All entries

PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues

arXiv:2604.18354v1 Announce Type: new Abstract: Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. Developing negotiation dialog

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models

DGX agent

arXiv:2601.15220v2 Announce Type: replace Abstract: We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle

safetyarxiv-cs-cl

21 Apr 2026

Privacy-Preserving Semantic Segmentation without Key Management

arXiv:2604.16523v1 Announce Type: new Abstract: This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed met

Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning

arXiv:2510.16054v2 Announce Type: replace-cross Abstract: When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send th

local-aiarxiv-cs-cl

PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces

arXiv:2510.18109v4 Announce Type: replace-cross Abstract: Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model bu

Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading

arXiv:2604.17476v1 Announce Type: cross Abstract: Multi-user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive comp

PRL: Prompts from Reinforcement Learning

arXiv:2505.14412v2 Announce Type: replace-cross Abstract: Effective prompt engineering remains a central challenge in fully harnessing the capabilities of LLMs. While well-designed prompts can dramati

Hardware

Probabilistic Programs of Thought

arXiv:2604.17290v1 Announce Type: new Abstract: LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to rea

hardwarearxiv-cs-cl

Agents

probably true (and agree on it being open)

This post by Harrison Chase (creator of LangChain) likely discusses the concept of statements or propositions that are 'probably true' and advocates for keeping such discussions or determinations open

agentsharrison-chase--x

Tutorials

Procedural Knowledge at Scale Improves Reasoning

arXiv:2604.01348v2 Announce Type: replace Abstract: Test-time scaling has emerged as an effective way to improve language models on challenging reasoning tasks. However, most existing methods treat ea

tutorialsarxiv-cs-cl

Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards

arXiv:2604.17957v1 Announce Type: new Abstract: Process Reward Models (PRMs) have emerged as a powerful tool for providing step-level feedback when evaluating the reasoning of Large Language Models (L

ProfVLM: A lightweight video-language model for multi-view proficiency estimation

arXiv:2509.26278v4 Announce Type: replace-cross Abstract: Most existing approaches formulate action quality assessment and skill proficiency estimation as discriminative prediction tasks, typically pr

Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics

model-releasesarxiv-cs-lg

arXiv:2604.17715v1 Announce Type: cross Abstract: Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations. However, they still l

Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions

model-releasesarxiv-cs-cv

arXiv:2604.18459v1 Announce Type: new Abstract: Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability th

Projected Coupled Diffusion for Test-Time Constrained Joint Generation

arXiv:2508.10531v3 Announce Type: replace Abstract: Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process

Prompt Sensitivity in Vision-Language Grounding: How Small Changes in Wording Affect Object Detection

arXiv:2604.17126v1 Announce Type: new Abstract: Vision-language models enable open-vocabulary object grounding through natural language queries, under the implicit assumption that semantically equival

researcharxiv-cs-cv

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery

model-releasesarxiv-cs-cv

arXiv:2604.17920v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) plays a critical role in maritime surveillance, yet deep learning for SAR analysis is limited by the lack of pixel-level

Proprietary lock-in is holding back enterprise AI ambition, says SUSE CEO

As enterprises race to adopt AI without sacrificing control or flexibility, open-source infrastructure is the stable foundation that enables organizations to modernize while maintaining full digital s

applicationssiliconangle

Industry

Proprietary stacks are failing enterprises when they need flexibility the most

As hybrid and multicloud strategies evolve, a new reality is coming into focus — one where open architectures are increasingly central to operational resilience, sovereignty and choice. The industry’s

industrysiliconangle

Safety

ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification

arXiv:2604.18444v1 Announce Type: cross Abstract: Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding

safetyarxiv-cs-cv

ProTrain: Efficient LLM Training via Memory-Aware Techniques

model-releasesarxiv-cs-lg

arXiv:2406.08334v2 Announce Type: replace-cross Abstract: Memory pressure has emerged as a dominant constraint in scaling the training of large language models (LLMs), particularly in resource-constra

Prune, Interpret, Evaluate: A Cross-Layer Transcoder-Native Framework for Efficient Circuit Discovery via Feature Attribution

arXiv:2604.16889v1 Announce Type: new Abstract: Existing feature-interpretation pipelines typically operate on uniformly sampled units, but only a small fraction of cross-layer transcoder (CLT) featur

Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition

arXiv:2510.08047v2 Announce Type: replace-cross Abstract: Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. Although p

PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation

model-releasesarxiv-cs-ro

arXiv:2603.04531v2 Announce Type: replace Abstract: Tactile dexterous manipulation is essential to automating complex household tasks, yet learning effective control policies remains a challenge. Whil

Tools

Publish an app in one click 💻

Replit announced a feature enabling users to publish applications with a single click, streamlining the deployment process from development to production. This capability reduces friction in the app p

toolsreplit--x

Pulse Shape Discrimination Algorithms: Survey and Benchmark

model-releasesarxiv-cs-lg

arXiv:2508.02750v2 Announce Type: replace Abstract: This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection, classifying nearly

Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability

arXiv:2601.22012v2 Announce Type: replace Abstract: Catastrophic forgetting in continual learning is often measured at the performance or last-layer representation level, overlooking the underlying me

applicationsarxiv-cs-lg

Tutorials

PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

arXiv:2206.14234v3 Announce Type: replace-cross Abstract: In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters

tutorialsarxiv-cs-lg

Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement

arXiv:2604.16858v1 Announce Type: new Abstract: Image Quality Assessment (IQA) models are increasingly deployed as perceptual critics to guide generative models and image restoration. This role demand

Safety

Q-SINDy: Quantum-Kernel Sparse Identification of Nonlinear Dynamics with Provable Coefficient Debiasing

arXiv:2604.16779v1 Announce Type: cross Abstract: Quantum feature maps offer expressive embeddings for classical learning tasks, and augmenting sparse identification of nonlinear dynamics (SINDy) with

safetyarxiv-cs-lg

Tools

QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard

QIMMA (قِمّة) is a quality-focused leaderboard for evaluating Arabic Large Language Models, developed by TIIUAE. The leaderboard benchmarks Arabic LLMs across various performance metrics and tasks to

toolshugging-face

QU-NLP at QIAS 2026: Multi-Stage QLoRA Fine-Tuning for Arabic Islamic Inheritance Reasoning

arXiv:2604.16396v1 Announce Type: new Abstract: Islamic inheritance law (ilm al-mawar{i}th) presents a challenging domain for evaluating large language models' structured reasoning capabilities, requi

QuadSync: Quadrifocal Tensor Synchronization via Tucker Decomposition

arXiv:2602.22639v2 Announce Type: replace Abstract: In structure from motion, quadrifocal tensors capture more information than their pairwise counterparts (essential matrices), yet they have often be

researcharxiv-cs-cv

Quantifying how AI Panels improve precision

arXiv:2604.16432v1 Announce Type: cross Abstract: AI in applications like screening job applicants had become widespread, and may contribute to unemployment especially among the young. Biases in the A

QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks

arXiv:2604.17842v1 Announce Type: new Abstract: LLM benchmarks are increasingly dynamic: instead of containing a fixed set of questions, they define templates and parameters that can generate an effec

Tools

Quoting Andreas Påhlsson-Notini

AI agents are already too human. Not in the romantic sense, not because they love or fear or dream, but in the more banal and frustrating one. The current implementations keep showing their human orig

toolssimon-willison

R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection

arXiv:2604.17321v1 Announce Type: new Abstract: Face morphing attacks pose a substantial risk to the reliability of face recognition systems used in passport issuance, border control, and digital iden

Safety

R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation

arXiv:2506.07826v2 Announce Type: replace Abstract: Validating autonomous driving (AD) systems requires diverse and safety-critical testing, making photorealistic virtual environments essential. Tradi

safetyarxiv-cs-cv

RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction

arXiv:2504.07415v2 Announce Type: replace-cross Abstract: Automated radiology report generation (RRG) holds potential to reduce the workload of radiologists, and recent advances in multimodal large la

applicationsarxiv-cs-cl

Hardware

RACE Attention: A Strictly Linear-Time Attention Layer for Training on Outrageously Large Contexts

arXiv:2510.04008v5 Announce Type: replace Abstract: Softmax Attention has a quadratic time complexity in sequence length, which becomes prohibitive to run at long contexts, even with highly optimized

hardwarearxiv-cs-lg

RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation

arXiv:2604.16310v1 Announce Type: cross Abstract: Evaluating Retrieval-Augmented Generation (RAG) systems using static multi-turn datasets fails to capture the dynamic nature of real-world dialogues.

applicationsarxiv-cs-cl

Hardware

RainFusion2.0: Temporal-Spatial Awareness and Hardware-Efficient Block-wise Sparse Attention

arXiv:2512.24086v2 Announce Type: replace Abstract: In video and image generation tasks, Diffusion Transformer (DiT) models incur extremely high computational costs due to attention mechanisms, which

hardwarearxiv-cs-cv

Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario

arXiv:2604.18450v1 Announce Type: cross Abstract: Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before over

Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning

arXiv:2604.16591v1 Announce Type: new Abstract: Large language models (LLMs) sometimes memorize undesirable knowledge, which must be removed after deployment. Prior work on machine unlearning has focu

Tutorials

Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus

arXiv:2604.18390v1 Announce Type: new Abstract: In self-supervised learning, self-distilled methods have shown impressive performance, learning representations useful for downstream tasks and even dis

tutorialsarxiv-cs-lg

Ranking Abuse via Strategic Pairwise Data Perturbations

arXiv:2604.17805v1 Announce Type: new Abstract: Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pa

applicationsarxiv-cs-lg

RASP-Tuner: Retrieval-Augmented Soft Prompts for Context-Aware Black-Box Optimization in Non-Stationary Environments

arXiv:2604.18026v1 Announce Type: new Abstract: Many deployed systems expose black-box objectives whose minimizing configuration shifts with an externally observed context. When contexts revisit a sma

applicationsarxiv-cs-lg

Rate-Distortion Optimization for Transformer Inference

arXiv:2601.22002v3 Announce Type: replace Abstract: Transformers achieve superior performance on many tasks, but impose heavy compute and memory requirements during inference. This inference can be ma