Search: “models”

MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms

arXiv:2603.02184v2 Announce Type: replace-cross Abstract: Multi-attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanis

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Research

oldsymbol{lambda}-Orthogonality Regularization for Compatible Representation Learning

DGX agent

arXiv:2509.16664v2 Announce Type: cross Abstract: Retrieval systems rely on representations learned by increasingly powerful models. However, due to the high training cost and inconsistencies in learn

researcharxiv-cs-cv

10 Aug 2026

Progressive Content Refinement with Decaying Reward Joint LinUCB

arXiv:2608.06750v1 Announce Type: cross Abstract: Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Ref

researcharxiv-cs-ai

ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

arXiv:2608.07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters,

Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination

arXiv:2608.07302v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely att

researcharxiv-cs-ai

Tutorials

Stochastic Autoregressive Learning

arXiv:2608.07224v1 Announce Type: new Abstract: Motivated by LLMs, which generate outputs by iteratively sampling from next-token distributions, we introduce a PAC-learning model for binary stochastic

tutorialsarxiv-cs-lg

Applications

Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools

arXiv:2608.07446v1 Announce Type: cross Abstract: Rapid adoption of large language models (LLMs) in enterprise settings has introduced operational, security, and governance risks. As generative AI app

applicationsarxiv-cs-ai

Safety

The Horizon Gap: Planning, Memory, Execution, Training, and Evaluation for Long-Horizon LLM Agents

arXiv:2608.06663v1 Announce Type: new Abstract: Frontier language models solve reasoning problems in a single forward pass that would have been research contributions years ago, yet fail at multi-hour

safetyarxiv-cs-cl

TRACE: A Multi-Layer Benchmark for Human AI Controller Coordination Under Drift and Failure

arXiv:2608.06657v1 Announce Type: new Abstract: Modern cyber-physical and AI-assisted systems couple human operators, AI decision modules, and automated controllers in a single control loop, so trustw

WebGrader: Training LLMs for Web Development with Self-Evolving Programmatic Grader

arXiv:2608.06474v1 Announce Type: new Abstract: Large language models increasingly generate complete websites from natural-language descriptions, and reinforcement learning has become a central approa

Local Ai

Activity Frames: Deterministic Screen-Activity Compilation for Agent Memory and Replay

arXiv:2608.05784v1 Announce Type: new Abstract: Computer-use agents pay full frontier inference to re-derive routines their user has already performed, because an agent's memory today records what the

local-aiarxiv-cs-ai

Align-RAG: Alignment Is All You Need for TSFM In-Context Learning

arXiv:2608.05571v1 Announce Type: new Abstract: Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods ty

Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap

arXiv:2608.05179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used across the scientific research lifecycle: ideation, literature search, experiment design and e

Beyond Rotations: AuroOFT for Expressive Quantized Orthogonal Fine-Tuning

arXiv:2608.05253v1 Announce Type: new Abstract: Quantized orthogonal fine-tuning (qoft) enables parameter-efficient adaptation of low-bit language models by learning structured activation rotations be

CASCADE: An Agentic Regulatory Network Framework for Patient-Data-Validated Downstream Perturbation Prediction

arXiv:2608.05359v1 Announce Type: new Abstract: CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbation from precomputed ARACNe regulatory networks, expos

ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution

arXiv:2608.05790v1 Announce Type: new Abstract: General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockch

Local Ai

ConceptADapt: Concept-guided Adaptive Feature Reconstruction with Dynamic Attention for Few-Shot Industrial Anomaly Detection

arXiv:2608.05743v1 Announce Type: new Abstract: Few-shot industrial anomaly detection (FS-IAD) focuses on detecting and localizing visual defects in industrial inspection during the cold-start phase,

local-aiarxiv-cs-cv

ConWriter: Transition-Constrained Stateful Long-Form Story Generation with Lightweight Neuro-Symbolic Consistency Control

arXiv:2608.05169v1 Announce Type: new Abstract: Long-form story generation requires models to preserve narrative consistency across extended contexts, yet existing prompting-based methods often accumu

Local Ai

Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation

arXiv:2608.05844v1 Announce Type: new Abstract: Radiology foundation models learn transferable representations that can be adapted to new tasks by training only small layers on top of a frozen encoder

local-aiarxiv-cs-cv

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining

arXiv:2608.05605v1 Announce Type: cross Abstract: Research and Education Networks (RENs) serve as critical infrastructure for scientific discovery, yet they face a unique security paradox: their norma

Enhancing Social Intelligence in LLMs with Hierarchical Reasoning and Utterance-Level Goal Rewarding

arXiv:2608.05832v1 Announce Type: new Abstract: Large language models (LLMs) excel in structured tasks but struggle with dynamic social interactions, where success requires long-term goal coordination

From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

arXiv:2608.05203v1 Announce Type: new Abstract: Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by

researcharxiv-cs-ai

Iterate or Widen? When Test-Time Refinement Helps LiDAR Scene Completion: A Controlled Study of Evidence Geometry, Training Coverage, and Compute

arXiv:2608.06014v1 Announce Type: new Abstract: Should a completion model spend extra test-time compute by iterating, or spend a similar parameter budget on a wider one-shot predictor? The answer is e

LLM Inference Under Bursty Workload Distribution: Modifying the WAIT Algorithm

arXiv:2608.06135v1 Announce Type: new Abstract: Large Language Models (LLMs) such as ChatGPT and Claude are widely used for information retrieval and problem-solving. Recent work has focused on improv

M^3R-Bench: A Unified Benchmark for Evidence-Grounded Multimodal Metaphor Understanding

arXiv:2608.05817v1 Announce Type: new Abstract: Metaphor enables the understanding of abstract concepts through cross-domain mappings while conveying affective attitudes. In multimodal scenarios, visu

