Search: “models”

Spectral-Target Physical Latent Structuring for JEPA-Style World Models

arXiv:2609.04264v1 Announce Type: new Abstract: Latent world models have become increasingly popular as a method to predict and plan in latent space rather than pixel space. Recent architectures, such

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TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation

DGX agent

arXiv:2609.05266v1 Announce Type: new Abstract: World-action models guide action generation with predicted future observations, but vision-centric predictions miss the local contact cues that decide c

local-aiarxiv-cs-ro

7 Sept 2026

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

arXiv:2609.05333v1 Announce Type: new Abstract: A transformer language model assigns a single, context-independent vector to a word type at its embedding layer, yet is widely believed to individuate t

local-aiarxiv-cs-ai

Think-Verify-Revise: Neuro-Symbolic Visual Reasoning with Vision-Language Models and Dynamic Logic Tensor Networks

model-releasesarxiv-cs-cv

arXiv:2609.05388v1 Announce Type: new Abstract: Visual reasoning tasks require a system to jointly perceive visual content and apply formal relational constraints---a combination that neither pure neu

VISTA: Dense Multi-Label Classroom Coding with Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.04550v1 Announce Type: new Abstract: Video-language benchmarks are usually constructed by the dataset authors without published reliability statistics, leaving the noise floor of the constr

Where Appearance Fails, Geometry Recognizes: A CAD-Free 3D Shape Prior That Complements Vision Foundation Models

model-releasesarxiv-cs-ai

arXiv:2609.04381v1 Announce Type: cross Abstract: Recognizing specific objects onboarded without a labeled training set recurs across manufacturing and service robotics, yet the conventional renderabl

Agents

Why Better Models Can Create Riskier Systems: Evidence from LLM Agents in Financial Markets

arXiv:2609.04373v1 Announce Type: new Abstract: Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We

agentsarxiv-cs-ai

DE-Venus: A Data-Efficient RLVR Framework for Large Language Models

arXiv:2609.03324v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-

High-Dimensional Learning Dynamics of Attention-Indexed Models

arXiv:2609.03858v1 Announce Type: new Abstract: Attention mechanisms are central to modern foundation models, yet their training dynamics remain poorly understood, especially when the attention matric

IchthyoNoma: Nomenclature and Context Sensitivity of Zero-Shot Biological Vision--Language Models for Bangladeshi Freshwater Fish Recognition

arXiv:2609.03985v1 Announce Type: cross Abstract: Zero-shot vision-language models (VLMs) are increasingly used as training-free species recognizers, but reported accuracy can reflect more than visual

Tutorials

Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views

arXiv:2609.04180v1 Announce Type: cross Abstract: Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulati

tutorialsarxiv-cs-ai

OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models

arXiv:2609.03972v1 Announce Type: new Abstract: Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accom

Toward an~Integrated Cognitive--Ergonomic Architecture for~Human--Machine Interaction: Combining Cognitive Models with~Human Factors Ergonomics

arXiv:2609.03704v1 Announce Type: new Abstract: This paper presents an integrated approach to modeling human competencies by combining the theoretical foundations of cognitive architectures with princ

safetyarxiv-cs-ro

Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models

arXiv:2609.03927v1 Announce Type: new Abstract: For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those act

safetyarxiv-cs-ro

VI3: Grounding Pretrained 3D Foundation Models with Inertial Cues

arXiv:2609.03824v1 Announce Type: new Abstract: 3D foundation models (3DFMs) excel at predicting camera poses and dense depth from multiple views of a scene, showcasing strong zero-shot generalization

researcharxiv-cs-cv

Tutorials

Zero-Shot Novel Depth Synthesis Using 3D Foundation Models Scene Representations

arXiv:2609.04174v1 Announce Type: new Abstract: 3D Foundation Models (3DFMs) such as VGGT have recently pushed the boundaries of 3D vision by predicting rich unified representations with feed-foward t

tutorialsarxiv-cs-cv

A computational approach to maximum likelihood thresholds for colored Gaussian graphical models

arXiv:2609.02382v1 Announce Type: cross Abstract: Gaussian graphical models (GGMs) are essential tools for interpretable structure learning. However, in high-dimensional, small-sample regimes, the ava

HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models

arXiv:2609.02029v1 Announce Type: new Abstract: Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughp

local-aiarxiv-cs-ai

Latent Cluster Analysis for Vision-Language-Action Models

arXiv:2609.02634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and perception into action, yet the internal

researcharxiv-cs-ro

When Persona Attributes Improve Population Alignment in Large Language Models

arXiv:2609.02526v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona prompting

Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models

arXiv:2509.25050v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a central paradigm for advancing Large Language Models (LLMs), where both pre-training and RL post-traini

AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages

arXiv:2606.12708v2 Announce Type: replace-cross Abstract: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP

