Learning-based global point cloud registration has achieved remarkable progress, yet its reliance on geometric representations makes existing methods sensitive to variations in point density, scan pattern, viewpoint, and sensor characteristics. We propose CVSD-Reg, a robust global LiDAR registration framework that distills visual semantic priors from a vision foundation model into LiDAR representations.
SCAPE is a scenario‑conditioned simulation‑augmented policy evaluation framework that predicts real‑world policy performance for specific scenarios using limited paired simulation‑and‑real samples and extensive simulation rollouts. It corrects sim‑to‑real bias in simulation labels before training the prediction model and calibrates prediction uncertainty via conformal prediction. Experiments on autonomous driving and quadruped velocity tracking show SCAPE reduces scenario‑level prediction error, improves testing sample efficiency, narrows calibrated prediction intervals, and generalizes better to out‑of‑distribution scenarios, enabling fine‑grained deployment strategies.
OpenAI announces that it will maintain Zero Data Retention for eligible API customers, ensuring that no user data is stored. The company also previews a new Private Safety Processing feature, which aims to enhance AI safety while preserving data privacy. These measures highlight OpenAI’s commitment to protecting user information in its frontier models.
Split-conformal prediction provides marginal coverage under exchangeability and is increasingly used as an abstention layer for zero-shot vision-language models (VLMs). We audit this practice under de...
The paper introduces NEAR, a neural‑anchor‑based retrieval framework that improves brain‑to‑image retrieval when only a few neural trials are available. By treating the high‑repetition center as an anchor, a denoiser pulls noisy queries toward it while a small network predicts pseudo anchors for candidate images, aligning both neural and visual representations. Across EEG, MEG, and fMRI datasets, NEAR consistently boosts retrieval accuracy, notably raising 200‑way Top‑1 accuracy on THINGS‑EEG2 by 5.7–9.3 percentage points with just one to four repetitions.
The paper presents a super‑resolution generative adversarial network (SRGAN) that boosts the throughput of electron backscatter diffraction (EBSD) for Li‑ion battery electrode materials. By training on LiNixMnyCozO2 cathode data, the SRGAN outperforms classical interpolation across upscaling factors of 2× to 12×, especially preserving small grains and realistic boundaries. A 5× upscaling yields a 25× speed‑up or larger field of view with acceptable accuracy, reducing grain‑size errors to within ±15%.
The paper introduces the privacy‑HSD trade‑off, highlighting that automatic hate speech detection systems can inadvertently compromise user privacy by encoding authorship. It demonstrates that such systems may achieve high performance at the expense of privacy, and proposes a new domain‑specific technique, AgnoSpeech, alongside other text privatization methods to balance these competing goals. The authors benchmark these methods, showing that while challenging, it is feasible to protect privacy without sacrificing hate‑speech detection effectiveness.
GrabVG is a visual grounding framework for UAV imagery that tackles the challenges of small, densely packed, and visually similar objects by separating the task into preattentive hypothesis search and graph-attentive feature binding. It first generates a compact set of reliable object hypotheses using distillation-guided proposal induction and text-aware filtering, then constructs a sparse graph where language-guided visual cues and inter-instance topological relationships are jointly bound and propagated via graph attention. Experiments on AerialVG and AerialSense demonstrate that GrabVG achieves a strong accuracy–speed trade‑off, reaching 67.31% and 80.34% Acc@0.5 and outperforming baselines by 10.55 and 8.76 percentage points.
The paper investigates how Vision Language Models (VLMs) can be fooled by tiny, human‑imperceptible changes to images. It introduces a gradient‑based attack that targets only the vision encoder, reducing computational cost while still effectively disrupting both untargeted and targeted multimodal interpretations. Experiments on open‑source VLMs such as Qwen2.5‑VL, Granite‑Vision, FastVLM, and Phi‑3.5‑Vision demonstrate that these small perturbations can dramatically alter the models’ textual outputs.
TestifAI is a deep learning testing framework that estimates model robustness against combinations of input perturbations efficiently. It allows users to define structured spaces of semantic perturbations and severity levels, then query robustness for any combination. By employing partial model tomography, TestifAI reconstructs higher‑order perturbation effects from low‑order tests, achieving less than 7% error while cutting inference counts by 60‑80% on image and language classification tasks.
