Search: “models”

Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation

arXiv:2605.14053v1 Announce Type: cross Abstract: The application of Large Language Models to Question Answering has shown great promise, but important challenges such as hallucinations and erroneous

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Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict

DGX agent

arXiv:2605.14473v1 Announce Type: cross Abstract: The Context-Compliance Regime in Retrieval-Augmented Generation (RAG) occurs when retrieved context dominates the final answer even when it conflicts

model-releasesarxiv-cs-ai

15 May 2026

Exemplar Partitioning for Mechanistic Interpretability

arXiv:2605.14347v1 Announce Type: new Abstract: We introduce Exemplar Partitioning (EP), an unsupervised method for constructing interpretable feature dictionaries from large language model activation

FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching

arXiv:2604.06757v2 Announce Type: replace Abstract: Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We chal

Fusion-fission forecasts when AI will shift to undesirable behavior

arXiv:2605.14218v1 Announce Type: new Abstract: The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self

GenExam: A Multidisciplinary Text-to-Image Exam

arXiv:2509.14232v5 Announce Type: replace Abstract: Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style bench

GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning

arXiv:2605.14841v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become the dominant paradigm for parameter-efficient fine-tuning (PEFT) of large language models (LLMs). However, its b

GradShield: Alignment Preserving Finetuning

arXiv:2605.14194v1 Announce Type: new Abstract: Large Language Models (LLMs) pose a significant risk of safety misalignment after finetuning, as models can be compromised by both explicitly and implic

safetyarxiv-cs-cl

GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations

model-releasesarxiv-cs-cl

arXiv:2605.14498v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems t

IPR-1: Interactive Physical Reasoner

arXiv:2511.15407v3 Announce Type: replace Abstract: Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarl

Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis

arXiv:2605.14392v1 Announce Type: new Abstract: We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the envi

LoRA in LoRA: Towards Parameter-Efficient Architecture Expansion for Continual Visual Instruction Tuning

arXiv:2508.06202v2 Announce Type: replace-cross Abstract: Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models (MLLMs) to incrementally learn new tasks over time. Howeve

Agents

MALLVI: A Multi-Agent Framework for Integrated Generalized Robotics Manipulation

arXiv:2602.16898v5 Announce Type: replace-cross Abstract: Task planning for robotic manipulation with large language models (LLMs) is an emerging area. Prior approaches rely on specialized models, fin

agentsarxiv-cs-ai

Mechanical Enforcement for LLM Governance:Evidence of Governance-Task Decoupling in Financial Decision Systems

arXiv:2605.14744v1 Announce Type: cross Abstract: Large language models in regulated financial workflows are governed by natural-language policies that the same model interprets, creating a principal-

Local Ai

Mistletoe: Stealthy Acceleration-Collapse Attacks on Speculative Decoding

arXiv:2605.14005v1 Announce Type: new Abstract: Speculative decoding has become a widely adopted technique for accelerating large language model (LLM) inference by drafting multiple candidate tokens a

local-aiarxiv-cs-cl

MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation

arXiv:2605.13857v1 Announce Type: cross Abstract: The creation of cinematic-quality animal effects necessitates the precise modeling of muscle and fur dynamics, a process that remains both labor-inten

MUON+: Towards More Effective Muon via One Additional Normalization Step for LLM Pre-training

arXiv:2602.21545v3 Announce Type: replace Abstract: Muon has recently emerged as a strong optimizer for large language model pre-training, orthogonalizing the momentum matrix via Newton--Schulz polar

Neural Signals Generate Clinical Notes in the Wild

arXiv:2601.22197v3 Announce Type: replace-cross Abstract: Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings

Research

RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution

arXiv:2605.15154v1 Announce Type: cross Abstract: Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attr

researcharxiv-cs-lg

SceneFunRI: Reasoning the Invisible for Task-Driven Functional Object Localization

arXiv:2605.14704v1 Announce Type: cross Abstract: In real-world scenes, target objects may reside in regions that are not visible. While humans can often infer the locations of occluded objects from c

SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples

arXiv:2507.07776v3 Announce Type: replace Abstract: Unrestricted adversarial attacks aim to fool computer vision models without being constrained by ell_p-norm bounds to remain imperceptible to humans

Tutorials

Support Before Frequency in Discrete Diffusion

arXiv:2605.13999v1 Announce Type: new Abstract: Discrete diffusion models are increasingly competitive for language modeling, yet it remains unclear how their denoising objectives organize learning. A

tutorialsarxiv-cs-lg

SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades

arXiv:2605.14415v1 Announce Type: cross Abstract: Coding agents powered by large language models are increasingly expected to perform realistic software maintenance tasks beyond isolated issue resolut

Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks

arXiv:2601.16312v2 Announce Type: replace-cross Abstract: Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this

Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning

arXiv:2605.14876v1 Announce Type: cross Abstract: Despite rapid advancements, current text-to-image (T2I) models predominantly rely on a single-step generation paradigm, which struggles with complex s

