Search: “models”

SemKV: Semantic Mixed-Precision KV Cache Quantization Guided by the Quality Cliff for Long-Context LLM Inference

arXiv:2608.28911v1 Announce Type: cross Abstract: The key-value (KV) cache is the dominant memory bottleneck of long-context large language model (LLM) inference, growing linearly with context length.

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Model Releases

Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?

DGX agent

arXiv:2602.11166v2 Announce Type: replace-cross Abstract: Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assumed

model-releasesarxiv-cs-ai

1 Sept 2026

Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse

arXiv:2608.30956v1 Announce Type: cross Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input

researcharxiv-cs-ai

TDDM-Melatt: A Decoupled Memory and Diffusion Framework for Generalizable Encrypted Traffic Classification

arXiv:2608.30745v1 Announce Type: new Abstract: The widespread adoption of encrypted traffic poses severe challenges to current security situational awareness systems based on network traffic monitori

Test-Time Scaling for Scientific Equation Discovery

arXiv:2608.28660v1 Announce Type: cross Abstract: Test-time scaling (TTS) improves language model reasoning by allocating additional test-time compute, but prior work mainly studies closed-ended tasks

Vocabulary Growth Fundamentals: Bernstein Functions and Hausdorff Sequences

arXiv:2608.29449v1 Announce Type: cross Abstract: We survey the theory of vocabulary growth founded in the setting of stochastic processes. In particular, we model the expected number of types through

researcharxiv-cs-cl

Local Ai

When Errors Become Memories: Causal Pathway Tracing in Multi-Turn Memory-Augmented LLMs

arXiv:2608.30198v1 Announce Type: new Abstract: Long-term memory enables large language models (LLMs) to preserve and reuse information across interactions, but it can also turn localized errors into

local-aiarxiv-cs-cl

When Hate Meets Facts: LLMs-in-the-Loop for Check-worthiness Detection in Hate Speech

arXiv:2603.25269v2 Announce Type: replace Abstract: Hateful content online is often expressed using fact-like, not necessarily correct information, especially in coordinated online harassment campaign

researcharxiv-cs-cl

When Patients Cut In: Extending Clinical Conversational AI Safety to Interruptions

arXiv:2608.29241v1 Announce Type: new Abstract: Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt. These systems typically use a cascad

WiT: Waypoint Diffusion Transformers for Alleviating Trajectory Conflict in Pixel-Space Image Generation

arXiv:2603.15132v3 Announce Type: replace Abstract: While recent Flow Matching models avoid the reconstruction bottlenecks of latent autoencoders by operating directly in pixel space, the raw pixel ma

A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation

arXiv:2608.28247v1 Announce Type: new Abstract: Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by i

Beyond Global Scalars: Synergizing Token-Level Statistics and Deep Semantics for Adversarial AIGC Text Detection

arXiv:2608.28009v1 Announce Type: new Abstract: The rapid evolution of large language models necessitates robust machine-generated text detection. Existing paradigms typically follow two isolated trac

CultureConverse: A Multilingual Multi-turn Simulation Harness for Culturally Grounded Assistance in East and Southeast Asia

arXiv:2608.28405v1 Announce Type: new Abstract: Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common u

Improved off-policy training of diffusion samplers

arXiv:2402.05098v5 Announce Type: replace Abstract: We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark s

Landau theory of quenched criticality in linear in-context learning

arXiv:2608.28059v1 Announce Type: cross Abstract: In-context learning (ICL) allows a pretrained model to infer a new task from examples supplied in its prompt without updating its parameters. In linea

Learning a Single Token to Replace Long System Prompts in LLMs

arXiv:2511.23271v2 Announce Type: replace Abstract: Long system prompts are widely used to steer Large Language Models (LLMs), but repeatedly processing them at inference time is inefficient and consu

Hardware

Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification

arXiv:2608.27954v1 Announce Type: cross Abstract: Post-deployment changes to large language models can alter behavior while leaving routine outputs largely unchanged, creating a challenge for AI gover

hardwarearxiv-cs-lg

Physics-Guided Flow Matching for CT Image Reconstruction

arXiv:2608.28256v1 Announce Type: cross Abstract: Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with diffusion-based approaches achievin

researcharxiv-cs-cv

Post-Training VLMs for Video Mistake Detection

arXiv:2608.28406v1 Announce Type: new Abstract: Human mistakes are inevitable when following instructions, yet they can lead to severe consequences. As such, there has been an increased interest in de

Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning

arXiv:2604.06079v2 Announce Type: replace Abstract: Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals i

Talk in Pieces, See in Whole: Disentangled and Hierarchical Representation Learning in Language-based Object Detection

arXiv:2509.24192v2 Announce Type: replace Abstract: Vision-language models (VLMs) have advanced multimodal perception, demonstrated by open-vocabulary object detection with simple language queries. St

Safety

The Autonomy Tax: Defense Training Breaks LLM Agents

arXiv:2603.19423v3 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously comp

safetyarxiv-cs-lg

A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation

arXiv:2510.11567v2 Announce Type: replace Abstract: Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real i

researcharxiv-cs-cv

A Very Big Video Reasoning Suite

arXiv:2602.20159v3 Announce Type: replace-cross Abstract: Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grou

