Search: “concepts”

DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation

arXiv:2609.02796v1 Announce Type: new Abstract: Sign language processing systems have traditionally operated at the sentence level, ignoring critical discourse phenomena fundamental to sign language c

researcharxiv-cs-cl

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Information Density Imbalance in Visual Object Detection

DGX agent

arXiv:2609.02369v1 Announce Type: new Abstract: In object detection, the number of instances is typically used to determine whether a dataset exhibits a long-tailed distribution, implicitly assuming t

safetyarxiv-cs-cv

3 Sept 2026

Agents

InsightSeg: Reusing Correction Insights for Guideline-Consistent Segmentation

arXiv:2609.02002v1 Announce Type: cross Abstract: Guideline-consistent semantic segmentation requires more than category recognition, as real-world labeling policies demand fine-grained, task-specific

agentsarxiv-cs-ai

The Ceiling Is in the Channel: Auditing Learner Gaps and Measurement Frontiers in Clinical Prediction

arXiv:2609.01909v1 Announce Type: new Abstract: Clinical prediction can saturate for two different reasons: a fitted learner may fail to extract available information, or the recorded variables may im

Agents

Towards a Foundational Ontology for Identifying and Resolving Contradictions in Dialogue-based Human-Robot Interactions

arXiv:2609.02364v1 Announce Type: cross Abstract: Existing Human-Robot Interaction (HRI) literature has focused on identifying and structuring errors, failures, conflicts, and knowledge issues (called

agentsarxiv-cs-ai

Counterfactual Fragility Certificates: Exposing High-Confidence Brittleness under Structured Evidence Failure

arXiv:2609.00366v1 Announce Type: cross Abstract: High test accuracy and good aggregate calibration do not show whether an individual prediction is structurally supported by its evidence. In tabular d

In-Context Neurofeedback: Can LLMs Control Their Internal Representations through Privileged Access?

arXiv:2609.00904v1 Announce Type: new Abstract: Whether large language models (LLMs) can control their own internal representations matters for both machine metacognition and AI safety. A recent study

Lagged Coupling: Internal Representations Become Readable Before They Become Causal

arXiv:2609.01048v1 Announce Type: cross Abstract: Across the full Pythia suite (160M-12B, eight checkpoints, four task families), a linear probe can read a target variable from the residual stream as

SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

arXiv:2603.17380v3 Announce Type: replace-cross Abstract: Virtual-cell models aim to predict how cell populations respond to perturbations, but control and treated cells are measured as unpaired popul

tutorialsarxiv-cs-ai

Sources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information Queries

arXiv:2609.00319v1 Announce Type: cross Abstract: Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compose

researcharxiv-cs-cl

Training-Free Inpainting Across Domains with a Frozen Text-to-Image Diffusion Model

arXiv:2609.00862v1 Announce Type: new Abstract: We show that a frozen generic text-to-image diffusion model can perform conditional inpainting across three evaluated natural-image domains with one fix

researcharxiv-cs-cv

Uncovering the Computational Ingredients of Human-Like Representations in LLMs

arXiv:2510.01030v2 Announce Type: replace Abstract: The human ability to translate diverse perceptual and linguistic inputs into structured behavior has been thought to rest on learning robust represe

When Does Online Adaptation Pay on the Edge? A Leakage-Free Evaluation of Warmup, Learning-Rate Selection, and Resource Trade-offs for Time-Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2609.01126v1 Announce Type: new Abstract: Online adaptation can help edge time-series forecasting under distribution drift, but its measured benefit is sensitive to evaluation choices. We study

Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment

arXiv:2609.00055v1 Announce Type: cross Abstract: Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-sp

A homotopy-type-theoretic generalization of neurosymbolic inference

arXiv:2606.17851v2 Announce Type: replace Abstract: A wide range of neurosymbolic (NeSy) systems compute one functional: a belief-weighted sum of a logical quantity over a space of sigma-structures, o

Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling

arXiv:2608.29291v1 Announce Type: new Abstract: Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generatio

