Search: “models”

SAGE: Semantic Audio Generative Encoder

arXiv:2609.32755v1 Announce Type: cross Abstract: Audio autoencoders compress waveforms into compact latent representations that serve as the interface between raw audio and downstream models. Current

Applications

Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2410.02331v3 Announce Type: replace Abstract: The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led

applicationsarxiv-cs-cv

29 Sept 2026

SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals

arXiv:2609.34228v1 Announce Type: new Abstract: Evaluating statistical discovery by large language model (LLM) agents requires verifiable analytical ground truth. Establishing such ground truth for re

SOLAR: A State-Driven Online Learning Rate Scheduler for LLM Pretraining

arXiv:2609.34681v1 Announce Type: new Abstract: Learning-rate (LR) scheduling plays a central role in large language model (LLM) pretraining, yet current practice still relies heavily on hand-crafted

SWT: Self-Supervised Video Object Segmentation via Sliding, Wavelet and Transportation

model-releasesarxiv-cs-cv

arXiv:2609.31725v1 Announce Type: new Abstract: Video Object Segmentation (VOS) aims to accurately segment target objects from consecutive video frames and track the changes of the objects in each fra

The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoning

arXiv:2609.33985v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks. Crowdsourcing user conversations is an established app

TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care

arXiv:2609.34088v1 Announce Type: new Abstract: TRACE (Text-Reinforced Analysis of Cardio ECGs) is a multimodal electrocardiogram (ECG) representation model that learns clinically grounded signal embe

A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Code

arXiv:2609.30642v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become integrated into software development workflows, concerns regarding unintentional biases in AI-generated code. A

FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators

arXiv:2609.30982v1 Announce Type: cross Abstract: Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights

FlatClip: A Geometry-Aware Surface-Level Baseline for fMRI Representation Learning

arXiv:2609.31204v1 Announce Type: cross Abstract: Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models are

researcharxiv-cs-cv

Guarded Gradient-Based Activation Steering of Shutdown Responses in Qwen3.5-0.8B: A Minimum-Step Policy

arXiv:2609.30326v1 Announce Type: new Abstract: Activation steering changes a model's internal activations during inference without updating its weights, but a useful intervention must determine both

LandscapeSHAP: Which Persistent Homology Class Gets the Credit?

arXiv:2609.31469v1 Announce Type: cross Abstract: Shapley values, a solution concept from cooperative game theory, have recently become a standard tool for feature credit allocation in machine learnin

safetyarxiv-cs-lg

Local Ai

LocUS: Head Selection and Subspace Projection for Targeted Activation Steering

arXiv:2609.31122v1 Announce Type: new Abstract: Activation steering is a powerful training-free paradigm for controlling large language models at inference time. However, standard approaches estimate

local-aiarxiv-cs-cl

Mentored Decoding: Faster Inference meets Boosting

arXiv:2609.30474v1 Announce Type: new Abstract: Speculative decoding is a successful technique speeding up inference of a target autoregressive language model via a fast drafter model. Lossy speculati

researcharxiv-cs-lg

RAZOR: Pruning Replaceable Experts in LLMs

arXiv:2609.30465v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models activate few experts per token but store the full expert pool. Expert pruning reduces this storage burden; at a fixed

Recursive Self-Improvement via On-Policy Distillation for Reasoning

arXiv:2609.30652v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student model by having it generate trajectories, then matching its next-token predictions with an external teache

safetyarxiv-cs-cl

Refining Cytology Predictions with Conditional Random Fields

model-releasesarxiv-cs-cv

arXiv:2609.31028v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong zero-shot (ZS) classification on histology images but do not perform as well on cytology, whose stains and

Agents

Rethinking Data Quality for AI-Driven Systems: Evidence from Practitioner Interviews

arXiv:2609.31191v1 Announce Type: cross Abstract: Data quality research has usually treated data as an input that is stored, processed, and validated. In AI-driven software-intensive systems, data als

agentsarxiv-cs-ai

SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages

arXiv:2609.30739v1 Announce Type: new Abstract: Multilingual text-vision embedding models are essential for cross-lingual image-text retrieval, but Southeast Asian languages remain poorly supported du

researcharxiv-cs-cl

Softmax Reparameterization for Output-Head Quantization

arXiv:2609.31291v1 Announce Type: cross Abstract: Large vocabularies make output heads a substantial inference cost in small language models. We propose softmax reparameterization, a post-training met

The Uncontrolled Variable: Vision-Language Refusal Is Conditioned on the Image-Attachment Interface, and Not Robust to Irrelevant Image Properties

arXiv:2609.26174v2 Announce Type: replace-cross Abstract: We show that aligned vision-language models also condition refusal on a property of a request's form: whether an image is attached, holding ev

Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach

arXiv:2609.31570v1 Announce Type: cross Abstract: Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not captu

Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach

arXiv:2609.30420v1 Announce Type: cross Abstract: Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multiva

Understanding the Role of Prompt Template in Knowledge Distillation for Safety Alignment

arXiv:2609.30802v1 Announce Type: new Abstract: Prior research has demonstrated that the choice of prompt template during Supervised Fine-Tuning (SFT) significantly impacts the robustness of safety al

Local Ai

A Simple Gripper Interface for Simulator-Agnostic Cloth Manipulation

arXiv:2609.29340v1 Announce Type: new Abstract: This paper presents a grasping model for cloth manipulation specifically tailored to ease the deployment of robotic control methods. The model is robust

local-aiarxiv-cs-ro

An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection

arXiv:2609.28703v1 Announce Type: cross Abstract: Hate speech on social media poses serious risks to social harmony, mental well-being, and public safety, making its timely and accurate detection esse

