Search: “reasoning”

Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits

arXiv:2610.13494v1 Announce Type: new Abstract: Existing LLM-based agent approaches for long-horizon machine learning engineering (MLE) often use context engineering, implemented through custom harnes

Human

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Model Releases

In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation

DGX agent

arXiv:2610.13411v1 Announce Type: cross Abstract: Traditional system identification typically involves two steps: initially, training data is processed to estimate a model of the system of interest; t

model-releasesarxiv-cs-ai

12 Oct 2026

Incremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support

arXiv:2610.12666v1 Announce Type: cross Abstract: We introduce iRAG, a human-in-the-loop decision-support system combining Retrieval-Augmented Generation (RAG) with Hybrid Decision-Making. Unlike conv

Tutorials

Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators

arXiv:2602.18655v3 Announce Type: replace Abstract: For fully actuated rigid robots, kinematic inversion is a purely geometric problem, efficiently solved by closed-loop inverse kinematics (CLIK) sche

tutorialsarxiv-cs-ro

Introducing DigitalOcean Agent Data Services: The Data Foundation for Agents

Agents are only as capable as the context they can reach. An agent with reliable access to the right information makes decisions grounded in fact; one without it guesses. And the work agents produce i

agentsdigitalocean

Introducing DigitalOcean Signals and Simulations: see how your agents and models really behave in production, then test the fix before it ships

model-releasesdigitalocean

You ship an agent. It mostly works. Then the feedback loop becomes a support ticket: a user got the wrong answer, a tool call looped four times, a refund went out that shouldn’t have. Standard observa

KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse

arXiv:2610.13060v1 Announce Type: new Abstract: KV-cache reuse in large language model serving is commonly limited to exact prompt prefixes, preventing reuse when the same retrieved document appears a

llm-meta-ai 0.2

model-releasessimon-willison

Release: llm-meta-ai 0.2 Meta AI release Muse Wonder (and Muse Wonder Full), their new 'most capable models'. These look like they may be Meta's first Fable class models - Artificial Analysis scores M

Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics

model-releasesarxiv-cs-lg

arXiv:2610.12518v1 Announce Type: cross Abstract: This position paper argues that the generative molecular dynamics (GenMD) community is not properly evaluating its progress: current evaluations let a

Hardware

Microsoft’s new AI chip makes it less reliant on Nvidia’s GPUs

Microsoft is announcing its third-generation Maia in-house AI chip today, designed to be the 'primary AI inference system' for Microsoft's products and services. Maia 300 not only improves the cost an

hardwarethe-verge-ai

Applications

Over the last few months at @river_ai_inc we’ve developed what we believe is the best system in the world for building your own internal cus…

applicationsclem-delangue--x

Over the last few months at @river_ai_inc we’ve developed what we believe is the best system in the world for building your own internal custom models, driven by 10 years of experience training top mo

Planning with Transformers: Chain of Computation and Structured Context Windows

model-releasesarxiv-cs-lg

arXiv:2607.17710v2 Announce Type: replace Abstract: Large Language Models (LLMs) have had a remarkable impact across many areas of machine learning. However, recent studies have shown that they strugg

QLoRA on ExLlamaV3 Quants - Train @ 2-8bpw

EXL3-QLoRA I've built this over the last few months and I think it's stable enough to share now. A fork of ExLlamaV3, this is a native ExLlamaV3 training implementation with plain PyTorch, without tra

Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing

arXiv:2610.13823v1 Announce Type: cross Abstract: Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executi

researcharxiv-cs-ai

Scaling Agentic Data for Long-Horizon Terminal Intelligence

arXiv:2610.13166v1 Announce Type: new Abstract: Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool

local-aiarxiv-cs-cl

Self-Aware Knowledge Probing: Evaluating Language Models' Relational Knowledge through Confidence Calibration

arXiv:2601.18901v2 Announce Type: replace Abstract: Knowledge probing quantifies how much relational knowledge a language model (LM) has acquired during pre-training. Existing knowledge probes evaluat

