Search: “retrieval”

First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations

arXiv:2512.01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety

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Forgetful Attention: A Trainable Support-Vector Memory with Certified Selection and Exact Unlearning

DGX agent

arXiv:2607.12204v1 Announce Type: new Abstract: Attention can be viewed as an online learner over context, yet existing test-time memories cannot certify that dropping a token leaves outputs unchanged

model-releasesarxiv-cs-lg

15 Jul 2026

From Words to Widgets for Controllable LLM Generation

arXiv:2604.10925v2 Announce Type: cross Abstract: Natural language remains the predominant way people interact with large language models (LLMs). However, users often struggle to precisely express and

researcharxiv-cs-cl

GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regions

arXiv:2607.12284v1 Announce Type: new Abstract: Image geolocation aims to infer the geographic origin of an image from visual content alone. However, this task remains challenging in regions where cou

researcharxiv-cs-cv

Good Benchmarks

arXiv:2607.12217v1 Announce Type: new Abstract: Good tasks are correct, solvable, verifiable, well-specified, and hard for interesting reasons. The best tasks describe a real problem an experienced pr

HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition

arXiv:2607.12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous p

safetyarxiv-cs-ai

Instance-Enriched Semantic Maps for Visual Language Navigation

arXiv:2607.12630v1 Announce Type: cross Abstract: Visual Language Navigation (VLN) aims to enable an embodied agent to navigate complex environments by following natural language instructions. Recent

agentsarxiv-cs-cv

Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes

arXiv:2607.12924v1 Announce Type: new Abstract: In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic acti

Learning When to Trust in Contextual Social Bandits

arXiv:2603.13356v2 Announce Type: replace Abstract: Robust reinforcement learning typically assumes that feedback sources are either globally trustworthy or corrupted within a fixed global budget. We

safetyarxiv-cs-ai

LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention

model-releasesarxiv-cs-lg

arXiv:2607.11976v1 Announce Type: new Abstract: Indexer-TopK, the operation to compute the scores and select the top-k candidates, is widely used by sparse attention kernels in large language models a

Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

model-releasesarxiv-cs-cl

arXiv:2510.18939v2 Announce Type: replace Abstract: Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling

Tutorials

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models

arXiv:2607.12782v1 Announce Type: new Abstract: Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm

tutorialsarxiv-cs-cv

MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning

model-releasesarxiv-cs-cv

arXiv:2607.12418v1 Announce Type: new Abstract: Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification

PRISM Edit: One Vector for All Temporal Answers

arXiv:2607.11327v2 Announce Type: replace-cross Abstract: Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locat

QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics

arXiv:2607.11019v2 Announce Type: replace Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineerin

RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories

arXiv:2512.04144v3 Announce Type: replace Abstract: Targeted interventions on language models, such as unlearning or model editing, aim to modify specific information, but their effects often propagat

Semidirect Fourier Delta Attention: Phase-Controlled Delta Memory with Constructive Chunk-WY Kernels

arXiv:2607.11897v1 Announce Type: new Abstract: Linear attention replaces softmax attention's growing KV cache with a fixed recurrent state, but this compression limits exact state tracking and long-c

researcharxiv-cs-lg

Speculate with Memory: Lossless Acceleration for LLM Agents

arXiv:2607.12236v1 Announce Type: cross Abstract: Speculative execution accelerates LLM agents by using a smaller, cheaper model to predict and pre-launch the next step while the environment is idle.

agentsarxiv-cs-cl

SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning

arXiv:2607.12042v1 Announce Type: new Abstract: Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing m

agentsarxiv-cs-cv

Local Ai

UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies

arXiv:2607.12892v1 Announce Type: cross Abstract: Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect

local-aiarxiv-cs-ai

Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation

arXiv:2607.12986v1 Announce Type: new Abstract: Plan evaluators can reward a strategic plan for becoming less explicit. This paper studies that failure in a staged expected-value scorer for LLM-genera

A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis

arXiv:2607.08038v1 Announce Type: new Abstract: Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task

Concept-as-Tree: A Controllable Synthetic Data Framework Makes Stronger Personalized VLMs

arXiv:2503.12999v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated exceptional performance in various multi-modal tasks. Recently, there has been an increasing i

Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text

arXiv:2607.08490v1 Announce Type: new Abstract: Biomedical language evolves rapidly as new discoveries emerge, causing traditional text models to lose semantic fidelity over time. Static embeddings an

