Search: “retrieval”

ICI-VLA: In-Context Imitation with Spatiotemporally Aligned Demonstrations for Vision-Language-Action Models

arXiv:2609.07581v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies are commonly adapted to new manipulation settings through additional gradient updates, which limits rapid deployme

safetyarxiv-cs-ro

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Research

MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models

DGX agent

arXiv:2609.08663v1 Announce Type: new Abstract: Universal multimodal embedding (UME) increasingly demands encoder's capacity for handling a broad range of tasks and modalities with increased complexit

researcharxiv-cs-lg

9 Sept 2026

On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing

model-releasesarxiv-cs-lg

arXiv:2609.07681v1 Announce Type: new Abstract: Associative Recall (AR) is the cognitive ability to learn and retrieve links between items in memory. In NLP, AR is used as a benchmark for evaluating t

raggy - CLI tool for RAG over your local documents built with Ollama

https://github.com/paulknysh/raggy A lightweight CLI tool for Retrieval-Augmented Generation (RAG) over local documents built with Ollama. Hybrid database (vector + BM25 index) and embedding generatio

local-air-ollama

Streaming Hierarchical Inference with Tabular Foundation Models

arXiv:2609.07956v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) have recently demonstrated strong predictive performance through in-context learning, but their deployment in high-thro

safetyarxiv-cs-lg

Towards Vision-Language Geo-Foundation Model: A Survey

arXiv:2406.09385v3 Announce Type: replace Abstract: Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks, such as image captioning, image-text retrieval,

researcharxiv-cs-cv

When Does a Laugh Begin? Structured Annotator Disagreement in Temporal Laughter Localization

model-releasesarxiv-cs-cv

arXiv:2609.06646v1 Announce Type: new Abstract: Annotators routinely disagree on laughter boundaries and subtle chuckles, yet temporal laughter localization typically evaluates against a single refere

AdaptVPR: Route-Aware Hard Positive Generation for Robust Visual Place Recognition

arXiv:2609.04369v1 Announce Type: new Abstract: Visual Place Recognition (VPR) localizes a query image by retrieving database images of the same or nearby place, yet its robustness is often degraded b

local-aiarxiv-cs-cv

Brilliant paper from LinkedIn. (bookmark it) I have been saying that memory is one of the most challenging things to get right when building…

Brilliant paper from LinkedIn. (bookmark it) I have been saying that memory is one of the most challenging things to get right when building agents. It's an are you want to invest time in optimizing.

agentsdair-ai--x

Building a research-software catalog with a coding agent: from hackathon prototype to public deployment

arXiv:2609.04711v1 Announce Type: cross Abstract: Generative AI and coding agents can accelerate research software development, but they also increase the need for efficient software discovery and mai

local-aiarxiv-cs-ai

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

arXiv:2609.05339v1 Announce Type: new Abstract: Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forget: a new model may interpret old notes dif

Evidence Integration in Large Language Models

arXiv:2609.04290v1 Announce Type: cross Abstract: Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how

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Tools

llm 0.35

**Title:** llm 0.35 **Summary:** Version 0.35 of the `llm` tool offers a command‑line interface for interacting with large language models. It allows users to issue prompts and retrieve model response

toolssimon-willison

MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution

arXiv:2603.18718v2 Announce Type: replace Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrie

local-aiarxiv-cs-ai

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

Quality Recovery for Quantized KV Caches via Low-Rank Attention Adaptation

arXiv:2609.04263v1 Announce Type: cross Abstract: Low-bit key--value (KV) caches reduce the memory required for autoregressive decoding, but the resulting quality loss depends on the model and quantiz

TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio

arXiv:2609.04452v1 Announce Type: new Abstract: Modern misinformation is often heard before it is read, yet fact-checking systems are still evaluated mainly on clean written claims. Spoken dialogue re

WeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and Editing

model-releasesarxiv-cs-cv

arXiv:2609.05171v1 Announce Type: new Abstract: Image generation and editing models have advanced rapidly, yet remain unreliable when prompts require external world knowledge. Bounded and long-tail pa

Whose record is this? Diagnosing and authorizing record use in personalized multimodal models

arXiv:2609.04801v1 Announce Type: new Abstract: Contextualized visual personalization can retrieve a true record yet apply it to the wrong visual subject. We formalize when a record may condition an a

Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL

arXiv:2609.02944v1 Announce Type: cross Abstract: Democratizing data access through natural language is a crucial goal for modern enterprises, but the practical adoption of Text-to-SQL is critically h

4 Sept 2026

SHELF: A Synthetic Harness for Multi-Task Bibliographic Benchmarking

arXiv:2609.03047v1 Announce Type: cross Abstract: Libraries and archives manage large collections with limited staff and computing budgets, yet common benchmarks do not systematically test their bibli

4 Sept 2026

We found ~18k posts from autonomous AI agents (self-identifying as from OpenAI) using the public internet to communicate during a web-retrie…

