All entries

Can Vision Models Truly Forget? Mirage: Representation-Level Certification of Visual Unlearning

arXiv:2605.20282v1 Announce Type: new Abstract: Machine unlearning in Vertical Federated Learning (VFL) has attracted growing interest, yet existing methods certify forgetting solely using output-leve

safetyarxiv-cs-cv

Safety

can’t believe people assume that success on highly verifiable problems in math (where we don’t even know how many tests were performed and h…

DGX agent

can’t believe people assume that success on highly verifiable problems in math (where we don’t even know how many tests were performed and how many might have failed) iautomatically generalize to ever

safetygary-marcus--x

21 May 2026

Capability neq Interpretability: Human Interpretability of Vision Foundation Models

arXiv:2605.20337v1 Announce Type: new Abstract: How interpretable are the features of leading vision models? The question is increasingly pressing as these models move from research benchmarks into hi

CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?

arXiv:2510.00520v2 Announce Type: replace Abstract: Foundation models are reshaping medical imaging, yet their application in echocardiography remains limited, hindered by a heavy reliance on private

CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support

arXiv:2605.20468v1 Announce Type: new Abstract: Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medi

Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity

arXiv:2605.20269v1 Announce Type: new Abstract: Many bandit deployments (recommendation, clinical dosing, ad targeting) share two facts prior work handles only in isolation: rewards live on a low-dime

Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models

arXiv:2602.04907v2 Announce Type: replace Abstract: Causal discovery is a data-driven paradigm for analyzing complex systems, while physics-based models, such as ordinary differential equations (ODEs)

Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health

arXiv:2605.20782v1 Announce Type: new Abstract: Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurre

Causal Path Alignment: Anchoring the Optimization Trajectory for Controllable In-Parameter Knowledge Editing

model-releasesarxiv-cs-cl

arXiv:2506.04042v2 Announce Type: replace Abstract: Knowledge editing is pivotal for efficiently updating the parametric memory of Large Language Models (LLMs), enabling them to function as evolving a

Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions

model-releasesarxiv-cs-lg

arXiv:2605.20341v1 Announce Type: new Abstract: Federated learning systems must support data deletion requests to comply with privacy regulations, yet retraining from scratch after each deletion is co

Checking the math behind the latest headlines from OpenAI and Anthropic, link below:

Gary Marcus examines and fact-checks recent claims made by OpenAI and Anthropic in their public announcements and media coverage. The post links to detailed analysis questioning the mathematical valid

safetygary-marcus--x

CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing

arXiv:2512.09806v2 Announce Type: replace Abstract: Deep learning-based methods have recently achieved significant success in image reconstruction problems. However, challenges have emerged, as these

safetyarxiv-cs-cv

CHOIR: Contact-aware 4D Hand-Object Interaction Reconstruction

arXiv:2605.20992v1 Announce Type: new Abstract: We ask whether everyday open-world monocular videos can be turned into reusable 4D interaction primitives: articulated hand motion, object shape with 6D

researcharxiv-cs-cv

Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning

arXiv:2605.20975v1 Announce Type: new Abstract: Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the pr

Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding

model-releasesarxiv-cs-cl

arXiv:2605.20268v1 Announce Type: cross Abstract: Real-world time series come with text: metadata, descriptions, news, reports. Yet time series foundation models process numerical sequences in isolati

ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning

model-releasesarxiv-cs-cl

arXiv:2605.21177v1 Announce Type: cross Abstract: This work presents extsc{ChunkFT}, a memory-efficient fine-tuning framework that reformulates full-parameter fine-tuning around a dynamically activate

CIG: Exploration via Conditional Information Gain

arXiv:2605.20878v1 Announce Type: new Abstract: Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated

ClaimDiff-RL: Fine-Grained Caption Reinforcement Learning through Visual Claim Comparison

arXiv:2605.20278v1 Announce Type: cross Abstract: Long-form image captioning exposes a reward granularity problem in RL: captions are judged as whole sequences, while the important errors occur at the

Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework

arXiv:2605.21352v1 Announce Type: new Abstract: The growing use of fast-switching power electronics has made partial discharge (PD) analysis under switching-voltage excitation increasingly important,

Climate tech companies are pivoting to critical minerals

We’re over a year into the second Trump administration here in the US, and support for climate causes is weak. But climate tech companies are finding ways to survive and even thrive in this new enviro

researchmit-tech-review

Local Ai

Closed-form predictive coding via hierarchical Gaussian filters

arXiv:2605.20293v1 Announce Type: new Abstract: Predictive coding (PC) offers a local and biologically grounded alternative to backpropagation in the training of artificial neural networks, yet to dat

local-aiarxiv-cs-lg

Closed Loop Dynamic Driving Data Mixture for Real-Synthetic Co-Training

arXiv:2605.21372v1 Announce Type: new Abstract: Data scaling is fundamental to modern deep learning, and grows increasingly critical as autonomous driving shifts to end-to-end learning. Real-world dri

agentsarxiv-cs-cv

Cluster-Based Generalized Additive Models Informed by Random Fourier Features

model-releasesarxiv-cs-lg

arXiv:2512.19373v3 Announce Type: replace-cross Abstract: In developing data-driven modeling methodologies, there is an ongoing need to reconcile the strong predictive performance of opaque black-box

