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arXiv:2610.07825v1 Announce Type: cross Abstract: Time series forecasting models are typically compared on pointwise error, which scores a prediction in isolation from the decision it is produced for,

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Research

How High Is 0.6? Floors, Ceilings, and Headroom in Interpretability Probing

DGX agent

arXiv:2610.08544v1 Announce Type: cross Abstract: Probes are the workhorse of interpretability. If a model's hidden states predict a variable, the model is said to represent it. But a probe score has

researcharxiv-cs-ai

7 Oct 2026

Latent space bias directions in LLMs capture confidence, not fairness

arXiv:2610.08559v1 Announce Type: cross Abstract: Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models. However, prior work reports t

safetyarxiv-cs-ai

Learning to Decide, Not to Reason: Parameter-Efficient Decision Operators via Low-Rank Activation Steering

arXiv:2610.06950v1 Announce Type: cross Abstract: Injecting skills into a frozen language model currently costs a million parameters and a reinforcement-learning pipeline. We introduce method{}, a Sys

LiLib: Lifelong Air-to-Ground Path-Loss Prediction on UAVs via a Drift-Triggered Model Library

arXiv:2610.07111v1 Announce Type: cross Abstract: UAVs that act as relays or base stations need accurate air-to-ground path-loss predictions for rate adaptation and placement, but propagation conditio

researcharxiv-cs-ai

MASKerade: Token-Routed Mask Experts for Dense-to-MoE Upcycling

arXiv:2610.07809v1 Announce Type: new Abstract: Sparsely activated Mixture-of-Experts (MoE) models increase model capacity without a proportional increase in per-token computation. Dense-to-MoE upcycl

Matching Object or Relation? Tracing Abstract Reasoning Inside VLMs

arXiv:2610.07646v1 Announce Type: new Abstract: Vision Language Models (VLMs) excel on visual benchmarks but fail systematically on tasks requiring abstract reasoning. Existing benchmarks document thi

Applications

NMPP: Nonlinear Model Predictive Planning for Agile UAV Flight in Cluttered Environments

arXiv:2610.08695v1 Announce Type: new Abstract: Flying a quadrotor through a cluttered environment requires not only planning a collision-free reference trajectory based on perceived obstacles, but th

applicationsarxiv-cs-ro

Agents

One for All, All for One: Coordinated Multi-Agent Diffusion Steering via Stochastic Optimal Control

arXiv:2610.08595v1 Announce Type: cross Abstract: Deep generative models often produce structured outputs composed of interacting components. Modelling these outputs with a single model requires learn

agentsarxiv-cs-ai

RAM-Net: Linear-Time Sequence Modeling with Sparsely Addressable State

arXiv:2602.11958v2 Announce Type: replace-cross Abstract: Linear attention offers an efficient alternative to full attention with a fixed-size recurrent state. However, this state is shared by all tok

researcharxiv-cs-cl

Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling

arXiv:2610.08677v1 Announce Type: new Abstract: Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly

local-aiarxiv-cs-lg

A local 27B model reads my lease and finds a leak in my bills | Row-Bot + qwen3.8 on Ollama

Row-Bot 5.0 with qwen3.8:27b in Ollama, on my own GPU. No API keys, no cloud. Gave it a year of bills, a tenancy agreement, an insurance policy and a rent increase letter (all made up). It spotted a l

local-air-ollama

BARQ: Balanced Codebook Refinement for Low-Bit LLM Quantization

arXiv:2610.04490v1 Announce Type: new Abstract: As large language models (LLMs) grow in parameter count, model storage and parameter memory traffic have become major bottlenecks to efficient deploymen

Tutorials

CurveCodec 2: Skeleton-agnostic animation compression with a learned entropy model

arXiv:2610.04211v1 Announce Type: cross Abstract: Skeletal motion is stored as every joint's transform at every frame, yet most of it is implied by the body rather than by what the motion is about. Co

tutorialsarxiv-cs-ro

DelegationBench: Measuring When AI Agents Should Ask Before Acting

arXiv:2610.05532v1 Announce Type: cross Abstract: AI agents that send emails, edit files, and make purchases must decide when to act on their own and when to check with the user first. This decision i

