Search: “models”

JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models

arXiv:2604.16171v1 Announce Type: cross Abstract: Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a lo

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing

DGX agent

arXiv:2604.15725v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have demonstrated strong capabilities in generating step-by-step reasoning chains alongside final answers, enabling thei

model-releasesarxiv-cs-ai

20 Apr 2026

RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2601.03699v2 Announce Type: replace Abstract: As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount.

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures

arXiv:2604.16042v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and

safetyarxiv-cs-ai

When Surfaces Lie: Exploiting Wrinkle-Induced Attention Shift to Attack Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2603.27759v3 Announce Type: replace Abstract: Visual-Language Models (VLMs) have demonstrated exceptional cross-modal understanding across various tasks, including zero-shot classification, imag

An Analysis of Regularization and Fokker-Planck Residuals in Diffusion Models for Image Generation

arXiv:2604.15171v1 Announce Type: new Abstract: Recent work has shown that diffusion models trained with the denoising score matching (DSM) objective often violate the Fokker--Planck (FP) equation tha

researcharxiv-cs-cv

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

arXiv:2603.01410v2 Announce Type: replace Abstract: Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large lan

Improving Language Models with Intentional Analysis

model-releasesarxiv-cs-cl

arXiv:2502.04689v4 Announce Type: replace Abstract: Intent, a critical cognitive notion and mental state, is ubiquitous in human communication and problem-solving. Accurately understanding the underly

Language of Thought Shapes Output Diversity in Large Language Models

arXiv:2601.11227v2 Announce Type: replace Abstract: Output diversity is crucial for Large Language Models as it underpins pluralism and creativity. In this work, we reveal that controlling the languag

Multi-Persona Thinking for Bias Mitigation in Large Language Models

arXiv:2601.15488v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit social biases, which can lead to harmful stereotypes and unfair outcomes. We propose extbf{Multi-Persona Thinki

Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization

arXiv:2508.10164v2 Announce Type: replace Abstract: Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning

tutorialsarxiv-cs-ai

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models

arXiv:2506.13139v2 Announce Type: replace-cross Abstract: Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, wher

RaTA-Tool: Retrieval-based Tool Selection with Multimodal Large Language Models

arXiv:2604.14951v1 Announce Type: cross Abstract: Tool learning with foundation models aims to endow AI systems with the ability to invoke external resources -- such as APIs, computational utilities,

Applications

Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization

arXiv:2604.15022v1 Announce Type: cross Abstract: Cost-aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the routing

applicationsarxiv-cs-cl

SelfGrader: Stable Jailbreak Detection for Large Language Models using Token-Level Logits

arXiv:2604.01473v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are powerful tools for answering user queries, yet they remain highly vulnerable to jailbreak attacks. Existing g

The Cost of Language: Centroid Erasure Exposes and Exploits Modal Competition in Multimodal Language Models

arXiv:2604.14363v1 Announce Type: new Abstract: Multimodal language models systematically underperform on visual perception tasks, yet the structure underlying this failure remains poorly understood.

local-aiarxiv-cs-cl

A closer look at how large language models trust humans: patterns and biases

arXiv:2504.15801v2 Announce Type: replace Abstract: As large language models (LLMs) and LLM-based agents increasingly interact with humans in decision-making contexts, understanding the trust dynamics

A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

arXiv:2604.13240v1 Announce Type: new Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are

safetyarxiv-cs-cv

Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments

arXiv:2508.08791v3 Announce Type: replace Abstract: Effective tool use is essential for large language models (LLMs) to interact with their environment. However, progress is limited by the lack of eff

local-aiarxiv-cs-cl

Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies

arXiv:2604.14111v1 Announce Type: new Abstract: Large Language Models (LLMs) are now capable of generating highly fluent, human-like text. They enable many applications, but also raise concerns such a

tutorialsarxiv-cs-cl

SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models

arXiv:2510.09541v3 Announce Type: replace Abstract: Diffusion large language models (dLLMs) are emerging as an efficient alternative to autoregressive models due to their ability to decode multiple to

Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI

arXiv:2603.18104v3 Announce Type: replace Abstract: Prevailing AI training infrastructure assumes reverse-mode automatic differentiation over IEEE-754 arithmetic. The memory overhead of training relat

researcharxiv-cs-ai

Agentic Control in Variational Language Models

arXiv:2604.12513v1 Announce Type: new Abstract: We study whether a variational language model can support a minimal and measurable form of agentic control grounded in its own internal evidence. Our mo

local-aiarxiv-cs-lg

Agents

ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods

arXiv:2603.21011v2 Announce Type: replace-cross Abstract: Finite element (FE) analysis guides the design and verification of nearly all manufactured objects. It is at the core of computational enginee

agentsarxiv-cs-ai

Beyond Perception Errors: Semantic Fixation in Large Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2604.12119v1 Announce Type: new Abstract: Large vision-language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from ru

Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models

arXiv:2604.12390v1 Announce Type: new Abstract: This paper addresses two limitations of large language models (LLMs) in solving complex problems: (1) their reasoning processes exhibit Bayesian-like st

tutorialsarxiv-cs-ai

HTDC: Hesitation-Triggered Differential Calibration for Mitigating Hallucination in Large Vision-Language Models

arXiv:2604.12115v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal performance, but still suffer from hallucinations caused by unstable visual grounding and

researcharxiv-cs-cv

Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation

arXiv:2604.13010v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teach

safetyarxiv-cs-ai

Applications

Surrogate models for diffusion on graphs via sparse polynomials

arXiv:2502.06595v3 Announce Type: replace-cross Abstract: Diffusion kernels over graphs have been widely utilized as effective tools in various applications due to their ability to accurately model th

applicationsarxiv-cs-lg

Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation

arXiv:2512.05812v5 Announce Type: replace-cross Abstract: Scalable multi-agent driving simulation requires behavior models that are both realistic and computationally efficient. We address this by opt

local-aiarxiv-cs-cv

Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations

arXiv:2604.09584v1 Announce Type: new Abstract: Flow physics and more broadly physical phenomena governed by partial differential equations (PDEs), are inherently continuous, high-dimensional and ofte

Assessing Privacy Preservation and Utility in Online Vision-Language Models

arXiv:2604.09695v1 Announce Type: cross Abstract: The increasing use of Online Vision Language Models (OVLMs) for processing images has introduced significant privacy risks, as individuals frequently

researcharxiv-cs-ai

CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models

arXiv:2604.11087v1 Announce Type: new Abstract: Despite the groundbreaking advancements made by large language models (LLMs), hallucination remains a critical bottleneck for their deployment in high-s

EdgeCIM: A Hardware-Software Co-Design for CIM-Based Acceleration of Small Language Models

arXiv:2604.11512v1 Announce Type: cross Abstract: The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, and embedded platforms, has exposed fun

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities

arXiv:2603.02578v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misalign

Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models

arXiv:2604.11609v1 Announce Type: new Abstract: Large language models exhibit sycophantic tendencies--validating incorrect user beliefs to appear agreeable. We investigate whether this behavior varies

LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models

arXiv:2604.09712v1 Announce Type: cross Abstract: Spatial reasoning is a cornerstone capability for intelligent systems to perceive and interact with the physical world. However, multimodal large lang

Locket: Robust Feature-Locking Technique for Language Models

arXiv:2510.12117v3 Announce Type: replace-cross Abstract: Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for

MIXAR: Scaling Autoregressive Pixel-based Language Models to Multiple Languages and Scripts

arXiv:2604.11575v1 Announce Type: new Abstract: Pixel-based language models are gaining momentum as alternatives to traditional token-based approaches, promising to circumvent tokenization challenges.

ProGAL-VLA: Grounded Alignment through Prospective Reasoning in Vision-Language-Action Models

model-releasesarxiv-cs-cl

arXiv:2604.09824v1 Announce Type: cross Abstract: Vision language action (VLA) models enable generalist robotic agents but often exhibit language ignorance, relying on visual shortcuts and remaining i

Self-Calibrating Language Models via Test-Time Discriminative Distillation

arXiv:2604.09624v1 Announce Type: new Abstract: Large language models (LLMs) are systematically overconfident: they routinely express high certainty on questions they often answer incorrectly. Existin

SinkTrack: Attention Sink based Context Anchoring for Large Language Models

arXiv:2604.10027v1 Announce Type: new Abstract: Large language models (LLMs) suffer from hallucination and context forgetting. Prior studies suggest that attention drift is a primary cause of these pr

researcharxiv-cs-cv

Towards Efficient Large Vision-Language Models: A Comprehensive Survey on Inference Strategies

arXiv:2603.27960v2 Announce Type: replace-cross Abstract: Although Large Vision Language Models (LVLMs) have demonstrated impressive multimodal reasoning capabilities, their scalability and deployment

Applications

Understanding Generalization in Role-Playing Models via Information Theory

arXiv:2512.17270v2 Announce Type: replace-cross Abstract: Role-playing models (RPMs) are widely used in real-world applications but underperform when deployed in the wild. This degradation can be attr

applicationsarxiv-cs-ai

Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization

arXiv:2604.10970v1 Announce Type: new Abstract: Background: Task-specific microscopy datasets are often small, making it difficult to train deep learning models that learn robust features. While self-

tutorialsarxiv-cs-cv

What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models

arXiv:2601.06165v2 Announce Type: replace-cross Abstract: Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries ar

When simulations look right but causal effects go wrong: Large language models as behavioral simulators

arXiv:2604.02458v2 Announce Type: replace-cross Abstract: Behavioral simulation is increasingly used to anticipate responses to interventions. Large language models (LLMs) enable researchers to specif

safetyarxiv-cs-ai

A Predictive View on Streaming Hidden Markov Models

arXiv:2604.09208v1 Announce Type: cross Abstract: We develop a predictive-first optimisation framework for streaming hidden Markov models. Unlike classical approaches that prioritise full posterior re