ViMU: Benchmarking Video Metaphorical Understanding

arXiv:2605.14607v1 Announce Type: new Abstract: Any new medium, once it emerges, is used for more than the transmission of overt content alone. The information it carries typically operates on two lev

Vision-LLMs for Spatiotemporal Traffic Forecasting

arXiv:2510.11282v2 Announce Type: replace Abstract: Accurate spatiotemporal traffic forecasting is a critical prerequisite for proactive resource management in dense urban mobile networks. While large

safetyarxiv-cs-lg

When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution

arXiv:2605.14504v1 Announce Type: new Abstract: Long-horizon household tasks demand robust high-level planning and sustained reasoning capabilities, which are largely overlooked by existing embodied A

A3 : an Analytical Low-Rank Approximation Framework for Attention

arXiv:2505.12942v4 Announce Type: replace-cross Abstract: Large language models have demonstrated remarkable performance; however, their massive parameter counts make deployment highly expensive. Low-

Building Interactive Real-Time Agents with Asynchronous I/O and Speculative Tool Calling

arXiv:2605.13360v1 Announce Type: new Abstract: There is a growing demand for agentic AI technologies for a range of downstream applications like customer service and personal assistants. For applicat

Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

arXiv:2605.13709v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely applied in educational practices, such as for generating children's stories. However, the generated stories ar

Collaborative Parameter Learning: Mitigating Forgetting via Parameter-Level Gradient Analysis

arXiv:2601.21577v2 Announce Type: replace Abstract: Catastrophic forgetting during knowledge injection impairs the ability of large language models to acquire new knowledge without overwriting previou

Dense vs Sparse Pretraining at Tiny Scale: Active-Parameter vs Total-Parameter Matching

arXiv:2605.13769v1 Announce Type: cross Abstract: We study dense and mixture-of-experts (MoE) transformers in a tiny-scale pretraining regime under a shared LLaMA-style decoder training recipe. The sp

Descriptive Collision in Sparse Autoencoder Auto-Interpretability: When One Explanation Describes Many Features

arXiv:2605.12874v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are now standard tools for decomposing language model activations into interpretable features, and automated interpretability

Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack

arXiv:2605.12673v1 Announce Type: new Abstract: Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hackin

Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion

arXiv:2605.11679v2 Announce Type: replace Abstract: In the realm of multi-objective alignment for large language models, balancing disparate human preferences often manifests as a zero-sum conflict. S

Exploring Multimodal LMMs for Online Episodic Memory Question Answering on the Edge

arXiv:2602.22455v2 Announce Type: replace Abstract: We investigate the feasibility of using Multimodal Large Language Models (MLLMs) for real-time online episodic memory question answering. While clou

FOAM: Blocked State Folding for Memory-Efficient LLM Training

arXiv:2512.07112v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable performance due to their large parameter counts and extensive training data. However

HCSG: Human-Centric Semantic-Geometric Reasoning for Vision-Language Navigation

model-releasesarxiv-cs-ro

arXiv:2605.13321v1 Announce Type: new Abstract: VLN has achieved remarkable progress by scaling data and model capacity. However, the assumption of a static environment breaks down in real-world indoo

LIFT: Last-Mile Fine-Tuning for Table Explicitation

arXiv:2605.13424v1 Announce Type: new Abstract: We propose last-mile fine-tuning, or Lift, a pipeline in which a pre-trained large language model extracts an initial table from unstructured clipboard

MedCore: Boundary-Preserving Medical Core Pruning for MedSAM

arXiv:2605.13688v1 Announce Type: new Abstract: Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large fo

MMCL-Bench: Multimodal Context Learning from Visual Rules, Procedures, and Evidence

arXiv:2605.12703v1 Announce Type: cross Abstract: We introduce MMCL-Bench, a benchmark for multimodal context learning: learning task-local rules, procedures, and empirical patterns from visual or mix

Pareto-Guided Optimal Transport for Multi-Reward Alignment

arXiv:2605.13155v1 Announce Type: new Abstract: Text-to-image generation models have achieved remarkable progress in preference optimization, yet achieving robust alignment across diverse reward model

safetyarxiv-cs-cv

Phasor Memory Networks: Stable Backpropagation Through Time for Scalable Explicit Memory

arXiv:2605.13370v1 Announce Type: new Abstract: For over a decade, explicit memory architectures like the Neural Turing Machine have remained theoretically appealing yet practically intractable for la

Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles

arXiv:2605.12803v1 Announce Type: new Abstract: Detecting concept drift in high-speed data streams remains challenging, particularly when models must operate on unlabeled data and avoid false alarms c

Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis

arXiv:2605.12869v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in a wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that ci

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

arXiv:2510.13999v3 Announce Type: replace-cross Abstract: Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create signi

Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency

arXiv:2605.13047v1 Announce Type: cross Abstract: Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Tr