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Jeff John Roberts / Fortune: Warehouse robotics startup Ultra raised a 12M seed and a 50M Series A, and expands its deal with Physical Intelligence to supply AI software for its robots — Finance edito

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WARP-VLA: Wrist-Camera Adaptation for View-Robust Policy Execution in Vision-Language-Action Models

model-releasesarxiv-cs-cv

arXiv:2610.11508v1 Announce Type: cross Abstract: Despite recent advances in Vision-Language-Action models (VLAs) for robotic manipulation, their performance remains sensitive to changes in camera con

Web automation with Ollama on a 4 GB GPU: Qwen 3.5 4B plans, a heuristic clicks, and saved tasks replay with no model at all

I've been building ANCHOR, a web automation agent that runs entirely on Ollama. You describe a task in plain language ('Register Maria Silva, IT department, contractor, and save'), point it at a site,

model-releasesr-ollama

Tutorials

We’re putting too much faith in AI’s ability to say no

Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is fu

tutorialsmit-tech-review

What 30,000 Hours of Ego-centric Video Does Not Teach

model-releasesarxiv-cs-cv

arXiv:2610.12464v1 Announce Type: new Abstract: World models offer a promising alternative to physics-based simulators, yet remain far from practical deployment. We ask how far scaling ego-centric hum

Tutorials

What can linear attention learn from nonlinear teachers in-context?

arXiv:2610.10761v1 Announce Type: cross Abstract: Linear attention is a tractable model for understanding the mechanisms governing in-context learning in transformers. For linear regression tasks, rec

tutorialsarxiv-cs-lg

Agents

What happens when a table contains tables? Usually, a mess. With LlamaParse, it comes out clean. Doc of the Week: Micron’s latest earnings d…

What happens when a table contains tables? Usually, a mess. With LlamaParse, it comes out clean. Doc of the Week: Micron’s latest earnings deck. Even with two business units and same row names, LlamaP

agentsjerry-liu--x

What is the goal of unsupervised machine learning?

arXiv:2610.11697v1 Announce Type: new Abstract: Unsupervised learning is one of the main branches of machine learning. Here I argue that unlike the other branches of machine learning (supervised and r

researcharxiv-cs-lg

Agents

What Output-Only Review Cannot Verify: Study Contracts for Research Agents

arXiv:2610.11754v1 Announce Type: new Abstract: Some defects in an AI-generated study can be identified from its artifacts; others require knowledge of what was approved before execution. We propose s

agentsarxiv-cs-ai

What to Admit and How to Present: Governing Persistent Memory in LLM Agents

arXiv:2610.11188v1 Announce Type: new Abstract: Persistent memory can improve personalization in LLM agents but can also induce sycophancy and cross-domain leakage. We distinguish two governance decis

When AI Finds Hidden Messages, Does It Report?

arXiv:2610.10620v1 Announce Type: cross Abstract: When an assistant encounters a message for another AI, does it tell its user? Four fixed model-provider deployments perform simulated source tasks in

When Can You Prune Your Network? A Study of Intermediate Neurons in Multilingual Speech Parsing

arXiv:2610.11520v1 Announce Type: new Abstract: End-to-end speech parsing, a task recently proposed, consists in predicting both the transcription and the syntactic tree for a spoken utterance. Existi

researcharxiv-cs-cl

When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection

arXiv:2610.10971v1 Announce Type: cross Abstract: Large language models are increasingly used in legal research and drafting, but they can still produce claims that sound convincing without being supp

When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better

arXiv:2610.11291v1 Announce Type: new Abstract: On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models. In this work, we ask a critical re

safetyarxiv-cs-cl

When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees

arXiv:2610.12212v1 Announce Type: new Abstract: Bayesian neural network predictions are commonly approximated using a fixed number of Monte Carlo samples per input, without controlling the resulting e

When History Helps and Hurts: Selective History Use across Multimodal Turns

arXiv:2610.11948v1 Announce Type: new Abstract: Reliable multimodal interaction depends on selective use of conversational history: an earlier question may remain relevant while its previous answer is

