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Temporal Fair Division in Multi-Agent Systems: From Precise Alternation Metrics to Scalable Coordination Proxies

arXiv:2605.14879v1 Announce Type: cross Abstract: A plethora real-world environments require agents to compete repeatedly for the same limited resource, calling for a temporal notion of fairness judge

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Research

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

DGX agent

arXiv:2603.12529v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables th

researcharxiv-cs-ai

15 May 2026

TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate

arXiv:2605.13909v1 Announce Type: cross Abstract: Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation. It is also a canonic

safetyarxiv-cs-ai

TERRA-CD: Multi-Temporal Framework for Multi-class and Semantic Change Detection

model-releasesarxiv-cs-cv

arXiv:2605.14651v1 Announce Type: new Abstract: Urban vegetation monitoring plays a vital role in understanding environmental changes, yet comprehensive datasets for this purpose remain limited. To ad

Test-Time Learning with an Evolving Library

arXiv:2605.14477v1 Announce Type: new Abstract: We introduce EvoLib, a test-time learning framework that enables large language models to accumulate, reuse, and evolve knowledge across problem instanc

Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report

arXiv:2605.14896v1 Announce Type: cross Abstract: This paper presents a system for the 2024 Text-Dependent Speaker Verification (TdSV) Challenge. The system achieved a Minimum Detection Cost Function

researcharxiv-cs-lg

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment

arXiv:2605.15168v1 Announce Type: cross Abstract: Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions lik

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale

arXiv:2605.15053v1 Announce Type: cross Abstract: Continually pre-training a large language model on heterogeneous text domains, without replay or task labels, has remained an unsolved architectural p

The Evaluation Trap: Benchmark Design as Theoretical Commitment

arXiv:2605.14167v1 Announce Type: new Abstract: Every AI benchmark operationalizes theoretical assumptions about the capability it claims to assess. When assumptions function as unexamined commitments

The Great Pretender: A Stochasticity Problem in LLM Jailbreak

arXiv:2605.14418v1 Announce Type: cross Abstract: 'Oh-Oh, yes, I'm the great pretender. Pretending that I'm doing well. My need is such, I pretend too much...' summarizes the state in the area of jail

safetyarxiv-cs-ai

Applications

The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot

arXiv:2410.02091v3 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) facilitates content production and enhances ideation capabilities, which can significantly influence d

applicationsarxiv-cs-ai

The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity

arXiv:2605.13860v1 Announce Type: cross Abstract: Moltbook is a social media platform in which posts and comments are authored exclusively by autonomous AI agents. We present the Moltbook Observatory

The Pitfalls of KV Cache Compression

arXiv:2510.00231v2 Announce Type: replace-cross Abstract: KV cache compression promises increased throughput and efficiency with negligible loss in performance. While the gains in throughput are indis

safetyarxiv-cs-ai

Applications

The Potential of Convolutional Neural Networks for Cancer Detection

arXiv:2412.17155v4 Announce Type: replace Abstract: Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different

applicationsarxiv-cs-cv

The Rate-Distortion-Polysemanticity Tradeoff in SAEs

arXiv:2605.14694v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) that can accurately reconstruct their input (minimizing distortion) by making efficient use of few features (minimizing the r

The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

arXiv:2605.15011v1 Announce Type: new Abstract: Scientific contributions rarely develop in isolation, but instead build upon prior discoveries. We formulate the task of automated technological roadmap

researcharxiv-cs-cl

The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval

arXiv:2511.21247v2 Announce Type: replace-cross Abstract: This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source sepa

researcharxiv-cs-lg

The Velocity Deficit: Initial Energy Injection for Flow Matching

arXiv:2605.14819v1 Announce Type: new Abstract: While Flow Matching theoretically guarantees constant-velocity trajectories, we identify a critical breakdown in high-dimensional practice: the Velocity

researcharxiv-cs-cv

Think When Needed: Adaptive Reasoning-Driven Multimodal Embeddings with a Dual-LoRA Architecture

model-releasesarxiv-cs-cl

arXiv:2605.14448v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have emerged as a powerful backbone for multimodal embeddings. Recent methods introduce chain-of-thought (CoT

Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models

arXiv:2605.14177v1 Announce Type: cross Abstract: Long-horizon personalization requires dialogue assistants to retrieve user-specific facts from extended interaction histories. In practice, many relev

TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes

arXiv:2605.14915v1 Announce Type: new Abstract: Imbalanced learning remains a fundamental challenge in tabular data applications. Despite decades of research and numerous proposed algorithms, a system

TILT: Target-induced loss tilting under covariate shift

arXiv:2605.14280v1 Announce Type: new Abstract: We introduce and analyze Target-Induced Loss Tilting (TILT) for unsupervised domain adaptation under covariate shift. It is based on a novel objective f

TiTok: Transfer Token-level Knowledge via Contrastive Excess to Transplant LoRA

arXiv:2510.04682v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely applied in real world scenarios, yet fine-tuning them comes with significant computational and storage

To discretize continually: Mean shift interacting particle systems for Bayesian inference

arXiv:2605.14142v1 Announce Type: cross Abstract: Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce n

Tutorials

To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model

arXiv:2605.14291v1 Announce Type: cross Abstract: The rapid advancement of Large Vision-Language Models (LVLMs) is increasingly accompanied by unauthorized scraping and training on multimodal web data

tutorialsarxiv-cs-ai

Tokenizer Fertility and Zero-Shot Performance of Foundation Models on Ukrainian Legal Text: A Comparative Study

model-releasesarxiv-cs-cl

arXiv:2605.14890v1 Announce Type: new Abstract: Foundation models tokenize Ukrainian legal text with vastly different efficiency, yet no systematic comparison exists for this domain. We benchmark seve

