Search: “context”

Planning in the Backbone: DiffAdapterVLA for Native Continuous Trajectory Generation with Driving VLMs

arXiv:2609.15322v1 Announce Type: cross Abstract: Pretrained driving vision-language models (VLMs) integrate visual, route, language, and driving context into rich driving priors, yet their representa

researcharxiv-cs-ai

15 Sept 2026

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ShopEase: A Generative AI-Based Multi-Agent Framework for Intelligent Enterprise Customer Support Using Hybrid Retrieval-Augmented Generation

DGX agent

arXiv:2609.13856v1 Announce Type: new Abstract: Enterprise customer support systems must answer customer questions correctly, retrieve the right policy information, use customer context, and pass diff

model-releasesarxiv-cs-cl

15 Sept 2026

TTDF: A Two-Stage Framework for Reliable Surgical Phase Transition Detection

arXiv:2609.14624v1 Announce Type: new Abstract: Reliable workflow transition detection is important for context-aware surgical assistance and downstream decision support. However, online surgical phas

researcharxiv-cs-cv

15 Sept 2026

Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking

arXiv:2609.12674v1 Announce Type: new Abstract: Advanced large language models (LLMs) with long context windows can substantially reduce input truncation in document-level machine translation (DocMT).

safetyarxiv-cs-cl

14 Sept 2026

Hoping for Optimized Smarter Upcoming Models .... Like DeepSeek-V4.1-Flash( KVCache + Engram) in Small/Medium/Big sizes

It's still a dream for many folks to run medium size(30B range) models @ Q8 with Unquantized KVCache (256K Context) on their GPUs. It would be awesome to have DeepSeek-V4.1-Flash's KVCache + Engram fo

13 Sept 2026

Qwen 3.8 27B UD-IQ4_XS even faster on 16GB CUDA

This is an evolution on top of Raymond's KV cache streaming fork - all credits to what enabled this goes to him. The basic idea behind what he enabled was a pool of memory in VRAM that is used differe

13 Sept 2026

This is all I will say on the matter: It’s alarming to see the constant effort to try to tell me how I should use AI, how I should build AI,…

This is all I will say on the matter: It’s alarming to see the constant effort to try to tell me how I should use AI, how I should build AI, and now how I should pace it. Is it just me, or are all the

safetydair-ai--x

13 Sept 2026

Demystifying the Privacy-Utility Trade-off in LLM Interactions

arXiv:2609.10992v1 Announce Type: new Abstract: The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current p

local-aiarxiv-cs-ai

This draft model is OP on 16 GB cards for Qwen 3.8 27b

https://huggingface.co/HermiHg/Qwen3.8-27B-DFlash2-Q2_K_S-MIX-GGUF I used this draft model with https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF with the IQ3_XXS with 128k context and I saw

Try GLM 5.3-flash with no reasoning effort set

Same 44-token prompt: low = 202 output tokens in 15s, max = 4,955 in 35s, unset = 10,579 in 68s. I'm using it via claude code with this: CLAUDE_CODE_MAX_CONTEXT_TOKENS=1048576 ollama launch claude --m

model-releasesr-ollama

When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting

arXiv:2609.11282v1 Announce Type: new Abstract: Multimodal forecasting models that combine time series with text annotations promise richer prediction through textual context, but how do we know wheth

A Voice-Interactive Multi-Agent System for Smart Operating Rooms: Architecture Design and Key Technologies

model-releasesarxiv-cs-cl

arXiv:2609.11231v1 Announce Type: cross Abstract: This paper presents SurgicalRoomAgent, a voice-interactive multi-agent system for smart operating rooms based on large language models (LLMs). The sys

Best Local Models for Claude Code with 8gb vram

Tested on Laptop with RTX 5060 8gb vram and 16gb ram Claude code - autocompact = false Ollama - Flash attention = 1, kv cache = q8_0 Gemma4 12B 64K - https://ollama.com/SetneufPT/Gemma4-12B-IT-QAT_Q4_

model-releasesr-ollama

PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations

arXiv:2609.09664v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as personalized assistants that interact with users over extended periods of time. As conversatio

Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management

arXiv:2609.09348v1 Announce Type: cross Abstract: Managing resources across IoT, edge, and cloud layers calls for continuous, context-aware decisions under constraints that rarely stay fixed. Deep rei

agentsarxiv-cs-ai

SWRouter: Similarity-Contractive Window Routing for Multi-Turn Large Language Model Conversations

arXiv:2609.11414v1 Announce Type: new Abstract: Large language models exhibit complementary strengths, motivating routing methods that dispatch each query to the most suitable model. Although existing

researcharxiv-cs-cl

Do LLMs Make More Mistakes If They Do Not Believe the Input Data?

arXiv:2609.09363v1 Announce Type: new Abstract: Large language models (LLMs) are prone to hallucinating or misinterpreting facts, which impairs their usability in retrieval-augmented generation or dat

local-aiarxiv-cs-cl

EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding

model-releasesarxiv-cs-lg

arXiv:2609.09728v1 Announce Type: new Abstract: Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps charact

Introducing the Google Cloud Developer Plugin for AI Coding Agents

model-releasesgoogle-cloud-ai

Agent skills fit well alongside documentation and remote MCP servers as ways of enabling the success of your AI workflows. They reduce context window usage for certain use cases, and they're straightf

Qwen3.8-Flash-Next on 2x3090 + DDR4, part 4: 2.2-2.5x faster prefill by kicking the expert cache off the GPU while the prompt runs

Part 4 of the same box. Part 1 was 17 -> 25-29 t/s with the expert cache PR, part 2 was 37-41 t/s after switching to UD-Q4_K_XL and stacking MTP on the cache, part 3 was the top-k fallback that was so

Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal

arXiv:2609.05593v1 Announce Type: new Abstract: Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low sp

agentsarxiv-cs-ro

CAM: Question Answering on Entity-Centric Videos with Continuous Extraction and Adaptive Querying

arXiv:2609.06504v1 Announce Type: new Abstract: Memory facilitates question answering over long videos by extracting and retrieving facts to fit within the limited context windows of multimodal LLMs (

researcharxiv-cs-cv

CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling

arXiv:2609.08686v1 Announce Type: new Abstract: Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models c

researcharxiv-cs-cv

I made a short explanation of KV Cache — is this understandable for beginners?

I’ve been experimenting with explaining AI/LLM concepts in a way that doesn’t assume too much technical background. This video is about KV Cache and why longer context windows require more memory duri

local-air-ollama

ICYMI: What landed for AI builders in August 2026

model-releasesaws-ml-blog

A recap of August 2026 launches for AI builders across Amazon Bedrock, Amazon Bedrock AgentCore, and Strands: million-token context for OpenAI models, cross-Region inference, agents that run for up to

Knowing When Not to Answer: Abstention and Refusal Reasoning in Vision--Language Models

arXiv:2609.05540v1 Announce Type: new Abstract: Many medical conditions require diagnosis through detailed, multi-context clinical assessment rather than from visual appearance alone. Despite this, vi

safetyarxiv-cs-cv

inclusionAI/Ling-3.0-flash-VL · Hugging Face

Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B total parameters

8 Sept 2026

Unifying ICL, SFT, KL-Regularized RL Through a Bayesian Lens

arXiv:2609.05111v1 Announce Type: new Abstract: Large language models are now trained and evaluated under a diverse set of paradigms: supervised fine-tuning (SFT), few-shot in-context learning (ICL),

7 Sept 2026

2x R9700, 64 GB DDR5 is an absolute beast machine with vLLM Radiance / R9V and Qwen 3.8 27b and Flash next

I've been tinkering with local LLMs since the beginning of the year when I had an Intel Arc B580 and 32 GB of DDR5. Curiosity got the best of me and I bought the first R9700 about half a year ago, als

6 Sept 2026

48 tg/s 440 prefill on my grandma's cluster (2xP40) (sort of)

TL;DR: switching KV cache to f16 may give a boost in speed if using MTP and ngrams. I have a self-built 'AI mega-cluster' with 2x P40s on a cheap Chinese motherboard and a Xeon CPU (around $1,100 to b

6 Sept 2026

BASP: Communication-Efficient Batch-Aware Sequence Parallelism for LLM Training

model-releasesarxiv-cs-lg

arXiv:2609.03151v1 Announce Type: cross Abstract: Long-context reasoning for large language models (LLMs) is becoming increasingly important, but training over long sequences remains challenging due t

