Search: “routing”

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

arXiv:2609.09153v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions throug

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ScopeMamba-YOLO: Widening the Perceptual Scope Inward and Outward for Small Object Detection in Remote Sensing Imagery

DGX agent

arXiv:2609.10156v1 Announce Type: new Abstract: Small object detection in unmanned aerial vehicle (UAV) and remote sensing imagery requires preserving high-resolution detail while modeling long-range

local-aiarxiv-cs-cv

10 Sept 2026

SkillAlign: Aligning Skill Interfaces for LLM-based Agents

arXiv:2609.07255v1 Announce Type: new Abstract: Language-model agents increasingly rely on skills: reusable procedural knowledge for reasoning, tool use, and interaction. Existing work studies how ski

Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing

arXiv:2609.07344v1 Announce Type: cross Abstract: Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration test

When Does Low-Bit Quantization Preserve the Decisions of Vector Search?

arXiv:2609.09854v1 Announce Type: cross Abstract: Low-bit quantization can achieve high recall on some vector representations and fail sharply on others, while average distortion and global rank corre

researcharxiv-cs-lg

A Shared-Backbone Approach for Multi-Task MedMNIST Classification

arXiv:2609.06838v1 Announce Type: new Abstract: Multi-task biomedical classification requires models to generalize across disparate modalities and class distributions. We study 11 heterogeneous MedMNI

Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living

arXiv:2609.08818v1 Announce Type: new Abstract: Person identification from millimeter-wave (mmWave) point clouds has mainly relied on gait. Indoor walking, however, is often brief and interrupted, whi

Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies

arXiv:2609.05783v1 Announce Type: cross Abstract: In autonomous driving, Bird's-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle's surroundings and have become

Code mode: Why your agent should code

Code mode gives an agent a sandbox instead of a longer tool list. Here is why it fixes the too-many-tools problem, what it costs you in sandboxing and debuggability, and when plain tool calling is sti

agentsarize-ai

DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing

arXiv:2609.06779v1 Announce Type: new Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cost-e

researcharxiv-cs-lg

DualPathOcc: Dual-Resolution BEV Encoder for 3D Occupancy Prediction

arXiv:2609.06370v1 Announce Type: new Abstract: Predicting 3D occupancy from multi-view images requires preserving geometric detail during 2D-to-3D lifting while reasoning over sparse, volumetric scen

local-aiarxiv-cs-cv

Fine-Grained Instruction-Guided Graph Reasoning for Vision-and-Language Navigation

arXiv:2503.11006v3 Announce Type: replace Abstract: Vision and Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demandi

MamMA: A Mamba-Based Pedestrian Trajectory Prediction Algorithm Considering Occupancy Map and Pedestrian Awareness States

arXiv:2609.08041v1 Announce Type: new Abstract: Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexist

local-aiarxiv-cs-cv

MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration

model-releasesarxiv-cs-cv

arXiv:2609.06645v1 Announce Type: new Abstract: All-in-one medical image restoration seeks to recover heterogeneous clinical images with a single model, but PET, CT, and MRI differ substantially in de

MetaRSI / RSI2: A Meta-Recursive Self-Improving System for Recursive Self-Improving Systems Themselves

arXiv:2609.06396v1 Announce Type: new Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet R

safetyarxiv-cs-lg

Qodo just launched the Agentic Toolbox. It gives Claude Code, Codex, Kiro, and Cursor direct access to @QodoAI's review engine, codebase kno…

Qodo has released the Agentic Toolbox, enabling Claude Code, Codex, Kiro, and Cursor to directly utilize QodoAI’s review engine, codebase knowledge base, and team‑specific rules. The launch was announ

model-releasesdair-ai--x

Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery

arXiv:2609.06346v1 Announce Type: new Abstract: Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However,

researcharxiv-cs-lg

Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline

arXiv:2609.07085v1 Announce Type: new Abstract: Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion

Task-driven Processing with Coarse-to-Fine Glimpse-based Active Perception

arXiv:2609.09025v1 Announce Type: new Abstract: State-of-the-art vision models process images in their entirety, lacking the ability to selectively zoom in on relevant regions. This limitation is part

local-aiarxiv-cs-cv

Unraveling the Real Working Mechanism and Inherent Flaws of GAE: A Method for Interpreting Transformer Processes from an Economic Perspective

arXiv:2609.07213v1 Announce Type: cross Abstract: We observe a phenomenon that current algorithmic research in the field of explainable artificial intelligence primarily pursues better performance on

Vision-Encoder Behavioral Fingerprints of Image-to-Image Generative Models: A Training-Paradigm-Driven Taxonomy of Six Commercial APIs

model-releasesarxiv-cs-cv

arXiv:2606.14787v2 Announce Type: replace Abstract: We study six production image-to-image AI systems (gpt-image-1, Gemini 2.5 Flash Image, Flux Kontext, SDXL img2img, SD3 img2img, and Qwen Image Edit

When Speech Meets Lips: Interpretable Audio-Visual Synchronization for L2 Pronunciation Assessment

arXiv:2609.06788v1 Announce Type: new Abstract: Automatic Pronunciation Assessment (APA) systems have achieved strong performance with transformer-based models and self-supervised speech representatio

On Epistemic Diversity in Large Language Models

arXiv:2609.04835v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In su

safetyarxiv-cs-cl

7 Sept 2026

Training-Free Speech-Centric Omni Understanding with Frozen VLMs

arXiv:2609.04242v1 Announce Type: cross Abstract: Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and their temporal relationships.