Context Staircase: Signature-Aligned Dynamics of Token Embeddings under Small Initialization

arXiv:2608.30315v1 Announce Type: cross Abstract: Token embeddings are the basic representational units that connect discrete tokens with continuous computation in language models. Although modern lan

safetyarxiv-cs-cl

Cross-lingual Functional Vectors for Emotion Detection in Large Language Models

arXiv:2608.29613v1 Announce Type: new Abstract: Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specif

Instruction Distillation: Text Instructions as Visual Examples

arXiv:2608.28696v1 Announce Type: new Abstract: Visual in-context learning (ICL) with multimodal large language models (MLLMs) is effective for fine-grained visual classification, but each retrieved i

tutorialsarxiv-cs-cv

MMPCBench: Benchmarking Multimodal Large Language Models on Proactive Critique of Flawed Inputs

arXiv:2608.29286v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructio

One note in three: a verified census of three deployed AI scribes, and the instrument that counted it

arXiv:2608.31017v1 Announce Type: cross Abstract: Ambient AI scribes draft clinical notes under the reassurance that a clinician signs every note. We audited three commercial AI scribes on the same 14

Applications

OrnaStyler: Ornament-Aware Latent Editing for Content-Preserving 3D Stylization

arXiv:2608.29905v1 Announce Type: new Abstract: Text-guided style editing of 3D assets is essential for adapting existing objects to diverse visual aesthetics in digital content creation. Despite rapi

applicationsarxiv-cs-cv

SHADOWBENCH: Toward Reliable Automatic Evaluation of Semantic Alignment in Autoformalization

arXiv:2608.29270v1 Announce Type: cross Abstract: Autoformalization translates informal mathematical theorems into code for proof assistants such as Lean. A central challenge is that current evaluatio

Test-Time Training with Next-Token Prediction

arXiv:2606.21803v2 Announce Type: replace Abstract: Next-token prediction is the self-supervised signal that trains language models, and every observed prompt token provides the same signal at test ti

Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation

arXiv:2608.29923v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) relies on vision-language alignment to recognize arbitrary text-defined categories, yet this alignment is f

Tracing distinguishability through transformer processing with stochastic LayerNorm

model-releasesarxiv-cs-lg

arXiv:2608.30720v1 Announce Type: new Abstract: Representational similarity is foundational to analyses of deep networks, yet distances between point-valued representations are not intrinsically tied

Local Ai

When Can We Work in Embedding Space? What Text Embeddings Preserve

arXiv:2608.31059v1 Announce Type: cross Abstract: When do text embeddings work as inputs to empirical analysis? Their use rests on an assumption: that we can trade text for its low-dimensional embeddi

local-aiarxiv-cs-cl

A Task-Centric Ontology and Deterministic Domain Rules as a Verifiable Core for AI-Assisted Chemistry Problem Solving

arXiv:2608.26164v1 Announce Type: new Abstract: Large language models can interpret natural-language chemistry questions, but their internal reasoning is difficult to inspect, constrain, and validate.

Cross-Lingual Alignment Without Joint Training: Do Monolingual Language Models Converge on Universal Representations?

arXiv:2608.27115v1 Announce Type: new Abstract: Cross-lingual alignment in multilingual language models is typically attributed to joint training: shared parameters, mixed-language batches, or explici

safetyarxiv-cs-cl

Emotional Preferences as Goal-Priority Regulation

arXiv:2608.27072v1 Announce Type: cross Abstract: A core question in decision-making for agents is whether the relative priorities of competing lower-level objectives can be determined by emotional pr

Graph-Guided Selective Unlearning for Language Models: Controlling Support Routes Beyond Forget Seeds

arXiv:2608.26743v1 Announce Type: new Abstract: Enterprises fine-tune language models on proprietary data that may later require removal due to privacy, contractual, or compliance obligations. Selecti

Parameter Efficient Continual Learning for Sparse Event-Based Transformers

model-releasesarxiv-cs-cv

arXiv:2608.26720v1 Announce Type: new Abstract: Robotic and edge intelligence systems operate in dynamic environments where data arrives continuously, requiring models to adapt while preserving previo

