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Concept Driven Domain Adaptation: Finding an Abstract Needle in a Haystack

model-releasesarxiv-cs-cv

arXiv:2610.00973v1 Announce Type: new Abstract: Science teachers frequently search for documentary excerpts not by describing what appears on screen, but by querying the abstract concepts they intend

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Human DGX agent

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Model Releases

Conflicting Supervision Moves Commitment, Not Capability: A 12.29{sigma} arrangement effect that is exactly zero under a convention-agnostic score

DGX agent

arXiv:2610.00234v1 Announce Type: new Abstract: 'Train a model on the same problems written under two incompatible conventions, both correct, and ask what the ordering of that data writes into the par

model-releasesarxiv-cs-ai

2 Oct 2026

Constant-Memory Recall: Learned Associations in a Fixed Matrix State

arXiv:2610.00232v1 Announce Type: new Abstract: Fixed-size recurrent memory limits storage growth during inference, but successful recall depends on the task and training. We study a small DeltaNet va

Constant-Time Planning for Chaining Collision-free Motion to Manipulation Behaviors

arXiv:2512.00939v3 Announce Type: replace-cross Abstract: Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines

Constitutional Value Potentials: reading and steering internal priority margins in language models

arXiv:2606.15420v2 Announce Type: replace-cross Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dash

Context-Aware Error Mitigation Orchestration for Hybrid Quantum Reinforcement Learning on NISQ Systems

arXiv:2610.01253v1 Announce Type: new Abstract: Quantum Reinforcement Learning (QRL) integrates reinforcement learning with parameterized quantum circuits and is a promising approach to combinatorial

safetyarxiv-cs-lg

Contextual trajectory and incremental contextual displacement: Towards using LLMs to understand dynamic, utterance-specific meaning construction

arXiv:2610.00840v1 Announce Type: cross Abstract: Transformer-based large language models (LLMs) such as RoBERTa represent text using contextual word embeddings (CWEs), which alter the embeddings asso

Contingent Exposure Routing for Financial AI: Outage Risk and the Cost of Indivisible Decisions

arXiv:2610.00239v1 Announce Type: new Abstract: Model failover restores availability, but changes which financial institutions share decision errors. We formulate outage-contingent routing through a l

agentsarxiv-cs-lg

Continual Concept Erasure in Diffusion Models by Suppressing Cross-Edit Interference

arXiv:2610.01989v1 Announce Type: new Abstract: Concept erasure removes copyright-protected, privacy-sensitive, or otherwise undesirable concepts from pretrained text-to-image diffusion models to supp

researcharxiv-cs-cv

Applications

Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations

arXiv:2610.01301v1 Announce Type: cross Abstract: Most current grasp synthesis systems are trained offline and remain fixed during deployment. While this works well when deployment conditions resemble

applicationsarxiv-cs-lg

Continual Reinforcement Learning with Neuroevolution

arXiv:2610.01583v1 Announce Type: cross Abstract: Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continual task changes, no RL method has yet co

Continue, Abort, or Fall: Viability-Aware Policy Selection (VAPS) for Safe Humanoid Acrobatics

arXiv:2610.01397v1 Announce Type: new Abstract: Dynamic humanoid motions such as flips risk hardware damage due to suboptimal policies, disturbances or sim-to-real gaps. A motion tracking policy offer

safetyarxiv-cs-ro

Continuous Conditioning of VLAs with Augmenting EMG and Visual Task Descriptors

arXiv:2610.01794v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models rely strongly on language for describing task information, despite having multimodal inputs. We hypothesize that oth

researcharxiv-cs-cv

Continuous Process-Level Evaluation for Evolving Enterprise AI Agent Skills

arXiv:2610.01833v1 Announce Type: new Abstract: Enterprise AI agent skills evolve as tool APIs, models, and specifications change, yet final-output evaluation can miss process-level behavioral drift.