Solving VeriContest with a Lean-Backed Rust Verifier

arXiv:2610.03994v2 Announce Type: replace-cross Abstract: VeriContest is a benchmark of 1007 competitive-programming problems in Rust, each with a Verus specification, a judge-accepted solution, and a

strix-llama new branch: prompt processing +21–54% over Vulkan and up to 2.7× over stock ROCm kernel at 128k, same outputs quality

I've been tuning llama.cpp on my Strix Halo box (Radeon 8060S, 128 GB) and the numbers are good enough to share. It's a branch called rdna35-perf on top of halo-box/strix-llama.cpp, a llama.cpp fork a

Structural Automata for Neural Semantic Parsing

arXiv:2610.13896v1 Announce Type: new Abstract: Neural semantic parsers generate meaning representations token by token, yet well-formedness depends on references, scope, and graph relations that unfo

local-aiarxiv-cs-cl

Safety

Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding

arXiv:2610.12939v1 Announce Type: cross Abstract: Decoder hidden states from different layers of large language models (LLMs) often encode complementary information. However, existing speculative deco

safetyarxiv-cs-ai

SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories

arXiv:2610.13356v1 Announce Type: new Abstract: Coding agents are increasingly deployed for end-to-end software engineering tasks, where a readily available oracle evaluator that defines the success c

TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability

arXiv:2606.19821v2 Announce Type: replace Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, exi

Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities

arXiv:2610.13456v1 Announce Type: new Abstract: Cyber-Physical Systems are developed through multiple model based activities, comprising several stages of development. As the development progresses th

Training User Simulators with Latent Intrinsic Feedback

arXiv:2610.13617v1 Announce Type: new Abstract: User simulators are gaining attention as environments to train LLM assistants that can better cope with the nuances of multi-turn conversations with rea

Vultr at NetApp INSIGHT 2026: Beyond Hyperscale and Into a New Era of Enterprise Infrastructure

At NetApp INSIGHT 2026, Vultr highlighted the emerging “beyond hyperscale” era of enterprise infrastructure, emphasizing that agentic AI and modernized applications require more than compute and stora

agentsvultr

When LLMs Sit Above Diagnostic Tools: Unrealized Complementarity in Industrial Fault Diagnosis

arXiv:2610.05031v2 Announce Type: replace Abstract: Large language models are increasingly used as integration layers above specialized tools, but a stronger component does not necessarily produce a s

2x 3090, 3x 3060 potential (smart and informed) future upgrade paths (send help)

I'm trying to figure out the best long-term upgrade path for my setup. Right now my GPUs are spread across several consumer PCs: Main: 5900X, 32GB DDR4, 2× RTX 3090 24GB. Secondary: i3-10320, 16GB DDR

local-air-localllama

Every Model That Can Be Run On 10-16GB VRAM Ranked

It's been 4 years since c.ai first hallucination model, and yet, we're nowhere good enough at LLMs in terms of spontaneity/interesting hallucination features. Fortunately, last year brought new archit

UPDATE: Qwen 3.8 27B 140 tok/s on single RTX 3090 Megakernel: KL divergence 0.0009 vs llama.cpp

This is a follow-up to my post from yesterday (https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/) detailing the CUDA megakernel. Thank you all for

[vllm] Is it possible to run Qwen3.8-27B-FP8 on a 5090?

Based on my experiments and feedback from claude, it seems extremely unlikely and even if I get it working, not practical because of minimum context. But just asking here as a last effort. I'm able to

I have an ESC4000 G3 with 8x T4s in it - what is the fastest way I can deploy Qwen3.5-9B for about 10-15 users concurrently: currently using llama.cpp

Hi All: I have an Asus ESC 4000 G3 with 128 GB DDR4 RAM - I tried putting in my V620s but couldn’t put more than 2. Sadly, pivoted to T4s, these are 72 watt passive cards and I thought I could use the

10 Oct 2026

Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel

I've been using Claude Opus 5.5 to speed up Qwen3.8-27B on my PC (rtx 3090), it wrote a CUDA megakernel that is 1.4-1.9x faster than llama.cpp depending on the task/context length. The results and cod

