Search: “claude”

Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering

model-releasesarxiv-cs-cv

arXiv:2610.08137v1 Announce Type: cross Abstract: AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These rout

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Running an open-source agent platform with no third party in the chain: what each piece needs (Ollama/vLLM, local embeddings, local OCR)

DGX agent

Disclosure first: I work at Vstorm and I build AgenticOS, an open-source (Apache-2.0), self-hosted platform for building AI agents in a browser. This post was drafted with an LLM's help and edited by

model-releasesr-ollama

7 Oct 2026

Same Feedback, Different Answer: Measuring Run-to-Run Instability in Frontier-Model Customer Feedback Analysis

arXiv:2610.08036v1 Announce Type: new Abstract: AI agents are increasingly being programmed to automate knowledge work over large collections of unstructured data. Such automation requires repeatabili

SquidAgent: Parallelize Wisely, Coordinate Efficiently

arXiv:2610.08647v1 Announce Type: new Abstract: LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency. In principle, parallelizing work across multiple a

Tool-calling retrieval versus vector RAG for a small Greek--English knowledge base: accuracy and robustness to how users type Greek

arXiv:2610.08205v1 Announce Type: cross Abstract: Assistants grounded in a small, frequently edited knowledge base can retrieve through tool calls to a live data interface or through vector retrieval-

WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?

arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical

AIProver: Agentic Auto-Formalization of Mathematical Research via Certificate-Driven Evolving Harness

arXiv:2610.05367v1 Announce Type: cross Abstract: Proof auto-formalization translates natural-language (NL) theorems and proofs into a formal language (FL) such as Lean, enabling mechanical verificati

Anthropic's IPO prospectus: Dario Amodei made 18M in 2025, ranking in the middle of top tech executives, and President Daniela Amodei, his sister, made 16.4M (Reuters)

Reuters: Anthropic's IPO prospectus: Dario Amodei made 18M in 2025, ranking in the middle of top tech executives, and President Daniela Amodei, his sister, made 16.4M — Dario Amodei made $18 million l

model-releasestechmeme

b11440

model-releasesllama-cpp-releases

llama : re-reserve the sched when the nextn extraction flags change (#30020) The speculative MTP init enables NextN extraction on the target and draft contexts after both were created and their schedu

Errors of LLM-Assisted Literature Retrieval in Environmental Science: A Comparison Study of Abstract versus Full-text Based Prompts

arXiv:2610.05690v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for literature search and synthesis. However, it is unclear whether they retrieve accurate bibliogr

I gave a 21M model a 6.4B-parameter lookup table. It matches a 114M dense model and runs with the table on an SSD (RX 9070)

I spent the last few weeks on a hobby research project and just made it public. The idea isn't new (product-key memory, Lample et al. 2019, and Meta's 'Memory Layers at Scale'): give a model a huge ta

Introducing GeoGuess Bench! A benchmark to measure how good AI models are at playing GeoGuessr. We gave each model 210 photos from around th…

model-releasestogether-ai--x

Introducing GeoGuess Bench! A benchmark to measure how good AI models are at playing GeoGuessr. We gave each model 210 photos from around the world, asked it to predict where each photo was taken, and

let me introduce you to the langchain stack 1. langgraph gives you this level of control 2. traces give you the complete run lineage 3. vers…

model-releasesharrison-chase--x

let me introduce you to the langchain stack 1. langgraph gives you this level of control 2. traces give you the complete run lineage 3. version your skills and instructions in context hub 4 & 5. deepa

MemLeak: Cross-User Semantic Leakage in Multi-Tenant AI Agent Memory

arXiv:2610.04195v1 Announce Type: cross Abstract: Personal AI agents in enterprise multi-tenant deployments share a common vector store for long-term memory. Shared embedding spaces create a surface f

Mistral Large 4

model-releasessimon-willison

My comment on Mistral Large 4 — Hacker News. wren6991: The benchmark is saturated. Frontier models are tested with an armadillo in fishnet tights jaywalking on Mars. OK well I couldn't resist this one

pi-optchat: never compact again - endless chat as a memory tree

I built a Pi extension that implements Victor Taelin's OptChat recipe: instead of compacting, every message is logged and summarized into a binary tree. Each turn starts from a fresh context with a bo

