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GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting

GoDeep achieves annotation-free open-vocabulary 3D segmentation by grounding structured image descriptions in language-only embeddings, outperforming CLIP-based lifting on out-of-vocabulary objects.

GoDeep uses a vision-language model purely as a translator, producing structured entity-level image descriptions that are grounded, projected, and aggregated in a general-purpose language-only embedding space, with no 3D training corpus or dedicated 3D encoder required. On ScanNet++ the pipeline is competitive with strong annotation-free baselines, and on a cultural-heritage benchmark a systematic vocabulary correction reverses initial CLIP-based rankings. Language-space embeddings separate genuinely out-of-vocabulary objects more sharply, localize them within scenes, and keep all predictions explainable as discrete text.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research

Zoom Annotation Flaws Could Let a Meeting Participant Hijack Another Attendee's Client

A Security researchers disclosed three Zoom annotation flaws enabling zero-click client hijacking; Zoom shipped fixes in June and July with no exploitation reported.

Researchers at A Security found three flaws in Zoom's annotation feature: CVE-2026-53413 (CVSS 8.3, buffer over-write), CVE-2026-53414 (CVSS 6.5, buffer over-read), and CVE-2026-53415 (CVSS 8.3, use-after-free). A crafted drawing object sent over the wrong message channel can overwrite adjacent memory and hijack another attendee's client with no user interaction. Fixes shipped in Zoom Workplace 7.1.5/7.0.6, VDI Client 7.0.11/6.6.16, and Zoom Rooms/Meeting SDK 7.1.0+ during June and July. No exploitation has been reported and the flaws are absent from CISA's Known Exploited Vulnerabilities catalog.

The Hacker News · Aug 11, 2026VulnerabilityCVE-2026-53413CVE-2026-53414CVE-2026-53415

Zoom Patches “Zoomsday” Zero-Click Flaw Enabling Remote Code Execution

Zoom patched CVE-2026-53413, a zero-click annotation flaw dubbed "Zoomsday" allowing remote code execution on meeting participants' devices across all platforms.

Zoom patched four vulnerabilities, including CVE-2026-53413, a stack buffer overflow in CAnnoFormatBlock::Deserialize in the annotation protocol that allows zero-click remote code execution on another participant's device. A Security also found CVE-2026-53414, a buffer overread enabling denial-of-service crashes, and CVE-2026-53415, a use-after-free Zoom had already discovered internally. Updates shipped for Workplace 7.1.5 and 7.0.6, Rooms 7.1.5, and Meeting SDK 7.1.5 across all supported platforms.

Security Affairs · Aug 11, 2026VulnerabilityCVE-2026-53413CVE-2026-53414CVE-2026-53415

ZDI-26-600: Foxit PDF Reader Annotation Use-After-Free Information Disclosure Vulnerability

Foxit PDF Reader annotation use-after-free (CVE-2026-57237, CVSS 3.3) can disclose sensitive information when users open malicious PDFs.

ZDI-26-600 documents a use-after-free in Foxit PDF Reader annotation handling that allows remote information disclosure, tracked as CVE-2026-57237 with CVSS 3.3. Exploitation requires user interaction such as opening a malicious file or visiting a malicious page. The advisory was published by the Zero Day Initiative on August 24, 2026.

ZDI Published Advisories · 23d agoAdvisoryCVE-2026-57237

ZDI-26-603: Foxit PDF Reader Annotation Use-After-Free Remote Code Execution Vulnerability

Foxit PDF Reader has a second annotation use-after-free vulnerability (CVE-2026-13127, CVSS 7.8) allowing remote code execution via malicious files or pages.

ZDI-26-603 describes a use-after-free vulnerability in the annotation feature of Foxit PDF Reader, tracked as CVE-2026-13127 with a CVSS score of 7.8. Successful exploitation allows remote attackers to execute arbitrary code, but requires user interaction such as visiting a malicious page or opening a malicious file. The advisory does not mention any exploitation in the wild.

ZDI Published Advisories · 23d agoVulnerabilityCVE-2026-13127

ZDI-26-675: Adobe Acrobat Reader DC Annotation Use-After-Free Remote Code Execution Vulnerability

ZDI disclosed a CVSS 7.8 use-after-free remote code execution flaw (CVE-2026-81976) in Adobe Acrobat Reader DC triggered via malicious PDFs.

