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Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAG

arXiv:2609.05152v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retrieved context inflates the input and degrades

researcharxiv-cs-cl

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Hardware

Compute-in-Memory Attention: A Time-Domain Analog Softmax Circuit with RC-Tunable Temperature

DGX agent

arXiv:2609.04266v1 Announce Type: cross Abstract: Softmax is a key operation in Transformer attention, but its exponentiation and normalization add significant overhead in compute-in-memory (CIM) acce

hardwarearxiv-cs-lg

7 Sept 2026

Conformal Prediction for Offensive Security

arXiv:2609.05165v1 Announce Type: cross Abstract: Despite its introduction more than a quarter century ago, Conformal Prediction (CP) has seen surprisingly few applications to the cyber security world

Conformity Breaks Conformal Prediction

arXiv:2609.04445v1 Announce Type: cross Abstract: A conformal certificate can be valid when an LLM answers alone and invalid when the same LLM sees peers that unanimously assert a wrong answer. The qu

agentsarxiv-cs-cl

Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks

arXiv:2609.05073v1 Announce Type: new Abstract: Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. The

ConfRAG: Confidence-Guided Retrieval-Augmenting Generation

arXiv:2506.07309v3 Announce Type: replace Abstract: Can Large Language Models (LLMs) be trained to avoid hallucinating factual statements, and can Retrieval-Augmented Generation (RAG) be triggered onl

researcharxiv-cs-cl

Consensus Group Relative Policy Optimization for Text Generation

arXiv:2602.03102v2 Announce Type: replace Abstract: Many strong decoding methods for text generation follow a sample-and-rerank paradigm: they draw multiple candidates, score each under a utility (rew

safetyarxiv-cs-lg

ConsensusBench: Benchmark of Consensus Nodes for LLM Reasoning via Outcome Reward Densifying

model-releasesarxiv-cs-cl

arXiv:2609.04648v1 Announce Type: new Abstract: Reinforcement learning (RL) has become one of the primary paradigms for reasoning enhancement of large language models (LLMs). In particular, Group Rela

Conserved Immune Topology Improves Pathology Foundation Model Generalization for Cross-Cancer MSI-H Prediction

arXiv:2609.05182v1 Announce Type: new Abstract: Pathology foundation models integrated with multiple instance learning achieve competitive accuracy within single-cancer cohorts, yet cross-cancer gener

researcharxiv-cs-cv

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor

arXiv:2510.12368v2 Announce Type: replace-cross Abstract: Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such

Constructing and Evaluating Clinical Reasoning Trajectories for Medical Agent

arXiv:2609.05090v1 Announce Type: new Abstract: Evaluation of medical artificial intelligence agents remains predominantly answer-centric, assessing only the correctness of final outputs while overloo

Local Ai

Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI

arXiv:2609.04552v1 Announce Type: cross Abstract: Unattended interactive autonomy - machines that step into danger in place of humans and complete tasks with human tools - remains a missing capability

local-aiarxiv-cs-ai

Continual Graph Memory for Adaptive Recommendation under Intent Drift

arXiv:2609.04651v1 Announce Type: new Abstract: This paper studies adaptive recommendation under intent drift, where feedback from each recommendation outcome can reveal whether the relational evidenc

CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls

arXiv:2609.05269v1 Announce Type: cross Abstract: LLM agent systems increasingly combine provenance tracking, authorization, policy enforcement, protocol adapters, and execution controls. However, ind

Continuous Cognitive Coverage for Autonomous Robots via Event-Dependent Cognitive Treatment and Learning

arXiv:2609.04770v1 Announce Type: new Abstract: Autonomous robots continuously encounter objects, changes, and situations, and every event admitted into cognition should receive an appropriate cogniti

agentsarxiv-cs-ro

Controlling and Assessing Appropriate Persona Use in LLM-based Dialogue Generation

arXiv:2609.04676v1 Announce Type: new Abstract: In persona-based dialogue generation (PDG), LLMs often overuse persona attributes by incorporating them regardless of dialogue context, resulting in unn

safetyarxiv-cs-cl

Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility

model-releasesarxiv-cs-lg

arXiv:2609.04269v1 Announce Type: cross Abstract: Deciding whether two supplier records belong to the same corporate family is a prerequisite for spend consolidation, credit exposure aggregation and s

Applications

Corporate Language Model (CLM): Transforming Tacit and Fragmented Enterprise Knowledge into a Sovereign, Auditable, and Executable Corporate Intelligence Layer

arXiv:2609.04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and

applicationsarxiv-cs-ai

CoSkill: Joint Reinforcement Learning of Reasoning and Meta-Skill Agents for Hierarchical Skill Evolution

arXiv:2609.04865v1 Announce Type: new Abstract: Skill libraries improve the sample efficiency of agentic reinforcement learning (RL) by enabling large language model (LLM) agents to reuse procedural k

Local Ai

Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution

arXiv:2609.04820v1 Announce Type: cross Abstract: Ransomware detection and family attribution require analysis of different modalities because it can use packing, obfuscation, process manipulation and

local-aiarxiv-cs-ai

Counting Beyond Instances: A Benchmark for Group-Individual Object Counting

model-releasesarxiv-cs-cv

arXiv:2609.04716v1 Announce Type: new Abstract: Visual counting is commonly formulated at the instance level, aiming to estimate how many objects of a queried category appear in an image. However, rea

Coupled Control and Wireless World Models for Resilient Remote Robotic Control

arXiv:2609.04851v1 Announce Type: cross Abstract: Remote robotic systems operating over wireless networks must maintain reliable control despite limited communication resources, changing channel condi

safetyarxiv-cs-lg

CPR-IE:A Compression-Prediction-Resource Intelligence Efficiency Metric

arXiv:2609.04809v1 Announce Type: new Abstract: Comparing intelligent systems under deployment constraints requires more than predictiveaccuracy.This paper develops Compression-Prediction-Resource Int

