by ena

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

arXiv:2607.17524v2 Announce Type: replace Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness predicti

safetyarxiv-cs-cl

Too Categorical to be Human: Emotion Concepts in LLMs and Humans

arXiv:2508.05880v3 Announce Type: replace-cross Abstract: Understanding human emotions is central to user-facing AI applications, safety alignment, and the simulation of human behavior. As emotional s

Tool-calling retrieval versus vector RAG for a small Greek--English knowledge base: accuracy and robustness to how users type Greek

arXiv:2610.08205v1 Announce Type: cross Abstract: Assistants grounded in a small, frequently edited knowledge base can retrieve through tool calls to a live data interface or through vector retrieval-

Local Ai

Topology-Consistent Task Planning over Cellular Workflow Complexes for LLM-based Agents

arXiv:2610.07004v1 Announce Type: new Abstract: Task planning for LLM agents requires workflows that satisfy both user intent and complex sub-task dependencies. While existing planners work well for s

local-aiarxiv-cs-ai

Toward Alignment Scaling Laws: A Framework and First Preregistered Measurements

arXiv:2610.08540v1 Announce Type: new Abstract: Whether alignment gets easier or harder as models grow is often argued from isolated findings, as if alignment were one property. We treat it as a famil

safetyarxiv-cs-ai

Toward Trustworthy Physical AI for Human Interaction

arXiv:2610.07382v1 Announce Type: new Abstract: Robots are entering human spaces faster than we can establish when they deserve trust. We propose a framework for trustworthy physical AI that integrate

safetyarxiv-cs-ro

Towards a Unified Misuse Monitoring Benchmark

arXiv:2610.07089v1 Announce Type: cross Abstract: LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful request

Towards an Extensible Benchmark for Spoken Dialogue with Social Robots

model-releasesarxiv-cs-ro

arXiv:2610.08733v1 Announce Type: new Abstract: Language models provide a plug-and-play interface between humans and robots, but important challenges remain when speech, dialogue, fast interaction, an

Towards benchmarking Western Bluebird detection in the wild

model-releasesarxiv-cs-cv

arXiv:2610.07802v1 Announce Type: new Abstract: Bird monitoring in natural environments is challenging due to the small size of some species of birds relative to the scene, background clutter, variabi

Towards Decentralized Formation of Minimum-Length Communication Networks Using Robot Swarms

arXiv:2610.07320v1 Announce Type: new Abstract: Multi-robot missions in infrastructure-denied environments frequently rely on reliable communication links between spatially separated locations. We pro

agentsarxiv-cs-ro

Towards Efficient Robotic Manipulation Models with Self-Recursive Pruning

model-releasesarxiv-cs-ro

arXiv:2610.08555v1 Announce Type: new Abstract: Network pruning can reduce parameter redundancy in robotic policies. However, generic pruning criteria are tailored for image recognition tasks and comm

Towards Explainable Benchmarking for Data-driven Post-Wildfire Debris Flow Prediction

model-releasesarxiv-cs-lg

arXiv:2610.07358v1 Announce Type: new Abstract: Post-wildfire debris flows (PFDFs) are destructive sediment-laden hazards triggered when intense rainfall strikes recently burned terrain, destabilizing