The Zero Day Initiative published advisory ZDI-26-675 describing a use-after-free remote code execution vulnerability in Adobe Acrobat Reader DC, tracked as CVE-2026-81976 with a CVSS 7.8 score. The flaw resides in the annotation feature. Exploitation requires user interaction: the target must visit a malicious page or open a malicious file. The advisory reports no evidence of in-the-wild exploitation.

ZDI Published Advisories · 6d agoVulnerability 19 sourcesCVE-2026-819761

ZDI-26-599: Foxit PDF Reader Annotation Use-After-Free Information Disclosure Vulnerability

ZDI published advisory ZDI-26-599 for a use-after-free information disclosure flaw (CVE-2026-57238, CVSS 3.3) in Foxit PDF Reader requiring user interaction.

The Zero Day Initiative released advisory ZDI-26-599 describing a use-after-free vulnerability in Foxit PDF Reader's annotation handling. The flaw allows remote attackers to disclose sensitive information when a target opens a malicious file or visits a malicious page. ZDI rated the issue CVSS 3.3 and assigned CVE-2026-57238.

ZDI Published Advisories · 23d agoAdvisoryCVE-2026-57238

ZDI-26-601: Foxit PDF Reader Annotation Use-After-Free Information Disclosure Vulnerability

Foxit PDF Reader has a use-after-free vulnerability (CVE-2026-13129, CVSS 3.3) allowing sensitive information disclosure when a user opens malicious content.

ZDI-26-601 describes a use-after-free vulnerability in the annotation feature of Foxit PDF Reader, tracked as CVE-2026-13129 with a CVSS score of 3.3. Successful exploitation allows remote attackers to disclose sensitive information, but requires user interaction such as visiting a malicious page or opening a malicious file. The advisory does not mention any exploitation in the wild.

ZDI Published Advisories · 23d agoVulnerabilityCVE-2026-13129

ZDI-26-604: Foxit PDF Reader Annotation Use-After-Free Remote Code Execution Vulnerability

Foxit PDF Reader use-after-free flaw (CVE-2026-13126) allows remote code execution when users open malicious files; CVSS 7.8 per ZDI advisory.

ZDI-26-604 describes a use-after-free vulnerability in Foxit PDF Reader's annotation handling, tracked as CVE-2026-13126 and rated CVSS 7.8. Exploitation requires user interaction, with the target visiting a malicious page or opening a malicious file, and allows remote attackers to execute arbitrary code. The advisory was published by the Zero Day Initiative on August 24, 2026.

ZDI Published Advisories · 23d agoAdvisoryCVE-2026-13126

ZDI-26-595: Foxit PDF Reader Annotation Use-After-Free Remote Code Execution Vulnerability

Foxit PDF Reader has a use-after-free vulnerability (CVE-2026-57254, CVSS 7.8) allowing remote code execution when a user opens a malicious file or page.

ZDI-26-595 describes a use-after-free vulnerability in the annotation feature of Foxit PDF Reader, tracked as CVE-2026-57254 with a CVSS score of 7.8. Successful exploitation allows remote attackers to execute arbitrary code, but requires user interaction such as visiting a malicious page or opening a malicious file. The advisory does not mention any exploitation in the wild.

ZDI Published Advisories · 23d agoVulnerabilityCVE-2026-57254

Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment

Researchers audit 31 NLP techniques for clinician-annotated suicide risk prediction, finding only 5 of 31 comparisons yield reliable gains.

A study of 1,635 clinician-annotated social media posts ran roughly 300 controlled experiments across 7 methodological families, auditing techniques such as model scaling, synthetic data, ensembling, and threshold tuning under severe class imbalance. The proposed system reformulates risk factor prediction as entailment between posts and codebook definitions, using architecturally diverse ensembles with class-balanced training and deployment-consistent calibration. It scores 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, ranking third among 53 teams.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem

Researchers release SpatialBlock-15k, a synthetic block-stacking dataset that improves 3D spatial reasoning in large vision-language models without dense geometric annotations.