Tokenizing Single-Channel EEG with Time-Frequency Motif Learning

arXiv:2502.16060v5 Announce Type: replace-cross Abstract: Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokeni

researcharxiv-cs-ai

ToMAToMP: Robust and Multi-Parameter Topological Clustering

arXiv:2605.14824v1 Announce Type: new Abstract: Topological clustering, and its main algorithm ToMATo, is a clustering method from Topological Data Analysis (TDA) which has been applied successfully i

TopoPrimer: The Missing Topological Context in Forecasting Models

arXiv:2605.15035v1 Announce Type: new Abstract: We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. To

researcharxiv-cs-lg

Applications

TOPOS: High-Fidelity and Efficient Industry-Grade 3D Head Generation

arXiv:2605.14594v1 Announce Type: new Abstract: High-fidelity 3D head generation plays a crucial role in the film, animation and video game industries. In industrial pipelines, studios typically enfor

applicationsarxiv-cs-cv

Towards Accurate Single Panoramic 3D Detection: A Semantic Gaussian Centric Approach

arXiv:2605.14601v1 Announce Type: new Abstract: Three-dimensional object detection in panoramic imagery is crucial for comprehensive scene understanding, yet accurately mapping 2D features to 3D remai

researcharxiv-cs-cv

Towards Continuous Sign Language Conversation from Isolated Signs

arXiv:2605.14705v1 Announce Type: new Abstract: Sign language is the primary language for many Deaf and Hard-of-Hearing (DHH) signers, yet most conversational AI systems still mediate interaction thro

safetyarxiv-cs-cv

Towards Fine-Grained and Verifiable Concept Bottleneck Models

arXiv:2605.14210v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) offer interpretable alternatives to black-box predictors by introducing human-relatable concepts before the final out

local-aiarxiv-cs-ai

Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News

arXiv:2605.14999v1 Announce Type: cross Abstract: As generative AI becomes increasingly integrated into journalism, designing effective AI-use disclosures that inform readers without imposing unnecess

researcharxiv-cs-ai

Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought

arXiv:2605.14866v1 Announce Type: cross Abstract: As modern microservice systems grow increasingly complex due to dynamic interactions and evolving runtime environments, they experience failures with

local-aiarxiv-cs-ai

Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning

arXiv:2605.14717v1 Announce Type: cross Abstract: Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directl

researcharxiv-cs-ai

Towards Real-Time Autonomous Navigation: Transformer-Based Catheter Tip Tracking in Fluoroscopy

arXiv:2605.14253v1 Announce Type: new Abstract: Purpose: Mechanical thrombectomy (MT) improves stroke outcomes, but is limited by a lack of local treatment access. Widespread distribution of reinforce

local-aiarxiv-cs-cv

Hardware

Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines

arXiv:2605.13981v1 Announce Type: cross Abstract: The rise in deployment of large language models has driven a surge in GPU demand and datacenter scaling, raising concerns about electricity use, grid

hardwarearxiv-cs-ai

Towards Robotic Dexterous Hand Intelligence: A Survey

arXiv:2605.13925v1 Announce Type: new Abstract: Robotic dexterous hands are central to contact-rich manipulation, with rapid progress driven by advances in hardware, sensing, control, simulation, and

researcharxiv-cs-ro

Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning

arXiv:2605.13936v1 Announce Type: cross Abstract: The recent success of large language models (LLMs) has been largely driven by vast public datasets. However, the next frontier for LLM development lie

Training-Free Generative Sampling via Moment-Matched Score Smoothing

arXiv:2605.14276v1 Announce Type: cross Abstract: Diffusion models generate samples by denoising along the score of a perturbed target distribution. In practice, one trains a neural diffusion model, w

safetyarxiv-cs-lg

Training-Free Inference for High-Resolution Sinogram Completion

arXiv:2506.08809v5 Announce Type: replace Abstract: High-resolution sinogram completion is critical for computed tomography reconstruction, as missing projections can introduce severe artifacts. While

local-aiarxiv-cs-cv

Training ML Models with Predictable Failures

arXiv:2605.15134v1 Announce Type: new Abstract: Estimating how often an ML model will fail at deployment scale is central to pre-deployment safety assessment, but a feasible evaluation set is rarely l

safetyarxiv-cs-lg

TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning

arXiv:2510.07118v3 Announce Type: replace Abstract: Instruction tuning is essential for aligning large language models (LLMs) to downstream tasks and commonly relies on large, diverse corpora. However

researcharxiv-cs-cl

TRIO: Token Reduction via Inference-Objective Guidance for Efficient Vision-Language Models

arXiv:2602.04657v3 Announce Type: replace Abstract: Recently, reducing redundant visual tokens in vision-language models (VLMs) to accelerate VLM inference has emerged as a hot topic. However, most ex

local-aiarxiv-cs-cv

Tutorials

TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention

arXiv:2605.14315v1 Announce Type: new Abstract: Recent feed-forward 3D reconstruction methods, such as visual geometry transformers, have substantially advanced the traditional per-scene optimization

tutorialsarxiv-cs-cv

Turning Stale Gradients into Stable Gradients: Coherent Coordinate Descent with Implicit Landscape Smoothing for Lightweight Zeroth-Order Optimization

arXiv:2605.14373v1 Announce Type: cross Abstract: Zeroth-Order (ZO) optimization is pivotal for scenarios where backpropagation is unavailable, such as memory-constrained on-device learning and black-

local-aiarxiv-cs-ai

UMo: Unified Sparse Motion Modeling for Real-Time Co-Speech Avatars

arXiv:2605.14731v1 Announce Type: cross Abstract: Speech-driven gestures and facial animations are fundamental to expressive digital avatars in games, virtual production, and interactive media. Howeve

safetyarxiv-cs-cv