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Error Detection for PET/CT Radiology Reports: Domain-Specific vs Large Language Models

arXiv:2608.30021v1 Announce Type: cross Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are ofte

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Research

Error-Driven Scene Editing for 3D Grounding in Large Language Models

DGX agent

arXiv:2511.14086v2 Announce Type: replace-cross Abstract: Despite recent progress in 3D-LLMs, they remain limited in accurately grounding language to visual and spatial elements in 3D environments. Th

researcharxiv-cs-ai

1 Sept 2026

Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labels

arXiv:2608.30827v1 Announce Type: new Abstract: Large language models are increasingly used to annotate datasets for training smaller, task-specialized models such as named entity recognition. While t

Estimating Population-Risk Curves Along Nonconvex Gradient Flows from the Training Sample

arXiv:2608.30261v1 Announce Type: cross Abstract: We estimate the conditional population-risk curve of a realized smooth nonconvex gradient flow from the training sample. Flow approximate leave-one-ou

Evaluating 2D and 3D-Aware Vision Foundation Models for Vehicle Attribute Recognition

model-releasesarxiv-cs-cv

arXiv:2608.29929v1 Announce Type: new Abstract: Vehicle attribute recognition is an important task in intelligent transportation systems, particularly when Automatic License Plate Recognition (ALPR) i

Evaluating and Improving LLM Self-Modeling

arXiv:2608.30980v1 Announce Type: cross Abstract: We study self-modeling: an LLM's ability to answer questions about its own behavior. We focus on verifiable behavioral questions, such as whether a pr

Evaluating and Mitigating Anti-LGBTQ Biases in German and Multilingual Language Models

arXiv:2608.30884v1 Announce Type: cross Abstract: While gender and racial biases in language models have been widely studied, anti-LGBTQ biases remain underexplored, particularly beyond English. Exist

Evaluating Constrained Iterative Refinement for Scalable Vector Graphics Generation with Off-the-Shelf VLMs

arXiv:2608.28678v1 Announce Type: new Abstract: Scalable Vector Graphics (SVGs) power much of the modern visual ecosystem, yet state-of-the-art generative models focus almost entirely on rasterized im

researcharxiv-cs-cv

Agents

Evaluating LLM-based AI agents integrated with materials synthesis tools: the case of atomic layer deposition

arXiv:2608.29309v1 Announce Type: cross Abstract: This work provides an overview of the different strategies that can be used to evaluate the performance of AI models and agents based on large languag

agentsarxiv-cs-ai

Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift

arXiv:2608.29543v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, ofte

Evaluating Multilingual Sentence Embeddings for Translation Error Detection:An English--Greek Contrastive Study

arXiv:2608.28776v1 Announce Type: new Abstract: Multilingual sentence embeddings are increasingly used to estimate semantic similarity across languages, yet their sensitivity to fine-grained translati

Evaluating Perspectival Biases in Cross-Modal Retrieval

arXiv:2510.26861v4 Announce Type: replace-cross Abstract: Multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. In practic

Agents

Evaluating the Capabilities of LLMs for Persuasive Dialogue

arXiv:2608.29738v1 Announce Type: new Abstract: Large language models (LLMs) can generate apparently highly persuasive text, but does sounding persuasive mean arguing well? We introduce extsc{Persuasi

agentsarxiv-cs-cl

Evaluating the Effects of Inter-Observer and Model Variability on Radiological Peritoneal Cancer Index Assessment

arXiv:2608.28716v1 Announce Type: cross Abstract: Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent impro

researcharxiv-cs-cv

Evaluating the Hidden Costs of Personalization in Large Language Models

arXiv:2608.28833v1 Announce Type: new Abstract: While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing

safetyarxiv-cs-ai

Evaluating the Safety of Deep Learning-Based Brain MRI Reconstruction

model-releasesarxiv-cs-cv

arXiv:2608.28714v1 Announce Type: cross Abstract: Objective: Deep learning accelerates brain MRI four- to tenfold, but models can erase lesions or synthesize false tissue - failures pixel-averaged met

