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Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding

model-releasesarxiv-cs-cl

arXiv:2604.07753v1 Announce Type: cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient c

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SymptomWise: A Deterministic Reasoning Layer for Reliable and Efficient AI Systems

DGX agent

arXiv:2604.06375v1 Announce Type: new Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end generative approaches ofte

safetyarxiv-cs-ai

10 Apr 2026

SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation

arXiv:2604.07513v1 Announce Type: cross Abstract: AI-based persona simulation -- often referred to as digital twin simulation -- is increasingly used for market research, recommender systems, and soci

safetyarxiv-cs-cl

SyncBreaker:Stage-Aware Multimodal Adversarial Attacks on Audio-Driven Talking Head Generation

arXiv:2604.08405v1 Announce Type: new Abstract: Diffusion-based audio-driven talking-head generation enables realistic portrait animation, but also introduces risks of misuse, such as fraud and misinf

Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation

arXiv:2604.07321v1 Announce Type: cross Abstract: Propositional Linear Temporal Logic (LTL) is a popular formalism for specifying desirable requirements and security and privacy policies for software,

Synthetic Data for any Differentiable Target

arXiv:2604.08423v1 Announce Type: new Abstract: What are the limits of controlling language models via synthetic training data? We develop a reinforcement learning (RL) primitive, the Dataset Policy G

safetyarxiv-cs-cl

Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity

arXiv:2509.09794v4 Announce Type: replace Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building and urban scale, supporting data-driven

T-Gated Adapter: A Lightweight Temporal Adapter for Vision-Language Medical Segmentation

model-releasesarxiv-cs-cv

arXiv:2604.08167v1 Announce Type: new Abstract: Medical image segmentation traditionally relies on fully supervised 3D architectures that demand a large amount of dense, voxel-level annotations from c

Tabular GANs for uneven distribution

model-releasesarxiv-cs-cv

arXiv:2010.00638v2 Announce Type: replace-cross Abstract: Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular

TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models

arXiv:2604.06291v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of Large Language Models (LLMs), and recent Mixture-of-Experts (MoE) extensions fur

Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation

arXiv:2604.07916v1 Announce Type: new Abstract: Referring Expression Segmentation (RES) aims to segment image regions described by natural-language expressions, serving as a bridge between vision and

Applications

TeaLeafVision: An Explainable and Robust Deep Learning Framework for Tea Leaf Disease Classification

arXiv:2604.07182v1 Announce Type: cross Abstract: As the worlds second most consumed beverage after water, tea is not just a cultural staple but a global economic force of profound scale and influence

applicationsarxiv-cs-ai

Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking

arXiv:2604.06424v1 Announce Type: cross Abstract: This paper presents a transformer-based approach to solving the SympTEMIST named entity recognition (NER) and entity linking (EL) tasks. For NER, we f

TeamLLM: A Human-Like Team-Oriented Collaboration Framework for Multi-Step Contextualized Tasks

arXiv:2604.06765v1 Announce Type: cross Abstract: Recently, multi-Large Language Model (LLM) frameworks have been proposed to solve contextualized tasks. However, these frameworks do not explicitly em

Tutorials

TEC: A Collection of Human Trial-and-error Trajectories for Problem Solving

arXiv:2604.06734v2 Announce Type: replace Abstract: Trial-and-error is a fundamental strategy for humans to solve complex problems and a necessary capability for Artificial Intelligence (AI) systems o

tutorialsarxiv-cs-cl

Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection

arXiv:2604.06332v1 Announce Type: cross Abstract: Autonomous highway driving, especially for long-haul heavy trucks, requires detecting objects at long ranges beyond 500 meters to satisfy braking dist

agentsarxiv-cs-lg

TEMPER: Testing Emotional Perturbation in Quantitative Reasoning

model-releasesarxiv-cs-cl

arXiv:2604.07801v1 Announce Type: new Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language. However, real-world quer

