Search: “benchmark”

Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding

arXiv:2609.13685v1 Announce Type: cross Abstract: Negation remains a longstanding challenge for both language models (LMs) and large language models (LLMs). Prior work mainly focuses on a small set of

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Safety

Not All Prompts Are Equal: Exploration-Guided Prompt Scaffolding for Multimodal Reinforcement Post-Training

DGX agent

arXiv:2609.15051v1 Announce Type: cross Abstract: Training prompts in online reinforcement learning (RL) differ substantially in how informative they are for the current policy: some are already satur

safetyarxiv-cs-ai

15 Sept 2026

Odds-Shift Slippage in One-vs-Rest Rankers: Diagnosing and Repairing Reweighting-Induced Top-K Errors

arXiv:2609.13810v1 Announce Type: cross Abstract: One-vs-rest rankers that show each user the top-K of many rare labels usually counter imbalance with a per-label positive-class weight, scale_pos_weig

Omni-Streaming Thinking

arXiv:2609.15128v1 Announce Type: new Abstract: Streaming omni-modal models must decide what and when to answer from the video chunks and synchronized audio observed so far. Visual cues often support

researcharxiv-cs-lg

Online Bayesian Node Classification on Inductive Graphs under Distribution Shift

arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and ca

safetyarxiv-cs-lg

Open-UniMo: Towards Unified Motion-Language Understanding and Generation in the Open World

arXiv:2609.14615v1 Announce Type: cross Abstract: Unified motion generation and understanding is crucial for embodied AI systems that can both synthesize and interpret human actions in open-world envi

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based fr

Overflip: Repetition-Induced Label Flips in Guardrail Models

arXiv:2609.15013v1 Announce Type: new Abstract: Guardrail models are classifiers deployed to screen malicious prompts and responses in LLM-based services. To meet latency constraints, many lightweight

Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting

arXiv:2609.15344v1 Announce Type: new Abstract: We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework, w

Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT

arXiv:2609.14106v1 Announce Type: new Abstract: We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) for liver tumor segmentation in abdominal CT of colorectal liver

Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection

arXiv:2609.13785v1 Announce Type: new Abstract: Oracle-style quantities, including virtual best solvers, selected-portfolio VBS, virtual-best encodings, and best-in-family summaries, are widely report

Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation

arXiv:2609.13328v1 Announce Type: cross Abstract: Anticipating whether a pedestrian will cross the road is safety-critical for autonomous vehicles, requiring real-time inference under challenging cond

Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression

arXiv:2609.15838v1 Announce Type: cross Abstract: Per-matrix singular value decomposition (SVD) truncation is Eckart-Young optimal in the whitened Frobenius norm, but errors from independently compres

PIDS-Bench: Evaluating Prompt-Injection Detectors Under Over-Defense, Obfuscation, and Distribution Shift

arXiv:2609.15017v1 Announce Type: cross Abstract: Prompt-injection detectors are typically evaluated using aggregate F1 on in-distribution test data, which offers limited insight into behavior under d

Planning or Learning: Reliability and Cost in Multi-Asset Maintenance

arXiv:2609.13566v1 Announce Type: new Abstract: Industrial maintenance systems involve multiple interacting assets and shared resources, making it challenging to balance reliability and operational co

PolicyMem: Geometric Policy Memory for LLM Governance

arXiv:2609.13734v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in real-world high-stakes applications, effective governance has become essential. Existing

safetyarxiv-cs-ai

Pre-PEFT Probing: Weight Statistics and Perturbation Robustness for Layer Selection in VLM Vision Encoders

arXiv:2609.15229v1 Announce Type: cross Abstract: We propose a pre-fine-tuning probing method for Parameter-Efficient Fine-Tuning (PEFT) layer selection, aiming to obtain more stable and higher gains

Local Ai

Preserving Subject-Clarity in Image Outpainting with Multiscale Wavelet Supervision

arXiv:2609.13251v1 Announce Type: new Abstract: Commercial and advertising images are frequently affected by poor framing, partially cropped subjects, truncated text or logos, and insufficient context

local-aiarxiv-cs-cv

Principal-timestep Restricted Init via Sparse Matrix-decomposition in Flow-matching

arXiv:2609.15643v1 Announce Type: new Abstract: Flow-matching diffusion models have recently emerged as a strong paradigm for high-fidelity visual generation. However, their prohibitively high fine-tu

safetyarxiv-cs-lg

PRISM-UDE: Physics-Regularized Iterative Symbolic Modeling of 3nm FinFETs via Universal Differential Equation

arXiv:2609.13200v1 Announce Type: cross Abstract: Compact transistor models are the mathematical backbone of circuit simulation. However, at advanced nodes such as 3nm, transport physics becomes too c

Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

arXiv:2609.15885v1 Announce Type: cross Abstract: This study proposes a privacy-enhanced federated learning framework to address secure collaborative training in distributed data environments. The fra

Proportional-Fair Resource Allocation and Dual-Threshold Early-Exit Inference for Secure Cooperative Multi-Layer Edge Intelligence

arXiv:2609.15847v1 Announce Type: cross Abstract: This paper proposes FREDI (Fair Resource Allocation for Edge Dual-Threshold Inference), a secure wireless edge-intelligence framework for event-trigge

PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement

arXiv:2609.14675v1 Announce Type: cross Abstract: This study addresses the PU classification problem under violations of the SCAR assumption. We investigate logistic regression-based approaches, namel

Pull: Lazy Materialization of Working Memory for Stateful LLM Conversations

model-releasesarxiv-cs-cl

arXiv:2609.14773v1 Announce Type: new Abstract: As LLM conversations grow to hundreds of turns, full-context injection incurs O(N^2) cumulative token costs, while lossy summarization or hard truncatio

Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection

arXiv:2609.14025v1 Announce Type: new Abstract: Forward-looking sonar object detection is essential for underwater perception, yet deployment on embedded platforms requires highly compact models. To a

Agents

Real-World Deployment and Performance Characterisation of Fog-Based Deep Learning for Cold-Chain Temperature Prediction over LoRaWAN

arXiv:2609.14036v1 Announce Type: cross Abstract: Fresh fruits and vegetables (FFVs) are highly perishable, and cold-chain breaks contribute significantly to global food waste. While Machine Learning

agentsarxiv-cs-lg

Realistic Continual Learning Approach using Pre-trained Models

arXiv:2404.07729v2 Announce Type: replace-cross Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic

researcharxiv-cs-cv

RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agents

model-releasesarxiv-cs-cl

arXiv:2609.15684v1 Announce Type: new Abstract: Language agents increasingly rely on reusable skills, but post-failure repair is often handled by opaque one-shot reflection: a model generates a skill

Reverse Spatio-Temporal Disease Progression Modelling

arXiv:2609.14590v1 Announce Type: new Abstract: Deep learning-based spatio-temporal disease progression models commonly overlook the incubation period of progressive diseases, limiting the use of thos

Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection

arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation,

ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments

arXiv:2508.13488v2 Announce Type: replace-cross Abstract: Loop closure detection is important for simultaneous localization and mapping (SLAM), which associates current observations with historical ke

Safe Meta-Reinforcement Learning via Information Space Reachability

arXiv:2609.15915v1 Announce Type: new Abstract: Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL i

safetyarxiv-cs-lg

Safety Signals to Verify NetOps Agents with Action-Level Granularity

arXiv:2609.14422v1 Announce Type: new Abstract: Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. Whil

safetyarxiv-cs-ai

SAILOR: Solver-Assisted Interactive LLM-based Optimization Recovery

arXiv:2609.13945v1 Announce Type: new Abstract: Natural-language descriptions of optimization problems may be incomplete or vague about numerical information that a solver requires, including costs, c

