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I’m building a personal local coding-agent project called Cody, using local 9B/14B models through Ollama. This is still a work in progress. It has not passed its full graduation process yet, and the l

local-air-ollama

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Research

Image-Derived PM10 Estimation in Cattle Feedlot Using Machine Learning: Addressing Concentration Ranges Beyond Existing Digital Imaging Methods

DGX agent

arXiv:2609.20975v1 Announce Type: new Abstract: Affordable dust monitoring remains a pressing need for the cattle feedlot industry, yet camera-based PM estimation, despite its growing body of research

researcharxiv-cs-cv

21 Sept 2026

Implicit Rule Induction with Test-Time Task Embeddings in ARC-like Tasks

arXiv:2609.21181v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus and related benchmarks evaluate whether AI models can solve novel reasoning tasks, but often leave unclear whether

Impressive paper on building recursive self-improving agent harnesses. It's rich with great insights on building effective agent harnesses. …

Impressive paper on building recursive self-improving agent harnesses. It's rich with great insights on building effective agent harnesses. If you maintain an agent harness, this paper names three def

model-releasesdair-ai--x

Impressive paper showing how much the first retrieval step matters for deep research agents. It helps to improve GPT-5.5 from 83.1% to 90.5%…

Impressive paper showing how much the first retrieval step matters for deep research agents. It helps to improve GPT-5.5 from 83.1% to 90.5% on BrowseComp-Plus with the same retriever and the same age

model-releasesdair-ai--x

Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling

arXiv:2609.21454v1 Announce Type: cross Abstract: We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests. Building on this

IncentRL: The Trade-Off Between Preference Guidance and Task Performance

arXiv:2609.21525v1 Announce Type: new Abstract: Preference-based reward shaping can guide reinforcement learning, but adding preference signals to the reward may unintentionally change the task being

safetyarxiv-cs-lg

Info3R: Information-Adaptive Test-Time Training for 3D Reconstruction

arXiv:2609.21938v1 Announce Type: new Abstract: Transformer-based models have recently achieved strong performance on 3D reconstruction from images, and recent works extend them to process video strea

safetyarxiv-cs-cv

Information-Gain Rewards over Diversity-Pruned Tests: GT-Anchored Verifier Co-Training for Reliable Code Generation

arXiv:2609.21208v1 Announce Type: new Abstract: Self-play methods that co-train a single language model as both coder and test author promise to move code-generation RL beyond fixed test suites, but t

safetyarxiv-cs-ai

IntBMoE: Integrating Block-Level Conditioning into Expert Composition for Full-Participation Mixture-of-Experts

arXiv:2609.21346v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) scales capacity, but existing designs cannot set three quantities independently. For a single token, participation is how many

Intelligence is improving exponentially

model-releaseselon-musk--x

Intelligence is improving exponentially Just a reminder that GLM 5.3 Flash, DeepSeek V4.1 Flash, Qwen 3.8 Next Flash, and even Qwen 3.8 27B are all outperforming (in both intelligence and capabilities

Hardware

Interference-Driven Clustered Optimisation for FM Spectrum Coordination

arXiv:2609.21441v1 Announce Type: cross Abstract: Cross-border FM spectrum coordination involves protecting foreign broadcasting services while preserving domestic coverage, amid increasingly large ra

hardwarearxiv-cs-ai

Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models

arXiv:2609.21906v1 Announce Type: new Abstract: Counterfactual simulation with a clinical world model means fixing a patient's history, changing the treatment, and reading off the predicted response.

Industry

Introducing Halo, the best framework for post-training of open-source models. Halo delivers up to 2.8x the throughput of stock TRL with less…

Introducing Halo, the best framework for post-training of open-source models. Halo delivers up to 2.8x the throughput of stock TRL with less peak memory, while models stay in their native HuggingFace

industryclem-delangue--x

Is Ollama + RAG could be the best for Pentesting and Bugbounty?

A few days ago, I came across a post/comment saying that you can get better data privacy by using local Ollama models in a RAG pipeline instead of relying on cloud-hosted models/APIs. The explanation

model-releasesr-ollama

Applications

It may be that people also feel safer giving their personal data to Meta (since they already use it) than to other AI companies. If you do d…

applicationsethan-mollick--x

It may be that people also feel safer giving their personal data to Meta (since they already use it) than to other AI companies. If you do decide to try out Muse, a reminder that you can turn off trai

Applications

It started with a simple question... 'How can I give my brother independence again?'

