Search: “context”

arXiv:2605.01920v1 Announce Type: cross Abstract: Large language models are increasingly used within larger systems ('LLM agents'). These make a sequence of LLM calls, each call providing the LLM with

arXiv:2602.04212v2 Announce Type: replace Abstract: Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier

arXiv:2604.27089v1 Announce Type: new Abstract: Large-language-models (LLMs) demonstrate enormous utility in long-context tasks which require processing prompts that consist of tens to hundreds of tho

arXiv:2604.27445v1 Announce Type: new Abstract: Many agents in real-world environments cannot reliably communicate their goals through language, including household pets, pre-verbal infants, and other

arXiv:2604.22335v1 Announce Type: new Abstract: Large language models (LLMs) often produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithf

feels like we have all the primitives for building powerful autonomous agent model task management tool use/auth memory & context management self improvement UI/UX what’s missing? verifiers (for self

arXiv:2601.11913v2 Announce Type: replace-cross Abstract: Effectively processing long contexts remains a fundamental yet unsolved challenge for large language models (LLMs). Existing single-LLM-based

arXiv:2601.21092v2 Announce Type: replace Abstract: Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying thei

arXiv:2505.21471v2 Announce Type: replace Abstract: With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large q

arXiv:2509.21623v2 Announce Type: replace-cross Abstract: The expanding long-context capabilities of large language models are constrained by a significant memory bottleneck: the key-value (KV) cache

arXiv:2604.13348v1 Announce Type: new Abstract: We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI

arXiv:2604.13066v1 Announce Type: new Abstract: In-context learning has established itself as an important learning paradigm for Large Language Models (LLMs). In this paper, we demonstrate that LLMs c

arXiv:2604.10344v1 Announce Type: new Abstract: The classical approach to detecting depression from vision emphasizes interpretable features, such as facial expression, and classifiers such as the Sup

A community-shared open source CLI agent project posted to r/ollama, designed specifically for use with 8K token context windows in local Ollama-based LLM setups. Version 0.3 focuses on improving Olla

This r/MachineLearning discussion thread centers on a hands-on workshop covering context engineering — the process of designing and optimizing the information an AI agent sends to and receives from a

arXiv:2511.22599v2 Announce Type: replace-cross Abstract: Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless na

Agentic workflows are already used for initiating action. To be successful, agents typically need to combine multiple steps and execute business logic reflective of real-life decisions. But, as develo

arXiv:2610.15640v1 Announce Type: new Abstract: Small language models (SLMs) used as on-device agents must balance accuracy and latency as their context grows. Mixed precision helps manage this tradeo

arXiv:2610.17867v1 Announce Type: new Abstract: Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. Howev

deepagents just hit 30k stars on github! deepagents is great for building agents that do complex work. they manage context well and use a durable runtime. though inspired by coding agent design, deepa

arXiv:2609.32119v2 Announce Type: replace Abstract: Language models (LMs) are often used as a tool to model human language processing. Recent studies suggest that severely restricting LMs' context win

arXiv:2610.09772v1 Announce Type: new Abstract: Small language-model agents on edge devices must hold a persona and reason correctly at once, inside one context window that fills with conversational h

arXiv:2610.08118v1 Announce Type: new Abstract: Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a differe

arXiv:2610.07074v1 Announce Type: cross Abstract: Estimating a decision threshold requires locating observations near an unknown boundary. We study how gradient-based pretraining learns this statistic

arXiv:2609.36417v1 Announce Type: new Abstract: Relational in-context learning (ICL) uses labeled support examples and their linked relational context to predict labels for new queries. This creates a

arXiv:2609.35333v1 Announce Type: new Abstract: Algorithm Distillation (AD) has demonstrated the remarkable ability of Transformers to perform in-context reinforcement learning without explicit weight

arXiv:2609.31401v1 Announce Type: new Abstract: Prior work established that a probe can decode an in-context binding on model errors and that probe-guided steering can repair some of them. We follow p

arXiv:2609.31458v1 Announce Type: cross Abstract: Transformers have become a central architecture for in-context learning (ICL), particularly through their state-of-the-art performance in large langua

arXiv:2609.27682v1 Announce Type: new Abstract: In-context learning (ICL) enables large vision-language models (LVLMs) to perform tasks by following patterns from in-context examples, yet its potentia

**Genie One MCP: Give any AI Agent the Right Business Context** The guide introduces Genie One MCP as a framework for embedding relevant business context in artificial intelligence agents, enabling th

arXiv:2609.23121v1 Announce Type: new Abstract: Multimodal Agentic Retrieval (MAR) requires agents to solve complex information-seeking tasks by iteratively invoking external tools. Typical frameworks

Hi. I saw some feedback that halogen was degrading at context depth. So I fixed that. Served through the image, same machine, same session, same prompts, 0.11.10 vs 0.12.0: decode at 1,004,581 tokens

arXiv:2609.19242v1 Announce Type: new Abstract: Block diffusion language models (BDLMs) combine autoregressive dependencies across blocks with parallel denoising within blocks, but long-context traini

arXiv:2609.18591v1 Announce Type: new Abstract: In-context learning (ICL) enables large language model (LLM) agents to improve decisions using interaction history, yet it remains unclear whether such

arXiv:2609.17376v1 Announce Type: cross Abstract: Large language models (LLMs) trained on next-token prediction exhibit remarkable in-context learning (ICL) abilities, yet the representations that sup

arXiv:2609.14011v1 Announce Type: new Abstract: Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them

arXiv:2511.03728v2 Announce Type: replace Abstract: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memor

arXiv:2609.13315v1 Announce Type: cross Abstract: We investigate whether a Chern-Simons (CS) context reservoir is a viable computational substrate and whether evolving its gauge connection provides a

arXiv:2502.15007v2 Announce Type: replace-cross Abstract: We introduce methods to quantify how Large Language Models (LLMs) encode and store contextual information, revealing that tokens often seen as

arXiv:2609.10613v1 Announce Type: cross Abstract: In-context learning (ICL) jailbreaks reveal a critical vulnerability in multimodal large language models (MLLMs): harmful demonstrations in the prompt

We’ve been building PipesHub for a while now, and I’d love to get more teams to try it and tell us where it breaks. The problem we kept running into was pretty simple: Building an AI app over company

arXiv:2608.31170v1 Announce Type: new Abstract: While WhisperX accelerates speech transcription via intra-audio batching, it isolates audio segments, losing the historical context needed for coherent

arXiv:2512.17452v4 Announce Type: replace-cross Abstract: Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear Key-Value (KV) cache growth. Prior approaches miti

arXiv:2608.24888v2 Announce Type: replace Abstract: Agents that act on a user's behalf must plan differently for different users, and increasingly do so from some structured representation of user con

arXiv:2608.30811v1 Announce Type: new Abstract: Long-context compression is essential for reducing the cost and latency of large language model inference. However, existing methods can fragment import

arXiv:2608.27882v1 Announce Type: new Abstract: Tabular foundation models based on in-context learning have recently emerged as strong alternatives to task-specific model fitting. However, the current