10 Oct 2026

Strata with Qwen3.8 Flash Next UD-Q4_K_XL

Been seeing quite a few posts about Strata lately, so I figured I'd give it a shot on my RTX PRO 6000 and I am very impressed. Most of the benchmarks I've seen are using IQ2 or IQ3 quants, so I wanted

10 Oct 2026

A 24KB stand-alone HTML-LLM that can generate consistent stories

https://preview.redd.it/7sknv6omnguh1.png?width=1023&format=png&auto=webp&s=4aef1dc464d008b00f104519eb5399ba6132ad69 Want to try? Just go here and click 'New seed' a lot. You will get different storie

local-air-localllama

Applications

Automated Assembly Instruction Generation from CAD Models Using Grounded Large Language Models: A Human-in-the-Loop Framework

arXiv:2610.11896v1 Announce Type: new Abstract: Assembly documentation is a downstream manufacturing artifact that is still usually authored by interpreting CAD models by hand. Structured product data

applicationsarxiv-cs-lg

AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda

arXiv:2511.02374v2 Announce Type: replace-cross Abstract: Current large language models excel at broad, general-purpose tasks, but consistently underperform when exposed to highly specialized domains

researcharxiv-cs-ai

Banger paper from Meta Superintelligence Labs on user simulators for agent training. Agent RL setups usually let an assistant LLM play the u…

Banger paper from Meta Superintelligence Labs on user simulators for agent training. Agent RL setups usually let an assistant LLM play the user, so the simulated user is too cooperative and too explic

model-releasesdair-ai--x

BrickBench: Evaluating Agentic Brick Design

arXiv:2610.12452v1 Announce Type: new Abstract: We propose BrickBench, a benchmark for agentic text-conditioned LEGO-set design. Given a prompt, an agent is tasked with producing an assembly that not

Tutorials

Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?

arXiv:2610.11135v1 Announce Type: new Abstract: Knowledge tracing (KT) models need many logged learners, so a new course or platform starts without a usable model. In LLM-based KT the LLM generates th

tutorialsarxiv-cs-cl

DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios

model-releasesarxiv-cs-cv

arXiv:2506.23292v3 Announce Type: replace Abstract: Recent advances in AIGC have exacerbated the misuse of malicious deepfake content, making the development of reliable deepfake detection methods an

Delphos: A reinforcement learning framework for assisting discrete choice model specification

model-releasesarxiv-cs-lg

arXiv:2506.06410v4 Announce Type: replace-cross Abstract: We introduce Delphos, a deep reinforcement learning framework for assisting discrete choice model specification process. Delphos aims to suppo

DGX Monarch - Dual Spark Rendering w/ ComfyUI

For those of you who have been wanting to utilize your dual Spark cluster for image/video gen with ComfyUI - today is your day. I started this personal project back in April of this year, and it’s at

DivMoE: Fine-Grained MoE Upcycling via Cross-Domain Expert Composition

arXiv:2610.11317v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures have become essential for scaling large language models, with recent work demonstrating the benefits of fine-grai

Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context Reinforcement Learning

arXiv:2610.11152v1 Announce Type: cross Abstract: LLM agents increasingly improve at inference time by accumulating experience in context rather than by updating parameters. This process is often desc

agentsarxiv-cs-cl

Evaluating Exact Output and Checkpoint-State Prediction in Real Programs

arXiv:2610.11889v1 Announce Type: cross Abstract: We present a benchmark for predicting final output and checkpoint state from source and input alone. It extends CRUXEval-style output prediction with

FlowState: Execution State as Memory for Long-Horizon LLM Agents

arXiv:2609.34565v2 Announce Type: replace Abstract: Long-horizon tasks require LLM agents to continually draw on information from earlier interactions. However, retaining the full history increases co

Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System

arXiv:2610.11899v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded as components in software systems, marketed under labels such as chatbot, copilot, retrieval-au

From a Prompt to Repertoires: Evolving Functional REpertoires Enable LLM Continual Learning

model-releasesarxiv-cs-cl

arXiv:2610.11373v1 Announce Type: cross Abstract: Continual learning remains challenging for large language models, which must enable models to acquire new skills and knowledge without degrading exist