MAC 2026: Advancing Micro-Action Analysis Towards Fine-Grained Understanding

arXiv:2607.16284v2 Announce Type: replace Abstract: Micro-Actions (MAs) are subtle and spontaneous human behaviors that provide important non-verbal cues in social interaction and affective communicat

Operating Multi-Node Full Fine-Tuning on NVIDIA B300: A Field Report on Telemetry-Based Triage, Negative Results, and Operational Hardening

arXiv:2608.05944v1 Announce Type: cross Abstract: We report operational experience full-fine-tuning a 32.76B-parameter dense model (Qwen3-32B) on 16 x NVIDIA B300 (two nodes, FSDP / ZeRO-3) -- among t

SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents

arXiv:2608.05212v1 Announce Type: new Abstract: Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning error

Stochastic Parrots or Singing in Harmony? Testing Five Leading LLMs for their Ability to Replicate a Human Survey with Synthetic Data

arXiv:2603.00059v3 Announce Type: replace-cross Abstract: How well can AI-derived synthetic research data replicate the responses of human participants? An emerging literature has begun to engage with

TAU-Bench: From Anomaly Instance Tracking to Fine-Grained Video Anomaly Understanding

arXiv:2608.05699v1 Announce Type: new Abstract: Humans understand anomalous events through a coherent perceptual process in which they identify the focal instance, follow its behavior as the event unf

Safety

Visual Grounding in Zero-Shot Vision-Language Control

arXiv:2608.06154v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as zero-shot controllers, but successful trajectories do not necessarily show that decisions are g

safetyarxiv-cs-ai

What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective

arXiv:2606.14299v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) such as CLIP have become a standard backbone for open-vocabulary recognition, yet their zero-shot predictions remain v

Who Checks the Citations? Benchmarking Legal Hallucination Detection

arXiv:2606.21155v2 Announce Type: replace Abstract: Attorneys, judges, and pro se filers increasingly use AI to draft legal documents, yet these tools frequently fabricate citations. Despite predictio

A Mechanistic Analysis of Transformers for Dynamical Systems

arXiv:2512.21113v2 Announce Type: replace Abstract: Transformers are increasingly adopted for modeling and forecasting time-series, yet their internal mechanisms remain poorly understood from a dynami

researcharxiv-cs-lg

A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents

arXiv:2602.06052v4 Announce Type: replace-cross Abstract: Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world

Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports

arXiv:2608.04682v1 Announce Type: cross Abstract: Coding agents powered by large language models (LLMs) are increasingly adopted in software engineering (SWE) scenarios, capable of fixing a specific b

Adaptive Finite-Budget Training for CVaR Risk-Aware Q-Learning

arXiv:2608.04305v1 Announce Type: new Abstract: Risk-aware Q-learning (RaQL) provides a model-free, two-timescale estimator for dynamic risk objectives, but its finite-budget behavior remains fragile:

Safety

BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation

arXiv:2608.05042v1 Announce Type: new Abstract: Leveraging pre-trained vision-language models (VLMs) to construct vision-language-action (VLA) models has emerged as a promising paradigm for 3D robot m

safetyarxiv-cs-ro

Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inference

arXiv:2608.04428v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have emerged as a key component in embodied AI. Among existing approaches, diffusion-based VLA models achieve supe

researcharxiv-cs-lg

EgoAfford: Task-Oriented Affordance Grounding via Egocentric Referring Segmentation

arXiv:2608.04533v1 Announce Type: new Abstract: Part-level affordance grounding has advanced the localization of functional object regions associated with elemental actions. Extending this capability

LaPrune: Controllable Differentiable Sparsity at Million Scale

arXiv:2608.04057v1 Announce Type: cross Abstract: Top-k selection determines which components of a sparse model remain active. Hard selection blocks gradients, while continuous relaxations often coupl

MediRec: Enhancing Chinese Medication Recommendation with Explainable Clinical Reasoning

arXiv:2510.21084v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong potential for clinical decision support through their advanced language understanding and reaso

OmniRouting: A Semantic-Coupled Multimodal Benchmark for Constraint-Aware Spatial Reasoning in PCB Routing

arXiv:2608.04434v1 Announce Type: new Abstract: Recent large language models (LLMs) have demonstrated remarkable progress in constraint-aware navigation, maze reasoning, and graph reasoning. However,

PhysMind: From Video to Executable Worlds for Training-Free Physical Reasoning

arXiv:2608.04575v1 Announce Type: cross Abstract: Reliable physical reasoning from video requires understanding how objects move, interact, and respond to interventions. Existing vision-language model

PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior

arXiv:2512.18367v2 Announce Type: replace-cross Abstract: Diffusion models are highly expressive image priors for Bayesian inverse problems. However, most diffusion models cannot operate on large-scal

researcharxiv-cs-lg

RESPClinBench: Benchmarking Multimodal Clinical Decision-Making and Longitudinal Disease Management in Respiratory Specialty Care

arXiv:2608.04514v1 Announce Type: new Abstract: Background: Respiratory specialty care requires multimodal interpretation, longitudinal risk assessment, guideline-concordant intervention, and whole-co

REZE: Recognition-Based Zero-Shot Extraction for Video Temporal Grounding

arXiv:2608.04480v1 Announce Type: new Abstract: Video temporal grounding (VTG) refers to the task of identifying the time interval in a video that corresponds to a given natural-language query. A comm

researcharxiv-cs-cv

Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load

arXiv:2608.05018v1 Announce Type: new Abstract: Short-term load forecasting (STLF) play a vital role in the electric power industry. It serves infrastructure that European and German law designate as