Search: “models”

PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs

arXiv:2608.02150v1 Announce Type: new Abstract: Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of

researcharxiv-cs-cv

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Model Releases

RAP: KV-Cache Compression via RoPE-Aligned Pruning

DGX agent

arXiv:2602.02599v4 Announce Type: replace Abstract: Long-context inference in large language models (LLMs) is bottlenecked by the memory and compute of the key-value (KV) cache. Structured pruning is

model-releasesarxiv-cs-lg

4 Aug 2026

S^4R: Selective Sampling, Subspaces, and Sparse Reconstruction for Compressed Long-Context KV Caching

arXiv:2608.00528v1 Announce Type: new Abstract: The growth of context window lengths in Large Language Models (LLMs) significantly enhances their long-context capabilities but incurs prohibitive memor

SCHEDBench: A Benchmark for Evaluating LLM Constraint Faithfulness in Natural-Language Combinatorial Scheduling

arXiv:2608.00991v1 Announce Type: cross Abstract: This paper introduces SCHEDBench, a natural-language benchmark for evaluating combinatorial scheduling constraint faithfulness under surface-form vari

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach

arXiv:2608.00030v1 Announce Type: new Abstract: Specialised retrieval agents typically surface higher quality results than general-purpose search, but selecting the optimal agent for a given query rem

Test-Time Curriculum for Open-Set AIGC Detection

arXiv:2608.00559v1 Announce Type: new Abstract: AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to e

UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation

arXiv:2608.00915v1 Announce Type: new Abstract: Uplift modeling (conditional-average-treatment-effect estimation) drives personalized targeting, yet published uplift benchmarks frequently disagree on

Writing-System-Level Tokenizer Adaptation for Byte-Level BPE

arXiv:2608.00582v1 Announce Type: new Abstract: Pretrained byte-level BPE tokenizers can segment underrepresented languages inefficiently. Replacing a tokenizer changes the meaning of nearly every tok

Z-PEFT: Zero-shot Backdoor Detection in Parameter-Efficient Fine-Tuning via Canonical Spectral Signatures

arXiv:2608.02271v1 Announce Type: new Abstract: Parameter-Efficient Fine-tuned (PEFT) models are frequently downloaded from open repositories by practitioners. This widespread practice creates a signi

Bootstrapping Self-Supervised Learning of Binary Classification Using Error Bounds: A Case Study on a Robotic Insertion Task

model-releasesarxiv-cs-ro

arXiv:2607.29640v1 Announce Type: new Abstract: Flexible manufacturing requires rapid deployment of solutions and minimal setup time to remain competitive. An essential attribute is the ability to con

Can LLMs Really Understand Item Difficulty Levels? Implications for Automated Item Generation Using LLMs

arXiv:2607.28634v1 Announce Type: new Abstract: The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments. This study explores h

Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning

arXiv:2607.16057v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are improving rapidly as reflected in benchmark scores, yet these AI benchmarks largely test capabilities such as

Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding

arXiv:2607.29196v1 Announce Type: new Abstract: Long-running multi-turn interactions with chatbots and agents are now common, and a correct response often depends on remembering earlier details, track

Incorporating data drift to perform survival analysis on credit risk

arXiv:2601.20533v2 Announce Type: replace-cross Abstract: Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing

safetyarxiv-cs-lg

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection

arXiv:2512.22179v3 Announce Type: replace Abstract: Detecting previously unseen attacks remains a major challenge for machine learning-based intrusion detection systems. Deep models trained on network

Locally Consistent Transductive Information Maximization for Few-Shot Remote Sensing Scene Classification

arXiv:2607.29192v1 Announce Type: new Abstract: Remote sensing scene classification is increasingly relying on foundation models pre-trained on large-scale Earth-observation data. Moreover, transducti

Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation

arXiv:2607.28645v1 Announce Type: cross Abstract: Recent multimodal large language models can convert visual designs directly into executable code, but real mobile products require multiple screenshot

Metaphor-Induced Algorithmic Steering: Cross-Domain Procedural Transfer in LLM Code Generation

arXiv:2607.28683v1 Announce Type: cross Abstract: Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve gener

researcharxiv-cs-ai

MirrorCraft: Paired Evaluation under Hidden Rule Changes in Minecraft

arXiv:2607.29218v1 Announce Type: new Abstract: With the prosperity of the large language models (LLMs), it has become an interesting topic: how do LLM-based agents work in Minecraft? Unfortunately, m

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

arXiv:2607.29462v1 Announce Type: cross Abstract: Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfi

Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization

arXiv:2607.29008v1 Announce Type: cross Abstract: Modern opaque AI models prize performance over interpretability, which makes testing difficult. However, formal statistical tests conducted on a model

safetyarxiv-cs-lg

Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks

arXiv:2607.28685v1 Announce Type: new Abstract: Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety. We treat four of them (R-Judge, I

SEDR-Seq2P: A Lightweight Dilated Residual Sequence-to-Point Network for Multi-Task Industrial NILM

arXiv:2607.28693v1 Announce Type: cross Abstract: Industrial NILM remains challenging because measurement noise and widespread concurrent machine operation reduce the generalization of models tuned on

Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution

arXiv:2605.25333v2 Announce Type: replace Abstract: Video world models should maintain evolving states when evidence is unobserved, yet current generators often freeze hidden states upon interruption.