Temporal Inversion for Learning Interval Change in Chest X-Rays

arXiv:2604.04563v2 Announce Type: replace-cross Abstract: Recent advances in vision--language pretraining have enabled strong medical foundation models, yet most analyze radiographs in isolation, over

Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling

arXiv:2604.06197v1 Announce Type: cross Abstract: Type 2 diabetes case reports describe complex clinical courses, but their timelines are often expressed in language that is difficult to reuse in long

Tensor-Augmented Convolutional Neural Networks: Enhancing Expressivity with Generic Tensor Kernels

model-releasesarxiv-cs-cv

arXiv:2604.08072v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) excel at extracting local features hierarchically, but their performance in capturing complex correlations hinges h

Tensor-Efficient High-Dimensional Q-learning

model-releasesarxiv-cs-lg

arXiv:2511.03595v2 Announce Type: replace Abstract: High-dimensional reinforcement learning(RL) faces challenges with complex calculations and low sample efficiency in large state-action spaces. Q-lea

Testimole-Conversational: A 30-Billion-Word Italian Discussion Board Corpus (1996-2024) for Language Modeling and Sociolinguistic Research

arXiv:2602.14819v2 Announce Type: replace Abstract: We present 'Testimole-conversational' a massive collection of discussion boards messages in the Italian language. The large size of the corpus, more

researcharxiv-cs-cl

Applications

exttt{SEM-CTRL}: Semantically Controlled Decoding

arXiv:2503.01804v4 Announce Type: replace Abstract: Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for r

applicationsarxiv-cs-cl

The AI Skills Shift: Mapping Skill Obsolescence, Emergence, and Transition Pathways in the LLM Era

arXiv:2604.06906v1 Announce Type: cross Abstract: As Large Language Models reshape the global labor market, policymakers and workers need empirical data on which occupational skills may be most suscep

The Art of Building Verifiers for Computer Use Agents

arXiv:2604.06240v1 Announce Type: cross Abstract: Verifying the success of computer use agent (CUA) trajectories is a critical challenge: without reliable verification, neither evaluation nor training

agentsarxiv-cs-ai

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training

arXiv:2604.07754v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve m

safetyarxiv-cs-cl

The ATOM Report: Measuring the Open Language Model Ecosystem

arXiv:2604.07190v1 Announce Type: cross Abstract: We present a comprehensive adoption snapshot of the leading open language models and who is building them, focusing on the ~1.5K mainline open models

The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail?

arXiv:2604.06436v2 Announce Type: cross Abstract: We prove that no continuous, utility-preserving wrapper defense-a function D: Xo X that preprocesses inputs before the model sees them-can make al

The Depth Ceiling: On the Limits of Large Language Models in Discovering Latent Planning

arXiv:2604.06427v1 Announce Type: cross Abstract: The viability of chain-of-thought (CoT) monitoring hinges on models being unable to reason effectively in their latent representations. Yet little is

The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

arXiv:2604.06613v2 Announce Type: cross Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three ben

The End of the Foundation Model Era: Open-Weight Models, Sovereign AI, and Inference as Infrastructure

arXiv:2604.06217v1 Announce Type: cross Abstract: The foundation model era -- roughly 2020 to 2025 -- is over. The forces that defined it have inverted. Open source models have reached frontier perfor

The Geometry of Forgetting

arXiv:2604.06222v1 Announce Type: cross Abstract: Why do we forget? Why do we remember things that never happened? The conventional answer points to biological hardware. We propose a different one: ge

The Human Condition as Reflected in Contemporary Large Language Models

arXiv:2604.06206v1 Announce Type: cross Abstract: This study seeks to uncover evidence of a latent structure in evolved human culture as it is refracted through contemporary large language models (LLM