Hey everyone, I wanted to share a story that changed my perspective on what's possible with modern AI tools, especially for everyday people with no background in software development. My brother Ben h

applicationsr-chatgpt

JEPA Guided Diffusion: Predictive Vision-Language Conditioning for Generative Traffic Forecasting

model-releasesarxiv-cs-cv

arXiv:2609.21379v1 Announce Type: new Abstract: Accurate traffic forecasting requires both understanding scene dynamics and synthesizing realistic future observations. Recent diffusion-based video gen

Tools

Jev and the System One Model: RLCD, intelligence/$, reliable AI, & the end of chat-first AI https://www.latent.space/p/jev @typesafeai CEO @…

Jev and the System One Model: RLCD, intelligence/, reliable AI, & the end of chat-first AI https://www.latent.space/p/jev @typesafeai CEO @CompleteSkeptic explains why AI can solve extraordinarily har

toolsswyx--x

Jev introduces a new shape of LLM - System One, aka Decision Models

model-releasessimon-willison

Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling 'System One models' (I'm with Maggie Appleton, I think 'decision models' is a better name for t

Tools

Jev pod tomorrow subscribe on apple /youtube @latentspacepod thanks to @allenpark and Ke for making this happen!

Jev pod tomorrow subscribe on apple /youtube @latentspacepod thanks to @allenpark and Ke for making this happen! After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not l

toolsswyx--x

Jev-style logit read on a 4B open VLM, measured: http://glance.yohei.me 👀 vs the same model writing JSON: ⏳ ~1/3 less time for a yes/no on …

model-releasesyohei-nakajima--x

Jev-style logit read on a 4B open VLM, measured: http://glance.yohei.me 👀 vs the same model writing JSON: ⏳ ~1/3 less time for a yes/no on a full photo 🤑 up to ~85% less GPU cost with many Qs per imag

Jev's calibration was measured. The LLMs won [D]

model-releasesr-machinelearning

Source: Jev Benchmarks Its training method is literally called 'Reinforcement Learning for Calibrated Decisions.' Calibration gap vs human labels (lower = better): Yes/no: Jev 5.0, Gemini 3.8 Flash 2.

Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data

arXiv:2609.21932v1 Announce Type: new Abstract: Joint remaining useful life (RUL) prediction and capacity estimation require representations of both gradual degradation and recent battery behavior. Th

Industry

JUST IN: Startups are increasingly building custom AI models using open-weight alternatives to cut costs & reduce dependence on OpenAI & Ant…

Recent Bloomberg reports indicate that a growing number of startups are developing custom AI models by leveraging open-weight alternatives to high-cost proprietary platforms like OpenAI’s and Anthropi

industryclem-delangue--x

Local Ai

Just interviewed a creative legend. ↓ @ingi_erlingsson is a 20-year creative vet. He built the world-renown Golden Wolf creative agency from…

Just interviewed a creative legend. ↓ @ingi_erlingsson is a 20-year creative vet. He built the world-renown Golden Wolf creative agency from the ground up. Sold it to @doodles. Became Doodles’ Chief C

local-aicomfyui--x

K2-V2: A 360-Open, Reasoning-Enhanced LLM

arXiv:2512.06201v3 Announce Type: replace Abstract: We introduce K2-V2, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and

safetyarxiv-cs-lg

Kinks vs. Smoothness: Identifiability of Real Analytic nICA for Laplace-like Sources

arXiv:2609.21926v1 Announce Type: new Abstract: Many machine learning systems try to explain complex data - like images or financial time series - in terms of hidden, independent factors that generate

KnowDemo: Knowledge-Guided Robot Demonstration Generation from Human Videos

arXiv:2609.21229v1 Announce Type: cross Abstract: Learning robot manipulation policies typically requires substantial demonstration data, which are costly to collect on real robots. Recent methods gen

safetyarxiv-cs-ai

Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting

model-releasesarxiv-cs-ai

arXiv:2609.21381v1 Announce Type: cross Abstract: We investigate whether coupling a time-series foundation model to hydraulic project knowledge improves surrogate forecasting of HEC-RAS water-surface

lambda-Controlled GRPO: Turning Flow-Matching Ratio Instability into a Budgeted Resource

arXiv:2609.22041v1 Announce Type: new Abstract: Reinforcement learning is increasingly used to align image generators with reward signals, and Flow-GRPO recently extended this paradigm to flow-matchin

safetyarxiv-cs-lg

Layerwise Decoupling for Stable Structured Sparsification of Fully Connected Layers

arXiv:2609.21126v1 Announce Type: new Abstract: We propose a decoupled, layerwise method for structurally sparsifying the fully connected layers of pretrained neural networks. Rather than penalizing a

