Search: “context”

arXiv:2604.12015v1 Announce Type: cross Abstract: In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heur

arXiv:2604.10027v1 Announce Type: new Abstract: Large language models (LLMs) suffer from hallucination and context forgetting. Prior studies suggest that attention drift is a primary cause of these pr

arXiv:2604.10848v1 Announce Type: new Abstract: Sequence modelling requires determining which past tokens are causally relevant from the context and their importance: a process inherent to the attenti

arXiv:2604.08597v1 Announce Type: cross Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowle

This Reddit thread discusses how to configure Ollama for use with Claude Code locally, covering two key setup steps. By default, Ollama uses a context window of only 4,096 tokens — insufficient for Cl

arXiv:2604.05429v2 Announce Type: replace-cross Abstract: Addressing the critical need for intelligent, context-aware energy management in renewable systems, we introduce the OpenCEM Simulator and Dat

arXiv:2604.06356v1 Announce Type: cross Abstract: In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain. Here, we inv

arXiv:2508.10068v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has advanced repository-level code completion by retrieving external code snippets, API documentation, or

arXiv:2508.04903v4 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However

arXiv:2605.06190v2 Announce Type: replace Abstract: We study constrained contextual bandits with adversarial contexts, where each action yields a random reward and incurs a random cost. The conditiona

arXiv:2610.15715v1 Announce Type: cross Abstract: Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support on

arXiv:2610.18769v1 Announce Type: new Abstract: Predicting spatial gene expression from standard H&E images could extend molecular analysis to tissues without spatial transcriptomic measurements. A st

arXiv:2610.16977v1 Announce Type: new Abstract: Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends c

Agentic artificial intelligence startup Uvi said today it has raised 7.5 million in seed funding to tackle an expensive problem around business context that’s draining millions of dollars annually fro

arXiv:2610.13690v1 Announce Type: new Abstract: Diffusion models typically learn the generative dynamics of a single target distribution or a family of distributions indexed by conditioning variables

I was running Qwen3.8-Flash-Next (base and later Swift-1.5 NVFP4) on a paid pod seat for about a month, and it started answering with short strings that echoed its own earlier reasoning. I assumed the

So, while doing a search to read the model card again, and because I didn't memorize the huggingface URL, I saw an AI 'answer' at the top of the search, stating that while it natively supports a ~244k

arXiv:2610.11907v1 Announce Type: cross Abstract: Hallucination remains a significant challenge in Large Vision-Language Models (LVLMs). Existing training-free methods generally mitigate hallucination

arXiv:2610.11132v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general

arXiv:2610.09124v1 Announce Type: new Abstract: Induction heads provide a mechanistic account of in-context learning in sequential data, but existing theory largely assumes that the context relevant t

arXiv:2610.07250v1 Announce Type: new Abstract: Wrapping an image generation model in an agentic harness can effectively boost Text-to-Image task performance: the harness can leverage memory, skills,

arXiv:2610.05195v1 Announce Type: new Abstract: The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single ta

arXiv:2604.05114v2 Announce Type: replace-cross Abstract: We study a QA curation pipeline for improving long-context complex reasoning in large language models (LLMs). Our approach, pi^2, constructs h

arXiv:2610.05852v1 Announce Type: new Abstract: Frozen representations are widely reused for downstream classification, yet each new task typically requires fitting a new predictor. We ask whether the

arXiv:2610.06156v1 Announce Type: new Abstract: A representation family is a distinct way of extracting features from time series. Ensemble algorithms that combine several representation families rema

arXiv:2610.03491v1 Announce Type: new Abstract: Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does

arXiv:2610.00562v1 Announce Type: cross Abstract: Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient

Hi, i got QFN running on my single r9700 but im not sure if i did everything right to get the best quality and speed out of this setup. Dont want to annoy anybody, maybe someone with the same card can

arXiv:2609.38530v1 Announce Type: new Abstract: Language models increasingly use architectures that vary attention span and positional encoding across layers, such as applying RoPE with sliding-window

arXiv:2609.38866v1 Announce Type: new Abstract: As preferences, goals, and facts change, LLM agents must use the current state while earlier versions remain in context. Yet they can answer with an old

arXiv:2609.36337v1 Announce Type: new Abstract: Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large da

arXiv:2609.37659v1 Announce Type: new Abstract: There has been significant work on understanding the In-Context Learning capabilities of Large Language Models, especially on the induction circuit. For

arXiv:2609.37012v1 Announce Type: new Abstract: For a long-horizon agent, context is the bottleneck: the history is resent with every request, the window caps task length, and reasoning degrades as th

arXiv:2609.37743v1 Announce Type: new Abstract: LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even

arXiv:2609.37988v1 Announce Type: new Abstract: As the context size of text processed with an LLM grows, the size of KV caches can outstrip the memory allocated for the original model weights. This im

arXiv:2609.36684v1 Announce Type: new Abstract: Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the wa

arXiv:2609.36995v1 Announce Type: cross Abstract: Few-step streaming audio--video generation requires both causal modeling and step distillation, yet standard training recipes face two context-related

arXiv:2609.37782v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential as training-free text encoders for long-context embeddings. Existing approaches primarily impro

arXiv:2512.11352v2 Announce Type: replace Abstract: Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (G

arXiv:2609.34786v1 Announce Type: new Abstract: Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual

arXiv:2609.32201v1 Announce Type: cross Abstract: On-Policy Context Distillation (OPCD) has recently emerged as a powerful technique for transferring context to student models and for self-improvement

arXiv:2609.34060v1 Announce Type: new Abstract: Prompt-specialized multi-agent systems enable multiple agents to share a model while performing complementary roles to solve complex tasks. However, age

arXiv:2609.30337v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates knowledge obsolescence and factual hallucination in large language models by introducing external context

arXiv:2605.06221v2 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, they are becoming increasingly capable while simultaneously demanding ever-longer conte

Not asking for anyone’s secrets of the trade, I’m more curious how people are thinking about context now that newer models chew through huge amounts of it for reasoning. the TLDR: I’m starting to thin

arXiv:2609.28427v1 Announce Type: new Abstract: Personalizing exoskeleton assistance across operating conditions is constrained by the time and physical effort required to collect user feedback. We ex