The paper addresses limited spatial intelligence in LVLMs by training on structured block-manipulation tasks instead of costly real-scene annotated datasets. SpatialBlock-15k contains 15,000 synthetic problems covering 3D-to-2D projection, viewpoint transformation, and structural combination, with color modulation as visual cues. LVLMs trained on it via direct answering or reasoning-based prediction outperform baselines and generalize to real-world spatial tasks. Code and data are released on GitHub.

Hugging Face daily papers · 9d agoAI research

TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation

TransNormal-2 improves monocular surface-normal estimation by fixing VAE edge degradation with geometry-aware losses and refinement, matching MoGe-2 with 1.4% of annotations.

TransNormal-2 is a FLUX.2-based rectified-flow framework for monocular surface-normal estimation with single-step deterministic inference. The authors quantify that VAE 8x spatial compression introduces 1.3-8.5 degrees of mean angular error even on ground-truth normals, with edge error up to 2.8x the global error. The method adds geometry-aware pixel-space losses and an RGB-guided Geometric Refinement Module to correct boundary-localized decoding errors. It matches or exceeds MoGe-2 on all eight reported metrics using only 1.4% as many task-specific annotations, and cuts transparent-object MAE by 4.2 degrees on ClearGrasp and 3.1 degrees on ClearPose.

Hugging Face daily papers · 10d agoAI research

Nebulon Enterprise Simulated Threats for Phishing Research (NEST-Phish): A Synthetic Enterprise Phishing Email Dataset for Behavioral and Machine-Learning Research

Researchers release NEST-Phish, a synthetic enterprise phishing email dataset with matched legitimate and phishing emails and cue annotations for detection research.

Academic researchers introduce NEST-Phish, a publicly released synthetic enterprise phishing email dataset built around a fictitious organization named Nebulon. It contains matched synthetic legitimate and phishing emails across a broad set of workplace communication themes, each with interpretable phishing-cue annotations. Human-subject categorizations and supervised classifier evaluations indicate the dataset supports meaningful variation in phishing judgments and provides learnable signal for detection models. It is intended to support work on phishing detection, human susceptibility, explainability, and benchmark development.

arXiv cs.CR · 12d agoResearch

Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication

UID-preserving multimodal framework plus GAVEL LLM judge improves clinical timeline reconstruction, boosting event recovery 43% over prior matching.

The paper introduces a UID-preserving framework linking each narrative clinical event to its source span through text-only estimation, structured-evidence retrieval, timestamped source-row grounding, and joint revision. GAVEL, an LLM judge, compares UID-aligned timelines against narrative and structured records. Across six open-weight models and 40 mixed-critical-care summaries, GLM 5.2 multimodal revision improved temporal agreement without reducing event recovery and performed competitively with clinician annotations, while DeepSeek V3.2 did not benefit from multimodality. The pipeline achieves 43% increased event recovery with occurrence-level provenance.

arXiv cs.AI / cs.LG / cs.CL · 4d agoAI research1

Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer

Domain-adversarial nnU-Net trained on 4,604 CT/MRI scans achieves 87.31% Dice pancreas segmentation with label-efficient subregion transfer.

A unified 3D pancreas segmentation framework applies domain-adversarial learning to 4,604 heterogeneous CT and MRI scans, aligning CT-MRI features via a latent domain discriminator on a shared nnU-Net encoder-decoder. Whole-pancreas segmentation reaches 87.31% Dice in-distribution and 84.20%-88.09% across external OOD datasets. The transferred encoder achieves 80.53% Dice on MRI and 83.05% on CT for downstream head-body-tail subregion segmentation using only limited MRI subregion annotations.

arXiv cs.AI / cs.LG / cs.CL · 4d agoAI research

RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems

Researchers introduce RESCUE-Bench, a video benchmark of 191 couple and family conversations evaluating LLMs on relation-aware multi-party emotional support.

RESCUE-Bench is built from real couple and family interview conversations, containing 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video. It defines six tasks measuring two capabilities: Relational Understanding and Relation-Sensitive Support. Experiments with ten LLMs show models handle local emotional cues but struggle with relation pattern prediction, viewpoint prediction, and support strategy prediction.

Hugging Face daily papers · 7d agoAI research