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale

arXiv:2607.14144v2 Announce Type: replace Abstract: The Platonic Representation Hypothesis (PRH) holds that as models scale, representations of heterogeneous networks converge toward a shared model of

researcharxiv-cs-ai

TokenSwap: Benchmarking and Reducing the Modality Gap in Multimodal LLMs

arXiv:2607.28640v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) should generate consistent responses given semantically equivalent inputs across modalities. However, we observ

researcharxiv-cs-cl

A Systems Engineering Framework for Vision-Language-Enabled UAV Triage and Disaster Response

arXiv:2607.27597v1 Announce Type: new Abstract: Recent advances in Vision Language Models (VLMs) have created new opportunities for disaster response, where responders must interpret large volumes of

safetyarxiv-cs-ro

AutoSupervision: Closing the Feedback Loop in Scientific Workflows with Grounded Revision Verification

arXiv:2607.27845v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled AI systems to assist scientific research and peer review. However, an essential capability

Agents

Beacon: Knowing When and How to Perform Agentic Visual Reasoning

arXiv:2607.28595v1 Announce Type: new Abstract: The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks, rather tha

agentsarxiv-cs-cv

Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning

arXiv:2607.27968v1 Announce Type: new Abstract: Machine unlearning seeks to selectively remove specific knowledge from trained language models without full retraining, a growing necessity under privac

Local Ai

Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton

arXiv:2607.27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linea

local-aiarxiv-cs-lg

HARGO: Heterogeneity-Aware Reward-Guided Optimization for RL Post-Training of LLMs on HPC Tasks

arXiv:2607.28301v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) can equip large language models (LLMs) with domain knowledge for high-performance computing (HPC) tasks such as data race d

JigShape: Evaluating Visual-Geometric Reasoning in VLMs through Jigsaw Puzzles

arXiv:2607.27670v1 Announce Type: new Abstract: Jigsaw puzzle solving requires jointly reasoning about visual content and geometric constraints, yet existing benchmarks use rectangular cuts that creat

MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning

arXiv:2607.27109v2 Announce Type: cross Abstract: With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-gra

MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning

arXiv:2607.26465v1 Announce Type: new Abstract: Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform seq

ProgFormer: Hierarchical Voxel Diffusion Transformer for Longitudinal Brain MRI Prediction

arXiv:2607.27537v1 Announce Type: new Abstract: Predicting future structural MRI of a brain is challenging because longitudinal changes are often subtle and confined to specific anatomical regions, wh

researcharxiv-cs-cv

Recursive transformers for semiconductor thermo-mechanical reliability

arXiv:2607.27251v1 Announce Type: new Abstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design. But conventional transf

RefCaptioner: Multi-Reference Image-Grounded Video Captioning

arXiv:2607.28509v1 Announce Type: new Abstract: Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference

Hardware

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation

arXiv:2607.27744v1 Announce Type: new Abstract: Modern recommendation models gain prediction quality by scaling feature-interaction and sequence modules, but production cost constraints cap how far sy

hardwarearxiv-cs-lg

UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks

arXiv:2607.26724v1 Announce Type: new Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks. However, existing methods typically rely on a limited set o

VESTIGE: A Knowledge-Guided Masking Strategy for Corruption-Aware Fine-Tuning of Genomic Transformers, Validated on Ancient DNA Reconstruction

arXiv:2607.27712v1 Announce Type: new Abstract: Standard masked-language-model fine-tuning applies a uniform masking probability across every token position, assuming reconstruction difficulty is posi

Agents

VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System

arXiv:2607.27380v1 Announce Type: new Abstract: Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal ev

agentsarxiv-cs-cv

Applications

ViewMind3D: Modular View-Aware Inference for Training-Free 3D-QA

arXiv:2607.28442v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled new possibilities for 3D question answering (3D-QA), a ke

applicationsarxiv-cs-cv

What Is The Performance Ceiling of My Classifier? Utilizing Category-Wise Influence Functions for Pareto Frontier Analysis

arXiv:2510.03950v2 Announce Type: replace Abstract: Data-centric learning seeks to improve model performance from the perspective of data quality, and has been drawing increasing attention in the mach

researcharxiv-cs-lg

AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing

arXiv:2601.11007v2 Announce Type: replace-cross Abstract: LLM role-playing aims to portray arbitrary characters in interactive narratives, yet existing systems often suffer from limited immersion and

Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time

arXiv:2607.26647v1 Announce Type: new Abstract: While text-to-image diffusion models achieve impressive visual quality, they frequently struggle to maintain precise alignment with complex compositiona

safetyarxiv-cs-cv

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

arXiv:2606.19651v2 Announce Type: replace-cross Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented

DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English

arXiv:2601.22888v4 Announce Type: replace Abstract: More than 80% of the 1.6B English speakers do not use Standard American English (SAE), yet LLMs often fail to correctly identify non-SAE dialects an