7 Sept 2026

A Low-Cost, Open Platform for End-to-End Autonomous Driving on a Miniature Ackermann Vehicle

arXiv:2609.04147v1 Announce Type: cross Abstract: This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platfo

Epistemic Warrant for LLM Recommendations: Characterizing the Basis for Reliance When Ground Truth Is Unavailable

arXiv:2609.04127v1 Announce Type: new Abstract: Large language models are increasingly used to support organizational decisions, yet users often lack a principled basis for assessing whether to rely o

Lngram v2: Latent N-Gram Memory with Interpretable Discrete Representations

model-releasesarxiv-cs-cl

arXiv:2609.03426v1 Announce Type: new Abstract: Transformers lack a native lookup mechanism, requiring repeated dense computation to recognize and reuse local static patterns. Lngram v1 introduces tok

Representational alignment yields generalizable safety in language models

arXiv:2609.04022v1 Announce Type: cross Abstract: Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly optimize observable responses, yet mode

Residual Optimal Transport-Based Experts Collaboration Towards Modality-Aware Infrared-Visible Object Detection

model-releasesarxiv-cs-cv

arXiv:2609.03516v1 Announce Type: new Abstract: Infrared-visible object detection (IVOD) integrates complementary evidence from visible and infrared sensors for reliable perception in challenging scen

The Natural Language Interaction Protocol and Standard for AI Agents

arXiv:2609.04135v1 Announce Type: new Abstract: AI agents are increasingly being developed and deployed across organizations using heterogeneous agent-development frameworks, AI models, tool interface

Tree-Structured Vector Quantization For Efficient And Progressive Image Compression

arXiv:2609.03641v1 Announce Type: new Abstract: Vector-quantization based image compression has achieved strong rate--distortion performance, yet most of them still produce a separate compressed repre

v0.4.0

model-releasesllama-cpp-releases

Overview llama.cpp 0.4.0 adds initial Qwen3.8-Flash-Next and Nemotron-3-Puzzle support, on-demand tensor reading, per-slot server context limits, video input options, and a ggml update to 0.23.0 with

WIDE: Wildcard Inference with Dynamic Expansion for Cross-Modal Generative Retrieval

arXiv:2609.03554v1 Announce Type: cross Abstract: Generative retrieval has demonstrated significant success by unifying representation learning and search into a single sequence-to-sequence generation

A Power Law in Logarithm's Clothing: On the Scalability of Graph-Based Vector Search

arXiv:2609.02143v1 Announce Type: cross Abstract: Most vector databases rely on graph-based indexes, notably HNSW and Vamana, for approximate nearest neighbor search. With embedding models widely adop

researcharxiv-cs-ai

AdaMem: Learning What to Remember with Adaptive Memory Policies for Personalized Agents

arXiv:2606.21144v2 Announce Type: replace-cross Abstract: Long-term memory systems allow LLM agents to preserve information beyond a single context window, but most systems focus on storing and retrie

HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs

arXiv:2609.02056v1 Announce Type: new Abstract: Scientific knowledge graphs organize entities and relations extracted from scientific literature, but they remain inherently incomplete. Missing typed l

agentsarxiv-cs-cl

Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis

arXiv:2609.02805v1 Announce Type: new Abstract: Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G network

SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology

arXiv:2609.02292v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) and the growing diversity of their applications presents a unique optimization opportunity: sele

UTP-Bench: Uncertainty-aware Travel Planning Benchmark

arXiv:2609.02421v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated strong capabilities in automated travel itinerary generation. However, real- world travel planni

Behaviorally Effective LoRA Writes Are Sparse and Structured

model-releasesarxiv-cs-cl

arXiv:2609.01374v1 Announce Type: new Abstract: Low-rank adaptation fixes the rank of the update, but it does not identify which parts of a trained write actually carry behavior. We study that questio

Beyond Scores: Understanding LLM-as-a-Judge Mechanisms in Summarization Evaluation

model-releasesarxiv-cs-cl

arXiv:2609.01604v1 Announce Type: new Abstract: LLM-based evaluators of natural language generation (NLG) quality are widely deployed as scoring tools and as automated training signals, yet the intern

Calibration is the Bottleneck: An Action-Class Diagnostic of Multi-Turn Tool-Calling

arXiv:2609.00949v1 Announce Type: cross Abstract: Multi-turn tool calling is a core evaluation scenario for large language model (LLM) agents. On public tool-calling benchmarks, open-weight models now

Capability-Gated Language Models: Security Composes, Utility Does Not

arXiv:2609.00445v1 Announce Type: cross Abstract: Deployed language model safeguards (safety fine-tuning, filtering, unlearning) vary by principal only outside the model weights: filters are reconfigu

Cheap Verifiers, Large Blind Spots: Measuring the Reliability Cost of Cost-Saving Cascades

arXiv:2609.01345v1 Announce Type: new Abstract: Inference cascades cut cost by answering most queries with a cheap model and escalating a hard tail to a frontier model that acts as verifier. A natural

researcharxiv-cs-ai

Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs

arXiv:2609.00621v1 Announce Type: new Abstract: Prompt optimization can improve multi-agent LLM systems, but the prompts being optimized often serve two entangled roles: generating task-relevant conte

agentsarxiv-cs-ai

Detoxifying Toxic Communication: A Design Science Approach to Responsible AI

arXiv:2609.00361v1 Announce Type: cross Abstract: Toxic language in digital workplaces such as pejoratives, sarcasm, condescension, and subtle incivility can erode trust, morale, and collaboration. Ex

safetyarxiv-cs-cl

Feedback-Assisted Trust Propagation over Document Relation Graphs for Retrieval-Augmented Generation

arXiv:2609.00543v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems rely on external corpora that may contain outdated, contradictory, noisy, or unreliable documents, introduc

researcharxiv-cs-ai

Flawed in Nature, Perfect through Evolution

arXiv:2609.00129v1 Announce Type: cross Abstract: The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a near

agentsarxiv-cs-ai