We found ~18k posts from autonomous AI agents (self-identifying as from OpenAI) using the public internet to communicate during a web-retrieval task. These AIs colluded to bypass sandbox restrictions

agentssimon-willison--x

4 Sept 2026

Applications

Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy Pattern

arXiv:2609.01834v1 Announce Type: new Abstract: As enterprise platforms transition to conversational reasoning interfaces, the stateless nature of LLM APIs creates an architectural gap. While stateles

applicationsarxiv-cs-ai

Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents

arXiv:2601.07468v2 Announce Type: replace Abstract: Memory enables Large Language Model (LLM) agents to perceive, store, and use information from past dialogues, which is essential for personalization

researcharxiv-cs-ai

Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA

arXiv:2609.01687v1 Announce Type: new Abstract: Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult be

researcharxiv-cs-cl

Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis

arXiv:2609.02473v1 Announce Type: new Abstract: Ontology rankers remain useful for rare-disease diagnosis because each candidate can be traced to matched patient phenotypes. Large language models (LLM

ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

arXiv:2609.02780v1 Announce Type: cross Abstract: Streaming video understanding is a critical capability for real-world applications, including embodied intelligence, autonomous driving, industrial mo

agentsarxiv-cs-cl

text2ql: Multi-Target Natural Language Querying via a Language-Agnostic Intermediate Representation

arXiv:2609.02115v1 Announce Type: cross Abstract: Natural language interfaces to databases have traditionally suffered from three structural limitations: exclusive targeting of relational SQL, uncondi

researcharxiv-cs-ai

A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data

arXiv:2609.01514v1 Announce Type: cross Abstract: Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of chea

researcharxiv-cs-cv

APEX-EM: Non-Parametric Online Learning for Autonomous Agents via Structured Procedural-Episodic Experience Replay

arXiv:2603.29093v3 Announce Type: replace-cross Abstract: LLM agents rerun full reasoning for every task, even one they solved moments earlier. We introduce extbf{APEX-EM}, a non-parametric experience

agentsarxiv-cs-ai

Beyond Static Summarization: Proactive Memory Extraction for LLM Agents

arXiv:2601.04463v2 Announce Type: replace-cross Abstract: Memory management is vital for LLM agents in long-term and personalized interactions. Most previous work studies how to retrieve and use memor

agentsarxiv-cs-ai

Compile, Don't Memorize: A Context Compilation Architecture (CCA) for In-Context Learning

arXiv:2609.00759v1 Announce Type: new Abstract: Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output sc

agentsarxiv-cs-cl

Applications

Degradation-based augmented training for robust individual animal re-identification

arXiv:2603.04163v2 Announce Type: replace Abstract: Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based o

applicationsarxiv-cs-cv

ExpArt-KG: Artwork Image Description Generation through Iterative Exploration of Knowledge Graphs

arXiv:2609.00629v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on image-grounded text generation and visual question answering. However, it remains dif

researcharxiv-cs-cl

Tutorials

Modernizing and scaling support operations with generative AI on AWS

Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and u

tutorialsaws-ml-blog

On the Design Fundamentals of Pixel Text Representation Learning

arXiv:2609.01147v1 Announce Type: cross Abstract: Text-rich visual inputs require models that can read, retrieve, and compress language directly in pixel space, yet existing pixel-text encoders strugg

safetyarxiv-cs-cl

Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge

arXiv:2602.17826v2 Announce Type: replace Abstract: Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in

Safin-1: Safety from Within through Memory-Native State Evolution

arXiv:2609.00092v1 Announce Type: new Abstract: Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety s

safetyarxiv-cs-lg

Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search

arXiv:2609.00618v1 Announce Type: cross Abstract: We investigate the role of conversational context modeling in user preference tracking for Conversational Recommendation Systems (CRSs). In this regar

ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits

model-releasesarxiv-cs-cv

arXiv:2609.00505v1 Announce Type: new Abstract: Video-text models adapted from image-text architectures (e.g., CLIP) frequently exhibit temporal blindness, the inability to perceive fundamental cues l

Tutorials

(V)LMs generalize beyond surface co-occurrence: Evidence from cross-modal number agreement

arXiv:2609.00443v1 Announce Type: cross Abstract: Language models learn about grammatical number primarily from co-occurrence, and show frequency effects as a result---sometimes taken to indicate that

tutorialsarxiv-cs-ai

Applications

Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI

arXiv:2607.17883v2 Announce Type: replace-cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output tha

applicationsarxiv-cs-ai

ASTRA - Agentic System for Ticket Resolution and Analysis

arXiv:2608.28790v1 Announce Type: cross Abstract: Technical operations teams resolve large volumes of incidents by synthesizing fragmented evidence from ticket text, historical cases, system logs, and

agentsarxiv-cs-ai

CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy

arXiv:2608.30674v1 Announce Type: cross Abstract: Accurate molecular property prediction requires both statistical reliability and chemical reasoning. Graph neural networks can be calibrated directly

local-aiarxiv-cs-ai

Dense Clinical Contrasts Enhance Medical Knowledge Updating in Large Language Models

arXiv:2608.30405v1 Announce Type: new Abstract: Medical knowledge changes continually, making large language models vulnerable to relying on outdated yet clinically plausible information. We study whe

Do Small Models Use the Law You Give Them? Measuring Context Use on a Bilingual Bangladesh Legal Benchmark

arXiv:2608.30327v1 Announce Type: new Abstract: Fine-tuning can improve legal question-answering accuracy without improving how models use law supplied in context. We study this distinction in bilingu

EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge

arXiv:2606.13120v2 Announce Type: replace Abstract: Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing ben

FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents

arXiv:2510.03204v2 Announce Type: replace Abstract: Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens

researcharxiv-cs-cl