ContractRL: Shielded Group-Relative Policy Optimization for Auditable Tool-Call Repair

arXiv:2610.00328v1 Announce Type: new Abstract: Structured tool calls often fail after only a small number of fields violate a schema or an execution contract. Regenerating the complete object enlarge

Contrastive Attention Mitigates Spectral Bias in Spiking Transformers

arXiv:2610.01403v1 Announce Type: new Abstract: Spiking Transformers merge the energy-efficiency of spiking neural networks (SNNs) with the representational power of self-attention, creating a promisi

Controllable Multi-label Video Safety Detection via Adaptive Tversky Policy Optimization

arXiv:2610.02019v1 Announce Type: cross Abstract: The rapid growth of video-based social media has increased users' exposure to harmful content, creating a need for reliable automated video safety det

safetyarxiv-cs-cv

Convergence Analysis of STORM Under Different Geometries

arXiv:2610.01599v1 Announce Type: cross Abstract: Stochastic recursive momentum (STORM) achieves fast convergence for nonconvex optimization via the variance reduction effect, but existing analyses re

researcharxiv-cs-lg

CortexBridge: Cortical Alignment of EEG Montages for Foundation Models

arXiv:2610.01124v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer diffic

Cost-augmented Schrodinger bridges on graphs are exactly solvable: a Feynman-Kac tilt replaces learned control

arXiv:2610.02195v1 Announce Type: new Abstract: The generalized Schrodinger bridge on a graph moves mass between two distributions while charging a cost for the states visited. It has been approached

researcharxiv-cs-lg

Local Ai

Counterfactual Auditing of Bias in Open-Source Large Language Models for Clinical Triage

arXiv:2610.01963v1 Announce Type: new Abstract: Emergency department (ED) triage is a high-stakes prioritization task in which demographic, socioeconomic, and system-context information may improperly

local-aiarxiv-cs-ai

Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates

arXiv:2610.01193v1 Announce Type: cross Abstract: Counterfactual generation seeks to sample outcomes under a hypothetical intervention or decision using observational data collected under the factual

Counting Moves, Weighing Voices: Bayesian Dialectical Argumentation for Calibrated Multi-LLM Councils under Persistent Adversaries

arXiv:2610.02005v1 Announce Type: new Abstract: A multi-LLM council lets several large language models (LLMs) deliberate on a question and return an answer together with a confidence estimate. As thes

Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Models

arXiv:2610.00277v1 Announce Type: new Abstract: Federated multimodal graph foundation models (GFMs) aim to adapt pretrained multimodal models to decentralized graph data, where each client owns a priv

researcharxiv-cs-lg

Cross-Benchmark Transfer from RL on Agentic Coding Tasks

arXiv:2610.00890v1 Announce Type: cross Abstract: Coding agents often fail in the last mile: they build most of a feature but drop a requirement, test only the cases their implementation already handl

Cross-entropy optimization with prioritized constraints

arXiv:2610.01319v1 Announce Type: new Abstract: When constraints conflict, an optimizer must determine which requirements to preserve and which to relax. On the one hand, a priority ordering specifies

researcharxiv-cs-ro

Cross-Lingual Alignment for Decoder-Only Models using MoE Routers

arXiv:2610.01921v1 Announce Type: cross Abstract: Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is

CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations

arXiv:2610.01649v1 Announce Type: new Abstract: Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained model

Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents

arXiv:2610.00759v1 Announce Type: cross Abstract: Cyber attack agents are typically trained and evaluated within a single simulator, making it unclear whether learned policies transfer beyond the envi

safetyarxiv-cs-lg

CtrlWAM: Controllable World Action Models with Aligned Intent and Foresight

arXiv:2610.00859v1 Announce Type: new Abstract: World action models (WAMs) jointly predict actions (intent) and visual future (foresight). Standard training adds noise to recorded actions and video si

agentsarxiv-cs-cv

Curvature Under Attack in hZACH-ViT: Gauge Symmetry, Boundary Saturation, and Adversarial Failure

arXiv:2610.00680v1 Announce Type: cross Abstract: Curvature is often treated as an intrinsic property of a representation, although its empirical effect also depends on coordinate scale, learned logit

researcharxiv-cs-cv

Cybernetic and Epistemic: A Missing Vocabulary for Trustworthy Agentic Delegation

arXiv:2610.00961v1 Announce Type: new Abstract: As code generation is increasingly delegated to AI systems, the bottleneck is shifting from writing code to supervising the systems that write it --- a