Evaluating the Semantic Specificity of Representation Steering in Language Models

arXiv:2608.29431v1 Announce Type: new Abstract: Localized Representation Steering (LRS) is widely used to correct reasoning pathologies in large language models. However, standard benchmark evaluation

Evaluating Tiny Recursive Models Across Training for Code Generation

arXiv:2608.29376v1 Announce Type: new Abstract: Code generation increasingly relies on large transformer models, whose capability advances with scale. Yet such a scale is costly, creating demand for s

EVAR: Evidence-Validated Hypothesis Admission for Budget-Aware Narrative Reasoning

arXiv:2608.29835v1 Announce Type: new Abstract: Large language models (LLMs) often produce fluent but weakly grounded conclusions when reasoning over non-interactive, long-form narratives. A central f

Hardware

Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware

arXiv:2608.30439v1 Announce Type: cross Abstract: Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space

hardwarearxiv-cs-lg

Event-triggered Control and Online Learning for Networked Systems under Computational Delays

arXiv:2608.29576v1 Announce Type: new Abstract: Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve

Every Token Leaves a Ripple in the Stream of Thought: Eliciting Model-Internal Token Saliency for Chain-of-Thought Compression

arXiv:2608.31066v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves multi-step problem solving, but long reasoning traces inflate inference cost. Token-level CoT compression redu

Tutorials

Everybody Tracking Every Body

arXiv:2608.29927v1 Announce Type: new Abstract: We address the problem of 3D body pose estimation of multiple interacting people from their egocentric views with centralized coordination. Each individ

tutorialsarxiv-cs-cv

EviAnchor: Mitigating Hallucinations in Large Vision-Language Models via Regional Visual Evidence Compensation

arXiv:2608.29092v1 Announce Type: new Abstract: Large vision-language models (LVLMs) frequently generate content unsupported by visual inputs. Preliminary experiments show that visual evidence is prim

Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols

arXiv:2608.31014v1 Announce Type: new Abstract: Computational mental health screening using multimodal speech and text has shown great promise. However, existing models often assume all clinical speec

EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge

arXiv:2606.13120v2 Announce Type: replace Abstract: Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing ben

EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants

arXiv:2608.29387v1 Announce Type: new Abstract: Large language models can generate interactive web interfaces, but reliable generative UI requires maintaining an executable artifact as user requests e

Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment

arXiv:2608.29978v1 Announce Type: new Abstract: Large language models are increasingly required to generate responses that satisfy multiple competing objectives. Since optimal trade-offs depend on bot

safetyarxiv-cs-cl

EvoSkill Injection: Red-Teaming Autonomous Skill Generation and Evolution in Self-Evolving Agents

arXiv:2608.30429v1 Announce Type: new Abstract: LLM-based agent systems increasingly adopt skill-based architectures to reduce repetitive reasoning costs and improve stable, efficient task execution.

Exact Recovery Thresholds for Weighted Data Selection in Vector-Valued Linear Regression

arXiv:2608.30254v1 Announce Type: new Abstract: We resolve the threshold part of Question 4 of the COLT 2025 open problem 'Data Selection for Regression Tasks' of Hanneke, Moran, Shlimovich and Yehuda

Industry

Excited to share that I joined @huggingface🤗 as a ML Advocate Engineer in Robotics ! Will work on showcasing what LeRobot can do on various…

Excited to share that I joined @huggingface🤗 as a ML Advocate Engineer in Robotics ! Will work on showcasing what LeRobot can do on various hardware, starting with @menloresearch Asimov robot, stay tu

industryclem-delangue--x

ExLlamav3 Recent Updates : CPU offload, GLM-5.3-FLASH, Qwen3.8-Flash, SC Quants ++

model-releasesr-localllama

More new massive updates from turboderp: - CPU offload of MoE experts - Qwen-3.8-Flash-Next ngram disk offload - GLM-5.3-Flash - New self-calibrated optimization technique - Countless other optimizati

