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arXiv:2503.14998v3 Announce Type: replace Abstract: Contrastive learning methods in computer vision typically rely on augmented views of the same image or multimodal pretraining strategies that align

tutorialsarxiv-cs-cv

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Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

DGX agent

arXiv:2605.07639v1 Announce Type: new Abstract: Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This c

applicationsarxiv-cs-ai

11 May 2026

TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation

model-releasesarxiv-cs-ro

arXiv:2510.04839v2 Announce Type: replace Abstract: Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environme

TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP

model-releasesarxiv-cs-cl

arXiv:2605.06886v1 Announce Type: new Abstract: This work introduces TajPersLexon, a curated Tajik--Persian parallel lexical resource of 40,112 word and short-phrase pairs for cross-script lexical ret

TAP: Two-Stage Adaptive Personalization of Multi-Task and Multi-Modal Foundation Models in Federated Learning

arXiv:2509.26524v3 Announce Type: replace-cross Abstract: In federated learning (FL), local personalization of models has received significant attention, yet personalized fine-tuning of foundation mod

local-aiarxiv-cs-ai

Target-Aware Data Augmentation for SAT Prediction

arXiv:2605.06931v1 Announce Type: new Abstract: Learning-based approaches to NP-hard problems have shown increasing promise, but their progress is fundamentally constrained by the high cost of generat

TAS-LoRA: Transformer Architecture Search with Mixture-of-LoRA Experts

model-releasesarxiv-cs-cv

arXiv:2605.07256v1 Announce Type: new Abstract: Transformer architecture search (TAS) discovers optimal vision transformer (ViT) architectures automatically, reducing human effort to manually design V

Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference

arXiv:2605.07354v1 Announce Type: cross Abstract: The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditiona

researcharxiv-cs-cv

Tutorials

Task Relevance Is Not Local Replaceability: A Two-Axis View of Channel Information

arXiv:2605.07086v1 Announce Type: new Abstract: Channel importance in vision networks is usually summarized by a single score. That summary hides two different questions: how much a channel is related

tutorialsarxiv-cs-cv

TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning

arXiv:2605.07943v1 Announce Type: cross Abstract: Active vision -- where a policy controls its own gaze during manipulation -- has emerged as a key capability for imitation learning, with multiple ind

TCMIIES: A Browser-Based LLM-Powered Intelligent Information Extraction System for Academic Literature

arXiv:2605.07507v1 Announce Type: new Abstract: The exponential growth of academic publications has created an urgent need for automated tools capable of extracting structured knowledge from unstructu

local-aiarxiv-cs-cl

TEA-Bench: A Systematic Benchmarking of Tool-enhanced Emotional Support Dialogue Agent

arXiv:2601.18700v2 Announce Type: replace Abstract: Emotional Support Conversation requires not only affective expression but also grounded instrumental support to provide trustworthy guidance. Howeve

Teacher-Feature Drifting: One-Step Diffusion Distillation with Pretrained Diffusion Representations

arXiv:2605.07327v1 Announce Type: new Abstract: Sampling from pretrained diffusion and flow-matching models typically requires many forward passes to generate diverse and high-fidelity images. Existin

researcharxiv-cs-cv

Teaching Language Models to Think in Code

model-releasesarxiv-cs-cl

arXiv:2605.07237v1 Announce Type: new Abstract: Tool-integrated reasoning (TIR) has emerged as a dominant paradigm for mathematical problem solving in language models, combining natural language (NL)

Teaching Prompts to Coordinate: Hierarchical Layer-Grouped Prompt Tuning for Continual Learning

arXiv:2511.12090v3 Announce Type: replace Abstract: Prompt-based continual learning methods fine-tune only a small set of additional learnable parameters while keeping the pre-trained model's paramete

local-aiarxiv-cs-cv

TeamBench: Evaluating Agent Coordination under Enforced Role Separation

arXiv:2605.07073v1 Announce Type: new Abstract: Agent systems often decompose a task across multiple roles, but these roles are typically specified by prompts rather than enforced by access controls.