Dataset Identity, Not Novelty: The Source of an Inflated OOD Detection Gain

model-releasesarxiv-cs-cv

arXiv:2610.01096v1 Announce Type: cross Abstract: A post-hoc out-of-distribution (OOD) detector reads the activations of a trained classifier and returns a score. It fits that score on in-distribution

DAYJOB: A Benchmark for Long-Horizon Professional Work

arXiv:2610.01306v1 Announce Type: new Abstract: Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the req

Debias Anything: Fairness with Diversity without Supervision in Diffusion Models

arXiv:2610.01815v1 Announce Type: new Abstract: Although diffusion models produce high-quality images, they also reproduce and amplify demographic imbalances in their training data. Debiasing their ge

safetyarxiv-cs-lg

Decentralized Safe Path Following for Multiple Quadrotors on Intersecting Paths with Theoretical Guarantees

arXiv:2610.00208v1 Announce Type: cross Abstract: This paper studies decentralized safe path following for multiple quadrotors on intersecting paths, where safety requires both collision avoidance and

safetyarxiv-cs-ro

Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning

arXiv:2610.01513v1 Announce Type: new Abstract: Long-term dependencies remain a major challenge for sequential decision-making in the field of AI: RNNs suffer from vanishing gradients and the limited

Decoding Looped Transformers Better for (Almost) Free

arXiv:2610.02185v1 Announce Type: new Abstract: Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate represe

Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping

arXiv:2610.00302v1 Announce Type: cross Abstract: Crowdsourced imagery provides timely, fine-grained, street-level observations for disaster mapping, complementing conventional remote sensing imagery

Applications

DecomVoxel: Harnessing 3D-Native Priors with Guided In-situ Denoising Optimization for Decompositional Scene Reconstruction

arXiv:2610.01914v1 Announce Type: new Abstract: Decompositional scene reconstruction aims to reconstruct high-quality objects and background, yet existing methods still struggle with the level of qual

applicationsarxiv-cs-cv

Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

arXiv:2610.00363v1 Announce Type: cross Abstract: Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep lea

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective

arXiv:2506.11641v2 Announce Type: replace-cross Abstract: Deep autoencoders have become a fundamental tool in various machine learning applications, ranging from dimensionality reduction and reduced o

DeepJEPA: Scaling World Models from Within

arXiv:2610.00368v1 Announce Type: cross Abstract: World-model planners typically scale outward by rolling farther, sampling more trajectories, or optimizing longer, while assigning the same computatio

DeFA: Dependency-Guided Failure Attribution for LLM Agents

arXiv:2610.01256v1 Announce Type: new Abstract: Errors in LLM agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understandi

Local Ai

Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection

arXiv:2610.01361v1 Announce Type: cross Abstract: Multilayer networks allow the modeling of interactions between the same entities across different contexts, such as temporal observations, varying set

local-aiarxiv-cs-lg

Hardware

Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving

arXiv:2610.00499v1 Announce Type: new Abstract: As diffusion large language models (dLLMs) become more capable, they are moving from research settings to real-world extit{serving}, where request manag

hardwarearxiv-cs-lg

Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems

arXiv:2610.00371v1 Announce Type: cross Abstract: Multi-agent systems derive their capabilities from sharing evidence, delegating tasks, and combining information across agents. The same process creat

Dependency-Aware Reward Shaping for Agentic Reinforcement Learning

arXiv:2610.01207v1 Announce Type: new Abstract: When training large language models with reinforcement learning, terminal rewards provide little guidance about which steps matter. Common methods for a