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arXiv:2609.40090v1 Announce Type: new Abstract: Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must tr

tutorialsarxiv-cs-ai

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Model Releases

QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs

DGX agent

arXiv:2609.39223v1 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive po

model-releasesarxiv-cs-lg

1 Oct 2026

Qodo 3.0 makes a statement: Engineering teams want to build their own software factory, not buy a black box. @QodoAI provides the quality, g…

Qodo 3.0 makes a statement: Engineering teams want to build their own software factory, not buy a black box. @QodoAI provides the quality, governance, plus a map of your software and your team’s triba

agentsitamar-friedman--x

QuantCode Model: Specializing Language Models for Executable Algorithmic Trading Code

model-releasesarxiv-cs-cl

arXiv:2609.39420v1 Announce Type: new Abstract: Large language models are strong general-purpose code generators, but executable algorithmic trading remains a demanding specialization target: a model

QuanVI: Score-based Variational Inference via Quantum Maximally Mixed States

arXiv:2609.39164v1 Announce Type: new Abstract: Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the variat

Quoting Matthew Green

[...] Put these pieces together and you have the two halves of a worm: a payload that hijacks the agent, and an agent that will carry the payload to the next agent. Agents in separately-isolated sandb

agentssimon-willison

Qwen Flash Next MTP work restarted

model-releasesr-localllama

If you're using MTP with Qwen Flash Next and llama.cpp, you can switch to: quants: https://huggingface.co/ggml-org/Qwen3.8-Flash-Next-GGUF PR: https://github.com/ggml-org/llama.cpp/pull/29761 Please n

Qwen3.8-27B Q4_K_M on one RTX 3090 + OpenCode: throughput, four coding tasks, and a reasoning-budget failure

model-releasesr-localllama

I put an old RTX 3090 to work as a local coding agent with Qwen3.8-27B, llama.cpp, and OpenCode. Here are the setup and results, including what failed. This is a summary of my own blog post, linked be

Qwen4Exp: add MTP by am17an · Pull Request #29761 · ggml-org/llama.cpp

model-releasesr-localllama

now you can use MTP with Qwen Flash Next, time to switch from Qwen 3.8 27B? (merged after 17h of development) quants: https://huggingface.co/ggml-org/Qwen3.8-Flash-Next-GGUF link to the previous discu

RAGScope: A Leakage-Controlled, Cost-Aware Evidence-Gating Protocol for RAG Hallucination Triage

arXiv:2609.39075v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems need inexpensive ways to route generated answers: accept low-risk outputs, review uncertain ones, and res

RAIM: Robust Aggregation of Inexpensive Models for Hallucination Detection

arXiv:2609.39229v1 Announce Type: cross Abstract: Automatic evaluation of faithfulness increasingly relies on a large language model acting as a judge, yet the most reliable judges are proprietary fro

RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling

arXiv:2609.39833v1 Announce Type: new Abstract: Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecasts are criti

Rank-Constrained Adaptation for Reliable Real-World Performance

arXiv:2602.06924v3 Announce Type: replace Abstract: Deep learning models trained to optimize average accuracy often exhibit systematic failures on particular subpopulations. In real-world settings lik

RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking Models

arXiv:2609.39551v1 Announce Type: new Abstract: Auto-research agents, LLM systems that propose, implement, train, and evaluate model changes across iterations, promise to automate applied ML's experim

Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis

arXiv:2609.40361v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based ob

researcharxiv-cs-cl

Local Ai

RAST: Resolution-Aware Privileged Structure Transfer for Low-Resolution Audio Activity Recognition

arXiv:2609.38780v1 Announce Type: cross Abstract: Audio is increasingly used for human activity recognition (HAR) because it captures object interactions, environmental events, and contextual cues in

local-aiarxiv-cs-ai

RATIO: Reasoning Analysis and Token-level Inference Optimization for Quantized Reasoning Models

arXiv:2609.39801v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become a widely adopted technique for reducing the memory footprint and inference cost of large language models (LL

Raw Imagery Impacting Your AI: Should You Care?

arXiv:2609.38265v1 Announce Type: cross Abstract: Onboard AI is gaining interest for space applications such as vessel, wildfire, and cloud detection, where real-time processing can improve mission re

Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models

arXiv:2609.39445v1 Announce Type: new Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup th

Re-ranking and Late Interaction Drive Retrieval Quality: A Controlled Comparison of RAG Strategies for Scientific Question Answering

arXiv:2609.38473v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is now the standard way to ground Large Language Models (LLMs) in external knowledge, yet the design space of ret

Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensembles

arXiv:2609.38448v1 Announce Type: new Abstract: Open-ended LLM homogeneity can create false plurality when several systems appear to offer independent perspectives while returning the same familiar de

Ready2Blend: From Natural-Language Instructions to Composable Alignment Prompts

arXiv:2609.39365v1 Announce Type: cross Abstract: Continual alignment requires LLMs to adapt to new requirements without forgetting previously acquired behaviors. Natural-language instructions are fle

RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce

arXiv:2609.38974v1 Announce Type: new Abstract: Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-le

Applications

Reasoning Externalization for Faithful Large Language Model Narratives of Stock Return Predictions

arXiv:2609.38869v1 Announce Type: new Abstract: In finance, interpreting machine learning predictions is essential, yet the numerical outputs of explainable AI can be difficult for non-experts to unde

applicationsarxiv-cs-ai

Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery

arXiv:2609.40212v1 Announce Type: new Abstract: This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery

researcharxiv-cs-cv

Applications

Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction

arXiv:2609.39960v1 Announce Type: new Abstract: 4D scene reconstruction aims to recover the evolving geometry, appearance, and motion of dynamic environments from visual observations. Despite substant

applicationsarxiv-cs-cv

Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks

arXiv:2602.23321v2 Announce Type: replace-cross Abstract: Using advanced machine learning techniques, we developed a method to reconstruct the arrival direction and energy of ultra-high-energy cosmic