The Illusion of Stochasticity in LLMs

arXiv:2604.06543v1 Announce Type: cross Abstract: In this work, we demonstrate that reliable stochastic sampling is a fundamental yet unfulfilled requirement for Large Language Models (LLMs) operating

agentsarxiv-cs-lg

The Illusion of Superposition? A Principled Analysis of Latent Thinking in Language Models

arXiv:2604.06374v1 Announce Type: cross Abstract: Latent reasoning via continuous chain-of-thoughts (Latent CoT) has emerged as a promising alternative to discrete CoT reasoning. Operating in continuo

The Impact of Steering Large Language Models with Persona Vectors in Educational Applications

arXiv:2604.07102v1 Announce Type: cross Abstract: Activation-based steering can personalize large language models at inference time, but its effects in educational settings remain unclear. We study pe

The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment

arXiv:2604.06377v1 Announce Type: cross Abstract: We investigate whether post-trained capabilities can be transferred across models without retraining, with a focus on transfer across different model

The Persistence of Cultural Memory: Investigating Multimodal Iconicity in Diffusion Models

arXiv:2511.11435v3 Announce Type: replace Abstract: The ambiguity between generalization and memorization in TTI diffusion models becomes pronounced when prompts invoke culturally shared visual refere

The Planetary Cost of AI Acceleration, Part II: The 10th Planetary Boundary and the 6.5-Year Countdown

arXiv:2604.04956v2 Announce Type: replace-cross Abstract: The recent, super-exponential scaling of autonomous Large Language Model (LLM) agents signals a broader, fundamental paradigm shift from machi

agentsarxiv-cs-ai

The Rhetoric of Machine Learning

arXiv:2604.06754v1 Announce Type: new Abstract: I examine the technology of machine learning from the perspective of rhetoric, which is simply the art of persuasion. Rather than being a neutral and 'o

The Stepwise Informativeness Assumption: Why are Entropy Dynamics and Reasoning Correlated in LLMs?

arXiv:2604.06192v1 Announce Type: cross Abstract: Recent work uses entropy-based signals at multiple representation levels to study reasoning in large language models, but the field remains largely em

The Sustainability Gap in Robotics: A Large-Scale Survey of Sustainability Awareness in 50,000 Research Articles

arXiv:2604.07921v1 Announce Type: new Abstract: We present a large-scale survey of sustainability communication and motivation in robotics research. Our analysis covers nearly 50,000 open-access paper

safetyarxiv-cs-ro

The Theorems of Dr. David Blackwell and Their Contributions to Artificial Intelligence

arXiv:2604.06621v1 Announce Type: cross Abstract: Dr. David Blackwell was a mathematician and statistician of the first rank, whose contributions to statistical theory, game theory, and decision theor

safetyarxiv-cs-lg

The Theory and Practice of Highly Scalable Gaussian Process Regression with Nearest Neighbours

model-releasesarxiv-cs-lg

arXiv:2604.07267v1 Announce Type: cross Abstract: Gaussian process (GP) regression is a widely used non-parametric modeling tool, but its cubic complexity in the training size limits its use on mass

The Traveling Thief Problem with Time Windows: Benchmarks and Heuristics

arXiv:2604.06724v1 Announce Type: cross Abstract: While traditional optimization problems were often studied in isolation, many real-world problems today require interdependence among multiple optimiz

The Unreasonable Effectiveness of Data for Recommender Systems

arXiv:2604.06420v2 Announce Type: cross Abstract: In recommender systems, collecting, storing, and processing large-scale interaction data is increasingly costly in terms of time, energy, and computat

The Weaponization of Computer Vision: Tracing Military-Surveillance Ties through Conference Sponsorship

arXiv:2604.07803v1 Announce Type: cross Abstract: Computer vision, a core domain of artificial intelligence (AI), is the field that enables the computational analysis, understanding, and generation of

Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach

arXiv:2602.18377v3 Announce Type: replace-cross Abstract: Quantum reservoir computers (QRCs) have emerged as a promising approach to quantum machine learning, since they utilize the natural dynamics o