Qwen Image 2.1 Uncensored MCP

model-releasesr-stablediffusion

https://github.com/hypersniper05/MCP-Image-Generator-Uncensored Just wanted to share with you guys my workflow converted into an MCP. It's a Qwen Image 2.1 Uncensored MCP with a few extra models I fou

SpatialChain: A Benchmark for Auditing Spatial Reasoning Faithfulness in VLMs

model-releasesarxiv-cs-cv

arXiv:2610.06413v1 Announce Type: new Abstract: Thinking-enabled vision-language models (VLMs) report ever-higher accuracy on spatial benchmarks, yet final-answer scores cannot reveal whether a correc

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

model-releasesarxiv-cs-cv

arXiv:2610.04432v1 Announce Type: new Abstract: Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manu

When Does Longer Reasoning Help? Predicting Mathematical Reasoning Through Discovery and Execution

arXiv:2610.05322v1 Announce Type: new Abstract: Test-time compute can improve mathematical reasoning, but can short-budget runs predict how mathematical reasoning scales with additional compute? We in

A benchmark for LLMs playing Civilization V. GLM-5.3 is ahead of Opus-5.5, and Qwen-3.8-27B holds up surprisingly well.

A while ago, I posted here getting OSS-120B and GLM-4.6 playing full games of Civilization V. Since then, models have moved pretty far, and we wanted a better understanding about models' capabilities

AptMQL-Bench: From Text-to-SQL to Text-to-MQL via Access-Pattern Schema Design and Data-Preserving Migration

arXiv:2610.02770v1 Announce Type: new Abstract: Document databases such as MongoDB are core infrastructure for modern applications, and natural-language interfaces to them---text-to-MQL---would let no

Automating the Application of HCI Principles: Skills for On-Demand UI Construction, the Human-AI Space to Think, and the Future of HCI

arXiv:2610.02369v1 Announce Type: cross Abstract: Human-computer interaction (HCI) is in the middle of a transition: large language models can now generate functional user interfaces (UIs) on demand f

D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?

arXiv:2610.03226v1 Announce Type: cross Abstract: GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal ho

Dissecting model behavior through agent trajectories

arXiv:2606.17454v3 Announce Type: replace Abstract: AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through

Evaluating the Retrieval Robustness of Large Language Models

arXiv:2505.21870v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG coul

Evaluating VQA in Vision Language Models using Cooperative Principles

arXiv:2610.02878v1 Announce Type: new Abstract: We evaluate the performance of Vision Language Models in Visual Question Answering (VQA) when questions violate Grice's maxims. To do this, we use VLMs

GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification

arXiv:2603.29112v2 Announce Type: replace Abstract: We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in rec

I ran 1,200+ Blender modeling runs across LLMs and agent harnesses and made them votable

Blind A/B arena where AI agents build things in Blender and you vote on which result is better: https://render-arena.izolight.xyz Each agent gets a prompt that describes its environment and the rules,

Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting

arXiv:2610.02571v1 Announce Type: cross Abstract: As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideratio

MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code

arXiv:2610.03080v1 Announce Type: cross Abstract: Large language models are moving from producing trading signals to writing the code that executes them. The failure mode of the second role is silent:

Pincer: Resource Authorization for Agents using a Digital Twin

arXiv:2610.02569v1 Announce Type: cross Abstract: Coding agents have become increasingly long-horizon, autonomous, reliant on general-purpose shell and maintain their own persistent memory for self-im

Speakrail - a low-latency fully-local voice assistant that runs on a single RTX 4090

tl;dr: I created a fully-local open-source full-duplex voice agent that rivals GPT-Live on some benchmarks. It uses Voxtral Realtime with a turn-taking head, a microturn-finetuned Gemma 4 12B and Bree

Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spotting

arXiv:2610.03567v1 Announce Type: new Abstract: We describe the Writerslogic team's participation in the CLEF 2026 SimpleText shared task, addressing Task 1 (text simplification) and Task 2 (complexit