FADE: Frame-Aware Diffusion-Transformer-based Multi-Concept Erasure for Video Unlearning

arXiv:2610.03980v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models can reproduce copyrighted, violent, or explicit content, which motivates concept erasure: removing designated conce

FlashMoE: Reducing SSD I/O Bottlenecks via ML-Based Cache Replacement for Mixture-of-Experts Inference on Edge Devices

arXiv:2601.17063v2 Announce Type: replace Abstract: Recently, Mixture-of-Experts (MoE) models have gained attention for efficiently scaling large language models. Although these models are extremely l

local-aiarxiv-cs-lg

google/embeddinggemma-2 · Hugging Face

model-releasesr-localllama

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimens

Greedy Multi-Path Block Verification for Faster Decoding in Speculative Sampling

arXiv:2602.16961v2 Announce Type: replace-cross Abstract: The goal of L-step speculative decoding is to accelerate autoregressive decoding of a target model by using a cheaper draft model to generate

Learning to Learn a Language: in-context learning of natural language from a synthetic non-linguistic prior [R]

model-releasesr-machinelearning

Learning from data as we observe it is easy for humans, but most machine learning models have limited ability to learn from new data that they have not seen during training. Prior-fitted networks (the

MedAD-R1: Consistency-Reinforced Policy Optimization for Interpretable Medical Anomaly Detection

arXiv:2602.01081v2 Announce Type: replace Abstract: Medical Anomaly Detection (MedAD) offers a promising direction for medical image analysis with Large Multimodal Models (LMMs). However, progress is

MiMo V2.6 Pro almost matched GPT-6 Astra at figuring out what a user actually wants

model-releasesr-localllama

Introducing InferBench: A benchmark testing how well frontier LLMs infer a user's priorities from their instructions. We tested 12 LLMs across 20 scenarios with 2.8k conversations to see which models

Optimal Control with Learned Critics under Unmodeled State Dependencies

arXiv:2610.05359v1 Announce Type: new Abstract: Model Predictive Control (MPC) provides a structured and constraint-aware mechanism for decision-making, but its reliance on optimization-friendly analy

local-aiarxiv-cs-ro

RoSA: Rotational Sparse Adaptation for Memory-Efficient Fine-Tuning

arXiv:2610.06243v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) reduces the cost of adapting foundation models by focusing training on a small parameter subset. Complementary to

Sharpen Without Search: On-Policy Distillation of Sequence-Level Power Distribution

arXiv:2610.06804v1 Announce Type: new Abstract: A language model can give a correct answer more probability than any single incorrect answer and still usually sample an incorrect one, because the inco

safetyarxiv-cs-lg

SpatialChain: A Benchmark for Auditing Spatial Reasoning Faithfulness in VLMs

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

SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts

arXiv:2610.06315v1 Announce Type: new Abstract: Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and com

safetyarxiv-cs-lg

Applications

Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data

arXiv:2610.05825v1 Announce Type: new Abstract: Classification problem in the context of highly imbalanced data is a major challenge in many real-world applications (e.g., FinTech, healthcare, etc.).

applicationsarxiv-cs-lg

We’re so back.

model-releaseslm-studio--x

We’re so back. Meet Mistral Large 4, aka Le Chonk. • 1T parameters, natively multimodal. 49B active. It is the best open weights model from US or Europe on aggregated benchmarks. • State-of-the-art on

What Does a Harness Repair? A Preregistered Study of Visibility, Baseline Adequacy and Evaluation Defects

arXiv:2610.05533v1 Announce Type: new Abstract: Harness search keeps a change to the prompts, reasoning switches, token budgets or parsers around a frozen model if the change raises a score. Such a ga