From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure

arXiv:2607.08288v1 Announce Type: cross Abstract: In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk as

ICDAR 2026 HIPE-OCRepair Competition on LLM-Assisted OCR Post-Correction for Historical Documents

arXiv:2607.08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents. OCR post-correction re

Latent Memory Palace: Reasoning for Control as Autoregressive Variational Inference

arXiv:2607.08724v1 Announce Type: new Abstract: Human decision-making is highly flexible -- some actions are taken immediately; others require longer deliberation. Language models have exhibited a sim

safetyarxiv-cs-lg

LESV: Language Embedded Sparse Voxel Fusion for Open-Vocabulary 3D Scene Understanding

arXiv:2604.01388v2 Announce Type: replace Abstract: Recent advancements in open-vocabulary 3D scene understanding heavily rely on 3D Gaussian Splatting (3DGS) to register vision-language features into

safetyarxiv-cs-cv

Local Ai

Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents

arXiv:2607.08395v1 Announce Type: cross Abstract: Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat assi

local-aiarxiv-cs-cl

Tutorials

VectorizationLLM: Smart Vectorization Based AI Assistant

arXiv:2607.07846v1 Announce Type: new Abstract: VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectori

tutorialsarxiv-cs-ai

VEGAS: Human-Aligned Video Caption Evaluation via Gaze

arXiv:2607.08489v1 Announce Type: cross Abstract: Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose V

A Word-Level Digital Reader of the Prasthanatrayi with Sankara's Bhasya: Corpus, Method, and an Open, Offline Reading Aid for the Advaita Vedanta Canon

arXiv:2607.07282v1 Announce Type: new Abstract: The Prasthanatrayi -- the ten principal Upanisads, the Brahmasutra, and the Bhagavadgita, with Sankara's commentaries (bhasya) -- is the foundational co

researcharxiv-cs-cl

Co-LMLM: Continuous-Query Limited Memory Language Models

arXiv:2607.07707v1 Announce Type: cross Abstract: Limited memory language models (LMLMs) externalize factual knowledge during pretraining to a knowledge base (KB), rather than memorizing it in their w

End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent

arXiv:2607.06964v1 Announce Type: cross Abstract: Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) a

Evaluating RAG Metrics in Applied Contexts: An Experiment, Its Findings and Its Limitations

arXiv:2607.07302v1 Announce Type: new Abstract: This paper reports an empirical study evaluating the relevance of several RAG metrics. The experiment is based on a question-answering dataset created b

researcharxiv-cs-cl

Future Confidence Distillation in Large Language Models

arXiv:2607.07626v1 Announce Type: cross Abstract: Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as

LEMUR 2: Unlocking Neural Network Diversity for AI

arXiv:2607.06839v1 Announce Type: cross Abstract: Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain

researcharxiv-cs-cv

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

model-releasesarxiv-cs-cv

arXiv:2607.07673v1 Announce Type: new Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundat

Tutorials

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in

tutorialsarxiv-cs-cl

MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning

arXiv:2607.07391v1 Announce Type: new Abstract: Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools,

Overview of the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation

arXiv:2607.06618v1 Announce Type: cross Abstract: Following the CMIVQA, MMI-VQA, and M4IVQA challenges in NLPCC 2023--2025, we introduce the Difficulty-Aware Medical Instructional Video Question Answe

RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning

arXiv:2601.00086v3 Announce Type: replace Abstract: Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or

agentsarxiv-cs-cl

Local Ai

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP

arXiv:2607.07135v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) relies on softmax-based self-attention, a strictly positive distribution that assigns probability mass to

local-aiarxiv-cs-cv

Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity

arXiv:2607.07386v1 Announce Type: new Abstract: Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention mod

researcharxiv-cs-lg

TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2607.07388v1 Announce Type: new Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion

The Key to Going Linear: Analysis-Driven Transformer Linearization

model-releasesarxiv-cs-lg

arXiv:2607.07706v1 Announce Type: new Abstract: The quadratic cost of causal self-attention severely bottlenecks long-context transformer inference. While numerous post hoc linearization pipelines exi

Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?

model-releasesarxiv-cs-cl

arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queri

Zoom In Disparities in Healthcare LLM Q&A

arXiv:2510.17476v2 Announce Type: replace Abstract: Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, rais

safetyarxiv-cs-cl