SAREF-based Ontology for Distributed AI Workflows across the Edge-Fog-Cloud Continuum

arXiv:2608.26160v1 Announce Type: new Abstract: Nowadays semantic models provide limited support for representing distributed AI workflows and their execution across heterogeneous edge, fog, and cloud

Self-OPD: On-Policy Distillation for Flow Matching Models without Teacher

arXiv:2608.26872v1 Announce Type: new Abstract: On-policy distillation (OPD), which leverages a pre-trained, specialized teacher model to provide dense supervisory signals, has achieved significant su

The BS-meter: Detecting Politics and Labour through ChatGPT's Language

arXiv:2411.15129v3 Announce Type: replace-cross Abstract: What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, w

tutorialsarxiv-cs-ai

Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales

arXiv:2608.26125v1 Announce Type: cross Abstract: Online hate against Muslim communities often appears in culturally coded, multilingual forms that evade conventional AI moderation. Such systems, thou

Unsupervised Adaptation of 3D CT Foundation Models for 3D CBCT Segmentation

arXiv:2608.27190v1 Announce Type: new Abstract: Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compoun

Can We Read the Mind of an Audio LLM? A Verbalizable, Multilingual Middle-Layer Workspace

arXiv:2608.24958v1 Announce Type: cross Abstract: An audio language model is a black box in a specific way: we see what it says, never what it works out on the way there, and chain-of-thought monitori

EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation

arXiv:2511.11635v2 Announce Type: replace-cross Abstract: In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirem

MObyGaze: a film dataset of multimodal objectification densely annotated by experts

model-releasesarxiv-cs-cv

arXiv:2505.22084v2 Announce Type: replace Abstract: Characterizing and quantifying gender representation disparities in audiovisual storytelling contents is necessary to grasp how stereotypes may perp

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation

arXiv:2412.12660v2 Announce Type: replace Abstract: Recently, developing unified medical image segmentation models gains increasing attention, especially with the advent of the Segment Anything Model

C3VDReg: A Benchmark for Local-to-Local Colonoscopic Registration toward Anatomical Localization

model-releasesarxiv-cs-cv

arXiv:2511.00260v2 Announce Type: replace Abstract: Anatomy-aware colonoscopic navigation requires localizing partial endoscopic observations on a stable 3D reference to support coverage assessment, r

Fidelity Preference, Not Demographic Preference: A Pixel-Level Attribute-Sensitivity Audit of Image Aesthetic/Preference Scorers

arXiv:2608.23593v1 Announce Type: cross Abstract: Text-to-image systems use learned aesthetic scorers to filter training data and guide generation, but whether these scores encode demographic attribut

Focal Calibration Loss: Controlling Posterior Distortion in Deep Neural Classifiers

arXiv:2410.18321v3 Announce Type: replace-cross Abstract: Confidence calibration matters wherever a classifier's probabilities, not just its labels, are consumed downstream. We study Focal Calibration

LumiXAI: A Modular Full-Stack Framework for Feature Attribution

arXiv:2608.24524v1 Announce Type: cross Abstract: Feature attribution is a central tool of model interpretability, yet the software through which it is applied remains fragmented: individual tools spe

Matched Excess-Outranker Regularization for Candidate-Set Interference in Continual Knowledge Graph Embedding

arXiv:2608.24273v1 Announce Type: new Abstract: Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forget

Mixture of Channel Experts: Static Sparse Supports with Input-Adaptive Mixing for Pointwise Projections

arXiv:2608.23794v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) scales language models by routing each input through a small set of independently parameterized experts. We show that copying

tutorialsarxiv-cs-cv

The Invisible Editorial Layer: Formalizing Undisclosed Inference-Time Steering, Probability Placement, and the Attribution Problem in Deployed Language Models

arXiv:2608.24662v1 Announce Type: new Abstract: Large language models (LLMs) are commonly evaluated under the assumption that their observable behavior is primarily determined by model weights, traini