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When More Is Not Better: Component Anti-Synergy in a P300 Speller

arXiv:2609.10961v1 Announce Type: new Abstract: P300 brain-computer interface (BCI) spellers can provide hands-free communication for people with severe motor impairments. Modern pipelines combine mul

safetyarxiv-cs-lg

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When Noise Fabricates Bias: The Fragility of LLM-as-a-Judge Bias Measurement under Noisy Text

DGX agent

arXiv:2609.11067v1 Announce Type: new Abstract: Large language models are increasingly used as judges to measure social bias in text, yet the passages they judge are often noisy, containing typos, inf

safetyarxiv-cs-cl

11 Sept 2026

When Synthetic Data Hurts: On Catastrophic Forgetting in Skill Retrieval for LLM Agents

arXiv:2609.10750v1 Announce Type: cross Abstract: LLM agents increasingly rely on external skills retrieved at runtime, making skill selection from large repositories a critical challenge. We present

Which Tokens Should SFT Actually Learn? A Token-Trimming Perspective on Mathematical Reasoning

arXiv:2609.09707v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) applies a uniform cross-entropy loss to all target tokens, even though different tokens provide unequal learning signals fo

Whisper-Based Speech Transcription from Videos Across Multiple Languages for Cross-Cultural Understanding

model-releasesarxiv-cs-cl

arXiv:2609.11772v1 Announce Type: cross Abstract: Cross-cultural understanding has become increasingly important in today's highly connected, cross-national world. The success of LLM-based technologie

Local Ai

(Whose defaults?) Is artificial intelligence reorienting archaeological methods?

arXiv:2609.11198v1 Announce Type: cross Abstract: Generative AI and the practice of 'vibe coding' are changing how archaeologists carry out computational research, but their effects on the discipline'

local-aiarxiv-cs-cl

Why Does Post-Training Quantization Work?

arXiv:2609.11716v1 Announce Type: cross Abstract: Post-training quantization compresses large language models (LLMs) by storing their weights at reduced precision, and each quantized weight introduces

researcharxiv-cs-cl

Why Sample What You Can Enumerate? Exact Policy Optimization for Genomic Tool Selection

arXiv:2609.10221v2 Announce Type: new Abstract: Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe

safetyarxiv-cs-ai

With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education

arXiv:2609.09856v1 Announce Type: cross Abstract: The rapid adoption of generative AI tools such as ChatGPT has sparked intense debate about their risks and opportunities for education, as well as the

agentsarxiv-cs-ai

Tutorials

World in World: Explore the World with World Models

arXiv:2609.11548v1 Announce Type: new Abstract: Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challenging. Exploring a source video from

tutorialsarxiv-cs-cv

XAI-Arena: Can LLMs Assess the Quality of XAI Explanations?

arXiv:2609.09428v1 Announce Type: new Abstract: Evaluating the quality of explanations produced by explainable AI (XAI) methods remains challenging because existing approaches often rely on subjective

Applications

Xiaomi-CocktailASR-1 Technical Report

arXiv:2609.11274v1 Announce Type: cross Abstract: Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios,

applicationsarxiv-cs-cl

Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models

arXiv:2609.11310v1 Announce Type: new Abstract: We address few-shot object detection with vision-language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using

safetyarxiv-cs-cv

Zero-shot rib design: merging training-free generative prior with topology optimization

arXiv:2609.10643v1 Announce Type: new Abstract: Natural load-bearing patterns such as leaf venation, trabecular bone, and spider webs achieve high stiffness per unit mass, yet classical topology optim

ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding

arXiv:2609.11642v1 Announce Type: cross Abstract: Neural audio codecs are a fundamental component of modern speech generation systems. While recent codecs achieve increasingly low bitrates, reducing f

3rd Place Solution to Human Motion Challenges in Real-World and Clinical Settings (MoCha) @ECCV2026: Language-Aligned Motion Representations for Domain-Generalizable UPDRS-Gait Severity Estimation

model-releasesarxiv-cs-cv

arXiv:2609.10187v1 Announce Type: new Abstract: In this work, we introduce language-aligned motion representations for domain-generalizable UPDRS-Gait severity estimation, aiming to learn semantically

