Search: “models”

PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models

arXiv:2606.03858v1 Announce Type: new Abstract: Despite the pivotal role of numerical reasoning as the cornerstone of mathematical capabilities in large language models (LLMs) across applications, few

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

Text-to-Image Models Need Less from Text Encoders Than You Think

DGX agent

arXiv:2606.03715v1 Announce Type: new Abstract: Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that conditi

tutorialsarxiv-cs-cv

3 Jun 2026

Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models

arXiv:2606.02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assu

TimeOmni-VL: Unified Models for Time Series Understanding and Generation

arXiv:2602.17149v2 Announce Type: replace-cross Abstract: Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generatio

Tutorials

Visual Graph Scaffolds for Structural Reasoning in Large Language Models

arXiv:2606.02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at t

tutorialsarxiv-cs-ai

When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models

arXiv:2606.03712v1 Announce Type: new Abstract: Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph

safetyarxiv-cs-lg

AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design

arXiv:2606.02386v1 Announce Type: new Abstract: Protein language models (PLMs) are passive oracles: they generate sequences in a single forward pass with no mechanism to consult external biophysical f

Applications

Are Large Reasoning Models Interruptible?

arXiv:2510.11713v4 Announce Type: replace Abstract: Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments. In this work, we challenge

applicationsarxiv-cs-cl

Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities

arXiv:2505.24621v3 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarkin

Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models

arXiv:2606.00039v1 Announce Type: cross Abstract: Text-to-Image (T2I) models have shown promising utility across various domains. However, such models are also amplifying harmful societal biases in th

Consistency Deep Equilibrium Models

arXiv:2602.03024v2 Announce Type: replace-cross Abstract: Deep Equilibrium Models (DEQs) have emerged as a powerful paradigm in deep learning, offering the ability to model infinite-depth networks wit

Controllable Value Alignment in Large Language Models through Neuron-Level Editing

arXiv:2602.07356v2 Announce Type: replace Abstract: Aligning large language models (LLMs) with human values has become increasingly important as their influence on human behavior and decision-making e

DASH: Dual-Branch Score Distillation for Guidance-Calibrated Compact Diffusion Models

arXiv:2606.00798v1 Announce Type: cross Abstract: Parameter compression of class-conditional diffusion models reveals an underexplored limitation in output-level distillation: the unconditional score

Distillation of Large Language Models via Concrete Score Matching

arXiv:2509.25837v3 Announce Type: replace-cross Abstract: Large language models (LLMs) deliver remarkable performance but are costly to deploy, motivating knowledge distillation (KD) for efficient inf

DLLM-JEPA: Joint Embedding Predictive Architectures for Masked Diffusion Language Models

arXiv:2606.00091v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) have reshaped self-supervised representation learning in vision. The recent LLM-JEPA ported JEPA to a

ELF: A Family of Encoder-Free ECG-Language Models

arXiv:2601.18798v2 Announce Type: replace-cross Abstract: ECG-Language Models (ELMs) extend recent advances in Multimodal Large Language Models (MLLMs) to automated ECG interpretation. However, most e

From 'Weak' Signals to Strong Models: Preference Delta Aggregation with LoRA Merging

arXiv:2606.00357v1 Announce Type: new Abstract: Training strong large language models (LLMs) requires high-quality supervision, which is often scarce. Recent work shows that paired preference data fro

GLENS: Global Search via Learning from Solver Iterates with Diffusion Models

arXiv:2606.00366v1 Announce Type: new Abstract: We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems.

IDLM: Inverse-distilled Diffusion Language Models

arXiv:2602.19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow in

InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning

arXiv:2509.12263v3 Announce Type: replace Abstract: Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge. It allows LMMs

Interpretability in Deep Time Series Models Demands Semantic Alignment

arXiv:2602.02239v2 Announce Type: replace Abstract: Deep time series models continue to improve predictive performance, yet their deployment remains limited by their black-box nature. In response, exi

safetyarxiv-cs-lg

Measuring and Mitigating Bias in Code Generated by Large Language Models

arXiv:2606.00049v1 Announce Type: cross Abstract: Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation

On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters

arXiv:2606.02437v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapt

Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence

arXiv:2606.00570v1 Announce Type: cross Abstract: Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted

Self-Regulating Annealing in Heavy-Tailed Diffusion Models

arXiv:2606.01645v1 Announce Type: cross Abstract: Diffusion models have emerged as a leading framework for deep generative modeling. While the standard Gaussian formulation is theoretically convenient

researcharxiv-cs-lg

SentGuard: Sentence-Level Streaming Guardrails for Large Language Models

arXiv:2606.02041v1 Announce Type: new Abstract: Large language models increasingly stream long, reasoning-intensive responses in real time, making when to moderate as critical as whether to moderate.

