All entries

TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy

arXiv:2610.13654v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are increasingly popular for their strong predictive performance. Many also provide posterior predictive distributions

Hardware

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tabular Foundation Models Are Effectively Shallow

DGX agent

arXiv:2610.13253v1 Announce Type: cross Abstract: In-context-learning Tabular Foundation Models (TFMs) are uniquely suited to scenarios with limited resources or privacy constraints, as they solve new

hardwarearxiv-cs-ai

12 Oct 2026

Tactile Sensing-based Secure and Gentle Grasping of Unknown Objects

arXiv:2610.12790v1 Announce Type: new Abstract: Reliable robotic manipulation requires maintaining a firm grasp while strictly avoiding both contact slip and structural damage to the object. Satisfyin

researcharxiv-cs-ro

Taking Tokenization off the Host for Agentic LLM Serving

arXiv:2610.13325v1 Announce Type: cross Abstract: Agentic workloads resend long contexts that barely change between calls. Prefix caching now skips most of their prefill, yet every request must still

TAME-Nav: Temporal Asymmetric Mixture-of-Experts Distillation for Agile and Versatile Robot Navigation

arXiv:2610.12877v1 Announce Type: new Abstract: Agile and reliable navigation is a core capability for embodied robots navigating complex spatial constraints and dynamic interactions. However, a singl

safetyarxiv-cs-ro

Target-Constrained Pooling for Trajectory Extrapolation

arXiv:2610.13034v1 Announce Type: new Abstract: Trajectory extrapolation from a partial target trajectory is underdetermined, but completed related trajectories provide information about the unresolve

Task Context Shapes Component Roles in Language Models

arXiv:2610.13895v1 Announce Type: cross Abstract: Large language models reuse the same attention heads across tasks, but identifying these shared heads does not explain what they do differently in eac

Teaching neural decoders with language

arXiv:2610.12921v1 Announce Type: new Abstract: Brain-computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as

Agents

TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability

arXiv:2606.19821v2 Announce Type: replace Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, exi

agentsarxiv-cs-ai

Temporal Evidence Forests for Long-Horizon Multi-Party Conversational Memory

arXiv:2610.12874v1 Announce Type: new Abstract: Long-term memory has been widely adopted in LLM-based agents to support persistent interaction and personalized assistance. Long-term multi-party intera

Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels

arXiv:2610.13221v1 Announce Type: new Abstract: Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets

TernaryLLM: Ternarized Large Language Model

arXiv:2406.07177v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computat

Test-Time Memory Construction

arXiv:2610.13348v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to answer questions over long interaction histories, where relevant evidence may be scattered across distant sessi

The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs

arXiv:2510.13154v3 Announce Type: replace Abstract: What happens inside a language model when alignment training conflicts with a cultural value it encodes? Across 16 MENA countries, 26 models, and 1.

safetyarxiv-cs-cl

The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs

arXiv:2610.12981v1 Announce Type: cross Abstract: Visual interventions provide a training-free way to reduce hallucinations in multimodal large language models. However, an intervention may have littl

The Expressive Power of Random-Feature MPNNs Across Graph Sizes

arXiv:2610.12809v1 Announce Type: new Abstract: Continuous random node features increase the expressivity of message-passing neural networks (MPNNs), but existing theoretical analyses are not fine-gra

Tutorials

The Limits of Query Monitoring against Distributed Model Extraction

arXiv:2610.13083v1 Announce Type: cross Abstract: Model providers now face distillation campaigns run through thousands of accounts, each of which looks like an ordinary user. When network and payment

tutorialsarxiv-cs-lg

The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging

arXiv:2610.13237v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) learn by predicting teacher representations of masked regions from visible context. Point prediction

safetyarxiv-cs-lg

Agents

The value of information and the emergence of communication in evolved e-puck robots

arXiv:2610.13118v1 Announce Type: new Abstract: We study when communication emerges between autonomous agents and what role an innate action selection architecture plays. Two e-puck robots, reconstruc

agentsarxiv-cs-ro

Time Delay System for Sequence Modeling

arXiv:2610.12864v1 Announce Type: new Abstract: Existing sequence models share a critical limitation: they lack an explicit, interpretable mechanism for content-aware temporal recall. LSTMs compress e

Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion

arXiv:2610.12697v1 Announce Type: cross Abstract: This paper studies an open network design problem of constructing graph topologies with maximum algebraic connectivity under fixed numbers of vertices

Towards a Measure Theory of Semantic Information

arXiv:2508.00525v2 Announce Type: replace-cross Abstract: A classic account of the quantification of semantic information is that of Bar-Hiller and Carnap. Their account proposes an inverse relation b

Towards a Unified Taxonomy of Agricultural Harvesting Technologies: Linking Physical Interaction, Automation, and Selectivity

arXiv:2610.13688v1 Announce Type: new Abstract: Harvesting technologies have evolved from conventional mechanized systems based on bulk mechanical interaction toward increasingly automated and robotic

researcharxiv-cs-ro

Towards Defining, Measuring, and Leveraging Creativity in AI Applications

arXiv:2610.12845v1 Announce Type: new Abstract: Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal defini

Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf

arXiv:2610.13036v1 Announce Type: new Abstract: Evaluating deep learning optimizer is challenging because confounding factors such as hyperparameter tuning and optimizer-model co-design often obscure

Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities

arXiv:2610.13456v1 Announce Type: new Abstract: Cyber-Physical Systems are developed through multiple model based activities, comprising several stages of development. As the development progresses th

Towards Safer Cities: A Design Process for Perception Datasets in Urban Driving Scenarios

arXiv:2610.13077v1 Announce Type: new Abstract: Urban environments present significant challenges for the perception systems of automated driving due to their high complexity, characterized by dense t

researcharxiv-cs-ro

Towards the Roof of the World: NLP Progress in Tibetan Languages

arXiv:2610.13398v1 Announce Type: new Abstract: Tibetan, spoken by some seven million people on and around the Qinghai-Xizang Plateau, remains underrepresented in natural language processing (NLP). We

Towards Understanding the Training of GFlowNets: A Theoretical Perspective

arXiv:2610.13711v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn policies that sample discrete objects in proportion to a positive reward. They are trained by minimizing bala

safetyarxiv-cs-lg

Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

arXiv:2610.13810v1 Announce Type: cross Abstract: Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate. They inject non existent entities and off-topic

safetyarxiv-cs-cl

Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion

arXiv:2610.13872v1 Announce Type: cross Abstract: Accurate detection of drivable area is essential for autonomous driving and advanced driver assistance systems. While state-of-the-art methods focus o

safetyarxiv-cs-ro

Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation

arXiv:2610.12806v1 Announce Type: cross Abstract: We study byte-level autoregressive language models whose hidden state is a small quantum register, whose transitions are learned token-conditioned qua

Training User Simulators with Latent Intrinsic Feedback

arXiv:2610.13617v1 Announce Type: new Abstract: User simulators are gaining attention as environments to train LLM assistants that can better cope with the nuances of multi-turn conversations with rea

Local Ai

Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL

arXiv:2610.13020v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. Yet long-horizon autoregressive rollouts introduc

local-aiarxiv-cs-lg

Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data

arXiv:2610.12747v1 Announce Type: cross Abstract: Ego-trajectory prediction in urban roadway work zones remains an underexplored topic in autonomous-driving research, with limited dedicated data and s

Transformer-based In-Context Score Operator Learning for Diffusion Models

arXiv:2610.13690v1 Announce Type: new Abstract: Diffusion models typically learn the generative dynamics of a single target distribution or a family of distributions indexed by conditioning variables

Tutorials

Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure

arXiv:2610.12544v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate discrete data by iteratively unmasking multiple tokens in parallel using a learned mask predictor. Their output

tutorialsarxiv-cs-lg

Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents

arXiv:2610.13058v1 Announce Type: new Abstract: Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dea

TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.12828v1 Announce Type: new Abstract: Outcome-supervised group-relative policy optimization (GRPO) assigns the same advantage to every token in an LLM agent trajectory, leaving useful and un

TTS-Q: Perception-Guided Post-Training Quantization of Text-to-Speech Diffusion Transformers

arXiv:2610.12474v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) can clone a voice from seconds of audio. Yet the quality comes from billion-parameter diffusion transformers whose size

Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes

arXiv:2610.12850v1 Announce Type: cross Abstract: Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows. We consider learning

U4RASD at KnowledgeGraphEval-2026: Ontology-Constrained Encoder and LLM Systems for Arabic Relation Extraction

arXiv:2610.12742v1 Announce Type: new Abstract: This paper details our submission to KnowledgeGraphEval 2026 Subtask 2 on directed Arabic relation extraction. We explored encoder-based and large langu

UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes

arXiv:2610.13783v1 Announce Type: new Abstract: Memes play a central role in modern online communication, especially in contexts of political conflict where visual and textual cues combine to convey p

Understanding Mamba-3 Through Oscillatory Dynamics

arXiv:2610.13341v1 Announce Type: new Abstract: Mamba-3 introduces input-dependent rotations into recurrent state updates, but their contribution beyond state retention remains unclear. We interpret t

Understanding Transformer-based Vision Models through Inversion

arXiv:2412.06534v5 Announce Type: replace-cross Abstract: Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promisi

UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning

arXiv:2506.02955v3 Announce Type: replace-cross Abstract: Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation

safetyarxiv-cs-ai

Universal Bounds for Self-Normalized Kullback-Leibler Estimation

arXiv:2610.12851v1 Announce Type: cross Abstract: This work studies an entropy-based estimator of Kullback-Leibler (KL) divergence constructed from normalized importance weights. We prove a universal

safetyarxiv-cs-lg

Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News

arXiv:2610.12863v1 Announce Type: new Abstract: Satirical news is a unique form of misinformation: it is intended to be humorous by the original publication source but can be misinterpreted. Explanati