When Interfaces Speak: Data-Aware Generative UI Harness for Active Interaction

arXiv:2610.11123v1 Announce Type: new Abstract: Most human-agent interaction today remains text-based. Natural language can impose cognitive overload, ambiguity, information chaos, and slow input for

When KL Regularization Misfires in Group Policy Optimization

arXiv:2610.12161v1 Announce Type: cross Abstract: Why does removing reference-policy KL regularization sometimes improve group policy optimization? This motivates studying how reference-policy informa

safetyarxiv-cs-cl

When Listening Becomes Easier: Scrubbing Visual Cues for Shortcut-Free VLAs

arXiv:2610.10912v1 Announce Type: cross Abstract: Shortcut learning is a prevalent issue in robot learning. The limited diversity of robot demonstration datasets can mislead policies into exploiting s

safetyarxiv-cs-cv

When Lower Reconstruction Loss Hurts: Distributionally Robust Refinement for Low-Bit LLM Quantization

arXiv:2610.11226v1 Announce Type: new Abstract: Weight-only post-training quantization (PTQ) relies heavily on reconstruction loss minimization to preserve model quality at low precision. We show that

When Routing Reveals Membership: Privacy Leakage from MoE Router Telemetry

arXiv:2610.10616v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models produce routing information during inference that may be logged or exposed for monitoring, debugging, load anal

safetyarxiv-cs-lg

Local Ai

When Scene Text Hijacks the Scene: Uncovering, Exploiting, and Mitigating Rendered-Text Semantic Leakage in Image Generation Models

arXiv:2610.11286v1 Announce Type: new Abstract: The reliability and accountability of image generative models (IGMs) are essential for building responsible and trustworthy AI systems. Recent IGMs, suc

local-aiarxiv-cs-cv

When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation

arXiv:2610.12061v1 Announce Type: new Abstract: Large language model (LLM)-based agents have demonstrated strong capabilities on complex tasks. They typically perform reasoning before each action thro

safetyarxiv-cs-ai

When to Intervene? State-Aware Sparse Manipulation in Federated Reinforcement Learning

arXiv:2610.11523v1 Announce Type: new Abstract: Federated reinforcement learning (FRL) enables distributed agents to collaboratively train decision-making policies, but its decentralized training proc

safetyarxiv-cs-lg

Where Draft Trees Lose Target Mass: Exit-Guided Speculative Decoding

arXiv:2610.11750v1 Announce Type: new Abstract: Tree-based speculative decoding verifies multiple draft continuations in one target-model pass, but finite trees built from draft scores face a fundamen

Where to Adapt Matters: Layer-Selective Fine-Tuning for Capability Retention

arXiv:2610.11620v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) enables large language models (LLMs) to adapt to specialized tasks, but often at the cost of degrading general ca

Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution

arXiv:2610.12367v1 Announce Type: new Abstract: Skills, reusable procedural guidance added at inference, can substantially improve LLM downstream performance (Li et al., 2026). Prior work retrieves sk

Agents

Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents

arXiv:2610.11464v1 Announce Type: new Abstract: We changed the agent: did it actually get better? Every self-improving agent loop answers this hundreds of times, and every answer comes from a verifier

agentsarxiv-cs-ai

Tutorials

Whose Ground Truth? Embracing Ambiguity in Human-Centered AI

arXiv:2610.10805v1 Announce Type: new Abstract: As AI systems increasingly interact with people and make decisions about them, understanding human interpretations becomes an important part of developi

tutorialsarxiv-cs-ai

Why LLM Agents Favor Their Group: Stakes, Observed Norms, and Reputation

arXiv:2610.11008v1 Announce Type: cross Abstract: Language-model agents favor their own group because they have watched their members favor each other. The group label alone does little once the decis

Why machines will still not rule the world

arXiv:2610.11424v1 Announce Type: new Abstract: In our book Why machines will never rule the world [13, 14] we argue that arti- ficial general intelligence is mathematically impossible. This is becaus

Why On-Policy Distillation Sometimes Fails: Vanishing Learning Signals

arXiv:2610.11247v1 Announce Type: cross Abstract: On-policy distillation (OPD) enables effective capability transfer between language models, yet the mechanisms underlying its failures are not fully u