5-Dialects-BN: Unmasking the Impact of Transliteration on Bangla Dialectal LLMs

model-releasesarxiv-cs-cl

arXiv:2609.09964v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable progress across natural language processing (NLP) tasks, yet their capabilities degrade sharply fo

A Block Tensor Train Burer-Monteiro Framework for Low-Rank Quantum State Tomography

arXiv:2609.09457v1 Announce Type: cross Abstract: Quantum state tomography is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evalua

A Computational Implementation of a Goal-Directed Theory of Affect

arXiv:2609.06654v1 Announce Type: new Abstract: Computational modeling of emotion has long faced a tension between descriptive, 'snapshot-based' appraisal models and granular, signal-driven architectu

agentsarxiv-cs-ai

A Confidence-Aware Multimodal Fusion Framework for Industrial Human-Robot Collaboration

arXiv:2609.10339v1 Announce Type: new Abstract: A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboratio

safetyarxiv-cs-ro

A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames

arXiv:2603.15038v2 Announce Type: replace-cross Abstract: A novel convolutional autoencoder and neural ODE (CAE-NODE) framework is proposed for a reduced-order model (ROM) applied to transient 2D coun

Applications

A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes

arXiv:2609.09137v1 Announce Type: new Abstract: Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives u

applicationsarxiv-cs-ai

A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospects

model-releasesarxiv-cs-ro

arXiv:2609.09403v1 Announce Type: new Abstract: In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. Thi

A Dominant Diffuse Phase in the Sparse Autoencoder Phase Diagram

arXiv:2609.10299v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are increasingly used to recover interpretable features from neural-network activations, yet systematic feature co-occurrence

A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning

arXiv:2109.02355v2 Announce Type: replace-cross Abstract: The last decade of progress in machine learning (ML), especially the deep learning era, has raised a number of scientific questions that chall

safetyarxiv-cs-lg

A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems

arXiv:2609.07075v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its re

researcharxiv-cs-ai

A Jump-Diffusion Framework for Irregular Time Series Generation

arXiv:2505.23215v2 Announce Type: replace-cross Abstract: We propose a framework for generative modeling of continuous-time processes from irregularly and asynchronously recorded data. It is based on

Applications

A Kernel-Based Modular Discriminant Analysis Framework for Small-Sample Learning

arXiv:2609.09910v1 Announce Type: new Abstract: The small-sample-size (SSS) problem remains a fundamental challenge in machine learning when labeled data are scarce due to cost, accessibility, or ethi

applicationsarxiv-cs-lg

A Later Test Set Is Not a New Domain: Pretraining Familiarity Survives a Contamination-Free Hold-Out

arXiv:2609.10357v1 Announce Type: new Abstract: Time-series foundation models are evaluated almost exclusively on public archives that predate them, so a strong score cannot be separated from having s

A Layered Analysis of Disagreement And Answer Quality in Multi-Agent LLM Debate

arXiv:2609.08016v1 Announce Type: new Abstract: Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagree

safetyarxiv-cs-ai

A Minimum-Energy Control Approach for Redundant Mobile Manipulators in Physical Human-Robot Interaction Applications

model-releasesarxiv-cs-ro

arXiv:2603.25259v2 Announce Type: replace Abstract: Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which co

A positive resolution of the gap-entropy conjecture

arXiv:2609.10529v1 Announce Type: new Abstract: We prove the gap-entropy conjecture for fixed-confidence best-arm identification with independent unit-variance Gaussian arms, means in [0,1], and a uni

A practical DIRECT-type algorithm for medium-scale black-box global optimization

arXiv:2609.09796v1 Announce Type: new Abstract: The DIRECT algorithm is a deterministic global optimization method known for its versatility and balanced exploration-exploitation strategy. However, DI