Tutorials

Expected flow networks in stochastic environments and two-player zero-sum games

arXiv:2310.02779v3 Announce Type: replace Abstract: Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied

tutorialsarxiv-cs-lg

Expert-validated STEM QA

arXiv:2608.28591v1 Announce Type: new Abstract: Recent advancements in AI are helping scientists achieve breakthroughs in fields such as mathematics, medicine, and materials sciences. New evaluation d

Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations

arXiv:2608.28820v1 Announce Type: new Abstract: Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and t

safetyarxiv-cs-ai

Applications

Explainable Machine Learning for Broadband Adoption Disparities: Tract-Level Prediction and SHAP-Based Factor Profiling

arXiv:2608.29110v1 Announce Type: new Abstract: The United States has allocated approximately $65 billion through the Infrastructure Investment and Jobs Act for broadband expansion, yet evidence-based

applicationsarxiv-cs-lg

Local Ai

Explainable Multi-Loss Distillation Framework for Efficient and Interpretable Shrimp Disease Text Classification

arXiv:2608.29027v1 Announce Type: new Abstract: Shrimp disease classification has become an urgent issue due to its significant impact on the import-export output of producing countries, particularly

local-aiarxiv-cs-cv

Explanations, Prompts, and Formalizations: Arguments for New Norms in LLM-Enabled Mathematical Research

arXiv:2608.29401v1 Announce Type: cross Abstract: As several mathematical conjectures have recently been settled using large language models (LLMs), the mathematical community has formulated norms and

Exponential random graph models with soft clique constraints

arXiv:2608.30869v1 Announce Type: cross Abstract: Let rgeq3 be fixed, and let mathbf{G}_n be the set of all simple graphs with vertex set [n]={1,ldots,n}. We consider an exponential random graph model

Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images

arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations

arXiv:2506.11635v2 Announce Type: replace-cross Abstract: Credit card fraud mitigation plays a significant role in modern society. While fraud detection systems are essential, they often struggle to k

Fable 5.1 is our best model yet for coding, data analysis, computer use, design, presentations, Tag, and the hardest long-running agentic wo…

model-releasesboris-cherny--x

Fable 5.1 is our best model yet for coding, data analysis, computer use, design, presentations, Tag, and the hardest long-running agentic work. This model is a pleasure to work with, and I've been usi

Fable 5.1 looks like a more usable model. And a potential daily-driver. Time will tell. IMO, Opus 5 is probably still good enough for most t…

Fable 5.1 looks like a more usable model. And a potential daily-driver. Time will tell. IMO, Opus 5 is probably still good enough for most tasks, while Fable 5.1 a more sophisticated coordinator amd v

model-releasesdair-ai--x

FaceSnap: Real-Time Personalized Lightstage Facial Performance Capture

model-releasesarxiv-cs-cv

arXiv:2608.31033v1 Announce Type: new Abstract: Lightstage facial capture produces production-quality digital humans, but it is resource and labor-intensive. Multi-camera setups, hours of computation,

Facts Without Rules: Boundary Metadata Collapse in Multi-Agent LLM Handoffs

arXiv:2608.29028v1 Announce Type: new Abstract: Multi-agent LLM systems often coordinate by compressing an upstream interaction into a handoff artifact that downstream agents treat as shared state. We

Applications

Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions

arXiv:2608.30690v1 Announce Type: new Abstract: Visual SLAM is commonly evaluated on clean trajectories, although deployment failures are often caused by adverse weather, illumination, blur, and senso

applicationsarxiv-cs-cv

Fairness in multi-class multi-group classification problems via contextial coherent risk measures

arXiv:2608.30223v1 Announce Type: cross Abstract: We propose a new design of fair classifiers for multi-class classification problems in the presence of vector-valued sensitive attributes. In that sce

safetyarxiv-cs-lg

Local Ai

FairReL: Deepfake Detection using Fairness-Aware Representation Learning

arXiv:2608.28777v1 Announce Type: new Abstract: Although recent deepfake detectors achieve high overall accuracy, their errors remain unevenly distributed across demographic subgroups, with real faces

local-aiarxiv-cs-cv