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HiPACE: Hierarchical Phase-Boundary Analysis and Controlled Evaluation of Feature Absorption in Sparse Autoencoders

arXiv:2609.29551v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose LLM activations into sparse dictionary atoms, so that each distinct concept gets its own feature. One recurring beh

researcharxiv-cs-ai

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Human DGX agent

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Safety

How Do Users Negotiate Harmful Value Conflicts with AI Companions? A Study with Minion, a Technology Probe for In-Situ Human-AI Conflict Response

DGX agent

arXiv:2411.07042v3 Announce Type: replace-cross Abstract: AI companions increasingly sustain long-term, emotionally engaging relationships but can also make discriminatory remarks or exert control, le

safetyarxiv-cs-ai

25 Sept 2026

Agents

How does Adversarial Influence Scale in Multi-Agent Systems?

arXiv:2609.30028v1 Announce Type: new Abstract: Multi-agent deliberation can improve performance, but what happens when some agents do not act in good faith? In practice, an agent may be deceptive and

agentsarxiv-cs-ai

How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure

arXiv:2609.30074v1 Announce Type: cross Abstract: Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deser

applicationsarxiv-cs-ai

Tutorials

How To Do Things With Prompts

arXiv:2609.29657v1 Announce Type: new Abstract: When users address large language models, they produce directive speech acts whose pragmatic features differ from those of both everyday conversation an

tutorialsarxiv-cs-cl

Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation

arXiv:2609.28859v1 Announce Type: new Abstract: Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality s

safetyarxiv-cs-ai

Human-in-the-Loop Geospatial Annotation for Rapid Dataset Construction in Field-Deployed UAV Systems

arXiv:2609.28767v1 Announce Type: new Abstract: Real-world perception systems must adapt to changing environments, but manual image annotation cannot scale to field data volumes. We present BirdsEye,

applicationsarxiv-cs-ro

Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment

arXiv:2609.28491v1 Announce Type: cross Abstract: Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating cu

Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution

arXiv:2609.29329v1 Announce Type: cross Abstract: Existing continual-learning methods protect parameters, replayed examples, or Euclidean feature subspaces. When applied to hyperbolic multimodal model

ICE: Task-Aligned Clifford Latent Fields for Multimodal Graph Foundation Models

arXiv:2609.29398v1 Announce Type: new Abstract: Multimodal attributed graphs connect entities, visual content, language, and observed relations. Learning one foundation across such graphs requires mor

researcharxiv-cs-lg

iCoder-27B: Recursive AI-Led Development of Frontier Industrial Coding Model

arXiv:2609.29626v1 Announce Type: new Abstract: Recursive AI, the prospect of AI taking an increasingly complete role in building and improving AI, is a crown jewel of AI for AI. Although recursive se

IDRBench: Benchmarking the Interactive Capabilities of Deep Research Agents

arXiv:2601.06676v3 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based deep research agents perform multi-step reasoning, web exploration, and long-form report generation. In these

Local Ai

Image Fidelity is Not Field Fidelity: Joint Thermodynamic Reconstruction and Error Localization in Neural Tomography

arXiv:2609.28868v1 Announce Type: new Abstract: Neural fields for scientific tomography are optimized from 2D images, but the actual quantity of interest is often a latent 3D physical field. Because t

local-aiarxiv-cs-lg

ImCorr: Sub-pixel Semantic Correspondence via Implicit Feature Decoding

arXiv:2609.29193v1 Announce Type: new Abstract: The strong performance that modern semantic correspondence methods achieve at standard thresholds plateaus sharply at fine-grained thresholds. We argue

researcharxiv-cs-cv

Implicit Behavior Coordination from Sub-Task Demonstrations by Exploiting Overlap-Induced Multimodality

arXiv:2607.09234v2 Announce Type: replace Abstract: Long-horizon robotic rearrangement is commonly formulated as a skill-sequencing problem, where distinct behaviors are explicitly represented and coo

safetyarxiv-cs-ro

Improving Calibration of Black-Box Radiology AI Using Test-Time Augmentation

arXiv:2609.29931v1 Announce Type: new Abstract: Radiology AI systems increasingly inform clinical decisions such as triage, follow-up imaging, and treatment planning. For these decisions to be made sa

researcharxiv-cs-lg

IndicBankBench: Evaluating Safety and Reliability of Language Model Assistants in Indian Retail Banking

arXiv:2609.29167v1 Announce Type: new Abstract: Banking assistants must use account-specific information to answer requests and, in many cases, take actions through tools. Evaluating only the final re

Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space

model-releasesarxiv-cs-cv

arXiv:2609.29457v1 Announce Type: new Abstract: Industrial anomaly detection (IAD) is evolving beyond conventional detection and localization toward multimodal inspection systems that can describe, ex

Agents

Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering over Revised Corpora

arXiv:2609.29661v1 Announce Type: new Abstract: Most agentic question answering (QA) systems do an important part of their semantic work at the worst possible time: every time someone asks a question.

agentsarxiv-cs-ai

Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure

arXiv:2609.30217v1 Announce Type: cross Abstract: A central concern in AI safety is that agents may treat oversight as an obstacle when it conflicts with completing their goals. We study instrumental

Integrating Local Detail and Global Context: A Dual-Input Multi-Task Learning Framework for Bone Tumor Diagnosis

arXiv:2609.28732v1 Announce Type: cross Abstract: Primary bone tumors are rare but clinically aggressive neoplasms whose diagnosis from radiographs is challenged by heterogeneous morphology, subtle le

researcharxiv-cs-cv

Interpretable Similarity of Synthetic Image Utility

model-releasesarxiv-cs-cv

arXiv:2512.17080v2 Announce Type: replace Abstract: Synthetic medical image data can unlock the potential of deep learning (DL)-based clinical decision support (CDS) systems through the creation of la

Intrinsic-Extrinsic Coupling in Learning Dynamics

arXiv:2609.30185v1 Announce Type: new Abstract: A learner's current observations need not determine its response to further training. We formulate intrinsic-extrinsic coupling through the continuation

safetyarxiv-cs-lg

Invertible Query-Key Coupling Composes with Attention Mechanisms

arXiv:2604.01683v2 Announce Type: replace-cross Abstract: Scaled dot-product attention forms its queries and keys as independent linear projections, so the two never interact before the dot product th

researcharxiv-cs-cl

Investigating White Blood Cells as a Source of False-Positive Malaria Parasite Detection in African Blood-Smear Images

arXiv:2609.29663v1 Announce Type: cross Abstract: White blood cells (WBCs) present on every Giemsa-stained thick blood smear share visual properties with early-stage Plasmodium falciparum ring-form tr

researcharxiv-cs-ai

IronViT: Toward Efficient Generalist Visual Representation Learning

arXiv:2609.29252v1 Announce Type: new Abstract: A generalist vision encoder must capture semantic, spatial, language-aligned, and action-relevant cues within a unified representation, yet softmax atte

researcharxiv-cs-cv

Is Reasoning Always Useful? Rethinking Reasoning Utility in Universal Multimodal Embeddings

arXiv:2609.29560v1 Announce Type: new Abstract: Reasoning-enhanced universal multimodal embeddings (UME) improve heterogeneous retrieval, but plausible rationales do not necessarily produce discrimina

safetyarxiv-cs-ai

IterSynth: Rethinking Deep Search Agents via Role-Decoupled Iterative Synthesis

arXiv:2609.29444v1 Announce Type: cross Abstract: Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents su

safetyarxiv-cs-ai

It's the Geometry, Not the Model: Effective Rank and Subspace Alignment in Functional Connectivity Classification

arXiv:2609.29932v1 Announce Type: cross Abstract: Resting-state functional connectivity (FC) is widely used to classify brain phenotypes and disorders. Most pipelines use the full connectome and seek

safetyarxiv-cs-cv

Local Ai

Jev-Mobile: Jev as an Executor for Mobile GUI Agents

arXiv:2609.30186v1 Announce Type: new Abstract: Vision-language models (VLMs) have become a common foundation for autonomous mobile GUI agents, but most existing systems rely on the VLM for both plann

local-aiarxiv-cs-ai

JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places

arXiv:2609.29769v1 Announce Type: new Abstract: We ask whether Jev, a typed classifier that returns probabilities over permitted answers without generating text, can replace an LLM rubric judge. We co

researcharxiv-cs-cl

JevOut: Natural Context Can Flip Decision Models

arXiv:2609.30243v1 Announce Type: new Abstract: Dedicated decision models such as Jev map unstructured language to probability distributions over finite choices, allowing their outputs to directly rou

applicationsarxiv-cs-cl

JoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence

arXiv:2606.14777v2 Announce Type: replace-cross Abstract: Many moments in the real world do not wait for a user to ask. A fire starts on a security monitor, an expression flickers across a video call,

