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arXiv:2607.06614v1 Announce Type: cross Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex

agentsarxiv-cs-ai

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Statistical inverse learning and ell^1-regularization

DGX agent

arXiv:2607.07468v1 Announce Type: cross Abstract: We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning. The unknown

model-releasesarxiv-cs-lg

9 Jul 2026

Strategies for Span Labeling with Large Language Models

arXiv:2601.16946v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for text analysis tasks, such as named entity recognition or error detection. Unlike encoder-base

researcharxiv-cs-cl

Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraints

model-releasesarxiv-cs-lg

arXiv:2607.07089v1 Announce Type: new Abstract: Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneou

STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning

arXiv:2607.06629v1 Announce Type: new Abstract: Brain age -- the age inferred from a physiological recording -- is an emerging biomarker whose deviation from chronological age tracks neurological and

Successor-Generator Planning with LLM-generated Heuristics

arXiv:2501.18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized

researcharxiv-cs-ai

Superstate Quantum Mechanics

arXiv:2502.00037v4 Announce Type: replace-cross Abstract: We introduce Superstate Quantum Mechanics (SQM), a theory that considers states in Hilbert space subject to multiple quadratic constraints, wi

SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation

arXiv:2607.07469v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, a

Applications

Synthetic Time Series Generation via Complex Networks

arXiv:2601.22879v2 Announce Type: replace Abstract: Time series data are essential for a wide range of applications, yet access to high-quality datasets is often constrained by privacy concerns, acqui

applicationsarxiv-cs-lg

T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs

arXiv:2511.16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training. Recent

safetyarxiv-cs-ai

T^{3}S: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series

model-releasesarxiv-cs-cv

arXiv:2506.12885v4 Announce Type: replace Abstract: Crop type classification from optical satellite time series remains limited in its ability to generalize across growing seasons, particularly when c

TACoS: Weakly Supervised Learning of Two-Dimensional Materials from Scribble Annotations to Precise Segmentation

arXiv:2607.07169v1 Announce Type: new Abstract: The precise pixel-level localization of 2D material flakes is crucial for high-throughput screening. However, traditional fully supervised methods rely

safetyarxiv-cs-cv

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling

arXiv:2607.06841v1 Announce Type: cross Abstract: Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approac

Applications

Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models

arXiv:2607.07008v1 Announce Type: cross Abstract: A common method for the representation and analysis of time-series data is the hidden Markov model (HMM), where each observation is associated with a

applicationsarxiv-cs-lg

TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2607.07388v1 Announce Type: new Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion

The Appeal and Reality of Recycling LoRAs with Adaptive Merging

model-releasesarxiv-cs-lg

arXiv:2602.12323v2 Announce Type: replace Abstract: The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs

The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression

arXiv:2607.06642v1 Announce Type: new Abstract: Active learning studies the fundamental question: what data should we choose to observe? The greedy algorithm in optimal experiment design is a common h

The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents

arXiv:2607.07436v1 Announce Type: new Abstract: A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the stru

safetyarxiv-cs-ai

The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI

arXiv:2607.06906v1 Announce Type: new Abstract: Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger rep

The Key to Going Linear: Analysis-Driven Transformer Linearization

model-releasesarxiv-cs-lg

arXiv:2607.07706v1 Announce Type: new Abstract: The quadratic cost of causal self-attention severely bottlenecks long-context transformer inference. While numerous post hoc linearization pipelines exi

The Optimal Sample Complexity of Learning Autoregressive Chain-of-Thought

arXiv:2607.07423v1 Announce Type: new Abstract: We prove that, in the realizable PAC setting, the sample complexity of exact-trace learning for full autoregressive Chain-of-Thought traces is upper bou

The Power of Backdoor Absorption in Community Training

arXiv:2607.06643v1 Announce Type: cross Abstract: Backdoor attacks severely threaten large-scale AI models. When model owners delegate training to external compute providers within a decentralized tra

safetyarxiv-cs-lg

The Rank-One Corner: How Much Value Equivalence Does a Task Need from a World Model?

arXiv:2607.06640v1 Announce Type: cross Abstract: A learned world model is usually judged by how faithfully it reconstructs its observations or predicts reward, as though quality were something the mo

researcharxiv-cs-ai

Tutorials

The Signs Were Always There: Training-Free Concept Detection and Steering in Raw Transformer Dimensions

arXiv:2606.12629v3 Announce Type: replace-cross Abstract: The standard basis of transformer hidden states is a training-free, architecture-general feature basis for detecting concepts and, in language

tutorialsarxiv-cs-ai

Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?

model-releasesarxiv-cs-cl

arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queri

Thinking Ahead: Foresight Intelligence in MLLMs and World Model

arXiv:2511.18735v3 Announce Type: replace-cross Abstract: In this work, we define Foresight Intelligence as the capability to anticipate and interpret future events-an ability essential for applicatio

Thinking Seeds: Leveraging Historical Diversity for Position-Aware RL in LLMs

arXiv:2601.21476v2 Announce Type: replace Abstract: On-policy reinforcement learning (RL) for language model post-training suffers from a fundamental tension: as training progresses, policy entropy co

safetyarxiv-cs-cl

TimEE: End-to-end Time Series Classification via In-Context Learning

arXiv:2607.07500v1 Announce Type: cross Abstract: Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pre

TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration

arXiv:2603.27742v2 Announce Type: replace Abstract: Vision-language agents that orchestrate specialized tools for image restoration (IR) have emerged as a promising method, yet most existing framework

safetyarxiv-cs-cv

Local Ai

TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation

arXiv:2607.07287v1 Announce Type: new Abstract: Dexterous manipulation in everyday environments requires both anticipation and reaction: a robot must predict how contact should evolve while rapidly co

local-aiarxiv-cs-ro

Toward Robust Open-set Adaptation: Synapse Consolidation Inspired by Rac1/MAPK Pathways

arXiv:2604.00533v2 Announce Type: replace Abstract: Large Language Models (LLMs) generalize across tasks through reusable representations and flexible reasoning, yet remain brittle in real deployment

Towards a Pseudo-Labeling Workflow for Celltype-Classification from Explanted Brain Slice Recordings

arXiv:2607.06569v1 Announce Type: cross Abstract: This paper proposes an unsupervised workflow to pseudo-label extracellular spikes from human brain slice MEA recordings into two putative cell types:

safetyarxiv-cs-lg

Hardware

Towards Accurate and Fast Clinical Body Composition: A Resource-Efficient Hierarchical Segmentation Framework for Multi-Source CT

arXiv:2607.07177v1 Announce Type: cross Abstract: Background: Automated 3D segmentation of muscles and adipose tissue from CT is vital for body composition analysis, but multi-source data heterogeneit

hardwarearxiv-cs-cv

Agents

Towards Agentic AI Governance: A Preliminary Assessment

arXiv:2607.07612v1 Announce Type: cross Abstract: Artificial intelligence is rapidly evolving from generative systems to agentic AI capable of autonomously planning and executing tasks. Widely charact

agentsarxiv-cs-ai

Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment

arXiv:2607.07350v1 Announce Type: new Abstract: Limited flight endurance significantly restricts the operational range of unmanned aerial vehicles (UAVs) in long duration missions such as surveillance

researcharxiv-cs-ro

Tutorials

Towards Understanding Steering Strength

arXiv:2602.02712v2 Announce Type: replace-cross Abstract: A popular approach to post-training control of large language models (LLMs) is the steering of intermediate latent representations. Namely, id

tutorialsarxiv-cs-cl

TRACE-Seg3D: Counterfactual Context Auditing For Robust 3D Glioma Segmentation Under Institutional Shift

model-releasesarxiv-cs-cv

arXiv:2607.07038v1 Announce Type: new Abstract: Medical image segmentation models can achieve strong benchmark performance while remaining sensitive to scanner, protocol, and institutional variation.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data

arXiv:2607.06583v1 Announce Type: cross Abstract: DNA methylation (DNAm) serves as one of the most robust molecular biomarkers of biological aging. While conventional epigenetic clocks accurately pred

safetyarxiv-cs-lg

Transformer-based segmentation of prosodic boundaries in Brazilian Portuguese

arXiv:2607.07408v1 Announce Type: new Abstract: Automatic prosodic segmentation identifies boundaries between speech units from acoustic and linguistic evidence. Although recent deep learning approach

researcharxiv-cs-cl

Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning

arXiv:2607.07117v1 Announce Type: cross Abstract: In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query

Hardware

Trees from Marginals: Autoregressive drafting with factorized priors

arXiv:2607.06763v1 Announce Type: cross Abstract: Speculative decoding greatly increases the interactivity of autoregressive language models by trading off computation for extra tokens generated in a

hardwarearxiv-cs-cl

Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes

arXiv:2507.10881v2 Announce Type: replace Abstract: Tubular tree structures, such as blood vessels and airways, are essential in human anatomy and accurately tracking them while preserving their topol

researcharxiv-cs-cv

Local Ai

TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation

arXiv:2607.06601v1 Announce Type: cross Abstract: Conditional computation can decouple language model quality from per-token inference cost, yet leading techniques act on a single axis in isolation: M

local-aiarxiv-cs-ai

Applications

tsbootstrap: Distribution-Free Uncertainty Quantification and Conformal Prediction for Time Series

arXiv:2607.06690v1 Announce Type: cross Abstract: Finance, sensing, and demand streams violate the exchangeability that IID conformal prediction and the IID bootstrap assume, and existing libraries im

applicationsarxiv-cs-ai

Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment

model-releasesarxiv-cs-cv

arXiv:2607.07438v1 Announce Type: new Abstract: Action Quality Assessment (AQA) aims to evaluate how well a person performs a movement, which is essential in applications such as sports scoring, skill

UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks

arXiv:2607.06638v1 Announce Type: new Abstract: Self-paced learning (SPL) is an effective learning paradigm that simulates the human learning process by progressing from easy to difficult samples base

Local Ai

Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities

arXiv:2607.07277v1 Announce Type: new Abstract: Harmful online communication often contains slang, coded terms, abbreviations, and community-specific expressions, which make messages difficult to inte

local-aiarxiv-cs-cl

Understanding Two-Layer Neural Networks with Smooth Activation Functions

arXiv:2507.14177v2 Announce Type: replace-cross Abstract: This paper aims to understand the training solution, which is obtained by the back-propagation algorithm, of two-layer neural networks whose h

researcharxiv-cs-ai