Can LLMs Extract Architectural Design Decisions from Source Code Commits? - A Preliminary Exploratory Study

arXiv:2609.03721v1 Announce Type: cross Abstract: Context: Architectural Design Decisions (ADDs) capture the rationale behind the structure and evolution of software systems but are rarely documented

GrowPage: On-Demand KV Budgeting for Efficient LLM Reasoning Serving

arXiv:2609.03494v1 Announce Type: new Abstract: Long-output reasoning has made the key--value (KV) cache a critical memory bottleneck for efficient LLM serving. Existing KV compression methods usually

researcharxiv-cs-ai

Qwen 3.8 Flash Next Can Build Funny Games

This is nothing impressive but, i had so much fun i wanted to share my experience with this model. (yes this post is written by human) I made an FPS with local Q4_K_XL 3.8 Flash Next (256k context) (i

The Civilization Framework: Sovereign-Anchored Communication Between Personal Multi-Agent Systems

arXiv:2609.03425v1 Announce Type: cross Abstract: Humans are the transport layer between AI systems, losing context at every hop. We present the Civilization Framework, whose addressable party is the

agentsarxiv-cs-ai

The Geometry of Ignorance: LLMs Know When to Temper Bayesian Priors

arXiv:2609.02959v1 Announce Type: cross Abstract: What does a language model predict when it has few clues? The answer lurks in its unembedding geometry: a single direction of the unembedding matrix e

Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM

arXiv:2609.04098v1 Announce Type: new Abstract: Hybrid LLMs pair softmax attention with linear-attention layers such as Gated DeltaNet (GDN), whose recurrent state summarizes the context in fixed size

local-aiarxiv-cs-ai

AdaMem: Learning What to Remember with Adaptive Memory Policies for Personalized Agents

arXiv:2606.21144v2 Announce Type: replace-cross Abstract: Long-term memory systems allow LLM agents to preserve information beyond a single context window, but most systems focus on storing and retrie

3 Sept 2026

CivBench: A Long-Horizon Benchmark for Tool-Mediated Agents in Civilization VI

arXiv:2609.02459v1 Announce Type: new Abstract: We present CivBench, an open-source benchmark for evaluating language model agents in long-horizon, tool-mediated environments through the Model Context

3 Sept 2026

FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment

arXiv:2603.19539v2 Announce Type: replace-cross Abstract: We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assess

3 Sept 2026

Getting started with Mantis, our open-source bug finding-and-fixing harness

AI models have clearly proven their ability to discover and exploit vulnerabilities without much, if any, human assistance. To help defenders gain the advantage with AI, we built the Mantis harness to

agentsgoogle-cloud-ai

2 Sept 2026

Qwen3.8-flash-next sees corruption everywhere

Hi, I've noticed that the model often sees 'garbled text' in its context. Sometimes it declare that the tools instructions are corrupted, sometimes it is the content of some .md files, ora other files

2 Sept 2026

b10737

model-releasesllama-cpp-releases

qwen4exp: fix seq_cp, block position keying, mtmd input, cuda abort, add tests (#27941) qwen4exp: follow up fixes -kvu NaN collapse fix Assisted-by: Claude indexer cache ext.x/ext.y restore fix Assist

Fine-Grained Multi Image Object Hallucination Benchmark

arXiv:2608.30653v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. Howeve

Applications

Improving Argument Saliency Coverage in Small LLMs for Long Legal Opinion Summarization via Sequence-Level Distillation

arXiv:2608.29884v1 Announce Type: new Abstract: We show that sequence-level distillation from a capable long-context teacher model is a simple, annotation-free, and data-efficient strategy for improvi

applicationsarxiv-cs-cl

R^2A: Learning Persona Policies Through Persona Representation Learning and Runtime Alignment

arXiv:2608.29798v1 Announce Type: cross Abstract: The same Persona behavior can be beneficial in one context but harmful in another, causing static Persona elicitation to perform inconsistently across

safetyarxiv-cs-ai

Tail-Replay: Escaping the Curse of Linear Attention in Prefix Caching for Hybrid LLMs

arXiv:2608.30310v1 Announce Type: cross Abstract: Hybrid large language models interleave full-attention layers with linear-attention layers to reduce the cost of long-context inference. This structur

researcharxiv-cs-ai

TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI

arXiv:2608.30567v1 Announce Type: new Abstract: We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-con