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PresentAgent-2: Towards Generalist Multimodal Presentation Agents

arXiv:2605.11363v1 Announce Type: cross Abstract: Presentation generation is moving beyond static slide creation toward end-to-end presentation video generation with research grounding, multimodal med

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Pretraining Exposure Explains Popularity Judgments in Large Language Models

DGX agent

arXiv:2605.12382v1 Announce Type: new Abstract: Large language models (LLMs) exhibit systematic preferences for well-known entities, a phenomenon often attributed to popularity bias. However, the exte

safetyarxiv-cs-cl

13 May 2026

Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study

arXiv:2605.12241v1 Announce Type: cross Abstract: Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the siz

Primal-Dual Policy Optimization for Linear CMDPs with Adversarial Losses

arXiv:2605.11535v1 Announce Type: new Abstract: Existing work on linear constrained Markov decision processes (CMDPs) has primarily focused on stochastic settings, where the losses and costs are eithe

Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling

arXiv:2605.11299v1 Announce Type: cross Abstract: Code generation is typically trained in the primal space of programs: a model produces a candidate solution and receives sparse execution feedback, of

safetyarxiv-cs-cl

Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation

arXiv:2605.10984v1 Announce Type: new Abstract: Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as

researcharxiv-cs-cv

Principled Design of Diffusion-based Optimizers for Inverse Problems

arXiv:2605.11506v1 Announce Type: new Abstract: Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference tim

researcharxiv-cs-cv

Principled Latent Diffusion for Graphs via Laplacian Autoencoders

arXiv:2601.13780v3 Announce Type: replace Abstract: Graph diffusion models achieve state-of-the-art performance in graph generation but suffer from quadratic complexity in the number of nodes -- and m

PriorZero: Bridging Language Priors and World Models for Decision Making

arXiv:2605.12289v1 Announce Type: new Abstract: Leveraging the rich world knowledge of Large Language Models (LLMs) to enhance Reinforcement Learning (RL) agents offers a promising path toward general

Local Ai

PRISM: A Geometric Risk Bound that Decomposes Drift into Scale, Shape, and Head

arXiv:2605.11608v1 Announce Type: new Abstract: Comparing post-training LLM variants, such as quantized, LoRA-adapted, and distilled models, requires a diagnostic that identifies how a variant has dri

local-aiarxiv-cs-cl

PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents

arXiv:2605.12260v1 Announce Type: new Abstract: Long-horizon language agents accumulate conversation history far faster than any fixed context window can hold, making memory management critical to bot

PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments

model-releasesarxiv-cs-ro

arXiv:2605.11534v1 Announce Type: new Abstract: When an LLM-based embodied agent fails at a household task, the culprit could be misidentified objects, forgotten sub-goals, or poor action sequencing -

PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior

arXiv:2605.12147v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human behavior, but their ability to simulate individual privacy decisions is not well

applicationsarxiv-cs-lg

Probabilistic Calibration Is a Trainable Capability in Language Models

arXiv:2605.11845v1 Announce Type: new Abstract: Language models are increasingly used in settings where outputs must satisfy user-specified randomness constraints, yet their generation probabilities a

Probabilistic Computers for Neural Quantum States

arXiv:2512.24558v2 Announce Type: replace-cross Abstract: Neural quantum states efficiently represent many-body wavefunctions with neural networks, but the cost of Monte Carlo sampling limits their sc

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks

arXiv:2509.06701v2 Announce Type: replace Abstract: We develop a theory of intelligent agency grounded in probabilistic modeling for neural models. Agents are represented as outcome distributions with

probably correct, from @polynoamial: “with today’s AI models, intelligence is a function of inference compute.” but what about tomorrow’s mo…

probably correct, from @polynoamial: “with today’s AI models, intelligence is a function of inference compute.” but what about tomorrow’s models? never forget that humans are remarkably intelligent (t

safetygary-marcus--x

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

arXiv:2605.12194v1 Announce Type: cross Abstract: Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access t