Local Ai

Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video

arXiv:2609.21624v1 Announce Type: new Abstract: Problematic internet use affects a growing share of the population, yet common interventions, e.g., time limits, blocking, forced breaks, are coercive a

local-aiarxiv-cs-cv

Learning Cardiac Features: ECG Biometrics Across Time and~Exercise

arXiv:2609.21962v1 Announce Type: new Abstract: Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authe

Learning Distance-Conditioned Object Transport for Humanoid Loco-Manipulation from a Single Motion Clip

arXiv:2609.21467v1 Announce Type: new Abstract: Motion tracking can reproduce humanoid loco-manipulation from a single retargeted motion clip, but a policy trained on a fixed reference primarily repro

Learning End-to-End Control for Omnidirectional Aerial Motion on Overactuated Tilt-rotor Quadrotors

arXiv:2602.21583v2 Announce Type: replace Abstract: While reinforcement learning (RL) has been successfully applied to conventional quadrotors for agile and robust flight, whether actuator-level RL ca

Learning Scene-Aware Humanoid Locomotion through 3D Clutter from Immersive Human Demonstrations

arXiv:2609.21107v1 Announce Type: new Abstract: While learning from human motions has enabled highly dynamic humanoid skills such as dancing and martial arts in obstacle-free space, traversal through

Learning Surrogate LPV State-Space Models with Uncertainty Quantification

arXiv:2603.29532v2 Announce Type: replace-cross Abstract: The Linear Parameter-Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high-dimensional systems, f

Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks

arXiv:2609.21945v1 Announce Type: new Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational control

Agents

Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks

arXiv:2609.21519v1 Announce Type: new Abstract: Artificial Intelligence (AI) is becoming a fundamental design principle of future AI-native communication networks, enabling autonomous resource managem

agentsarxiv-cs-ai

LEGIT: Credentialing Protocol for Trustworthy AI Agent Marketplaces

model-releasesarxiv-cs-ai

arXiv:2609.21325v1 Announce Type: new Abstract: Agentic marketplaces are emerging where AI agents with varying capabilities autonomously complete specialized tasks for buyers. A major challenge of suc

LEMCA: LLM-Guided Synthesis of Efficient Mode-Switching Control Architectures

arXiv:2609.21319v1 Announce Type: new Abstract: Physical control tasks in the natural world, such as driving or object manipulation, frequently exhibit dramatic variations in sensory and compute compl

researcharxiv-cs-ro

Let’s be clear: LLMs don’t feel pain. And they don’t deserve moral standing. This new paper reports a series of experiments identifying pain…

Let’s be clear: LLMs don’t feel pain. And they don’t deserve moral standing. This new paper reports a series of experiments identifying pain-related representations in LLMs. The authors also show that

safetygary-marcus--x

LIMBO: Learning and Internalizing Model-Free Barrier Objectives for Agile and Safe Whole-Body Control

arXiv:2609.22075v1 Announce Type: new Abstract: Safe whole-body control requires coordinating collision avoidance and balance under high-dimensional, nonlinear dynamics--making safety certificates dif

Listen Before You Speak: Response Planning from Listener Facial Reactions for Conversational Speech Generation

arXiv:2609.21683v1 Announce Type: new Abstract: Conversational speech depends on dialogue context and the listener's immediately preceding behavior. We propose ReACT-TTS, a two-stage framework that us

Agents

LittleHorse adds agent controls and free serverless trial to its ‘Business-as-Code’ orchestration platform

LittleHorse Enterprises LLC, a maker of workflow orchestration software spanning multiple applications, today announced version 1.3 of its Saddle Command Center, adding no-code agent deployment, prebu

agentssiliconangle

LLM-Generated Feature Pools for Time Series Anomaly Detection

arXiv:2609.21801v1 Announce Type: new Abstract: We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extracts

LLMs as Feature Engineers for Text-and-Tabular Prediction

arXiv:2609.21894v1 Announce Type: new Abstract: We introduce an iterative framework that automates the extraction of interpretable, schema-bound categorical features from unstructured text for tabular