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PsychoPass: Geometric Profiling of Multi-Turn Adversarial LLM Conversations

arXiv:2606.03136v1 Announce Type: cross Abstract: Multi-turn jailbreak attacks on large language models (LLMs) reveal a mismatch in current guardrails: they operate on individual turns, while attacks

safetyarxiv-cs-cl

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PubTables-v2: A new large-scale dataset for full-page and multi-page table extraction

DGX agent

arXiv:2512.10888v3 Announce Type: replace Abstract: Table extraction (TE) is a key challenge in document understanding. Traditional approaches detect tables first, then recognize their structure. Rece

model-releasesarxiv-cs-cv

3 Jun 2026

PURGE: Projected Unlearning via Retain-Guided Erasure

arXiv:2606.03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning

tutorialsarxiv-cs-ai

PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models

arXiv:2606.03858v1 Announce Type: new Abstract: Despite the pivotal role of numerical reasoning as the cornerstone of mathematical capabilities in large language models (LLMs) across applications, few

q0: Primitives for Hyper-Epoch Pretraining

arXiv:2606.03938v1 Announce Type: cross Abstract: Multi-epoch training is becoming the standard now that compute is growing faster than the supply of high-quality text. But pretraining a single model

Qift: Shift-Friendly No-Zero W2 Post-Training Quantization for Rotated W2A4/KV4 LLM Inference

arXiv:2606.02823v1 Announce Type: new Abstract: Two-bit weight quantization is attractive for memory-efficient LLM inference, but the standard W2 level set {-2,-1,0,+1} often collapses under aggressiv

Qiskit Fall Fest 2026: Applications open

Qiskit Fall Fest 2026 is an event organized by IBM that focuses on quantum computing education and community engagement through the open-source Qiskit framework. The event likely provides opportunitie

researchibm-research

Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent

arXiv:2606.03935v1 Announce Type: cross Abstract: The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing. However, for

Quantifying Faithful Confidence Expression in Large Reasoning Models

arXiv:2606.03969v1 Announce Type: cross Abstract: Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic

safetyarxiv-cs-ai

QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards

arXiv:2606.03968v1 Announce Type: cross Abstract: Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treat

safetyarxiv-cs-ai

QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models

arXiv:2606.02785v1 Announce Type: new Abstract: Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce QUIVER (Q

Hardware

Quobly raises $150M for its silicon spin qubit technology

French quantum computing startup Quobly SAS today announced that it has raised €130 million, or about 150 million, to commercialize its technology. The Series A round was led by publicly traded chipma

hardwaresiliconangle

Qwen-Image-Flash: Beyond Objective Design

arXiv:2606.03746v1 Announce Type: cross Abstract: Few-step distillation has become an effective strategy for accelerating advanced visual generative models, yet prior work has largely focused on disti

Qwen3.6-35B-A3B on 2× GTX 1080 Ti with Ollama: ~20 tok/s + 3 gotchas (driver 570+, cuda_v12 for Pascal, quant fit on 22GB)

This post documents running the Qwen3.6-35B-A3B language model on dual GTX 1080 Ti GPUs using Ollama, achieving approximately 20 tokens per second. The author highlights three critical configuration r

local-air-ollama

R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks

arXiv:2504.01250v2 Announce Type: replace Abstract: This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of robust recurrent neural networks for machine learning a

researcharxiv-cs-lg

R^{2k} is Theoretically Large Enough for Embedding-based Top-k Retrieval

arXiv:2601.20844v3 Announce Type: replace-cross Abstract: This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of m object vectors so th

RadarSFD: Single-Frame Diffusion with Pretrained Priors for Radar Point Clouds

model-releasesarxiv-cs-ro

arXiv:2509.18068v2 Announce Type: replace Abstract: Millimeter-wave radar provides robust perception in fog, smoke, dust, and low light, making it attractive for size-, weight-, and power-constrained

Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains

arXiv:2505.16014v5 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems deployed in sensitive domains must provide interpretable evidence selection and robust safeguards again

researcharxiv-cs-cl

Re-Evaluating Continual Learning with Few-Shot Adaptation

arXiv:2606.03843v1 Announce Type: cross Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standa

tutorialsarxiv-cs-ai

Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA

model-releasesarxiv-cs-cl

arXiv:2606.03728v1 Announce Type: new Abstract: Retrieval-augmented generation systems for legal question answering typically retrieve passages based on semantic similarity and provide them to a langu

RealClawBench: Live OpenClaw Benchmarks from Real Developer-Agent Sessions

model-releasesarxiv-cs-cl

arXiv:2606.03889v1 Announce Type: new Abstract: Agent benchmarks should reflect what users actually ask deployed agents to do, yet existing benchmarks often miss key realism properties of real develop

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

arXiv:2510.09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and

Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?