TestifAI is a deep learning testing framework that estimates model robustness against combinations of input perturbations efficiently. It allows users to define structured spaces of semantic perturbations and severity levels, then query robustness for any combination. By using partial model tomography, TestifAI reconstructs higher-order perturbation effects from low-order tests, achieving less than 7% error while reducing inferences by 60–80%.
SIDScope is a diagnostic tool that evaluates Semantic-ID mappings used between item tokenizers and generative recommenders. It normalizes artifacts, verifies provenance, profiles mapping structure, compares revisions, and tracks path-to-item outcomes in generated traces. Using data from Amazon and Yelp, SIDScope shows that interface health depends on multiple signals, revealing gaps in prefix alignment, trace accounting, and refresh handling that affect model reuse.
Skin diseases represent a major global public health burden, yet machine learning tools developed to assist in their diagnosis suffer from two critical limitations: reliance on only one modality for d...
The paper introduces CurriPO, a tree‑structured curriculum that adapts to diverse user reward models in AI alignment. By automatically building a curriculum that branches and reuses reward models, it addresses the problem of users whose reward models are hard to optimize, a group often underserved by conventional methods. Experiments on personalized continuous control demonstrate that CurriPO improves population satisfaction by 1.2–2.1× over the best baseline while cutting training time.
The paper investigates how latent world models (specifically JEPA-style models) use Euclidean distance to a goal latent as a cost for model‑predictive control (MPC). It introduces two metrics—Plan‑Real Spearman and CEM‑stage Spearman—to evaluate how well latent‑space distances align with real‑task progress, a property termed decision‑metric alignment. By identifying encoder distortion, terminal rollout error, and candidate margins as key factors, the authors propose DA‑LeWM, which augments the base model with inverse‑dynamics and demonstration‑conditioned goal‑action heads, leading to faster convergence and higher online success while maintaining similar probe scores.
EgoHRV is a method that estimates heart rate variability (HRV) and heart rate (HR) from the gaze cameras in egocentric headsets. It uses a 3D backbone and a low–high decomposition module to extract the blood volume pulse signal from gaze video, and aligns frequency‑domain representations of contact‑based and camera‑derived signals through cross‑domain pretraining. The approach achieves state‑of‑the‑art accuracy for HR and HRV estimation and, when integrated into EgoExo4D’s proficiency estimator, improves accuracy by 17.8%.
The paper presents the first systematic evaluation of uncertainty quantification (UQ) methods applied to a foundation model for semantic segmentation. By fine‑tuning a lightweight DPT decoder on the pretrained SAM2 encoder, the authors benchmark four UQ approaches—Monte Carlo Dropout, Deep Sub‑Ensemble, Test‑Time Augmentation, and Evidential Deep Learning—across Cityscapes, NYUv2, and two out‑of‑domain settings. The study compares segmentation accuracy, calibration, uncertainty quality, and inference time, revealing trade‑offs between predictive performance, reliability, and computational cost.
MemFuse introduces a multi‑source memory fusion system and a corresponding benchmark, MemFuseBench, designed to evaluate agents that must integrate fragmented observations from multiple applications, devices, users, and time points. The benchmark uses a Scene‑to‑Sensor pipeline to generate source‑tagged observations, evidence‑grounded questions, and adversarial distractors, enabling systematic assessment of temporal reasoning, cross‑source evidence fusion, and noise robustness. Experiments show that MemFuse outperforms other memory systems across all evaluated LLM settings, especially on questions requiring cross‑source evidence fusion.
Aligned vision‑language models (VLMs) are designed to combine grounded visual reasoning with safe generation. The study finds that when safety constraints are applied, these models often abstain from answering questions that they could answer under default instruction, yet visual evidence continues to influence the decoding process. The authors show that safety‑induced abstention alters late‑stage hidden‑state dynamics, and that targeted interventions can restore grounded answering without retraining or changing visual inputs.
QuantumNovelty is an open‑source, skill‑orchestrating language agent that both creates quantum‑computing artifacts—such as papers, ansatz candidates, and patent drafts—and evaluates them through simulated referee and patent‑examiner panels. Its core innovation is an audit‑and‑falsify layer of deterministic gates (Pareto domination, numerical recomputation, Wilson intervals, and cross‑vendor consensus) that restricts claims to those that survive rigorous checks, with every model call logged for transparency. In initial tests on a planted adversarial corpus and a small real‑world deployment, the system successfully flagged all overclaims without false positives and produced panels that were more conservative than typical public acceptance rates.
By Shlomo Kashani