Temporal Attention for Adaptive Control of Euler-Lagrange Systems with Unobservable Memory

arXiv:2605.06877v1 Announce Type: new Abstract: Adaptive control of Euler-Lagrange systems is challenging when friction is governed by a finite-horizon internal state that is not directly observable f

safetyarxiv-cs-lg

Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing

arXiv:2605.05958v2 Announce Type: replace Abstract: Knowledge Tracing (KT) is fundamental to intelligent education systems, yet relies on educational logs that are selectively observed. The non-random

Tessellations of Semi-Discrete Flow Matching

arXiv:2605.07513v1 Announce Type: new Abstract: We study Flow Matching in a semi-discrete setting where a Gaussian source is transported toward a discrete target supported on finitely many points. Thi

researcharxiv-cs-lg

Test-Time Compositional Generalization in Diffusion Models via Concept Discovery

arXiv:2605.07078v1 Announce Type: new Abstract: Compositional generalization requires models to produce novel configurations from familiar parts. In diffusion models, prior compositional generation me

local-aiarxiv-cs-lg

Test-Time Compute Games

arXiv:2601.21839v2 Announce Type: replace-cross Abstract: Test-time compute has emerged as a promising strategy to enhance the reasoning abilities of large language models (LLMs). However, this strate

Testing Noise Assumptions of Learning Algorithms

arXiv:2501.09189v3 Announce Type: replace Abstract: We pose a fundamental question in computational learning theory: can we efficiently test whether a training set satisfies the assumptions of a given

researcharxiv-cs-lg

Text-to-CAD Evaluation with CADTests

arXiv:2605.07807v1 Announce Type: cross Abstract: Text-to-CAD has recently emerged as an important task with the potential to substantially accelerate design workflows. Despite its significance, there

TextLDM: Language Modeling with Continuous Latent Diffusion

arXiv:2605.07748v1 Announce Type: new Abstract: Diffusion Transformers (DiT) trained with flow matching in a VAE latent space have unified visual generation across images and videos. A natural next st

Agents

The AI-Native Large-Scale Agile Software Development Manifesto

arXiv:2605.07717v1 Announce Type: cross Abstract: Despite the widespread adoption of agile methods, achieving true agility at scale remains elusive. Large-scale agile frameworks remain largely human-c

agentsarxiv-cs-ai

Agents

The Context Gathering Decision Process: A POMDP Framework for Agentic Search

arXiv:2605.07042v1 Announce Type: new Abstract: Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories

agentsarxiv-cs-ai

The Convergence Gap: Instruction-Tuned Language Models Stabilize Later in the Forward Pass

arXiv:2605.07282v1 Announce Type: new Abstract: Final outputs hide when a checkpoint commits to its next-token prediction. We introduce the convergence gap, a model-diffing diagnostic that decodes eac

The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search

arXiv:2605.06988v1 Announce Type: cross Abstract: Distributed agents in real-world settings frequently must coordinate under uncertainty with only partial observations. Coordination is necessary to sh

safetyarxiv-cs-ro

The Coupling Tax: How Shared Token Budgets Undermine Visible Chain-of-Thought Under Fixed Output Limits

arXiv:2605.07686v1 Announce Type: new Abstract: Chain-of-thought reasoning is often treated as a monotone way to improve language-model accuracy by letting a model think longer. We identify a counterv

The Effect of Mini-Batch Noise on the Implicit Bias of Adam

arXiv:2602.01642v2 Announce Type: replace-cross Abstract: With limited high-quality data and growing compute, multi-epoch training is gaining back its importance across sub-areas of deep learning. Ada