Y'all this is a sexy paper; context language models

Paper linky - Context Language Models The central idea of the paper is incredibly simple. Give a model the ability to edit its context on-the-go like a file has major benefits on task performance, con

A Strata fork for IBM AC922 running Qwen3.8-FN UD-Q4_K_XL is doing up to 7,357 tk/s prefill and 113 tk/s decode

I forked Strata and worked with Opus 5.5 with some heavy changes to it to make it work on an IBM AC922 I have access to. The IBM AC922 is a 2018 era beast with two POWER9 20 core SMT4 CPUs that are co

Banger paper from Google. It's standard practice to sample several agent rollouts and trust the answers they agree on. This Google paper sho…

Banger paper from Google. It's standard practice to sample several agent rollouts and trust the answers they agree on. This Google paper shows that agreement can hide shared errors, while disagreement

model-releasesdair-ai--x

Built a quick, sub-15ms Rust CLI/TUI to pack repos into prompts without burning 40k tokens on lockfiles and junk

Whenever I feed codebases into local models (Qwen, DeepSeek R1) or API models, the biggest annoyance is prompt pollution: - Lockfiles (`Cargo.lock`, `package-lock.json`) burning 30,000+ tokens for zer

For dual DGX spark users; GLM 5.3 flash got a 50%+ performance boost

For the last few months, I ran DeepSeek v4.0 flash (NVFP4). First 0731, then visionexp because it was a free improvement. I got around 65 tps decode and almost 2k prefill, and ran 4-5 agents in parall

HuggingFace just closed a major gap in harness engineering! (the ultimate guide to multi-harness RL) the same open-weight model can perform …

model-releasesclem-delangue--x

HuggingFace just closed a major gap in harness engineering! (the ultimate guide to multi-harness RL) the same open-weight model can perform well inside one harness, then lose accuracy or produce inval

I Distilled an LLM into two 287M encoders (GLiNER + multiple choice) for document extraction, can't match teacher. did i do something wrong?

A while ago I asked here how to turn ~5 million court decisions into structured graphs without running an expensive LLM on every document thanks for the advice . I went with the 'small extractor + cla

Nonobench: an open benchmark of 49 LLMs on nonogram puzzles, public and open source [P]

model-releasesr-machinelearning

Nonobench measures how well LLMs solve nonograms (picross). Each model gets the row and column clues once and returns the full grid. No tools, one attempt per puzzle. Method: - Standard mode: 30 puzzl

poorman inference engine for 16GB GPU and 35B moe Qwen 3.6for coding

I forked llama.cpp's server into AgrillaMoE, a dedicated build for Qwen3.6-35B-A3B (~A4B) with Unsloth quants. On a (vant.ai) rented V100 16GB with the 2-bit UD-Q2_K_XL quant it generates at ~57-60 to

b11366

model-releasesllama-cpp-releases

ggml-quants : avoid invalid rounding in qkx3 scale search (#29817) ggml-quants : avoid invalid rounding in qkx3 scale search The imatrix scale search can produce an infinite, NaN, or otherwise out-of-

b11377

model-releasesllama-cpp-releases

chat : honor json_schema in Ling 3.0 parser (#29813) chat : honor json_schema in Ling 3.0 parser Ling 3.0 only built a grammar for tool calls and did not handle inputs.json_schema, so response_format

I just made the most expressive real-time motion harness for the Reachy Mini. It was created by: > synthesizing a massive motion dataset usi…

model-releasesclem-delangue--x

I just made the most expressive real-time motion harness for the Reachy Mini. It was created by: > synthesizing a massive motion dataset using Astra (over 10000 samples) > fine-tuning Qwen 3.5 4B usin

Must-read paper from Meta on reliable auto-research agents. If you let coding agents run ML experiments, one silent bug like leaked eval dat…

Must-read paper from Meta on reliable auto-research agents. If you let coding agents run ML experiments, one silent bug like leaked eval data or a disconnected gradient can invalidate hours of trainin

model-releasesdair-ai--x

Research

Cool eval. Simply ask an LLM “Land or Water?” and give it a latitude and longitude coordinate as text. Ask 16,200 times, plot as image. The …