Agents

Agentic Multimodal Models for Environmental Hyperspectral Unmixing

arXiv:2609.01289v1 Announce Type: new Abstract: Hyperspectral unmixing is a key task in remote sensing that aims to decompose mixed pixels in hyperspectral images into their constituent material signa

agentsarxiv-cs-cv

Causal Probing for Internal Visual Representations in Multimodal Large Language Models

arXiv:2605.05593v2 Announce Type: replace Abstract: Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode a

local-aiarxiv-cs-ai

Good Memory Has ECC: Evaluating the Memory of Vision-Language Models Beyond Accuracy

model-releasesarxiv-cs-ai

arXiv:2609.00103v1 Announce Type: cross Abstract: Memory is widely viewed as an important unsolved problem for LLMs and VLMs, and current benchmarks typically evaluate it by testing accuracy over long

Jailbreaking Text-to-Image Models Through Cracks: Navigating Heterogeneous Safety Filters via Multi-Agent Debate

arXiv:2609.01168v1 Announce Type: new Abstract: Text-to-image (T2I) models remain vulnerable to jailbreak attacks that elicit Not-Safe-For-Work (NSFW) content, despite increasingly being guarded by he

Latent-Space No-Arbitrage Geometry of Generative Models for Implied Volatility Surfaces

arXiv:2609.00332v1 Announce Type: cross Abstract: Generative models for implied volatility surfaces must produce outputs that satisfy static no-arbitrage constraints. We study these constraints in lat

Prompt-Robust Language Models: Which Training Strategies Work?

arXiv:2609.01217v1 Announce Type: new Abstract: Despite their strong performance, large language models remain highly sensitive to prompt formulation. Prior work addresses this through refined data co

Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training

model-releasesarxiv-cs-cv

arXiv:2504.01547v3 Announce Type: replace Abstract: Supervised deep learning achieves strong performance in biomedical image segmentation but relies on costly pixel-wise annotations, motivating semi-s

SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models

arXiv:2609.01004v1 Announce Type: cross Abstract: Despite their strong multimodal understanding ability, multimodal large language models (MLLMs) incur substantial computational overhead when processi

Applications

The Privacy-Hallucination Tradeoff in Differentially Private Language Models

arXiv:2609.00492v1 Announce Type: new Abstract: Both privacy and factual accuracy are paramount in high-stakes domains like healthcare. Concerningly, we uncover and investigate a privacy-hallucination

applicationsarxiv-cs-ai

Towards unsupervised representation learning for quantum data: quantum models with inference and generation

arXiv:2609.00372v1 Announce Type: cross Abstract: With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rather

Amortizing intractable inference in large language models

arXiv:2310.04363v3 Announce Type: replace-cross Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This lim

Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models

arXiv:2603.00763v2 Announce Type: replace Abstract: Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets

researcharxiv-cs-cv

Learning diverse attacks on large language models for robust red-teaming and safety tuning

arXiv:2405.18540v3 Announce Type: replace Abstract: Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large langu

Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models

arXiv:2601.15436v3 Announce Type: replace Abstract: We propose a novel perspective for probing LLM sycophancy in a direct and neutral way, mitigating various forms of uncontrolled bias, noise, or mani

Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models

arXiv:2608.29996v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background c

PEPPER: Perception-Guided Perturbation for Robust Backdoor Defense in Text-to-Image Diffusion Models

arXiv:2511.16830v4 Announce Type: replace Abstract: Recent studies show that text-to-image (T2I) diffusion models are vulnerable to backdoor attacks, where a trigger in the input prompt can steer gene

researcharxiv-cs-cl

Robustness of Vision Language Models Against Split-Image Harmful Input Attacks

arXiv:2602.08136v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are now a core part of modern AI. Recent work proposed several visual jailbreak attacks using single/ holistic i

Agents

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

arXiv:2608.30910v1 Announce Type: cross Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direc

agentsarxiv-cs-cl

Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence

arXiv:2608.31075v1 Announce Type: new Abstract: Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoni

State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models

arXiv:2608.28698v1 Announce Type: new Abstract: Compared with typical vision-language tasks, document parsing places stronger demands on fine-grained visual perception. Existing vision-language model

local-aiarxiv-cs-cv

Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation

arXiv:2608.29923v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) relies on vision-language alignment to recognize arbitrary text-defined categories, yet this alignment is f

safetyarxiv-cs-cv

TPvG: A Moral Decision Framework for Large Language Models from One-Shot to Sequential Feedback

arXiv:2608.28610v1 Announce Type: new Abstract: Existing LLM moral evaluations typically present models with isolated moral vignettes and elicit a single-shot decision, neglecting a factor known to pr

Universal Redundancies in Time Series Foundation Models

arXiv:2602.01605v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need for

Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation

arXiv:2604.03592v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps rema

Where Identity Lives: Localized, Retain-Free Identity Unlearning in Multimodal Large Language Models

arXiv:2608.30649v1 Announce Type: new Abstract: Removing a specific individual's information from multimodal large language models (MLLMs) is often needed after deployment, but existing methods rely o

local-aiarxiv-cs-cl

CamoDocs: A Poisoning Attack Against Retrieval-Augmented Language Models Using Camouflaged Documents