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

KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS

arXiv:2609.28761v1 Announce Type: cross Abstract: We design, implement, and evaluate KathDB-FAO, a new query evaluation subsystem for our KathDB multimodal DBMS. KathDB-FAO takes as input a query in n

researcharxiv-cs-ai

Hardware

KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization

arXiv:2609.30059v1 Announce Type: cross Abstract: Deep learning inference and training performance depends critically on GPU kernel efficiency. Modern compilers such as PyTorch Inductor automatically

hardwarearxiv-cs-ai

KeyGen: Unsupervised Keypoint based Object-Centric Representations for Category-Level Policy Generalization

arXiv:2609.28818v1 Announce Type: cross Abstract: Generalization in robotic manipulation requires policies to perform tasks across diverse unseen object instances that vary in shape, size, and pose. H

Know Your Body: A Harness for Direct and Self-Improving Robot Control with VLMs

arXiv:2609.28530v1 Announce Type: new Abstract: A general-purpose vision-language model can understand a task goal without knowing how a particular robot's motion and functional parts produce the inte

researcharxiv-cs-ro

Koopman-Accelerated Model-Based Diffusion for Real-Time Robot Control

arXiv:2609.28920v1 Announce Type: new Abstract: Conventional model-based diffusion (MBD) achieves effective trajectory optimization by leveraging noise annealing. However, its high computational cost,

researcharxiv-cs-ro

Agents

LabFactory: Building and Evaluating Executable AI Labs

arXiv:2609.28697v1 Announce Type: new Abstract: Scientific tasks specify a desired capability, but realizing it often requires building a computational system tailored to the task---acquiring data, de

agentsarxiv-cs-lg

Language Specificity vs. Domain Diversity: Benchmarking Transformers for Bangla Medical NER

model-releasesarxiv-cs-lg

arXiv:2609.29101v1 Announce Type: new Abstract: Medical Named Entity Recognition (NER) for low-resource languages remains a challenging task due to high linguistic variability and a scarcity of domain

Large Language Models for Programming: Actually Fixing or Reimplementing Incorrect Code?

model-releasesarxiv-cs-cl

arXiv:2609.29410v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models can effectively solve problems and fix bugs in diverse programming environments, including competit

LastOPD: Taming Collapse in Latent On-Policy Distillation

arXiv:2609.28845v1 Announce Type: cross Abstract: On-policy distillation (OPD) corrects a student on the responses it writes, but its signal is the teacher's next-token distribution: it tells the stud

safetyarxiv-cs-cl

LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping

model-releasesarxiv-cs-cv

arXiv:2512.13130v2 Announce Type: replace Abstract: High-resolution phenotyping at the level of individual leaves offers fine-grained insights into plant development and stress responses. However, the

Local Ai

Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs

arXiv:2609.28567v1 Announce Type: new Abstract: Scanning electron microscopy (SEM) is routinely used to characterize the microstructural changes caused by hydrogen embrittlement (HE) in structural ste

local-aiarxiv-cs-lg

Learnable Time-Frequency Masks for Explaining Time-Series Classifiers

arXiv:2609.29270v1 Announce Type: new Abstract: Time-series explainability remains challenging because discriminative information is often encoded in latent frequency or time-frequency features rather

applicationsarxiv-cs-lg

Learned Cross-Task Relationships in Multi-Task Models

arXiv:2609.28776v1 Announce Type: new Abstract: We propose a framework that learns cross-task relationships in multi-task models by approximating the joint distribution of task labels through targeted

applicationsarxiv-cs-ai

Learning a Flow to Self-Supervised Representations

arXiv:2609.29350v1 Announce Type: new Abstract: Explicit geometric references offer a direct way to structure self-supervised representations. Existing adversarial distribution-matching formulations,

safetyarxiv-cs-cv