Procedural-skill SFT across capacity tiers: A W-Shaped pre-SFT Trajectory and Regime-Asymmetric Mechanism on 0.8B-4B Qwen3.5 Models

arXiv:2605.11907v1 Announce Type: new Abstract: We measure procedural-skill SFT contribution across three Qwen3.5 dense scales (0.8B, 2B, 4B) on a 200-task / 40-skill holdout, with Claude Haiku 4.5 as

Prompting from the bench: Large-scale pretraining is not sufficient to prepare LLMs for ordinary meaning analysis

arXiv:2510.25356v2 Announce Type: replace Abstract: In the U.S. judicial system, a widespread approach to legal interpretation entails assessing how a legal text would be understood by an `ordinary' s

applicationsarxiv-cs-cl

Protein in Homo erectus teeth suggests Denisovans gave us some of their DNA

Analysis of proteins in six 400,000-year-old Homo erectus teeth from China identified genetic variants shared with Denisovans, providing the first evidence of genetic mixing between the two groups. On

industryars-technica

Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection

arXiv:2603.23677v2 Announce Type: replace Abstract: Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to e

safetyarxiv-cs-cv

Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function

arXiv:2602.02406v2 Announce Type: replace-cross Abstract: Data-driven algorithm design automates hyperparameter tuning, but its statistical foundations remain limited because model performance can dep

Agents

P.S. Join our Discord to view all of the submissions and get involved with the Hermes Agent community https://discord.gg/nousresearch

Nous Research invites community members to join their Discord server to view submissions and participate in the Hermes Agent community. The Discord link provided offers access to community discussions

agentsnous-research--x

Pure Exploration Beyond Reward Feedback: The Role of Post-Action Context

arXiv:2502.03061v2 Announce Type: replace Abstract: We introduce the problem of best arm identification (BAI) with post-action context, a new BAI problem in a stochastic multi-armed bandit environment

PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution

model-releasesarxiv-cs-cv

arXiv:2504.14129v4 Announce Type: replace Abstract: The challenge of tracing the source attribution of forged faces has gained significant attention due to the rapid advancement of generative models.

Q&A with Alexandr Wang on rebuilding Meta's AI stack, launching Muse Spark, personal superintelligence, acquiring Assured Robot Intelligence, and more (Ashlee Vance/Core Memory)

Ashlee Vance / Core Memory: Q&A with Alexandr Wang on rebuilding Meta's AI stack, launching Muse Spark, personal superintelligence, acquiring Assured Robot Intelligence, and more — Last June, Meta pri

industrytechmeme

Q&A with Amazon SVP of Devices and Alexa Panos Panay on Alexa+, the company's focus on devices for the home, its Leo satellite broadband business, and more (Rafe Rosner-Uddin/Financial Times)

Rafe Rosner-Uddin / Financial Times: Q&A with Amazon SVP of Devices and Alexa Panos Panay on Alexa+, the company's focus on devices for the home, its Leo satellite broadband business, and more — Execu

industrytechmeme

Q&A with Anthropic CFO Krishna Rao on the 'cone of uncertainty' in AI, allocating compute, returns to frontier intelligence, platform vs. application, and more (Invest Like The Best on YouTube)

Invest Like The Best on YouTube: Q&A with Anthropic CFO Krishna Rao on the “cone of uncertainty” in AI, allocating compute, returns to frontier intelligence, platform vs. application, and more — In th

industrytechmeme

QDSB: Quantized Diffusion Schrodinger Bridges

arXiv:2605.11983v1 Announce Type: new Abstract: Learning generative models in settings where the source and target distributions are only specified through unpaired samples is gaining in importance. H

applicationsarxiv-cs-lg

Quantifying Rodda and Graham Gait Classification from 3D Makerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort

arXiv:2605.11314v1 Announce Type: new Abstract: Cerebral Palsy (CP) is a neurological disorder of movement and the most common cause of lifelong physical disability in childhood. Approximately 75% of

researcharxiv-cs-cv

Quantifying the Reconstructability of Astrophysical Methods with Large Language Models and Information Theory: A Case Study in Spectral Reconstruction

arXiv:2605.11154v1 Announce Type: cross Abstract: Modern astrophysical studies rely heavily on complex data analysis pipelines; however, published descriptions often lack the detail required for compu

applicationsarxiv-cs-lg

Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring

arXiv:2605.12398v1 Announce Type: new Abstract: Estimating question difficulty is a critical component in evaluating and improving large language models (LLMs) for question answering (QA). Existing ap

safetyarxiv-cs-cl

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization

arXiv:2605.10959v1 Announce Type: new Abstract: There is currently no unified metric for evaluating the efficiency of quantized neural networks. We propose QuIDE, built around the Intelligence Index I

Quite excited about llama-eval, a proposed eval tool for llama.cpp. Could be a nice step toward more comparable community evals 🎉 https://g…

model-releasesgeorgi-gerganov--x

Llama-eval is a proposed evaluation tool for llama.cpp designed to standardize and improve comparability of community-run evaluations. The tool aims to address inconsistencies in how different users b

Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning

arXiv:2605.11289v1 Announce Type: new Abstract: Average-reward reinforcement learning requires estimating the gain and the bias, which is defined only up to an additive constant. This makes direct dis

Agents

Quoting Boris Mann

“11 AI agents” is meaningless as a phrase. If I said “I have 11 spreadsheets” or “I have 11 browser tabs” to do my work, it means about the same thing. — Boris Mann Tags: ai-agents, ai, agent-definiti

agentssimon-willison

Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models

arXiv:2605.11887v1 Announce Type: new Abstract: Large language models have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque, li

🚀Qwen3.6-Plus is on Nous Portal now and FREE for a limited time. Hermes Agent, here we go!! ⚡️ @NousResearch

🚀Qwen3.6-Plus is on Nous Portal now and FREE for a limited time. Hermes Agent, here we go!! ⚡️ @NousResearch Qwen 3.6 Plus by @Alibaba_Qwen is now FREE for a limited time on Nous Portal! Nous Portal i

model-releasesqwen--x

RACC: Representation-Aware Coverage Criteria for LLM Safety Testing

arXiv:2602.02280v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face severe safety risks from jailbreak attacks, yet current safety testing largely relies on static datasets and

safetyarxiv-cs-cl

Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion

arXiv:2605.11697v1 Announce Type: new Abstract: This paper presents a kinematics-aware deep reinforcement learning framework based on Rainbow Deep Q-Networks (DQN) for cooperative peg-in-hole manipula

safetyarxiv-cs-ro

Agents

Random-Set Graph Neural Networks

arXiv:2605.11987v1 Announce Type: cross Abstract: Uncertainty quantification has become an important factor in understanding the data representations produced by Graph Neural Networks (GNNs). Despite

agentsarxiv-cs-lg

Local Ai

Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models

arXiv:2605.11142v1 Announce Type: new Abstract: Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, commu

local-aiarxiv-cs-lg

RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking

arXiv:2605.11151v1 Announce Type: cross Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key chall

safetyarxiv-cs-ro

Local Ai

Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction

arXiv:2605.05749v2 Announce Type: replace Abstract: Dense 3D reconstruction from continuous image streams requires both accurate geometric aggregation and stable long-term memory management. Recent fe

local-aiarxiv-cs-cv

Read, Extract, Classify: A Tool for Smarter Requirements Engineering

arXiv:2605.11045v1 Announce Type: cross Abstract: This paper presents the ReXCL tool, which automates the extraction and classification processes in requirements engineering, enhancing the software de

READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling

arXiv:2312.06950v3 Announce Type: replace-cross Abstract: Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal

ReAD: Reinforcement-Guided Capability Distillation for Large Language Models

arXiv:2605.11290v1 Announce Type: new Abstract: Capability distillation applies knowledge distillation to selected model capabilities, aiming to compress a large language model (LLM) into a smaller on

researcharxiv-cs-cl