Search: “benchmark”

Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning

arXiv:2610.12684v1 Announce Type: new Abstract: Non-prehensile manipulation with mobile robots has primarily targeted placement in coarse goal regions. Precision placement has been studied with manipu

safetyarxiv-cs-ro

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Research

NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo

DGX agent

arXiv:2610.12985v1 Announce Type: cross Abstract: Neural-network variational Monte Carlo provides an accurate route to solving the electronic Schrodinger equation, but many neural wavefunctions are op

researcharxiv-cs-lg

12 Oct 2026

NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection

arXiv:2610.12754v1 Announce Type: new Abstract: We propose NV-Tesseract-AD, a curriculum learning-enhanced conditional diffusion framework for multivariate time-series anomaly detection. Our central h

OccludeBench: Can VLMs Find What's Missing?

model-releasesarxiv-cs-cl

arXiv:2610.13790v1 Announce Type: cross Abstract: How well can vision-language models (VLMs) reason about what is missing from an image? We study this question with OccludeBench, a manually curated be

On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models

arXiv:2610.13676v1 Announce Type: new Abstract: Time series foundation models (TSFMs) increasingly offer covariate support, with calendar information being the most common type of categorical covariat

Tutorials

One Thing Leads to Another: The Essentials of Time Series Causal Discovery

arXiv:2610.13430v1 Announce Type: cross Abstract: Time series causal discovery --- the process of detecting causation between time series --- has its roots in signal processing research from over a ha

tutorialsarxiv-cs-lg

PANDA: Prior-guided Attentional Dual-path Architecture

arXiv:2610.13635v1 Announce Type: new Abstract: Hypergraph Neural Networks (HNNs), designed to model higher-order relations beyond pairwise interactions, typically rely either on fixed structural weig

PlacePulse: Learning Transferable Place Representations from Structural Geographic Data

arXiv:2610.13390v1 Announce Type: new Abstract: Geospatial machine learning often depends on task-specific feature engineering over large collections of sociodemographic, business, and environmental v

Private and Distributionally Robust Federated Multiclass Classification

arXiv:2610.13631v1 Announce Type: new Abstract: Fault diagnosis in asset-intensive industries requires multiclass models trained on noisy, sensitive data collected across geographically distributed cl

Proactive Agentic Orchestration

arXiv:2610.13485v1 Announce Type: new Abstract: Agentic orchestration delegates tasks to sub-agents with independent contexts, providing a key path to extending the effective execution horizon of larg

Provenance-Aware Belief Updating in Human-AI Feedback Loops

arXiv:2610.13008v1 Announce Type: new Abstract: In repeated human-AI interaction, a single observation can be quoted, summarized, and returned to a model as multiple reports, making repetition look li

Provenance-grouped protocol for evaluating machine learning in perovskite photovoltaics

arXiv:2610.12766v1 Announce Type: new Abstract: Machine-learning models for perovskite solar cells are typically evaluated using random data splits, which we show systematically leak information throu

QLoRA on ExLlamaV3 Quants - Train @ 2-8bpw

model-releasesr-localllama

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

RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics

arXiv:2610.13889v1 Announce Type: new Abstract: As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance bu

ReAT: Residual Action Tokenization for Autoregressive Robot Policies

arXiv:2610.13093v1 Announce Type: new Abstract: Autoregressive robot policies rely on action tokenizers to convert continuous action chunks into compact sequences of discrete prediction targets. Exist

safetyarxiv-cs-ro

Recurrent Harness for Agentic Continual Learning

arXiv:2610.12668v1 Announce Type: cross Abstract: Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learnin

agentsarxiv-cs-ai

Robot-WATCHDOG : failure detection through object-centric graph representation

model-releasesarxiv-cs-ro

arXiv:2610.13403v1 Announce Type: new Abstract: Reliable real-time failure detection is critical for deploying learned robotic policies, yet defining failures explicitly is infeasible in open-world se

Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids

arXiv:2607.16004v2 Announce Type: replace-cross Abstract: Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distributio

Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits

arXiv:2610.13438v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a key paradigm for improving large language model (LLM) agents, enabling stronger reasoning, planning, and to

safetyarxiv-cs-ai

RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance

arXiv:2602.00183v2 Announce Type: replace-cross Abstract: Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the perva

safetyarxiv-cs-lg

Hardware

SBNO : Schrodinger Bridge Neural Operator for the Forward and Inverse Problems under Missing Information

arXiv:2610.13317v1 Announce Type: new Abstract: Partial differential equation (PDE) surrogate modeling often relies on either dense observations or access to governing equations/physics constraints. I

hardwarearxiv-cs-lg

Scalable Circuit Learning for Interpreting Large Language Models

arXiv:2606.16939v2 Announce Type: replace-cross Abstract: A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly prod

Local 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

SEEDS: Preview-Guided Evidence Admission for Search Agents

arXiv:2610.12932v1 Announce Type: new Abstract: Search agents extend large reasoning models with multi-round access to external knowledge, yet most existing systems largely conflate retrieving evidenc

agentsarxiv-cs-cl

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

arXiv:2610.13207v1 Announce Type: new Abstract: Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive pattern

SOAR: Source-Aware Reinforcement Learning with Precise Credit Assignment for Search Agents

arXiv:2610.13157v1 Announce Type: new Abstract: Reinforcement learning (RL) for search agents often suffers from evidence fabrication, a failure mode where agents bypass external information (E_{ext})

