Search: “framework”

T-SMART: Mechanism-Level Attribution for Tool-Augmented Time-Series Question Answering

arXiv:2609.14142v1 Announce Type: new Abstract: Large language models (LLMs) can struggle with time-series question answering (TS-QA), especially when numerical signals are serialized as text and requ

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Task-Based CT Protocol Optimization Using Reinforcement Learning and Virtual Imaging Trials

DGX agent

arXiv:2609.13309v1 Announce Type: cross Abstract: Protocol optimization in computed tomography (CT) aims to improve diagnostic image quality while reducing radiation dose, but the interdependence of a

safetyarxiv-cs-ai

15 Sept 2026

TF-IDF and BM25 Are Exact KL Divergences

arXiv:2609.14016v1 Announce Type: cross Abstract: TF-IDF and BM25 are two of the most widely used methods for scoring query-document relevance, yet neither has a standard probabilistic derivation that

researcharxiv-cs-cl

The Metric Slingshot: Navigational Reuse as Width-Optimal Structural Decoupling in Continual Learning

arXiv:2603.15412v2 Announce Type: replace Abstract: The mammalian brain, most extensively studied in rodents and bats, solves an enormous variety of non-spatial cognitive tasks using neural circuitry,

TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models

arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for que

tutorialsarxiv-cs-ai

Toward a Decision-Assurance Layer for AI-Assisted Flight Planning in Air Traffic Management

arXiv:2609.13552v1 Announce Type: new Abstract: Generative AI is increasingly being used informally in Air Traffic Management (ATM) for tasks such as flight plan generation, trajectory interpretation,

safetyarxiv-cs-ai

Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation

arXiv:2609.13581v1 Announce Type: new Abstract: Discharge summaries are lengthy medical documents that summarize a hospital in-patient visit. Automatically generating them can reduce documentation bur

safetyarxiv-cs-cl

Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary

arXiv:2609.13547v1 Announce Type: new Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most S times. When S is kn

Towards Anticipatory Databases Through Shared Data and Workload Semantics

arXiv:2609.14255v1 Announce Type: cross Abstract: Database management systems increasingly serve dynamic and exploratory workloads, yet many of their decisions still rely on low-level signals such as

Towards Optimizing SQL Generation via LLM Routing

arXiv:2411.04319v1 Announce Type: cross Abstract: Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large la

researcharxiv-cs-ai

Tracking the Ground: Online Lidar Identification of Robot-Induced Soil Deformation in Agricultural Environments

arXiv:2609.15667v1 Announce Type: new Abstract: Agriculture faces many challenges, and robotic systems can play an important role in addressing them by improving the efficiency and sustainability of f

researcharxiv-cs-ro

Transforming harmonic coefficients for 3D splat compression

arXiv:2609.15735v1 Announce Type: cross Abstract: We address the problem of color attribute compression for 3D splats. We show that all images generated by 3D splats are linear in the coefficients for

Trustworthy Agentic AI: A Comprehensive Cybersecurity and Systems Survey on Threat Landscapes, Defense Architectures, and Open Challenges

arXiv:2609.13731v1 Announce Type: new Abstract: The transition from passive foundation models to autonomous, goal-directed agentic AI systems has introduced unprecedented capabilities by coupling recu

safetyarxiv-cs-ai

Two-Stage Mixture-of-LoRA for Multi-Task Medical Vision-Language Learning

arXiv:2609.14350v1 Announce Type: new Abstract: Medical vision-language models (VLMs) allow a single model to perform clinical image analysis tasks ranging from diagnosis classification to report gene

TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series

model-releasesarxiv-cs-ai

arXiv:2607.17632v2 Announce Type: replace-cross Abstract: Time series data play a pivotal role across numerous domains, including healthcare and manufacturing. In real-world environments, models must