Local Ai

A radiographic world model for clinical reasoning and evidence generation

arXiv:2609.07719v1 Announce Type: new Abstract: Medical imaging artificial intelligence (AI) is commonly developed as separate mappings from radiographs to diagnostic outputs or from clinical descript

local-aiarxiv-cs-ai

A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving

arXiv:2609.09650v1 Announce Type: new Abstract: Reinforcement learning (RL) has demonstrated considerable potential for autonomous driving decision-making. However, its deployment in urban autonomous

safetyarxiv-cs-ro

A Sharp Barrier for Consistent Submodular Maximization: Any Improvement over 2-sqrt{2} Entails Exponential Queries or Linear Recourse

arXiv:2609.09986v1 Announce Type: cross Abstract: Consistent submodular maximization studies the tradeoff between solution quality and stability when elements arrive over time. For a monotone submodul

A solution to the Erdos Problem #1040

arXiv:2609.06050v1 Announce Type: cross Abstract: For a compact set KsubsetC, let artheta(K) be the infimum of the planar areas of the unit lemniscates of all monic polynomials with zeros in K, allowi

researcharxiv-cs-ai

A statistical approach to bias in zero-shot learning: the lens of handwriting recognition

arXiv:2609.10084v1 Announce Type: cross Abstract: Generalized zero-shot learning (GZSL) has emerged as an important paradigm for visual recognition systems that must generalize to classes that were no

safetyarxiv-cs-cv

Tutorials

A Statistical Approach to Estimating Sample Size of Machine Learning Models

arXiv:2609.09547v1 Announce Type: new Abstract: Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predicto

tutorialsarxiv-cs-lg

A Subsampled Davis-Kahan Bound for Large-Scale Eigenspace Estimation

arXiv:2609.09211v1 Announce Type: cross Abstract: The Davis-Kahan theorem is a fundamental tool in spectral analysis, providing quantitative control over the distance between the eigenspaces of a symm

A Systematic Evaluation of Molecule Generation Models for De Novo Drug Design: From Benchmarks to Practical Insights

arXiv:2609.10099v1 Announce Type: new Abstract: Molecule generation has emerged as a powerful computational tool for de novo drug design, enabling the exploration of chemical space beyond the limits o

A Three-Tier Persona Vector for Controllable User Simulation in Agentic Evaluation

arXiv:2609.08592v1 Announce Type: new Abstract: Evaluating tool-augmented LLM agents requires diverse, realistic user inputs yet most evaluation frameworks use flat role descriptions ('you are an angr

agentsarxiv-cs-ai

A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis

arXiv:2609.07586v1 Announce Type: new Abstract: Software repositories contain vast amounts of data on code contributions, bug reports, and project activities, yet this information remains challenging

A traffic management system for large and heterogeneous vehicles in narrow industrial environments

arXiv:2609.10400v1 Announce Type: new Abstract: The coordination of Automated Guided Vehicles (AGVs) in high-density industrial environments represents a critical challenge within Logistics 4.0, as tr

agentsarxiv-cs-ro

A Translational Note on AI Safety Evaluation

arXiv:2609.06573v1 Announce Type: new Abstract: Recent studies report that automated red-teaming finds more vulnerabilities, at lower cost, than human red-teaming on standard AI safety benchmarks, and

Local Ai

A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction

arXiv:2609.10108v1 Announce Type: new Abstract: Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks

local-aiarxiv-cs-lg

A Trustworthy Watermarking Framework for LLM-Generated Food Safety Content

arXiv:2609.06708v1 Announce Type: cross Abstract: Large language models are transforming many industries with their text generation abilities. However, their outputs can be easily tampered with, creat

safetyarxiv-cs-ai

A TTP by TTP Approach: Precise Malware Detection via Malicious TTP Recognition

arXiv:2609.06579v1 Announce Type: cross Abstract: Machine learning methods, and especially neural networks, are now routinely used for malware detection in network traffic. Though very effective, syst

researcharxiv-cs-ai