arXiv:2606.03782v1 Announce Type: new Abstract: Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic res

tutorialsarxiv-cs-cl

Reasoning Structure of Large Language Models

arXiv:2606.03883v1 Announce Type: new Abstract: Large reasoning models (LRMs) are often evaluated using metrics such as final-answer accuracy or token count. However, identical scores on these metrics

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

arXiv:2502.02748v4 Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal struct

local-aiarxiv-cs-lg

Reconstructing Objects along Hand Interaction Timelines in Egocentric Video

model-releasesarxiv-cs-cv

arXiv:2512.07394v2 Announce Type: replace Abstract: We introduce the task of Reconstructing Objects along Hand Interaction Timelines (ROHIT). We first define the Hand Interaction Timeline (HIT) from a

Reducing container cold start times using SOCI index on DLAMI and DLC

In this post, we look at how to use SOCI on publicly available Deep Learning AMIs and Containers, when to use the various SOCI modes provided by the tool, and how to quickly and efficiently use this t

tutorialsaws-ml-blog

Ref-DGS: Reflective Dual Gaussian Splatting

arXiv:2603.07664v3 Announce Type: replace-cross Abstract: The reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface

Regime-Arrival Uncertainty in Generalization Bounds under Distribution Shift

arXiv:2606.02657v1 Announce Type: new Abstract: The standard generalization bounds assume that the training and deployment distributions are the same, or are static, and don't consider regime switchin

researcharxiv-cs-lg

Regret Pre-training: Bridging Prior and Posterior Views for Enhanced Knowledge Grounding

arXiv:2606.03080v1 Announce Type: cross Abstract: Causal language models factorize sequence probabilities using only preceding context, leaving future information unexploited during training despite i

local-aiarxiv-cs-ai

Agents

Reinforcement Learning from Cross-domain Videos with Video Prediction Model

arXiv:2606.03201v1 Announce Type: cross Abstract: Reinforcement learning from expert videos across visually distinct domains is challenging due to the absence of reward signals and the presence of dom

agentsarxiv-cs-ai

Relational Linearity is a Predictor of Hallucinations

arXiv:2601.11429v2 Announce Type: replace-cross Abstract: Hallucination is a central failure mode of language models (LMs). We focus on hallucinations in response to questions like: 'Which instrument

Hardware

Releasing vui an open source voice mode 300M TTS model Runs on a single consumer gpu / apple sillicon Context aware speech 6 minutes of cont…

Jeremy Howard announced the release of Vui, an open-source voice mode text-to-speech (TTS) model with 300 million parameters that can run on consumer GPUs and Apple Silicon. The model features context

hardwarejeremy-howard--x

Applications

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases

arXiv:2606.03040v1 Announce Type: new Abstract: Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging du

applicationsarxiv-cs-ai

Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps

model-releasesarxiv-cs-ro

arXiv:2606.03421v1 Announce Type: new Abstract: Specular glare on reflective floors, glass boundaries, and glossy indoor surfaces frequently corrupts active-stereo RGB-D depth measurements, producing

ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services

arXiv:2606.02606v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously

Representational Capacity: Geometric Limits on Feature Representation in Transformer Language Models

arXiv:2606.02765v1 Announce Type: cross Abstract: Model dimension (d_{model}) is a fundamental hyperparameter in transformer language models, yet its role in setting the geometric limits of feature re

Agents

Reproducibility is the New Copyleft: Defining AGI-oriented Reproducible Builds

arXiv:2606.03019v1 Announce Type: cross Abstract: Copyleft, as implemented in licenses such as the GNU General Public License, was a legal hack that used copyright to guarantee user freedom by tying t

agentsarxiv-cs-ai

RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting

arXiv:2606.02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand res

ResCLIP: Residual Attention for Training-free Dense Vision-language Inference

arXiv:2411.15851v2 Announce Type: replace Abstract: While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-leve

local-aiarxiv-cs-cv

Resource-Constrained Adaptive Inference for Sequential Pricing

arXiv:2606.03736v1 Announce Type: cross Abstract: Resource-constrained pricing controllers can make fixed-price inference impossible: the controller's resource state may remove the target price neighb

local-aiarxiv-cs-lg

Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606.03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a sys

Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies

arXiv:2606.03125v1 Announce Type: new Abstract: Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts

safetyarxiv-cs-lg

Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning

arXiv:2606.03817v1 Announce Type: new Abstract: Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability i

researcharxiv-cs-cl

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression

arXiv:2606.03465v1 Announce Type: cross Abstract: Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints. Tensor decompositions have emerged

Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.03784v1 Announce Type: new Abstract: Embodied chain-of-thought (CoT) aims to bridge linguistic reasoning and robotic control, but its effective form and integration strategy remain underexp

Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers

arXiv:2502.08834v4 Announce Type: replace-cross Abstract: Deep generative models based on neural differential equations have become state-of-the-art for many generation tasks. These models rely on ODE

RGMem: Renormalization Group-inspired Memory Evolution for Language Agents

arXiv:2510.16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory h