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Row-Bot 5.0 is available: a new React interface, persistent agent goals, and a workspace for every conversation

Row-Bot 5.0 is a major rebuild of the local-first AI assistant. The NiceGUI interface is gone, replaced by one React app across desktop, browsers, phones and tablets. The biggest change is how work is

model-releasesr-ollama

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Model Releases

ROWBench: Do Video Models Render What the Program Specifies?

DGX agent

arXiv:2610.02205v1 Announce Type: new Abstract: Programmable world models separate executable dynamics from visual generation, offering a promising foundation for next-generation game engines. However

model-releasesarxiv-cs-cv

2 Oct 2026

RPTune: Learned Context Curation for LLM Catalog Search

arXiv:2610.00964v1 Announce Type: cross Abstract: For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-sta

Rules Amortize, Pairings Don't: Linguistic Structure Determines What Latent Task Representations Can Replace In-Context Learning

arXiv:2610.00526v1 Announce Type: cross Abstract: In-context learning (ICL) can be amortized into latent objects (task vectors, function vectors, context vectors) that recover few-shot behavior at zer

Rules to Tools: Executable Checks for LLM Agents in Scientific Computing

arXiv:2610.00313v1 Announce Type: new Abstract: Scientific coding agents receive equations, boundary conditions, and output requirements in writing, then must assess the programs they revise. Rules to

Safety in Self-Evolving Agents: A Survey

arXiv:2610.00093v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong general capabilities, yet their parameters typically remain fixed after deployment, limiting learning from

SAGE: Similarity-Based Cleaning of Poisoned Training Data from Verified Examples

arXiv:2610.01788v1 Announce Type: new Abstract: As machine learning increasingly relies on public, untrusted data sources, data poisoning attacks, which inject malicious examples into training data to

SALD: Self-Referenced Advantage Learning for Diffusion Models

arXiv:2610.01496v1 Announce Type: new Abstract: Recent work on language-model adaptation has shown that single models can obtain informative training signals by evaluating their behavior in demonstrat

researcharxiv-cs-cv

Same Reward, Different Skills: When Multimodal RL Learns to Look

arXiv:2610.01908v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves vision-language benchmark scores even without visual information during training. With im

Same Scene, Different Task: Skill Alignment for Compositional Generalization in VLAs

arXiv:2610.00524v1 Announce Type: cross Abstract: Vision-language-action (VLA) models often struggle to generalize to skill combinations absent from their fine-tuning demonstrations, even when every c

safetyarxiv-cs-lg

Local Ai

Sample complexity bounds for categorical Markov random fields via Discrete Diffusions

arXiv:2610.02128v1 Announce Type: cross Abstract: Many applications in statistics, economics, and physics require sampling from high-dimensional categorical distributions with local dependence structu

local-aiarxiv-cs-lg

Sapien: A Stateful Policy Engine for Autonomous AI Agents

arXiv:2610.00797v1 Announce Type: new Abstract: Contextual security defenses prevent AI agents from taking rogue actions by synthesizing a task-specific policy and enforcing it on the agent's tool cal

Applications

ScaffoldM3C: A Multimodal Sequential Monte Carlo Framework for Generative Stable Construction Planning

arXiv:2610.00487v1 Announce Type: cross Abstract: Autonomously constructing physically realizable 3D structures remains a significant challenge due to combinatorial action spaces, interchangeable comp

applicationsarxiv-cs-lg

Scalable Delphi: Large Language Models for Structured Risk Estimation

arXiv:2602.08889v2 Announce Type: replace Abstract: Quantitative risk assessment relies on structured expert elicitation to estimate unobservable properties. The Delphi method produces calibrated, aud

Scalable Multi-Task Inverse Reinforcement Learning

arXiv:2610.00758v1 Announce Type: new Abstract: By learning transferable rewards, inverse reinforcement learning (IRL) enables counterfactual evaluation of agents under modified environments. Such tra

safetyarxiv-cs-lg

Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)

arXiv:2610.02092v1 Announce Type: cross Abstract: Data selection is critical for training large language models on massive and heterogeneous corpora. Meta-learning for Training-data Selection offers a

