Search: “context”

arXiv:2608.26184v1 Announce Type: new Abstract: AI programming tutors provide scalable support, yet lack the behavioral context human tutors rely on to adapt support to learners' needs. We present Tut

arXiv:2608.27128v1 Announce Type: new Abstract: Long-context inference is bottlenecked by the memory footprint of the key-value (KV) cache, especially for small models under tight resource budgets. Ex

arXiv:2608.25489v1 Announce Type: cross Abstract: Graph retrieval-augmented generation places retrieved subgraphs into the model's context window at query time, paying a recurring token cost and expos

arXiv:2608.25774v1 Announce Type: new Abstract: EXAONE Tabular is a compact tabular foundation model family for classification and regression via in-context learning, producing predictions without dat

arXiv:2608.23843v1 Announce Type: new Abstract: Long-context inference in large language models (LLMs) is increasingly limited by the memory required for the key-value (KV) cache. KV cache compression

arXiv:2608.24275v1 Announce Type: new Abstract: Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware saf

arXiv:2608.24302v1 Announce Type: new Abstract: Long-video understanding depends critically on how a limited model context is constructed from a much longer video. Existing approaches improve this pro

arXiv:2608.21386v1 Announce Type: cross Abstract: Given a task described by a few examples, how should a model be specialized to it? Four mechanisms are available -- zero-shot, in-context attention, t

arXiv:2608.22179v1 Announce Type: cross Abstract: Membership inference asks whether a text was used to train a language model, whereas AI-generated text detection asks whether it was generated by a la

arXiv:2608.20617v1 Announce Type: new Abstract: Multi-agent LLM systems split work across models, so answering often requires knowledge that sits in another agent's context: a Sharer has encoded infor

arXiv:2608.20548v1 Announce Type: new Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplored,

arXiv:2608.20342v1 Announce Type: new Abstract: Large language model (LLM) coding agents start each session with an empty context window, discarding accumulated knowledge from prior work. We present P

arXiv:2608.19212v1 Announce Type: new Abstract: Out-of-context (OOC) misinformation pairs authentic images with misleading captions to construct false narratives without image manipulation, making det

arXiv:2608.18659v1 Announce Type: cross Abstract: Tsetlin Machines (TMs) learn interpretable Boolean clauses using finite-state automata. We introduce the Quantum-Logic Tsetlin Machine (QL-TM), which

arXiv:2608.18260v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (

arXiv:2608.17151v1 Announce Type: new Abstract: Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context

arXiv:2608.14309v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can generate pathological descriptions from histological images, but gigapixel Whole Slide Images (WSIs) exceed

arXiv:2608.13420v1 Announce Type: new Abstract: Embodied intelligent virtual agents are expected to operate as persistent, adaptive, and context-aware entities within complex virtual and Metaverse wor

arXiv:2608.13040v1 Announce Type: cross Abstract: Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distill

arXiv:2608.10760v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has become the de-facto interface for connecting LLM agents to enterprise tools, and adoption has been explosive: wit

arXiv:2608.06849v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by quadratic attention computation and growing KV-cache costs. Existing sparse attention and KV-compression

arXiv:2603.14158v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are entering clinical workflows, yet evaluations rarely assess how clinician reasoning shapes model behavior duri

arXiv:2608.04569v1 Announce Type: new Abstract: Hard prompt compression reduces long-context inference cost by independently scoring tokens, sentences, or chunks and retaining the highest-scoring unit

arXiv:2608.03769v1 Announce Type: cross Abstract: Masked diffusion language models (MDLMs) enable parallel generation and bidirectional context modeling, but their positional context differs fundament

arXiv:2608.03839v1 Announce Type: new Abstract: Training-free speculative decoding drafts by matching an exact suffix of the context against a pool of earlier context. That lookup misses correct draft

arXiv:2608.03137v1 Announce Type: new Abstract: Large language model (LLM) agents must retain reusable information, control a bounded active context, and recover earlier evidence during long-horizon i

arXiv:2607.29484v1 Announce Type: new Abstract: Interventional data is widely regarded as the gold standard for teaching models causal reasoning. We test this assumption in a fully controlled syntheti

arXiv:2607.27269v1 Announce Type: new Abstract: Multi-head latent attention (MLA) is increasingly important for long-context LLM inference because compact latent states replace the growing key-value (

arXiv:2607.28611v1 Announce Type: new Abstract: Visual generation increasingly requires high-resolution images, long videos, and multimodal context, making the quadratic cost of full attention prohibi

arXiv:2607.26726v1 Announce Type: new Abstract: Emotion Recognition in Conversation (ERC) aims to predict utterance-level emotions in dialogues and has largely advanced through context-centric modelin

arXiv:2607.24260v1 Announce Type: new Abstract: Modern LLM systems increasingly rely on knowledge-selection processes that produce high-value structured priors, such as ranked evidence, graph topology

arXiv:1805.11546v3 Announce Type: replace-cross Abstract: We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architectur

arXiv:2607.22610v1 Announce Type: new Abstract: When a language model produces a response in a multi-turn conversation, which tokens from prior turns shaped that answer, and how did those dependencies

arXiv:2508.13899v2 Announce Type: replace Abstract: Accurate ultrasound image segmentation is fundamentally challenged by target-context entanglement, where lesion cues are easily mixed with surroundi

arXiv:2607.21922v1 Announce Type: new Abstract: Clinical irregular multivariate time series are shaped not only by physiological dynamics but also by the measurement process that determines when and w

arXiv:2607.12215v1 Announce Type: new Abstract: Accurately assessing personality from text is challenging because traits are latent, context-dependent, and often subtly expressed across long narrative

arXiv:2607.12334v1 Announce Type: new Abstract: Retrieval-augmented question answering depends on selecting evidence passages that jointly support answer generation. However, many RAG pipelines rely o

arXiv:2607.12216v1 Announce Type: cross Abstract: Multi-agent and memory-augmented LLM systems often place coordination content, shared state, prior discussion, tool outputs, summaries, and role instr

arXiv:2607.06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX. TimesX contains a wide selection of high-quality real-world t

arXiv:2607.05708v1 Announce Type: new Abstract: Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows. Replaying

arXiv:2607.02402v2 Announce Type: replace Abstract: Vision-language models (VLMs) can follow complex textual instructions, yet they struggle to reason from purely visual context. In particular, curren

arXiv:2607.02805v1 Announce Type: cross Abstract: High-throughput long-context generation is one of the central challenges for large language models. Generation is typically memory-bandwidth-bound rat

arXiv:2607.01523v1 Announce Type: cross Abstract: Recurrent memory agents extend LLMs to arbitrarily long contexts by iteratively consolidating input into a fixed-size memory window. Despite their sca

arXiv:2607.00368v1 Announce Type: new Abstract: Large language model test-time training (TTT) is often evaluated through local proxy metrics: models are updated on recent tokens, retrieved context, ta

arXiv:2606.31844v1 Announce Type: cross Abstract: A local-to-global context mismatch arises when autoregressive traffic simulators trained on ego-centric driving logs are deployed in globally observab

arXiv:2606.29328v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding. This assumption breaks dow