Knowledge catalogue
arXiv:2604.18398v1 Announce Type: new Abstract: Creativity has become a core competence in the era of LLMs and human-AI collaboration, underpinning innovation in real-world problem solving. Crucially,
arXiv:2601.06394v3 Announce Type: replace Abstract: Understanding student behavior in the classroom is essential to improve both pedagogical quality and student engagement. Existing methods for predic
arXiv:2601.10306v2 Announce Type: replace-cross Abstract: While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards
arXiv:2510.02370v3 Announce Type: replace Abstract: Large language models leverage both parametric knowledge acquired during pretraining and in-context knowledge provided at inference time. Crucially,
arXiv:2604.13226v1 Announce Type: new Abstract: Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: re
Banger paper from NVIDIA. Agentic reasoning needs models that are not just capable, but efficient at long-context inference. The agent model layer is moving toward open, long-context, high-throughput
arXiv:2604.12452v1 Announce Type: new Abstract: Large language models (LLMs) face significant challenges in processing long contexts due to the linear growth of the key-value (KV) cache and quadratic
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: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
arXiv:2609.24352v1 Announce Type: new Abstract: Large language models (LLMs), when acting as agents, are expected to take observed data in context, infer the latent state space underlying the world, a
arXiv:2607.09175v2 Announce Type: replace-cross Abstract: Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the
arXiv:2609.20888v1 Announce Type: new Abstract: Massive KV caches can cause severe memory-bandwidth bottlenecks during long-context decoding. Sparse attention methods mitigate this via selective loadi
arXiv:2609.17943v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by attention, whose repeated KV-cache reads make decoding memory-bound. Self-speculative decoding alleviate
arXiv:2609.19138v1 Announce Type: cross Abstract: Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstration
arXiv:2609.16382v1 Announce Type: cross Abstract: A language model's representation geometry is not predetermined; it evolves as the model runs. A faithful account of that geometry must capture that d
arXiv:2501.03008v2 Announce Type: replace-cross Abstract: Context-aware machine translation can expose the evidence needed for pronoun choice, but standard fine-tuning does not explicitly prioritize t
arXiv:2607.17768v2 Announce Type: replace Abstract: Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds. Existing methods ty
arXiv:2606.21203v2 Announce Type: replace Abstract: Psycholinguistics studies show that human readers fall for coherence illusions: an incoherent discourse can seem coherent simply because a distracto
arXiv:2609.07663v1 Announce Type: cross Abstract: A wrong number is worse than no answer. Across factuality-critical domains -- audience metrics, scheduling and rights in media; dosages and lab values
arXiv:2609.07070v1 Announce Type: new Abstract: Spatio-temporal context has become increasingly crucial for visual tracking. However, most existing approaches extract spatio-temporal cues via discrete
LayerStoRm: GLM-5.3-Flash UD-Q4_K_XL (186 GiB) at 1M context on 2× RTX 5090 + 2× RTX 5080 (96 GB VRAM total) using RAM for the pinned experts. LayerStoRm is a (still experimental) MIT-licensed continu
arXiv:2609.04288v1 Announce Type: cross Abstract: Voice AI applications are gaining popularity as advances in large language models (LLMs) enable more natural and accessible spoken interactions. Servi
arXiv:2609.03384v1 Announce Type: new Abstract: In-Context Segmentation (ICS) aims to precisely segment arbitrary semantic concepts, such as objects or parts, given one or a few annotated visual exemp