Silent Failures in Federated Personalization of Foundation Models

arXiv:2606.00947v1 Announce Type: cross Abstract: Foundation models are increasingly personalized on decentralized private data through federated learning and are now deployed at scale under growing r

SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models

arXiv:2601.22276v2 Announce Type: replace-cross Abstract: As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributor

safetyarxiv-cs-cv

The Shape of Wisdom: Decision Trajectories in Language Models

arXiv:2606.01202v1 Announce Type: new Abstract: Language models do not simply choose an answer at the output layer. In a 9,000-trajectory MMLU study across Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct,

Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models

arXiv:2606.00919v1 Announce Type: new Abstract: Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - re

Vision-language Models for Driver Monitoring Systems: A Driver Activity Description Dataset

model-releasesarxiv-cs-cv

arXiv:2606.02273v1 Announce Type: new Abstract: Understanding subtle driver actions is essential for building reliable driver monitoring systems. Existing visionlanguage models (VLMs) are trained on g

World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications

arXiv:2606.00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artifici

safetyarxiv-cs-lg

3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models

arXiv:2603.07751v2 Announce Type: replace-cross Abstract: Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks

researcharxiv-cs-cl

Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education

arXiv:2605.31212v1 Announce Type: cross Abstract: AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully rep

dgMARK: Decoding-Guided Watermarking for Diffusion Language Models

arXiv:2601.22985v2 Announce Type: replace Abstract: We propose dgMARK, a decoding-guided watermarking method for discrete diffusion language models (dLLMs). Unlike autoregressive models, dLLMs can gen

researcharxiv-cs-lg

Domain Adaptation and Reasoning Frameworks in Language Models: A Controlled Experiment with Historical Cosmology

arXiv:2605.30415v1 Announce Type: cross Abstract: We investigate how domain adaptation reshapes explanatory behavior in language models using historical cosmology as a controlled setting. In Phase 1,

Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models

arXiv:2509.24319v4 Announce Type: replace-cross Abstract: Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during tra

Hardware

Elastic ViTs from Pretrained Models without Retraining

arXiv:2510.17700v2 Announce Type: replace Abstract: Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes, forcing sub-optimal deploym

hardwarearxiv-cs-cv

Fine-Tuning Improves Information Conveyance in Language Models

arXiv:2605.30844v1 Announce Type: cross Abstract: Fine-tuning is often believed to reduce uncertainty and diversity in large language models, but existing analyses overlook output length, a key confou

Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task

arXiv:2605.31480v1 Announce Type: new Abstract: Do neural models, such as Large Language Models, genuinely acquire compositional abilities for interpretation of natural language? When we talk about se

researcharxiv-cs-cl

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2511.16940v3 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) pose significant individual-level privacy risks by linking fragmented multimodal data to identifiable individua

Applications

Performance and Complexity Trade-off Optimization of Speech Models During Training

arXiv:2601.13704v3 Announce Type: replace-cross Abstract: In speech machine learning, neural network models are typically designed by choosing an architecture with fixed layer sizes and structure. The

applicationsarxiv-cs-ai

SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders

arXiv:2509.21379v3 Announce Type: replace-cross Abstract: Concept unlearning in diffusion models is hampered by feature splitting, where concepts are distributed across many latent features, making th

Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models

arXiv:2509.06856v2 Announce Type: replace-cross Abstract: We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares E

Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO

arXiv:2605.30789v1 Announce Type: cross Abstract: We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollou

The Illusion of Generalization in Tabular Language Models

arXiv:2602.04031v2 Announce Type: replace Abstract: Tabular Language Models (TLMs) have been claimed to achieve strong generalization for tabular prediction. We conduct a systematic re-evaluation of T

Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints

arXiv:2605.30825v1 Announce Type: cross Abstract: Unlearning in diffusion models aims to remove undesirable data or concepts while preserving the utility of pretrained models -- two fundamentally conf

Vision-Language Models Suppress Female Representations Under Ambiguous Input

arXiv:2605.31556v1 Announce Type: cross Abstract: Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succeed. Far l