AEScorer: An Agentic Evidence-Grounded Framework for Graded Factuality Verification

arXiv:2601.03605v2 Announce Type: replace Abstract: Despite the significant advancements of Large Language Models (LLMs), their factuality remains a critical challenge, creating a growing need for mor

Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmark

arXiv:2608.26224v1 Announce Type: cross Abstract: Event triggering sits at the heart of high-energy physics, where the rare events of interest must be retained while an overwhelming background is disc

Safety

Decoupling Planning and Control for Instructable Agents

arXiv:2608.26788v1 Announce Type: new Abstract: Recent work shows that pre-trained, instruction-tuned vision-language models (VLMs) perform well at mapping from instructions and observations to high-l

safetyarxiv-cs-ai

Local Ai

DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research

arXiv:2608.26990v1 Announce Type: new Abstract: Large language models can summarize financial information, but an operational stock-research system must first assemble heterogeneous evidence, expose u

local-aiarxiv-cs-ai

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities

arXiv:2608.26950v1 Announce Type: new Abstract: Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing

Learning to Reason with Curriculum I: Provable Benefits of Autocurriculum

arXiv:2603.18325v2 Announce Type: replace Abstract: Chain-of-thought reasoning, where language models expend additional computation by producing thinking tokens prior to final responses, has driven si

researcharxiv-cs-lg

LLMs for Academic Workflows: An Evaluation of Literature Reviews Generated with Short and Long Context Windows of LLMs

arXiv:2608.26145v1 Announce Type: new Abstract: Our research focuses on evaluating literature reviews generated in short and long context settings of large language models (LLMs) to investigate the im

LoRA as Oracle

arXiv:2601.11207v2 Announce Type: replace-cross Abstract: Practitioners increasingly deploy neural networks they did not train, and must audit them after the fact for hidden backdoors, without the tra

Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

arXiv:2608.27429v1 Announce Type: new Abstract: Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through extit{de novo} ge

Applications

Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs

arXiv:2608.26729v1 Announce Type: new Abstract: In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledg

applicationsarxiv-cs-lg

Safety

NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation

arXiv:2608.26222v1 Announce Type: cross Abstract: Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated t

safetyarxiv-cs-ai

Not Just Reason, Not Just Scan: Reinforcement Learning for Proactive Scientific Error Verification over Academic Paper

arXiv:2608.26596v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly capable scientific assistants, yet they remain far from fully autonomous research. This transi

Redwood: A Frontier AI Accelerator Designed, Verified, and Deployed from Scratch in 2 Weeks by AI

arXiv:2608.26418v1 Announce Type: cross Abstract: Modern AI workloads and the hardware that runs them evolve on different timescales: architectural definition precedes volume silicon by years, while t

StreamAV-Bench: A Comprehensive Benchmark for Streaming Audio-Video Generation

arXiv:2608.26336v1 Announce Type: cross Abstract: Recent advancements in generative models are pushing video generation toward unbounded streaming audio-video generation for real-time interactive worl

TwinKV: A Composable Repair Pass for KV Cache Eviction via Pairwise Key Redundancy

arXiv:2608.27128v1 Announce Type: new Abstract: Long-context inference is bottlenecked by the memory footprint of the key-value (KV) cache, especially for small models under tight resource budgets. Ex

What the 'Spotless' Mind Remembers: How Knowledge Entanglement Shapes What Leaks After Unlearning in LLMs

arXiv:2510.25732v2 Announce Type: replace-cross Abstract: Unlearning in large language models (LLMs) is usually evaluated as whether an 'unlearned' fact can be recovered. We instead ask whether a fact

researcharxiv-cs-ai

A Comedy of Estimators: On KL Regularization in RL Training of LLMs

arXiv:2512.21852v4 Announce Type: replace Abstract: The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL ob

A Storage-Retrieval Gap in Parametric Knowledge Graph Memory

arXiv:2608.25489v1 Announce Type: cross Abstract: Graph retrieval-augmented generation places retrieved subgraphs into the model's context window at query time, paying a recurring token cost and expos

Ban&Pick: Enhancing Performance and Efficiency of MoE-LLMs via Smarter Routing

arXiv:2509.06346v3 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs) efficiently. Recent fine-grained MoE designs

CaSKG: Counterfactual-Causal Skill Graphs for Scalable Agent Skill Retrieval

arXiv:2608.25500v1 Announce Type: cross Abstract: Reusable skill libraries allow large language model (LLM) agents to reuse procedural knowledge across tasks, but they also turn memory access into a c

FAMPWQ: Fisher Information-based Adaptive Mixed Precision Weight Quantization for Effective LLM Inference

arXiv:2608.24945v1 Announce Type: new Abstract: Recent years have witnessed remarkable achievements of Large Language Models (LLMs) in multiple domains, while the excessive resource requirements of LL

researcharxiv-cs-lg

From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations

arXiv:2608.25894v1 Announce Type: new Abstract: Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon w

GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction

arXiv:2601.13207v2 Announce Type: replace Abstract: Geo-localization aims to infer the geographic location where an image was captured using observable visual evidence. Traditional methods achieve imp

LDAC-Net: A Learnable Multi-Lag Differencing Attention-Convolution Network for Drift-Robust Recognition with Low-Cost MOX Gas Sensors

arXiv:2608.25646v1 Announce Type: new Abstract: Portable electronic-nose systems based on low-cost metal-oxide (MOX) gas sensors offer a practical solution for gas and odour recognition, but their sig