Records that span multiple pages: 85.5% → 96.5%. That's where we most often see customers get stuck with existing tools. With citations on e…

Records that span multiple pages: 85.5% → 96.5%. That's where we most often see customers get stuck with existing tools. With citations on every value, the output is easy to trust. Great work from the

agentsjerry-liu--x

Recovering Off-Policy Supervision for Speculative Decoding

arXiv:2609.38795v1 Announce Type: new Abstract: Block drafters for speculative decoding are commonly trained on corpora written by external models, where a single off-policy token invalidates supervis

safetyarxiv-cs-cl

ReDiF: Resource-Efficient Few-Step Diffusion Distillation via Reinforcement Learning

arXiv:2512.22802v2 Announce Type: replace-cross Abstract: Step distillation accelerates diffusion sampling by training a few-step student to imitate a many-step teacher, but distillation itself remain

safetyarxiv-cs-cv

RefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent

arXiv:2609.39143v1 Announce Type: new Abstract: Long-horizon agent interactions generate useful but noisy experience, and retraining models to absorb it is expensive. Context-evolving agents therefore

agentsarxiv-cs-ai

Referential Uncertainty in Human--AI Collaboration

arXiv:2609.39518v1 Announce Type: new Abstract: Effective human-AI collaboration requires partners to establish references through interaction, which becomes fragile when descriptions are ambiguous, s

Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners

arXiv:2609.38971v1 Announce Type: cross Abstract: Embodied vision-language models (VLMs) are increasingly deployed as high-level planners for robots because they generalize across diverse environments

safetyarxiv-cs-ro

ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images

arXiv:2609.38680v1 Announce Type: cross Abstract: Text-to-image diffusion models are personalized to a subject by DreamBooth fine-tuning on a handful of its images. Increasingly, these images come fro

safetyarxiv-cs-ai

Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization

arXiv:2609.40159v1 Announce Type: cross Abstract: Sparse triangular solve (SpTRSV) is a fundamental kernel in numerous scientific and engineering applications. However, the data dependencies inherent

agentsarxiv-cs-lg

Reinforcement Learning with Complex (valued) Memories

arXiv:2609.38598v1 Announce Type: new Abstract: Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from ob

safetyarxiv-cs-lg

Reinforcing Multimodal Reasoning via Token-Level Perception-Grounded Advantage Estimation

arXiv:2609.39168v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing

Applications

ReLaG: A Scalable Framework Generalizing Random Splits to Data with Latent Relations

arXiv:2609.38538v1 Announce Type: cross Abstract: Random splitting can yield non-independent train--test subsets when a dataset contains related samples, as is common in certain applications such as b

applicationsarxiv-cs-ai

Reliability-Aware Checkpoint Selection for Domain Generalization

arXiv:2609.39934v1 Announce Type: cross Abstract: Checkpoint selection in domain generalization often relies on source-validation accuracy, yet the selected checkpoint need not provide reliable probab

researcharxiv-cs-cv

Remote work is so cooked If your job can be done on the other side of a screen the AI can do it better

model-releasesemad-mostaque--x

Remote work is so cooked If your job can be done on the other side of a screen the AI can do it better Introducing Griffin, the first model to pass the video Turing test. 48% of people who talked to i

Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text

arXiv:2609.40359v1 Announce Type: new Abstract: We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the in

Local Ai

Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents

arXiv:2609.39149v1 Announce Type: new Abstract: Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existin

local-aiarxiv-cs-ai

Replay on Demand: An Emergent Curriculum for Balancing Adaptation and Forgetting in Continued Pretraining

arXiv:2609.40089v1 Announce Type: new Abstract: Continued pretraining enables language models to adapt to new domains and knowledge, but often at the cost of forgetting previously acquired capabilitie

Report: Anthropic targets pre-Thanksgiving IPO launch, despite warning of AI’s ‘existential risks’

Artificial intelligence giant Anthropic PBC is reportedly forging ahead with its plans to go public and has set a new target date of Nov. 9, according to a report from Bloomberg that cited anonymous s

safetysiliconangle

Representation Risk in Pretrained Image Encoders

model-releasesarxiv-cs-cv

arXiv:2609.35470v1 Announce Type: cross Abstract: Applied researchers increasingly convert images into features with pretrained encoders, then use those features in a downstream prediction model. The

ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation

arXiv:2609.39306v1 Announce Type: cross Abstract: Iterative self-distillation enables LLM agents to learn from successive deployments, offering a path toward recursive self-improvement (RSI). Yet our

agentsarxiv-cs-ai

ResARC: Residual-Aware AutoRegressive Coding for Ultra-Low Bitrate Image Compression

model-releasesarxiv-cs-cv

arXiv:2609.39451v1 Announce Type: new Abstract: Progressive autoregressive image codecs provide an appealing paradigm for generative compression by quantizing continuous latents into discrete tokens,

Local Ai

ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning

arXiv:2609.38833v1 Announce Type: new Abstract: Federated continual learning must integrate new tasks over time without losing earlier-task knowledge. Most existing methods attach an anti-forgetting m

local-aiarxiv-cs-lg