Beyond Average Safety: Chance-Constrained LLM Fine-tuning

arXiv:2609.29960v1 Announce Type: cross Abstract: Fine-tuning large language models on new objectives can improve helpfulness, instruction following, or domain-specific performance, but it can also in

Delay-of-Gratification as a Multi-Agent Survival Micro-benchmark for Long-Horizon LLMs: Social Exposure, Personas, and Tool Use Budgets

arXiv:2609.29509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as multi-turn agents that must sustain goals, use tools, and adapt to other agents over extended

Hallucination Neurons and Where to Find Them: An Investigation into the existence of Hallucination Neurons

arXiv:2609.29781v1 Announce Type: new Abstract: Interpretable machine learning for Large Language Models (LLMs) increasingly relies on sparse probing methods that identify small sets of neurons claime

Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures

arXiv:2609.29429v1 Announce Type: new Abstract: Detectors of alignment failures screen deployed language models and score alignment benchmarks. Most are generative judges that spend a decoding pass on

MoVISA: Multi-Token Reasoning for Video Object Segmentation

model-releasesarxiv-cs-cv

arXiv:2609.28956v1 Announce Type: new Abstract: Recent advances in video object segmentation with Multimodal Large Language Model (MLLM) reasoning have demonstrated the effectiveness of using a single

Online Task Adaptation via Self-Organisation

arXiv:2609.29281v1 Announce Type: new Abstract: Neural networks are typically adapted by computing gradients and updating model parameters. We investigate whether task-specific adaptation can instead

Polite but Misaligned: Evaluating LLM Politeness Judgments Against Human Pragmatic Norms

arXiv:2609.29001v1 Announce Type: new Abstract: Despite strong performance on standard benchmarks, it remains unclear whether large language models (LLMs) evaluate social pragmatics in ways that align

safetyarxiv-cs-cl

Robo-Harness K1: Harnessing Robot-Use Agents via Perception Augmentation

model-releasesarxiv-cs-ro

arXiv:2609.29389v1 Announce Type: new Abstract: Foundation vision-language models (VLMs) understand objects, instructions, and spatial relations, yet translating this capability into robotic manipulat

Tutorials

Self-Play Pretraining with Zero Data

arXiv:2609.30063v1 Announce Type: new Abstract: Advances in language modeling have been driven by scaling pretraining on ever more data. Yet, the training data is still largely curated on the model's

tutorialsarxiv-cs-ai

Synthetic Hospital: An Open, Verifiable, Physician-Validated Longitudinal EHR Benchmark

arXiv:2609.30027v1 Announce Type: new Abstract: Frontier language models are rarely used in clinical workflows because the realistic, longitudinal benchmarks needed to develop them are scarce. Real el

TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction

arXiv:2602.11700v2 Announce Type: replace-cross Abstract: Tabular prediction can benefit from in-table rows as few-shot evidence, yet existing tabular models typically perform instance-wise inference

Tracking States or Tracking Cosets? An Algebraic Account of Learned State Tracking

arXiv:2609.29951v1 Announce Type: cross Abstract: State tracking requires composing a sequence of updates, but accuracy alone does not reveal what a model has learned. We study neural networks trained

Who Put the I in AI? Provenance and the Admissibility of Machine Self-Report

arXiv:2609.29494v1 Announce Type: cross Abstract: Large language models make statements concerning their own 'minds'. When asked whether or not they are conscious, they usually say that they are not;

A Systematic Benchmark of Explainable Methods for Temporal Attribution in Sequential Recommendation Systems

arXiv:2609.27201v1 Announce Type: new Abstract: Sequential RecSys are central to modern personalization, exploiting user's historical interaction sequences to drive next-step decisions. Deep learning

Alignment of LRMs via Counter-Aligned Few-Shot Conversation Exposure

arXiv:2609.27763v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) rely on explicit chain-of-thought (CoT) reasoning and large context windows to achieve strong performance on complex tasks

Anomaly-Free Self-Optimization via AUC Bounds

arXiv:2609.27362v1 Announce Type: new Abstract: Anomalies are rare, and anomalous data are often unavailable during development, making it difficult to determine which anomaly detection models and con

Backdoors in Learning-Based Industrial Robotic Arm Manipulation: An Empirical Security Study

arXiv:2609.26868v1 Announce Type: new Abstract: Learning-based models (e.g., visuomotor and Vision-Language-Action (VLA)) are increasingly explored for industrial robotic manipulation, where model pre

researcharxiv-cs-ro

BranchDrive: A Branch-Structured Dataset for Action-Conditioned Driving Prediction

model-releasesarxiv-cs-ro

arXiv:2609.27275v1 Announce Type: new Abstract: Most autonomous-driving datasets record only the action executed by a behavior policy and the single future that followed, providing limited supervision

ChipMEM: Verification-Grounded Memory for EDA Agents

arXiv:2609.27067v1 Announce Type: cross Abstract: Large language model (LLM)-based agents use Electronic Design Automation (EDA) tools to generate and revise register-transfer-level (RTL) designs unde

Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court

arXiv:2609.26945v1 Announce Type: new Abstract: Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the

Counterfactual Constraint-Conditioned On-Policy Distillation for Multi-Constraint Instruction Following

arXiv:2609.27421v1 Announce Type: new Abstract: Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tune

DCRL: Decoupling and Coupling Reinforcement Learning via Policy-Reward Manifold Alignment

arXiv:2609.27572v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a key paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing