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Rethinking the Information Bottleneck: Structured Decomposition under Label-Induced Partitions

arXiv:2610.01175v1 Announce Type: new Abstract: Standard information bottleneck (IB) regularization constrains representations via a single scalar I(Z;X), implicitlytreating all information as homogen

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Retrospective Open-Vocabulary Memory for Long-Term Object Search

DGX agent

arXiv:2610.00330v1 Announce Type: cross Abstract: Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment. We formulate r

model-releasesarxiv-cs-cv

2 Oct 2026

Reusing Past Samples in Proximal Policy Optimization: When and How Does It Help?

arXiv:2610.01399v1 Announce Type: new Abstract: Among on-policy deep reinforcement learning methods, Proximal Policy Optimization (PPO) has become the de facto standard, due to its consistently strong

REVEAL: Robust Evolution of Vision-Language Models for Explainable AI-Video Detection

arXiv:2502.14994v2 Announce Type: replace Abstract: The rapid advancement of AI-generated video poses challenges to digital authenticity and security. Current detection methods, often trained on speci

Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices

arXiv:2610.00002v1 Announce Type: new Abstract: We introduce reverse Item Response Theory (IRT) to pharmacogenomic drug-response analysis by treating cancer types as latent 'subjects' with resistance

Revision-Aware Independent Agent Graphs for Dynamic Reasoning

arXiv:2610.01249v1 Announce Type: new Abstract: Conventional reasoning protocols present a fixed, preselected task, so they cannot test whether an agent propagates relevant updates, preserves unaffect

Revisiting Cross-Reconstruction for Generalizable Deepfake Detection

arXiv:2610.01544v1 Announce Type: new Abstract: Existing image forgery detectors often suffer from generalization to unseen manipulation methods due to the limited ability to capture transferable fore

Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation

arXiv:2610.00729v1 Announce Type: new Abstract: This paper explores a reward-based policy to achieve zero-shot transfer between source and target environments with completely different observation spa

Right In-Place (RiP) Convolution: A Simple, General, and Near-Optimal Strategy for Memory-Efficient CNN Inference

arXiv:2610.00586v1 Announce Type: cross Abstract: Activation memory, not compute, limits CNN inference on constrained hardware such as microcontrollers. Direct in-place convolution removes the dual-bu

researcharxiv-cs-cv

RIQE: a NIQE-style reference model for Computed Tomography

arXiv:2610.00384v1 Announce Type: cross Abstract: The Natural Image Quality Evaluator (NIQE) scores an image by its statistical distance from a model fitted on pristine images, and its distributed mod

researcharxiv-cs-cv

Local Ai

RISED: RubrIcs for agentic multi-environment Selection and sElf-Distillation

arXiv:2610.00979v1 Announce Type: new Abstract: Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing

local-aiarxiv-cs-ai

Robot Learning on Discrete Surfaces: Theory and Applications

arXiv:2610.01910v1 Announce Type: new Abstract: All the objects composing our world are enclosed within surfaces. Yet, most robot learning and motion generation frameworks treat surfaces as constraint

researcharxiv-cs-ro

Robust Evidential Learning Through Latent Consistency

arXiv:2610.01384v1 Announce Type: new Abstract: Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial i

Robust Is Salient: An Informed Adversary Moves the Optimal Signal onto the Salience Pole

arXiv:2610.00233v1 Announce Type: new Abstract: When an informed adversary shares the audience of a constrained signalling channel, the signal that best protects the truth is the signal that best desc

researcharxiv-cs-ai

Robust Nash Alignment under Preference Uncertainty

arXiv:2610.00715v1 Announce Type: new Abstract: Preference-based alignment methods typically optimize against a single preference model, and can therefore be brittle when pairwise preferences are unce

Robust Non-Clairvoyant Scheduling with Classification Models

arXiv:2610.01343v1 Announce Type: new Abstract: We study the classical single-machine scheduling problem of minimizing the sum of completion times of jobs in a non-clairvoyant setting, where the proce

Robust Online Aero-Engine Blade Defect Detection via Dual-Alignment Test-Time Adaptation

arXiv:2610.00067v1 Announce Type: new Abstract: Reliable visual inspection is essential for quality assurance in aero-engine blade manufacturing, where defect appearance may vary across production lin

Roto-translated Local Coordinate Frames For Interacting Dynamical Systems

arXiv:2110.14961v4 Announce Type: replace Abstract: Modelling interactions is critical in learning complex dynamical systems, namely systems of interacting objects with highly non-linear and time-depe

ROWBench: Do Video Models Render What the Program Specifies?

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

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

agentsarxiv-cs-ai

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

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

researcharxiv-cs-ai

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

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

agentsarxiv-cs-ai

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

researcharxiv-cs-ai

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

agentsarxiv-cs-ai