Search: “models”

IFHierBench: Hierarchical Instruction Following for Large Language Models

arXiv:2607.27912v1 Announce Type: cross Abstract: Instruction-following ability is critical for deploying large language models in real-world applications, where downstream components depend on the ou

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Memory Efficient Tabular Foundation Models

DGX agent

arXiv:2607.27546v1 Announce Type: new Abstract: Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learn

researcharxiv-cs-lg

31 Jul 2026

Applications

TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction

arXiv:2607.28391v1 Announce Type: new Abstract: World Action Models (WAMs) combine future-state prediction with robot action generation, but existing approaches largely rely on visual futures. Visual

applicationsarxiv-cs-ro

WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models

arXiv:2607.26621v2 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation mod

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution

arXiv:2607.27308v1 Announce Type: new Abstract: We introduce ZUNA1.1, a 380M-parameter diffusion autoencoder for flexible EEG signal reconstruction. ZUNA1.1 is capable of reconstructing variable lengt

Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning?

arXiv:2607.26952v1 Announce Type: new Abstract: We introduce CreditCardQA, the first financial literacy benchmark for numerical reasoning derived from real credit card agreements. The dataset contains

HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models

arXiv:2607.27030v1 Announce Type: cross Abstract: LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs acros

Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States

arXiv:2607.26929v1 Announce Type: new Abstract: The same diagnostic result can support or challenge one causal claim yet fail to address another when the claims concern different populations, outcomes

TPCD: Tone-Pressure Contrastive Decoding and the Label-Free Gating Bottleneck in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2607.26536v1 Announce Type: new Abstract: High-pressure prompts can push vision-language models (VLMs) into unsupported commitments, such as reading illegible text, reporting indeterminate times

Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation

arXiv:2607.25527v1 Announce Type: cross Abstract: Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data d

researcharxiv-cs-ai

Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualization

arXiv:2607.25021v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can connect visualization patterns to external causes, consequences, and domain knowledge, but the evidential b

Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks

arXiv:2607.25227v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly

Evaluation of Adversarial Robustness in Arabic Language Models

arXiv:2607.25814v1 Announce Type: new Abstract: The emergence of the recent outstanding capabilities of Arabic Language Models has opened doors for exposing their vulnerabilities. One of the major sec

safetyarxiv-cs-cl

GAUGE: Grading Agent-Built Financial Models Without a Golden Answer

arXiv:2607.24889v1 Announce Type: cross Abstract: Financial models combine public disclosures with analyst assumptions to produce forecasts and valuations. While some components can be checked mechani

Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications

arXiv:2607.25642v1 Announce Type: cross Abstract: Instruction-based Image Editing (IIE) aims to transform a given image into a new one based on textual instructions. Advances in Large Language Models

Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models

arXiv:2607.25907v1 Announce Type: cross Abstract: Activation steering controls model behavior by editing internal activations at inference time. We study its input-side dual: optimizing a fluent promp

Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising

arXiv:2607.24841v1 Announce Type: new Abstract: Autoregressive (AR) large language models (LLMs) are inherently inefficient at inference time because each generated token requires accessing the full s

PilotRL: Training Language Model Agents via Global Planning-Guided Progressive Reinforcement Learning

arXiv:2508.00344v5 Announce Type: replace Abstract: Large Language Models (LLMs) have shown remarkable advancements in tackling agent-oriented tasks. Despite their potential, existing work faces chall

All in One: Generative Modeling as Mean-Field Game Design

arXiv:2607.23026v1 Announce Type: cross Abstract: Mean-field games (MFGs) offer a unifying lens on continuous-time generative modeling: a cost tuple recovering twelve prominent models---Continuous Nor

researcharxiv-cs-ai

Cheap Probes Predict Expensive Training in 3D-CT Vision--Language Models

arXiv:2607.22771v1 Announce Type: cross Abstract: Picking the frozen image encoder for a 3D~CT vision--language model (VLM), together with the token-compression scheme on top of it, is a search over m

DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models

model-releasesarxiv-cs-cv

arXiv:2607.24016v1 Announce Type: new Abstract: Recent advances in generative models have shifted AI-generated image detection from identifying easily distinguishable, fully synthetic images to identi

DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning

arXiv:2607.24159v1 Announce Type: cross Abstract: Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vi

safetyarxiv-cs-cv

DualityCert: Verifier-Gated Language-Model Repair of Broken Duality Claims in Quantum Field Theory

arXiv:2607.23614v1 Announce Type: cross Abstract: We present DualityCert, a symbolic verifier for candidate Seiberg-duality claims in four-dimensional N=1 quiver gauge theories. The verifier evaluates

Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation

arXiv:2607.22637v1 Announce Type: new Abstract: Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state informa

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models

arXiv:2607.23226v1 Announce Type: new Abstract: Despite the empirical success of score-based diffusion models, a complete theoretical understanding of how finite-sample learning, network parameterizat

researcharxiv-cs-lg

GNM Head: A Generative aNthropometric Model of the human head

arXiv:2607.23687v1 Announce Type: new Abstract: Parametric models of the human head are essential tools traditionally used in computer vision and graphics for animation, rendering, and reconstruction.

researcharxiv-cs-cv

Interpreting Quantum Learning Models via Stochastic Processes

arXiv:2607.17327v2 Announce Type: replace-cross Abstract: Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement. While such models

researcharxiv-cs-lg

Tutorials

Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning

arXiv:2411.15469v3 Announce Type: replace Abstract: Continual Learning (CL) aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowl

tutorialsarxiv-cs-cv

MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models

arXiv:2607.22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However

Same Predictions, Different Reasons: The Effect of Quantization on Model Explanations

arXiv:2607.22872v1 Announce Type: cross Abstract: Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressin

researcharxiv-cs-cv

What do Reward Models Memorize?

arXiv:2607.24484v1 Announce Type: cross Abstract: This paper studies what discriminatively trained reward models (RMs) memorize by measuring counterfactual memorization on two human preference dataset

researcharxiv-cs-cl

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification

arXiv:2607.21839v1 Announce Type: cross Abstract: Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing

safetyarxiv-cs-lg

Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model

arXiv:2607.22221v1 Announce Type: cross Abstract: Zermelo's algorithm is a classical method for computing the maximum likelihood estimator in the Bradley--Terry (BT) model, but its convergence can be

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

arXiv:2607.16811v2 Announce Type: replace Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributiona

On the Identifiability of Controlled World Models

arXiv:2607.22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to plan

safetyarxiv-cs-lg

Spectral Prior for Reducing Exposure Bias in Diffusion Models

arXiv:2607.22091v1 Announce Type: new Abstract: Diffusion models typically suffer from error accumulation during iterative sampling, commonly referred to as exposure bias. We reveal systematic frequen

safetyarxiv-cs-cv

Applications

Token-Operations-Oriented Inference Optimization Techniques for Large Models

arXiv:2606.20295v2 Announce Type: replace-cross Abstract: Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large mode

applicationsarxiv-cs-cl

What Matters When Building Universal Multilingual Named Entity Recognition Models?

arXiv:2601.06347v2 Announce Type: replace Abstract: Recent progress in universal multilingual named entity recognition (NER) has been driven by multilingual transformer models, task-specific architect

researcharxiv-cs-cl

Why Large Language Models and Humans Converge and Diverge in Evaluating Creativity

arXiv:2607.22218v1 Announce Type: new Abstract: Despite the growing use of large language models (LLMs) as creativity evaluators, evidence of their alignment with human evaluations remains mixed, rais

safetyarxiv-cs-cl

Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment

arXiv:2607.20476v1 Announce Type: new Abstract: We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data. Testing 60 scenarios across three c

Benchmarking the Personalization Capabilities of Large Language Models

arXiv:2607.20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradit

ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues

arXiv:2604.01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit bias

Profiling Lightweight Large Language Models

arXiv:2607.20806v1 Announce Type: new Abstract: Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resour

Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning

arXiv:2607.20913v1 Announce Type: new Abstract: Fine-tuning large diffusion models for new domains or styles involves a trade-off: improving target-specific generation often degrades the pretrained mo

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

arXiv:2607.19847v1 Announce Type: cross Abstract: Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise i

Bayesian Wind Tunnels for Model Selection

arXiv:2607.19379v1 Announce Type: new Abstract: Prior work has shown that transformers can perform exact Bayesian filtering within a fixed hypothesis class. Can they also perform Bayesian model select

ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models

arXiv:2607.20092v1 Announce Type: cross Abstract: Contextual entrainment is the tendency of a model to let auxiliary context in its input pull its output, independently of whether that context is rele

Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning

arXiv:2602.00072v2 Announce Type: replace Abstract: The performance of machine learning surrogates is critically dependent on data quality and quantity. This presents a major challenge, as high-fideli