The Effective Depth Paradox: Evaluating the Relationship between Architectural Topology and Trainability in Deep CNNs

arXiv:2602.13298v3 Announce Type: replace-cross Abstract: This paper investigates the relationship between convolutional neural network (CNN) topology and image recognition performance through a compa

researcharxiv-cs-ai

The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting

arXiv:2605.07671v1 Announce Type: cross Abstract: Eliciting truthful reports from autonomous agents is a core problem in scalable AI oversight: a principal scores the agent's report using a strictly p

The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty

arXiv:2605.07979v1 Announce Type: new Abstract: The rise of machine learning has shifted targeted resource allocation in policy and humanitarian settings toward algorithmic targeting based on predicte

The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agents

arXiv:2605.08060v1 Announce Type: cross Abstract: Context window expansion is often treated as a straightforward capability upgrade for LLMs, but we find it systematically fails in multi-agent social

The Minimax Rate of Second-Order Calibration

arXiv:2605.07808v1 Announce Type: new Abstract: We characterize the minimax rate of estimating the second-order calibration error for binary classification, which quantifies whether a higher-order pre

researcharxiv-cs-lg

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment

arXiv:2605.07462v1 Announce Type: cross Abstract: Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious sa

The Position Curse: LLMs Struggle to Locate the Last Few Items in a List

model-releasesarxiv-cs-cl

arXiv:2605.07127v1 Announce Type: cross Abstract: Modern large language models (LLMs) can find a needle in a haystack (locating a single relevant fact buried among hundreds of thousands of irrelevant

The Proxy Presumption: From Semantic Embeddings to Valid Social Measures

arXiv:2605.07409v1 Announce Type: new Abstract: Natural Language Processing is rapidly evolving into a primary instrument for Computational Social Science, with researchers increasingly using embeddin

The Single-File Test: A Longitudinal Public-Interface Evaluation of First-Output LLM Web Generation with Social Reach Tracking

arXiv:2605.06707v1 Announce Type: cross Abstract: This paper presents an eight-week observational comparison of 68 single-file HTML generations collected across 17 public experiments in the 'HTML AI B

The Text Uncanny Valley: Non-Monotonic Performance Degradation in LLM Information Retrieval

arXiv:2605.07186v1 Announce Type: cross Abstract: Existing Large Language Model (LLM) benchmarks primarily focus on syntactically correct inputs, leaving a significant gap in evaluation on imperfect t

The Translation Tax Is Not a Scalar: A Counterfactual Audit of English-Source Cue Inheritance in Chinese Multilingual Benchmarks

arXiv:2605.07093v1 Announce Type: cross Abstract: The Translation Tax is often treated as a scalar: translated benchmarks are assumed to inflate scores by preserving English-source cues. We audit this

Theoretical Limits of Language Model Alignment

arXiv:2605.07105v1 Announce Type: cross Abstract: Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities of the base model. The most common

Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model

arXiv:2602.04774v2 Announce Type: replace-cross Abstract: Setting the learning rate (LR) for a deep learning model is a critical part of successful training. Choosing LRs is often done empirically wit

Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles

arXiv:2512.03454v4 Announce Type: replace-cross Abstract: Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) meth

local-aiarxiv-cs-ai

Think-with-Rubrics: From External Evaluator to Internal Reasoning Guidance

arXiv:2605.07461v1 Announce Type: new Abstract: Rubrics have been extensively utilized for evaluating unverifiable, open-ended tasks, with recent research incorporating them into reward systems for re

THINKSAFE: Self-Generated Safety Alignment for Reasoning Models

arXiv:2601.23143v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-

ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models

arXiv:2510.01290v2 Announce Type: replace Abstract: The long-output context generation of large reasoning models enables extended chain of thought (CoT) but also drives rapid growth of the key-value (

Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention

arXiv:2605.07199v1 Announce Type: new Abstract: Marketing decisions reflect the interaction of latent consumer heterogeneity, time-varying internal states, and explicit interventions, a structure that

researcharxiv-cs-ai