Wieszcz-XIX: A 3.1-Billion-Word Corpus of Pre-1918 Polish and Temporally Bounded Language Models Trained From Scratch

arXiv:2610.10592v1 Announce Type: new Abstract: Historical Polish is well documented as a language but annotated in machine-readable form only to about a million words for the period this paper covers

researcharxiv-cs-cl

Workerville: Towards an Organizational Behavior Account of Agent Safety

arXiv:2610.11561v1 Announce Type: new Abstract: LLM-based agents now interact with their environments continuously, shaped by such organizational channels as user instructions, peer messages, and long

World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning

arXiv:2610.10932v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) has produced a diverse set of goal-reaching algorithms, yet no single algorithm performs best acr

safetyarxiv-cs-lg

WorldAlign: Decoupled 4D Reward for World-Consistent Video Generation

arXiv:2610.12382v1 Announce Type: new Abstract: Faithful visual world simulation requires generated videos to maintain 4D world consistency, encompassing both static and dynamic consistency. Static co

safetyarxiv-cs-cv

WorldBench: Evaluating LLMs on Three.js Voxel World Generation

arXiv:2610.10622v1 Announce Type: cross Abstract: Large language models can now write complete, interactive 3D worlds as code, but grading those worlds automatically is unreliable. Existing judges tak

Local Ai

WorldCast: Distributed Multiplayer World Models

arXiv:2610.12412v1 Announce Type: new Abstract: Multiplayer world models must generate independently controlled views with consistent representations of both players and their shared environment. Most

local-aiarxiv-cs-cv

WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models

arXiv:2605.25077v2 Announce Type: replace Abstract: Recent video world models enable interactive camera navigation but provide limited control over individual objects. We study persistent object-state

researcharxiv-cs-cv

WorldFact-Bench: Beyond Image-Internal Plausibility to Image-World Consistency

model-releasesarxiv-cs-cv

arXiv:2610.11184v1 Announce Type: new Abstract: Advances in image generation have made visual authenticity increasingly difficult to assess. Although image forensics now examines both generation artif

WorldGuide: Goal-Directed Video World Model for Procedural Task Execution

arXiv:2610.12459v1 Announce Type: new Abstract: Video generators and video-based world models can synthesize plausible visual trajectories, but long-horizon procedural tasks require generation to adap

researcharxiv-cs-cv

WOVEN: Weaving Visual World Modeling into Multimodal LLMs

arXiv:2610.12417v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) struggle with spatial, embodied, physical, and temporal reasoning. We hypothesize that these failures reflect

Writing for the Reviewer: Defensive Writing in GPT Models

arXiv:2610.11355v1 Announce Type: new Abstract: Researchers increasingly use ChatGPT to revise their papers, and recent GPT versions often narrow or even retract the authors' claims. We call such chan

YOCO: You Only Calibrate Once! Fast Mocap Calibration for Dexterous Teleoperation

arXiv:2610.11657v1 Announce Type: new Abstract: Dexterous teleoperation requires reliable human-hand state estimations. However, common low-cost motion-capture gloves and markerless trackers often exh

researcharxiv-cs-ro

Zatom-2: Multitask Pretraining on Atomistic Data for Generative Modeling across Domains

arXiv:2610.11454v1 Announce Type: cross Abstract: Unified atomistic modeling has the potential to accelerate discovery in chemistry, materials science, and biology by bridging data-rich chemical domai

1/ My heart is heavy. I made a number of mistakes in my life, and not asking earlier for an immediate slowdown is one of them. If OpenAI sta…

model-releasesyoshua-bengio--x

1/ My heart is heavy. I made a number of mistakes in my life, and not asking earlier for an immediate slowdown is one of them. If OpenAI starts making similar breakthroughs in biology, we risk somethi

8 Oct 2026

2 months (meme-d) research, Do People Notice the Difference Between AI Models?

model-releasesr-localllama

Preamble and disclaimer, Sample size: 8, at any size of form this research is just screwing around being writing it down. This was just a little,(well... big), curiosity test I wanted to run to see wh

8 Oct 2026

$2800 rig with 8x Radeon Pro V620 (256 GB VRAM) + custom vLLM fork = Qwen3.8-Flash-Next at 60 to 100 t/s decode and 3000+ t/s prefill