Scaling Clinical Judgment to Evaluate Medical AI

arXiv:2609.12822v2 Announce Type: replace Abstract: Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical reasoning in large language models (LLMs). T

Scaling Collider Event Generation with Residual-Quantized Tokens

arXiv:2610.00569v1 Announce Type: cross Abstract: Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider,

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

arXiv:2610.02202v1 Announce Type: new Abstract: What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prio

Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papers

arXiv:2610.00531v1 Announce Type: new Abstract: AI-generated content, often called AI slop, is increasingly common everywhere, particularly in academia. Slop in AI-generated scientific papers, however

Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems

arXiv:2610.01257v1 Announce Type: cross Abstract: Scientific progress emerges from a longitudinal ecosystem in which researchers, institutions, funding agencies, collaboration networks, and the scient

Scientific Agents: Evaluating Profession-Specific System Prompts on Scientific Tasks

arXiv:2610.00084v1 Announce Type: new Abstract: Detailed profession-specific system prompts raise token use and estimated cost per response without a consistent accuracy gain. We evaluate Scientific A

Scientific Discovery under Validation Congestion via Multi-Fidelity Pairwise Rankings

arXiv:2610.01827v1 Announce Type: new Abstract: Modern computational methods can now propose candidate molecules, materials, and other scientific designs at an unprecedented scale, creating a validati

agentsarxiv-cs-lg

SCOPE-AD: Sequential cost-aware ordinal-belief planning with energy-based models for diagnostic agents

arXiv:2610.01278v1 Announce Type: new Abstract: Alzheimer's disease (AD) diagnosis requires sequential evidence acquisition under heterogeneous test costs and patient burden. Fixed-modality predictors

Score the Update, Not the Token: Descent-Aligned Routing for Combinatorial LoRA Experts

arXiv:2610.00493v1 Announce Type: new Abstract: Mixture-of-LoRA-experts methods raise the capacity of low-rank adaptation by routing each token to a few low-rank experts. Nearly all of them tie one in

Scores That Hold, Benchmarks That Leak: Measuring Dataset Contamination in Public Brain-Tumor MRI Classification

arXiv:2610.00421v1 Announce Type: cross Abstract: Automated classification of brain tumors from MRI is a heavily published application of deep learning in medical imaging, with reported accuracies on

Screw Attention: Rigid-Body Algebra Inside a Transformer

arXiv:2610.00904v1 Announce Type: cross Abstract: Learned manipulation policies rediscover from data the spatial relations that rigid-body mechanics supplies in closed form. This costs data, and it le

SeedFlood: A Step Toward Scalable Decentralized Fine-Tuning of LLMs

arXiv:2602.18181v2 Announce Type: replace Abstract: This work presents SeedFlood, a new approach to decentralized LLM fine-tuning designed to scale across large models, large collaborations, and compl

Tutorials

Seeing the City or Recognizing the Place? What Street-View Imagery Adds Beyond Existing Urban Data in VLM Urban Sensing

arXiv:2610.00031v1 Announce Type: new Abstract: Street-view imagery is increasingly used to infer urban attributes, but predictive accuracy alone does not reveal how much a photograph contributes beyo

tutorialsarxiv-cs-cv

Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents

arXiv:2610.01892v1 Announce Type: cross Abstract: Multimodal agents commonly generate free-form reasoning before each action. For small models, limited model capacity can result in lengthy reasoning t

Self-Evolving Coding Rules for AI Coding Agents

arXiv:2610.00650v1 Announce Type: cross Abstract: The performance of AI coding agents is highly dependent on their underlying coding rules. However, existing coding rules are typically hand-crafted an

Self-hosting AI does not save money, and I do it anyway

model-releasesr-localllama

Hi folks, I'm a long-time lurker and big fan of this subreddit and a massive self-hosting fan (also outside of AI). I doubt many people will disagree with me here because I see the same arguments bein

Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems

arXiv:2607.18255v2 Announce Type: replace Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, me

Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos

arXiv:2610.01205v1 Announce Type: new Abstract: Objective assessment of microsurgical technical skill is essential for competency-based training and quality assurance, yet existing video-based approac

researcharxiv-cs-cv

SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation

arXiv:2610.00686v1 Announce Type: new Abstract: Recent video-based world models pair the scalability of autoregressive (AR) prediction with the visual quality of diffusion models. The choice of scene

safetyarxiv-cs-cv

Sentence Specificity Scores for Collaborative Technical Documentation: A Domain-Transfer Study

arXiv:2610.01046v1 Announce Type: cross Abstract: Collaboration depends on shared context, and technical documentation is one way that context persists across people and AI teammates. Specificity, the

Sequential Capacity of Quantum Processes with Finite Memory

arXiv:2610.02068v1 Announce Type: cross Abstract: How complex can the responses of a quantum device become as it runs longer with a fixed internal memory? We quantify this complexity through sequentia

Sequential Functional Structured Tucker Compression for Large Language Model Attentions

arXiv:2610.00717v1 Announce Type: cross Abstract: Post-training compression of LLM attention is often formulated as independent matrix approximation, ignoring both the shared structure among attention

SETA got accepted to NeurIPS 2026! 🎉 We released the full training scripts for DeepSeek-V4-Flash, GLM-5.2 (LoRA), Inkling-Small, and Qwen3.…

model-releasesclem-delangue--x

SETA got accepted to NeurIPS 2026! 🎉 We released the full training scripts for DeepSeek-V4-Flash, GLM-5.2 (LoRA), Inkling-Small, and Qwen3.8-27B, with both GRPO and PPO, alongside 4,500+ open-source,

Sharing some notes from OpenAI DevDay in parts. The first part matters, since it's on everyone's mind. How do you optimize for cost and perf…

Sharing some notes from OpenAI DevDay in parts. The first part matters, since it's on everyone's mind. How do you optimize for cost and performance? If you are building agentic applications today, the

agentsdair-ai--x

Sharp Non-Asymptotic Analysis of the Penalized Challenger in eta-EB-TCI for Bernoulli Bandits

arXiv:2610.01951v1 Announce Type: new Abstract: Top-two algorithms are simple and effective for fixed-confidence best-arm identification, but their sharp non-asymptotic behavior is still not well unde

Sharp Oracle-Regret Tradeoffs for Projection-Free Online Convex Optimization

arXiv:2610.00254v1 Announce Type: new Abstract: We characterize the regret attainable in online convex optimization when access to the feasible set is limited to an exact linear optimization oracle. T

Sharpen Before You Adapt: Data-Free Entry-State Sharpening for Test-Time Reinforcement Learning

arXiv:2610.00903v1 Announce Type: new Abstract: Test-time reinforcement learning (TTRL) adapts language models on unlabeled test problems using supervision derived from their own samples. This makes t

safetyarxiv-cs-lg

Sharpening Tax in Post-Training

arXiv:2610.01509v1 Announce Type: new Abstract: An emerging hypothesis about reinforcement learning (RL) post-training of large language models (LLMs) is that it merely sharpens existing behaviors of

SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

arXiv:2610.00838v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) trains a large language model (LLM) to act over long, multi-step interactions. However, a single localized error c

ShatterQuant: Breaking Uniform Precision with Block-Wise Mixed-Precision on a Systolic Transformer Hardware Accelerator

arXiv:2610.00207v1 Announce Type: cross Abstract: Due to limited support for intra-tensor heterogeneous precision in conventional accelerators, neural network quantization remains largely restricted t

Applications

ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring

arXiv:2610.01283v1 Announce Type: new Abstract: Reliable shelf monitoring is an important capability for retail automation, yet existing out-of-stock detection methods mainly operate in image space an

applicationsarxiv-cs-cv

Should we plead opensource labs to still produce great non thinking (instruct) models?

model-releasesr-localllama

The results are in, no thinking / instruct mode for new models degrade performance more than on old models such as 3.6 vs 3.8, where 3.6 takes the lead on several coding benches in instruct mode. I wo