Salesforce Koa: An Enterprise Language Model for Agentic Tool Use

arXiv:2609.15066v1 Announce Type: cross Abstract: We present Salesforce Koa, an enterprise language model built by post-training the open-weight Nemotron-3-Super-120B foundation model with reinforceme

Local Ai

SCOUT-SLAM: Structurally-Coupled Dual Uncertainty-Aware 3DGS SLAM in the Wild

arXiv:2609.14634v1 Announce Type: new Abstract: Recently, 3D Gaussian Splatting SLAM (3DGS-SLAM) has gained significant momentum in simultaneous localization and 3DGS scene reconstruction. In real-wor

local-aiarxiv-cs-cv

Self-Evolving AI for Humanoids: Mechanisms, Safety, and Evaluation of Post-Deployment Self-Improvement

model-releasesarxiv-cs-ro

arXiv:2609.13236v1 Announce Type: new Abstract: Humanoid robots are becoming an important part of embodied artificial intelligence, driven by advances in reinforcement learning for locomotion, world m

Semantic Privacy Protection with Utility Preservation for 3D Point Clouds

arXiv:2609.13823v1 Announce Type: new Abstract: Point cloud data face serious semantic privacy risks during acquisition, transmission, and cross-institutional sharing. Existing methods mostly rely on

ShieldVLA: Feasibility-Aware Safety Alignment for Vision-Language-Action Models

arXiv:2609.13231v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existing fine-tuning methods provide

safetyarxiv-cs-ai

Agents

SkillLift: Learning Dense Rubrics from Sparse Oracles for Efficient Skill Evolution

arXiv:2609.15396v1 Announce Type: new Abstract: LLM-based agents increasingly rely on persistent skills, i.e., reusable procedural prompts, to adapt without weight updates. Existing skill self-evoluti

agentsarxiv-cs-ai

SL(n) Representation Learning: An Intrinsic Mixed-Curvature Space with Higher Curvature Capacities and Deeper Order-Aware Composition

arXiv:2609.15083v1 Announce Type: new Abstract: Mixed-curvature representation learning seeks to capture rich geometric structures that cannot be adequately modeled by a single curvature regime. Exist

researcharxiv-cs-lg

STAGE: Diagnosing Semantic Transfer at Grounded Execution in Embodied Agents

model-releasesarxiv-cs-cl

arXiv:2609.13458v1 Announce Type: cross Abstract: Embodied language grounding requires more than identifying the referent of an instruction: recovered semantics must also control the action an agent e

Steering Generative Robot Policies with Lexicographic Preferences

arXiv:2609.15014v1 Announce Type: cross Abstract: Pretrained generative robot policies can produce effective behaviors across diverse environments, but deployment can lead to requirements and preferen

Applications

STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

arXiv:2609.15172v1 Announce Type: new Abstract: Spatio-temporal traffic forecasting is a fundamental big data analytics task for intelligent transportation systems, where massive urban sensor streams

applicationsarxiv-cs-ai

Local Ai

SURE-Map: Self-Correcting Streaming Geometric Foundation Model

arXiv:2609.15795v1 Announce Type: new Abstract: Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental issue

local-aiarxiv-cs-cv

Surprising Effectiveness of Self-Demonstrations in Enhancing Schema-Ontology Mapping with LLMs

arXiv:2609.13776v1 Announce Type: new Abstract: Integrating heterogeneous relational databases into a centralized ontology remains a persistent challenge in enterprise knowledge representation, primar

Survey of Novel Deep Learning Architectures for Denoising Gravitational-wave Signals

arXiv:2609.13272v1 Announce Type: cross Abstract: Gravitational-wave denoising must handle the full diversity of spinning, precessing binaries, since the recovered waveform underpins parameter estimat

Tabby: An Open Pretraining Recipe for Time Series Foundation Models

arXiv:2609.13956v1 Announce Type: new Abstract: In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was buil