Cross-dataset transportability of pediatric chest X-ray deep learning across three countries: discrimination, calibration, operating-point failure, and limited-label recovery

arXiv:2609.05140v1 Announce Type: cross Abstract: Background and Objective: External evaluation of medical-imaging AI is often collapsed into discrimination. We evaluated a computational protocol that

safetyarxiv-cs-cv

Cross-Domain Tracker Adaptation Without Target-Domain Labels via Vision-Language Agents

model-releasesarxiv-cs-cv

arXiv:2609.05239v1 Announce Type: new Abstract: We present a system that uses a Vision-Language Model (VLM) as a diagnostic agent for adapting a detect-to-track pipeline to a new target domain without

Cross-modal triage network: a multimodal deep learning framework for severity-based triage and visual explainability in chest radiographs

arXiv:2609.04357v1 Announce Type: cross Abstract: Purpose: Increased number of chest radiograph (CXR) scans create a triage bottleneck, queueing urgent examinations behind routine ones. Existing AI to

Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation

model-releasesarxiv-cs-cl

arXiv:2603.25183v2 Announce Type: replace Abstract: Context-aware machine translation (MT) leverages document-level information, yet it does not consistently outperform sentence-level MT, as contextua

Cross-Task Generalization Between Understanding and Generation in Unified Vision-Language Models: A Controlled Study

arXiv:2505.23043v2 Announce Type: replace-cross Abstract: Unified vision-language models (VLMs) aim to support both visual understanding and generation within a single framework, but it remains unclea

CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation

arXiv:2609.05397v1 Announce Type: new Abstract: Reliable 3D understanding of the surrounding environment is a core requirement for autonomous driving. Multi-view surround camera rigs provide broad sce

agentsarxiv-cs-cv

CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents

arXiv:2609.05374v1 Announce Type: new Abstract: Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still act mostly through the GUI, often producing inefficient traject

Cultural Misalignment in Large Language Models: Detection, Measurement, and Mitigation Through Targeted Fine-Tuning

arXiv:2609.04485v1 Announce Type: cross Abstract: We evaluate three open-weight LLMs (Gemma3-12B from the USA, Bielik-11B-v3 from Poland, and Qwen3-4B from China) against World Values Survey Wave 7 da

Tutorials

DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models

arXiv:2609.04555v1 Announce Type: new Abstract: Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained backbone can provide rich representations

tutorialsarxiv-cs-cv

Data-Driven Discovery of Composition-Dependent Constitutive Models for Hyperelasticity and Viscoelasticity of Digital Materials

arXiv:2609.04541v1 Announce Type: new Abstract: Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective r

Data-Driven Learning of Unknown Nonlinear Differential Equations Using Functional Analysis

arXiv:2609.04329v1 Announce Type: cross Abstract: In this paper, the problem of data-driven discovery of nonlinear ordinary differential equations (ODEs) is recast, and a new interpretable machine lea

Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security

arXiv:2609.04300v1 Announce Type: new Abstract: Ensuring the security of the power system is essential for stability and reliability, especially in the event of disruption. Effective classification of

DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems

arXiv:2609.04749v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite their promise, such systems remain fragile,

Deep Learning-Driven Peptide Classification in Biological Nanopores

arXiv:2509.14029v2 Announce Type: replace Abstract: Nanopore-based single-molecule sensing is a promising route to fast, low-cost disease diagnosis and protein sequencing: as an analyte such as a pept

Deep Microcompression: Structured Pruning and Bit-packed Quantization for Microcontrollers

arXiv:2609.05081v1 Announce Type: new Abstract: This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on bare-metal microcontrollers. DMC integrates

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

arXiv:2609.05364v1 Announce Type: cross Abstract: Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are inval

Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts

model-releasesarxiv-cs-cl

arXiv:2606.12807v2 Announce Type: replace Abstract: Summaries of real-world events can become outdated as contexts evolve and new information arrives. A common response is to generate a new summary fr

Development and Evaluation of Ultrasound Image Learning Pipelines for MASLD Risk Stratification

arXiv:2609.04390v1 Announce Type: cross Abstract: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30% of the general population. Ultrasound-based imaging, includ

researcharxiv-cs-cv

Local Ai

Development of a Humanoid Robot Prototype for Multimodal Human-Robot Interaction

arXiv:2609.05361v1 Announce Type: new Abstract: Human-robot interaction (HRI) enables intuitive and intelligent collaboration between humans and robots in real-world environments. This paper introduce

local-aiarxiv-cs-ro

Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges

arXiv:2609.04778v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through

Diffusion TV: Experiencing Diffusion Models through Tangible, Embodied Interaction

arXiv:2609.05404v1 Announce Type: cross Abstract: Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion models through a modified CRT TV. By ph

Dimension-Adaptive Batched Lipschitz Narrowing Without Knowing the Zooming Dimension

arXiv:2609.05214v1 Announce Type: new Abstract: The Appropriately Combined Edge-length (ACE) sequence in A-BLiN depends on the zooming dimension d_z. This note removes that dependence. The next edge l

Discourse Dependency: A Continuous Criterion for Translation Difficulty

arXiv:2609.04959v1 Announce Type: new Abstract: Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeas

researcharxiv-cs-cl

Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures

model-releasesarxiv-cs-lg

arXiv:2609.04407v1 Announce Type: new Abstract: Deep neural operators learn mappings between input functions and complete PDE solution fields, enabling forward evaluations of new problem instances ord

Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

model-releasesarxiv-cs-lg

arXiv:2609.05363v1 Announce Type: new Abstract: Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent while offering upgraded benefits. Large langua