Sources: DeepSeek agrees to lead robotic AI startup Winverse AI's seed funding round of tens of millions of dollars, valuing the startup at ~$400M post-money (Bloomberg)

Bloomberg: Sources: DeepSeek agrees to lead robotic AI startup Winverse AI's seed funding round of tens of millions of dollars, valuing the startup at ~$400M post-money — DeepSeek aims to lead the fir

model-releasestechmeme

SynAct: Synergistic Active Feedback Learning for LLM Agents

arXiv:2610.12651v1 Announce Type: new Abstract: LLM agents executing multi-step workflows fail in two fundamentally different ways: recoverable slips, which further attempts can repair, and knowledge

agentsarxiv-cs-lg

TabMF: Learning Dataset Representations with Tabular Foundation Models

arXiv:2610.13818v1 Announce Type: new Abstract: Representations of supervised datasets support higher-level machine-learning tasks such as dataset retrieval and distribution-shift monitoring. However,

Taking Tokenization off the Host for Agentic LLM Serving

model-releasesarxiv-cs-cl

arXiv:2610.13325v1 Announce Type: cross Abstract: Agentic workloads resend long contexts that barely change between calls. Prefix caching now skips most of their prefill, yet every request must still

Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels

arXiv:2610.13221v1 Announce Type: new Abstract: Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets

TernaryLLM: Ternarized Large Language Model

arXiv:2406.07177v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computat

The Bitter Lesson for harness design and formal math! Instead of hand-designing how an AI should prove (decompose, plan, assign agents), wha…

The Bitter Lesson for harness design and formal math! Instead of hand-designing how an AI should prove (decompose, plan, assign agents), what if we just give a frontier model good Lean feedback and le

agentsyohei-nakajima--x

Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

arXiv:2610.13810v1 Announce Type: cross Abstract: Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate. They inject non existent entities and off-topic

Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation

arXiv:2610.12806v1 Announce Type: cross Abstract: We study byte-level autoregressive language models whose hidden state is a small quantum register, whose transitions are learned token-conditioned qua

Local Ai

Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL

arXiv:2610.13020v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. Yet long-horizon autoregressive rollouts introduc

local-aiarxiv-cs-lg

TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.12828v1 Announce Type: new Abstract: Outcome-supervised group-relative policy optimization (GRPO) assigns the same advantage to every token in an LLM agent trajectory, leaving useful and un

U-Space: Uncovering When and Why Uncertainty Arises in Language Models [R][D]

model-releasesr-machinelearning

If you build on open-weight reasoning models, you have probably wanted a 'how sure is the model about this answer?' signal that does not cost 5x sampling, a judge model, or a labeled dataset. Hence, w

Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News

model-releasesarxiv-cs-cl

arXiv:2610.12863v1 Announce Type: new Abstract: Satirical news is a unique form of misinformation: it is intended to be humorous by the original publication source but can be misinterpreted. Explanati

Verifiable Sampling for Delegated Generative Quantum Machine Learning

arXiv:2610.13211v1 Announce Type: cross Abstract: Generative machine learning has shown remarkable results across a wide range of interdisciplinary tasks, motivating significant efforts toward Quantum

Web$hell: A Unified Interface for Agentic Web Access

arXiv:2610.13416v1 Announce Type: new Abstract: Large language model (LLM) agents are becoming direct users of the open web. As they take on increasingly complex and information-intensive tasks, effec

agentsarxiv-cs-ai

What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training

arXiv:2610.12995v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introdu

safetyarxiv-cs-ai

When Solvers Get Moody: Quantifying and Predicting Performance Variability in Mixed Integer Programming

arXiv:2610.12876v1 Announce Type: cross Abstract: Mixed-integer programming (MIP) solvers are widely used, yet their performance can vary substantially even under seemingly neutral perturbations. Such

A new work suggesting that division of labor among self-improving AI agents can help them avoid reinforcing their own errors. I found this w…

model-releasesdavid-ha--x

A new work suggesting that division of labor among self-improving AI agents can help them avoid reinforcing their own errors. I found this work interesting from a metascience perspective. Science is a

Building a 4x R9700 setup for a 10 person startup

model-releasesr-localllama

Just wanted to share a build I am doing for a client. $18k. Specs: Threadripper 9970x 128gb DDR5 ECC 5600 4x AMD Radeon R9700 AI Pro 32gb(128gb total VRAM) 1600W PSU(GPUs are undervolted and under 210

Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence Blog: https://pub.sakana.ai/mass…

Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence Blog: https://pub.sakana.ai/mass/ As AI systems tackle increasingly open-ended research prob

safetydavid-ha--x

microsoft/AesCode 8B and 32B

model-releasesr-localllama

https://preview.redd.it/r2ledkyuntuh1.png?width=1684&format=png&auto=webp&s=9ed4518b2d0f20f44835ebbfac605581da186a57 AesCode generates information-rich visual artifacts such as slides, posters, and da

New paper from the team at Sakana AI: Recursive Self-Improvement through Multi-Agent Self-Supervision (MASS) How do we keep improving AI whe…

New paper from the team at Sakana AI: Recursive Self-Improvement through Multi-Agent Self-Supervision (MASS) How do we keep improving AI when the tasks become too complex for humans to reliably evalua

agentsdavid-ha--x