A Composable AI-Accelerated Iterative Solver for 3D-IC Thermal Modeling

arXiv:2610.02461v1 Announce Type: new Abstract: Accurate thermal analysis of heterogeneous 2.5D/3D-IC packages is essential yet computationally prohibitive. A single full-package FEM simulation can ta

researcharxiv-cs-lg

Adaptive Second-Order Solvers for Fast Stochastic Diffusion Sampling

arXiv:2610.03034v1 Announce Type: cross Abstract: Diffusion models rely on numerical solvers requiring time-discretization, which has a large influence on the tradeoff between sampling cost and qualit

researcharxiv-cs-cl

Are you Synthesizing or Recalling? Evaluating LLMs on Algorithmic Code Retrieval

arXiv:2610.02438v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance in code generation, where success depends on both recalling relevant algorithmic kno

Below what training size do deep tabular generators stop beating trivial baselines? A preregistered benchmark on a size ladder of clinical and standard datasets

arXiv:2610.03500v1 Announce Type: new Abstract: Deep tabular generative models are benchmarked on datasets with tens of thousands of rows; clinical datasets have hundreds. We preregistered and ran a s

Dynamic LLM Routers are Often Misguided

arXiv:2610.02762v1 Announce Type: new Abstract: Dynamic LLM routers promise to cut inference costs by sending each query to the cheapest model that can answer it correctly. We analyze six commercial r

EvoRiskBench: An Evolving Benchmark for Runtime Security Risks in Workspace Agents

arXiv:2610.03153v1 Announce Type: cross Abstract: Workspace agents combine large language models with execution harnesses to perform stateful, multi-step tasks that access or modify external resources

HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning

model-releasesarxiv-cs-cl

arXiv:2610.03039v1 Announce Type: new Abstract: Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high infere

Tutorials

Learning a Fact Is Not Learning How to Retrieve It

arXiv:2610.03251v1 Announce Type: new Abstract: A model trained on 'The capital of X is Y' may produce 'Y' after 'The capital of X is' but fail after 'The capital of X:'. We call these different ways

tutorialsarxiv-cs-ai

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

model-releasesarxiv-cs-cl

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:

Mitigating Social Sycophancy via Pluralistic Preference Optimization

arXiv:2610.02568v1 Announce Type: new Abstract: Personal advice, including relationship advice, now ranks among the most common uses of generative AI. But language models (LMs) exhibit sycophancy: the

researcharxiv-cs-ai

Moving Forward with Video Saliency: A New Dataset and Benchmark where Motion Matters

arXiv:2610.03276v1 Announce Type: new Abstract: Video saliency prediction is inherently harder to model than static image saliency due to the additional temporal dimension. Video saliency benchmarks r

Objects Without Morphisms: What LLMs for Mathematics Do Not Represent

arXiv:2610.03551v1 Announce Type: new Abstract: Large language models (LLMs) have reached expert-level performance on competition mathematics largely through the volume of search placed around them: c

Refinement Buys Intelligibility, Search Buys Identity: What Test-Time Compute Buys in Masked-Diffusion TTS

arXiv:2610.03320v1 Announce Type: new Abstract: Diffusion language models for text-to-speech combine two forms of computation: model depth (parameters) and refinement steps (inference budget). We ask

researcharxiv-cs-ai

Applications

Seer: Maximum Likelihood Regression for Learning-Speed Curves

arXiv:2610.02610v1 Announce Type: new Abstract: The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations

applicationsarxiv-cs-lg

When Can We Trust the Matching Principle? Robust Deployment Geometry Under Finite-Sample and Model Uncertainty

arXiv:2610.02894v1 Announce Type: cross Abstract: Match only geometry you can identify; otherwise spread the penalty. We quantify that decision by the trust ratio tau = epsilon / gamma (estimation unc

safetyarxiv-cs-ai

World Editing: Intervening on Executable Worlds at Increasing Depth

arXiv:2610.02331v1 Announce Type: new Abstract: Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable wor

A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems [R]

model-releasesr-machinelearning

In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems (DS)” (preprint: https://arxiv.org/abs/2607.14937) we reduce a DS foundation model to

4 Oct 2026

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