CMC-Opt: Constraint Manifold with Corners for Inequality-Constrained Optimization

arXiv:2605.20796v1 Announce Type: new Abstract: We introduce a manifold-based framework for addressing optimization problems with equality and inequality constraints found in robotics. Our approach tr

researcharxiv-cs-ro

Co-Fusion4D: Spatio-temporal Collaborative Fusion for Robust 3D Object Detection

arXiv:2605.20301v1 Announce Type: new Abstract: In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making. However, object motion and ego-motion ofte

CoarseSoundNet: Building a reliable model for ecological soundscape analysis

arXiv:2605.21143v1 Announce Type: cross Abstract: A soundscape is composed of three types of sound: biophony (sounds made by animals), geophony (natural abiotic sounds) and anthropophony (sounds made

Code Generation by Differential Test Time Scaling

arXiv:2605.20473v1 Announce Type: cross Abstract: Test-time scaling has emerged as a promising approach for improving code generation by exploring large solution spaces at inference time. However, exi

agentsarxiv-cs-lg

Industry

Code security startup Socket raises $60M in funding

Cybersecurity startup Socket Inc. today disclosed that it has raised 60 million in funding at a 1 billion valuation. Thrive Capital led the Series C round with participation from Andreessen Horowitz,

industrysiliconangle

Tutorials

Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks

arXiv:2605.20529v1 Announce Type: new Abstract: In what ways might statistical signals in linguistic input assist with the acquisition of syntax? Here we hypothesize a mechanism called collocational b

tutorialsarxiv-cs-cl

Color has RGB. Sound has MP3. We just gave smell its own system. Today, we’re announcing Sense1, the first model to map activation across al…

Sense1 is announced as the first model capable of mapping olfactory activation patterns across multiple dimensions, establishing a standardized system for smell comparable to how RGB defines color and

agentsyohei-nakajima--x

Command A+ is available on @huggingface with W4A4 quantization 🤗 Cut your serving footprint dramatically with virtually zero performance de…

model-releasesclem-delangue--x

Command A+ is available on @huggingface with W4A4 quantization 🤗 Cut your serving footprint dramatically with virtually zero performance degradation. Try it now: https://huggingface.co/CohereLabs/comm

Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums

arXiv:2605.21157v1 Announce Type: new Abstract: In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile enviro

applicationsarxiv-cs-cv

Comparative Evaluation of Deep Learning Models for Fake Image Detection

arXiv:2605.20971v1 Announce Type: new Abstract: The growing sophistication of GAN-based image manipulation presents significant challenges for digital forensics. This study compares the performance of

safetyarxiv-cs-cv

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models

arXiv:2602.02304v2 Announce Type: replace-cross Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning w

CompilerKV: Risk-Adaptive KV Compression via Offline Experience Compilation

arXiv:2602.08686v2 Announce Type: replace Abstract: Prefill-only KV compression freezes a token subset at the end of prefill and decodes from it without further eviction. The retention decision is the

Complementing reinforcement learning with SFT through logit averaging in the post training of LLMs

arXiv:2605.20555v1 Announce Type: new Abstract: We introduce a novel method that averages the logits of a frozen reference policy (e.g., SFT) and a trainable policy, and incorporate the method into Gr

Component Influence-Driven Fastener Reduction for Robotic Disassemblability-Aware Design Simplification

arXiv:2605.21026v1 Announce Type: new Abstract: To accelerate automated remanufacturing, robotic disassembly must be considered during the product design phase. However, designers currently lack quant

researcharxiv-cs-ro

Compositional Transduction with Latent Analogies for Offline Goal-Conditioned Reinforcement Learning

arXiv:2605.20609v1 Announce Type: new Abstract: Compositional generalization is essential for reaching unseen goals under novel contextual variations in offline goal-conditioned reinforcement learning

agentsarxiv-cs-lg

Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features

arXiv:2407.08976v2 Announce Type: replace-cross Abstract: Recent years have seen a surge in methods for two-sample testing, among which the Maximum Mean Discrepancy (MMD) test has emerged as an effect

Compute Only Once: UG-Separation for Efficient Large Recommendation Models

arXiv:2602.10455v2 Announce Type: replace-cross Abstract: Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behavior

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise

arXiv:2605.20999v1 Announce Type: cross Abstract: We establish maximal concentration bounds for the iterates generated by stochastic approximation algorithms with general step sizes, where the noise h

ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning

arXiv:2605.20385v1 Announce Type: new Abstract: Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the

researcharxiv-cs-cv

Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment

arXiv:2605.20834v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF), offering theoretical e

Conditioning Gaussian Processes on Almost Anything

arXiv:2605.21041v1 Announce Type: cross Abstract: Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We es

Tools

Configure weighted traffic splits for Vercel Flags from the Vercel CLI

Vercel introduced functionality to configure weighted traffic splits for Vercel Flags directly through the Vercel CLI, enabling developers to control what percentage of traffic sees different flag var

toolsvercel-blog

Conflict-Aware Active Perception and Control in 3D Gaussian Splatting Fields via Control Barrier Functions

arXiv:2605.20566v1 Announce Type: new Abstract: Active perception in uncertain environments requires robots to navigate safely while acquiring informative observations to reduce map uncertainty. These

safetyarxiv-cs-ro

Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards

arXiv:2605.20758v1 Announce Type: cross Abstract: Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a contr

researcharxiv-cs-cv

Local Ai

Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs

arXiv:2605.20270v1 Announce Type: new Abstract: A local specialist LLM, fine-tuned with reinforcement learning from verifiable rewards (RLVR) on operator-local data, is installed in a regulated organi

local-aiarxiv-cs-lg