Search: “context”

Interesting paper from Tencent. Tencent trains an agent to manage its own working context, and assigns credit at the level of individual context edits. Long-horizon tasks force a model to retrieve, in

arXiv:2512.15206v3 Announce Type: replace Abstract: A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes

arXiv:2608.21690v1 Announce Type: new Abstract: LLM agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier in

Graph: A cumulative metric of Speed x Context Size - the higher the dot - the better. Helpful for looking for the most balanced solution. The research relies on DFlash2 Q2 work by AnalogAlok: https://

arXiv:2603.06505v2 Announce Type: replace Abstract: Automatic speech recognition (ASR) has benefited from advances in pretrained speech and language models, yet most systems remain constrained to mono

arXiv:2607.29553v1 Announce Type: new Abstract: Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metri

arXiv:2511.04249v2 Announce Type: replace Abstract: Sim-to-real transfer remains a major challenge in reinforcement learning (RL) for robotics, as policies trained in simulation often fail to generali

arXiv:2607.22575v1 Announce Type: new Abstract: Human episodic memory supports the retrieval of experiences that unfold over extended timescales, yet the computational mechanisms underlying this abili

arXiv:2606.10209v1 Announce Type: new Abstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can ca

arXiv:2605.30842v1 Announce Type: new Abstract: Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this p

arXiv:2605.25354v1 Announce Type: new Abstract: While LLMs excel at reasoning over prompts using static pretrained knowledge, they struggle significantly with context learning-the ability to dynamical

arXiv:2604.27043v1 Announce Type: new Abstract: Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for mode

arXiv:2604.23277v1 Announce Type: cross Abstract: Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context co

arXiv:2604.11462v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle with long-horizon tasks due to the 'context bottleneck' and the 'lost-in-the-middle' phenomenon, where accumulated

arXiv:2604.07320v1 Announce Type: cross Abstract: Low-resource languages pose a challenge for machine translation with large language models (LLMs), which require large amounts of training data. One p

Great overview of context compression in LLM Agents And one interesting, unexpected finding. If your compaction policy is tuned to cut tokens, it may be making your agent run slower. Great study from

arXiv:2610.02404v1 Announce Type: cross Abstract: We study long context language models. Instead of training long context natively, or designing a long context harness, we train a model over the simpl

arXiv:2609.38379v1 Announce Type: new Abstract: Large language models (LLMs) are frequently updated for various use cases, where filtering out misaligned training samples is a common practice for prev

arXiv:2609.37791v1 Announce Type: new Abstract: Every day, people continuously infer situational context and adjust the way they understand and remember the world. Context, signaled by the prefrontal

arXiv:2609.13141v1 Announce Type: new Abstract: Post-training attention sparsification reduces the quadratic cumulative attention cost of pretrained Transformers by selecting a small set of context un

I can't stand kv cache quantization. Even at q8_0, I can feel the difference. But realistically, when running Qwen3.8-27B-UD-Q4_K_XL on my 32 GB GPU, I only have room for ~170k tokens (with mtp and mm

Hello, I'm a bit obsessed with cache management on local LLMs. For the last few days I've been working on cache management on vLLM with my 2x DGX Spark and DeepSeek v4 Flash 0731. I felt something was

arXiv:2608.28875v1 Announce Type: cross Abstract: Supplying context at inference time to a large multimodal model is an inexpensive lever for adapting speech transcription to a domain, and earlier res

arXiv:2608.17616v1 Announce Type: new Abstract: We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retrain

arXiv:2608.17607v1 Announce Type: new Abstract: Whole-slide pathology reasoning requires models to integrate gigapixel-scale visual evidence across complete case-linked slides, yet current question-an

arXiv:2608.12158v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made rapid progress, yet they still exhibit object hallucination, generating plausible but incorrect descr

arXiv:2608.10743v1 Announce Type: new Abstract: Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are s

arXiv:2608.05333v1 Announce Type: new Abstract: In-context learning (ICL) adapts medical image segmentation models to unseen structures and modalities without retraining by conditioning on a task-spec

arXiv:2607.26628v1 Announce Type: new Abstract: TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts te

arXiv:2607.24425v1 Announce Type: new Abstract: Large language models place structured concepts on geometrically faithful manifolds: weekdays lie on a circle, months on another, usually taken to be a

arXiv:2607.23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response. Recent works show the ini

arXiv:2607.21535v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive generation by having a cheap draft propose tokens that a target verifies in parallel. Frontier models

arXiv:2607.11906v1 Announce Type: new Abstract: The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest i

arXiv:2508.10031v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and

arXiv:2603.13748v3 Announce Type: replace Abstract: Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is

arXiv:2606.09900v1 Announce Type: cross Abstract: Long-term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround -- replaying the whole history into the p

arXiv:2601.00376v2 Announce Type: replace-cross Abstract: Repository-level code generation has attracted growing attention in recent years. Unlike function-level code generation, it requires the model

arXiv:2604.16883v1 Announce Type: new Abstract: In long-context decoding for LLMs and LMMs, attention becomes increasingly memory-bound because each decoding step must load a large amount of KV-cache

arXiv:2604.08426v1 Announce Type: cross Abstract: With the growing demand for long-context LLMs across a wide range of applications, the key-value (KV) cache has become a critical bottleneck for both

arXiv:2610.13132v1 Announce Type: new Abstract: LLM-as-a-Service has become a dominant paradigm for accessing large language models without requiring local deployment. However, the process raises seve

arXiv:2510.13276v2 Announce Type: replace Abstract: The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an exten

arXiv:2610.05885v1 Announce Type: new Abstract: Many works approach continual learning through the lens of infinite context windows. As an agent puts more observation into context (concretely the KV c

arXiv:2609.36935v1 Announce Type: new Abstract: Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length i

arXiv:2609.14320v1 Announce Type: new Abstract: Recently, linear attention layers have been increasingly adopted to replace softmax attention at scale for long-context modeling. However, existing cont

arXiv:2609.14168v1 Announce Type: cross Abstract: Context parameterization enables large language models (LLMs) to internalize contexts into reusable model parameters, avoiding repeated processing acr

arXiv:2603.12530v3 Announce Type: replace Abstract: Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This ``contexts ar