Search: “retrieval”

arXiv:2508.06869v4 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) demonstrate exceptional performance in vision-language tasks, yet their processing of long videos is

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Auto-generated index of all models mentioned across the wiki.

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arXiv:2604.08504v1 Announce Type: cross Abstract: We initiate the study of language generation in the limit, a model recently introduced by Kleinberg and Mullainathan [KM24], under the constraint of d

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arXiv:2604.07490v1 Announce Type: new Abstract: Representation learning for geospatial and spatio-temporal data plays a critical role in enabling general-purpose geospatial intelligence. Recent geospa

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arXiv:2604.05268v2 Announce Type: replace-cross Abstract: Multi-modal retrieval-augmented generation (MM-RAG) relies heavily on re-rankers to surface the most relevant evidence for image-question quer

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arXiv:2604.06197v1 Announce Type: cross Abstract: Type 2 diabetes case reports describe complex clinical courses, but their timelines are often expressed in language that is difficult to reuse in long

arXiv:2604.06177v1 Announce Type: cross Abstract: Specialized web tasks in finance, biomedicine, and pharmaceuticals remain challenging due to missing domain priors: queries drift, evidence is noisy,

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In production systems today, most AI agents operate as lightweight dispatchers. They parse a prompt, make an API call, retrieve a record, and hand back control. Their execution is fast, stateless and

arXiv:2610.22191v1 Announce Type: cross Abstract: In Hebbian learning from examples a Hopfield network of N neurons is not supplied with the K archetypes it has to retrieve but with M blurred examples

arXiv:2610.21424v1 Announce Type: new Abstract: Scientific agents must connect research problems, methods, and findings dispersed through narrative articles. We introduce SPRA (Structured Paper Repres

arXiv:2610.21327v1 Announce Type: new Abstract: Multimodal long-document question answering requires models to seek sparse evidence across pages and modalities, understand heterogeneous document conte

arXiv:2610.21937v1 Announce Type: new Abstract: Vision--language pre-training has enabled substantial progress in image--text retrieval and zero-shot recognition by learning transferable cross-modal r

arXiv:2610.21165v1 Announce Type: new Abstract: Long-context capability has become essential for large language models (LLMs) to handle complex tasks, yet continuously expanding the context window doe

arXiv:2610.21167v1 Announce Type: new Abstract: Deep research agents are increasingly deployed to retrieve precise information from large corpora, yet multi-hop, long-horizon tasks remain challenging.

arXiv:2609.33465v3 Announce Type: replace Abstract: Improving language model efficiency requires not only cheaper operations but also eliminating redundant computation. We argue that effective modelin

arXiv:2610.21300v1 Announce Type: new Abstract: To show that a medical device complies with regulation, experts judge whether documents and records meet each requirement and record the judgment for ot

arXiv:2610.22070v1 Announce Type: cross Abstract: Streaming vision-language models (VLMs) must continuously integrate information from the past while remaining responsive to current observations and u

arXiv:2610.21512v1 Announce Type: new Abstract: DeepSearch agents achieve strong performance on complex information-seeking tasks through iterative search and reasoning. However, they often issue inte

arXiv:2610.20964v1 Announce Type: cross Abstract: Existing long-document QA benchmarks rarely combine large-scale human-authored questions with detailed visual evidence annotations, limiting diagnosis

arXiv:2610.14877v1 Announce Type: new Abstract: Experience-based learning enables large language model (LLM) agents to improve over time by reusing knowledge from past executions. However, existing me

arXiv:2610.19754v1 Announce Type: new Abstract: Biomedical multi-hop reasoning over knowledge graphs requires identifying a semantically valid relation sequence that supports the answer. Existing meth

arXiv:2610.19811v1 Announce Type: cross Abstract: LLM workflows and agents rely on a set of binary decisions like allow/block an input, or discard/retain a retrieved document. When human deferral is u

arXiv:2606.17029v2 Announce Type: replace Abstract: Deep research agents synthesize long-form reports by searching and reasoning over retrieved evidence. Reinforcement learning with rubric-based rewar

arXiv:2610.19373v1 Announce Type: new Abstract: Does a language model answer better because its compressed memory contains useful document information? Replacing memory vectors with zeros can lower a

arXiv:2610.19481v1 Announce Type: new Abstract: Wrist-worn inertial measurement units (IMUs) provide a common sensing modality for tasks ranging from activity and gesture recognition to pose estimatio

arXiv:2610.18616v1 Announce Type: cross Abstract: Searching text-rich knowledge graphs requires following relations while examining textual evidence. Predefined graph interfaces constrain what evidenc

arXiv:2610.19257v1 Announce Type: new Abstract: Memory for long-horizon video generation must satisfy two competing requirements: it must remain bounded so generation can continue indefinitely, while

arXiv:2607.28103v2 Announce Type: replace Abstract: Memory-augmented LLM-based agents are vulnerable to memory injection attacks: Agents may retrieve poisoned memory from attackers, which diverts thei

arXiv:2610.16141v1 Announce Type: new Abstract: LLM agents increasingly operate over multiple entities, where reliable response generation requires not only retrieving relevant evidence but also prese

arXiv:2610.19131v1 Announce Type: cross Abstract: Maintenance work orders are the largest written record of how industrial equipment fails, yet in most plants they are close to unusable for analysis.

arXiv:2610.14570v1 Announce Type: new Abstract: Large language models (LLMs) support social simulation, but faithfully reproducing real users requires grounding both structured platform decisions and

arXiv:2512.23044v3 Announce Type: replace Abstract: Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypas

arXiv:2610.15492v1 Announce Type: new Abstract: Skill internalization aims to convert externally specified procedural skills into persistent model capabilities, allowing language-model agents to execu

arXiv:2610.18656v1 Announce Type: new Abstract: Language model systems repeatedly make test-time decisions by comparing similarity: they retrieve exemplars, rank candidates, and revise responses. Yet

arXiv:2610.14226v1 Announce Type: new Abstract: Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation. M