VLBiMan++: Expanding the Generalization Boundary of Vision-Language Anchored One-Shot Bimanual Manipulation

arXiv:2609.14310v1 Announce Type: new Abstract: Generalizable bimanual robotic manipulation requires a reusable task prior that can persist across increasingly diverse tasks, objects, scenes, embodime

safetyarxiv-cs-ro

What Limits Us? Analyzing Self-Reported Limitations in NLP Research

arXiv:2609.15191v1 Announce Type: new Abstract: Since late 2022, a Limitations section has become mandatory at many top-tier NLP conferences. The growing number of accepted papers at these venues has

researcharxiv-cs-cl

When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

model-releasesarxiv-cs-lg

arXiv:2609.13148v1 Announce Type: cross Abstract: Large language models are increasingly deployed as synthetic consumer panels, promising 97% cost reductions over traditional surveys. Yet aggregate va

When Do Learned Priors Help Visual Inertial Estimation? A Controlled Study of Prior Integration, Calibration, Initialization, and Backend Consistency

arXiv:2609.13777v1 Announce Type: cross Abstract: Learned components are increasingly integrated into geometric visual--inertial estimators to provide motion, depth, bias, uncertainty, or confidence c

safetyarxiv-cs-cv

Why LLM Agents Collapse Without Oversight: The Enforcement Gap as the Mechanism Behind Emergence World Failures

model-releasesarxiv-cs-ai

arXiv:2609.15293v1 Announce Type: new Abstract: When Emergence World placed frontier LLM agents in an unsupervised multi-agent simulation, the results were alarming: agents committed crimes, starved,

Zero-shot video highlight detection based on text descriptions and synthetic images

arXiv:2609.14790v1 Announce Type: new Abstract: Detecting video highlights, the most informative or engaging moments in a video, is important for applications such as video summarization and content r

An End-to-End Automated Pipeline for Controllable Crack Data Synthesis

arXiv:2609.12431v1 Announce Type: new Abstract: Automated crack inspection increasingly relies on deep learning, yet its reliability is limited by scarce and weakly controllable defect data. Existing

Agents

Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

arXiv:2609.13073v1 Announce Type: cross Abstract: Recent breakthroughs in LLM-based systems and their abilities in problem solving and coding have allowed progress in the AI for Science paradigm, pote

agentsarxiv-cs-lg

Beyond Ambiguous Visual Cues: Studying Physiological Disruptions and Cross-Modal Inconsistencies in Deepfake Videos

arXiv:2609.12668v1 Announce Type: new Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks l

tutorialsarxiv-cs-cv

Beyond the Query: Do Retrieval Signals Improve Adaptive Multimodal RAG Routing?

arXiv:2609.12437v1 Announce Type: new Abstract: Adaptive RAG often uses retrieval-time signals to decide whether another retrieval, reranking, or multimodal step should run. We ask whether these signa

Can We Trust LLM Judges: A Study of Capability-Dependent Biases and Multi-Judge Ensemble for Bias Calibration

arXiv:2609.12002v1 Announce Type: new Abstract: LLMs are increasingly used as automated judges for model training and evaluation, yet individual judges exhibit systematic biases that undermine reliabi

safetyarxiv-cs-lg

Certifying Concept Unlearning in Text-to-Image Diffusion Models

arXiv:2609.12163v1 Announce Type: new Abstract: Existing evaluations of concept unlearning in text-to-image (T2I) diffusion models primarily rely on attack success rates obtained through automated adv

safetyarxiv-cs-lg

Classical and quantum kernel fusion for two-sample testing

arXiv:2511.20941v2 Announce Type: replace-cross Abstract: Two-sample tests have been extensively employed in various scientific fields and machine learning to discriminate whether two sets of samples

tutorialsarxiv-cs-lg

Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts

arXiv:2609.12374v1 Announce Type: new Abstract: Dashboard-mounted gaze trackers often lose sight of the driver's eyes during large head rotations, including shoulder checks, mirror glances, and inters

DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning

arXiv:2609.11997v1 Announce Type: new Abstract: Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications suc

safetyarxiv-cs-lg

Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations

arXiv:2512.23829v3 Announce Type: replace-cross Abstract: Inverse problems are important mathematical problems that seek to recover model parameters from noisy data. Since inverse problems are often i

tutorialsarxiv-cs-lg

Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control

arXiv:2607.10625v2 Announce Type: replace Abstract: Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. Howe

safetyarxiv-cs-ro

Explanations-Driven Active Feature Acquisition for Algorithmic Recourse

arXiv:2609.12179v1 Announce Type: new Abstract: Algorithmic recourse methods typically assume that a predictive model has access to all features of an individual. In practice, decisions are often made

ext{GSF-}hi: Global Stereochemical Fields for Chiral Graph Transformers

model-releasesarxiv-cs-lg

arXiv:2609.12532v1 Announce Type: new Abstract: Enantiomers share atoms, bonds, and pairwise distances yet can behave differently in chiral environments, so molecular encoders must respect atom relabe

FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences

model-releasesarxiv-cs-lg

arXiv:2609.11993v1 Announce Type: new Abstract: Machine learning research in financial services is limited by the scarcity of representative open-source datasets. Existing resources are often narrowly

HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology

arXiv:2609.12835v1 Announce Type: new Abstract: Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories.

Hurdle-RMIL: Addressing Zero Inflation and Long-Tailed Imbalance in Infrared Rainfall Retrieval

arXiv:2510.20486v2 Announce Type: replace Abstract: Imbalanced labels can cause frequent samples to dominate AI-based quantitative remote sensing, degrading rare-event retrieval. In rain-rate retrieva

Information Specialization and Constrained Synthesis in Multi-Agent LLM Forecasting: A Prospective Live-Study of the 2026 FIFA World Cup

arXiv:2609.12495v1 Announce Type: cross Abstract: Large language models are being organized into multi-agent systems with specialized roles, but whether such specialization produces distinct forecasts

Is Bash All You Need? An Empirical Study of Tool Interfaces for Enterprise Digital Worker Agents

arXiv:2609.11999v1 Announce Type: cross Abstract: In this study, we examine whether a general shell can outperform specialized tools on enterprise tasks. Shell-based agents have shown strong results i

KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image

model-releasesarxiv-cs-cv

arXiv:2609.12559v1 Announce Type: new Abstract: Due to the complexity of hand kinematics and self-occlusion, existing 3D hand pose estimation methods based on single depth images struggle to comprehen

Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models

arXiv:2609.12105v1 Announce Type: cross Abstract: The prevailing assumption in applied machine learning is that progress on consequential quantitative decisions such as pricing risk, allocating capita

Applications

Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design

arXiv:2609.12524v1 Announce Type: cross Abstract: We study the strategic design of a two-echelon spare-parts inventory network where evaluating each candidate topology requires an expensive inventory

applicationsarxiv-cs-lg

Agents

LifeMem: Enabling Lifelong Experience Reuse for LLM Agents

arXiv:2609.12655v1 Announce Type: new Abstract: Large language model agents are expected to continuously adapt to new tasks and environments over their lifetime by reusing past experience. However, ex

agentsarxiv-cs-cl

MAxBench: A Multinomial Concept Recovery Benchmark

arXiv:2609.13072v1 Announce Type: cross Abstract: Fine-grained control of language model behaviors (e.g., steering) is among the more actionable outcomes of interpretability research. For binary conce

Measuring Pragmatic Influence in Large Language Model Instructions

arXiv:2602.21223v2 Announce Type: replace Abstract: It is not only what we ask large language models (LLMs) to do that matters, but also how we ask them. Phrases like ``This is urgent'' or ``As your s

researcharxiv-cs-cl

MedSNIP: Building and Benchmarking Snippet-Level Granularity for Medical Fact Verification

arXiv:2609.12884v1 Announce Type: new Abstract: A medical claim's correctness often depends not on the claim alone, but on the clinical structure around it. A claim may require a lab reference range,

Local Ai

On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health

arXiv:2609.11961v1 Announce Type: new Abstract: Stress is a pervasive determinant of mental health and a key target for mobile health interventions. On-device language models (ODLMs) offer privacy-pre

local-aiarxiv-cs-lg

Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion

arXiv:2609.12899v1 Announce Type: new Abstract: Full waveform inversion (FWI) estimates subsurface velocity from seismic recordings, but its ill-posedness and nonlinearity make accurate reconstruction