Search: “models”

arXiv:2610.22236v1 Announce Type: cross Abstract: On-policy distillation consolidates specialist models into one student at the price of rollouts, teacher passes and gradient steps for every teacher.

safetyarxiv-cs-ai

16 Oct 2026

ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence

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arXiv:2610.14381v1 Announce Type: new Abstract: Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system

model-releasesarxiv-cs-ai

15 Oct 2026

Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection

arXiv:2610.16049v1 Announce Type: cross Abstract: Existing audio deepfake detection (ADD) methods frequently exhibit limited generalization, as fixed bottleneck architectures fail to adapt to the hete

Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages

arXiv:2610.20263v1 Announce Type: new Abstract: Every multilingual speech pipeline begins with a language decision, an error there propagates silently through recognition, translation and routing. In

AuthGateBench: Benchmarking LLMs as Authorization Gate

arXiv:2610.16426v1 Announce Type: new Abstract: Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a classi

Agents

Choosing LLMs for Strategic Agent Interactions

arXiv:2610.18354v1 Announce Type: new Abstract: Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capabili

agentsarxiv-cs-ai

Agents

ClawLoop: Self-Improving Agents via Harness-Native Training Loop

arXiv:2610.14118v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents op

agentsarxiv-cs-ai

Customers love choice! @AIatMeta Muse Spark Open and @nvidia Nemotron 3.5 Super VL are now available on the Dell Enterprise Hub with @huggin…

model-releasesclem-delangue--x

Customers love choice! @AIatMeta Muse Spark Open and @nvidia Nemotron 3.5 Super VL are now available on the Dell Enterprise Hub with @huggingface 🤗 https://dell.huggingface.co/updates/muse-spark-open-

Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection

arXiv:2610.19178v1 Announce Type: new Abstract: Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shifts between tr

researcharxiv-cs-lg

Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison

arXiv:2610.18415v1 Announce Type: cross Abstract: Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps. However, standard RL alignment ste

Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution

arXiv:2610.17816v1 Announce Type: new Abstract: Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-

From Brewing to Resolution: Tracing the Internal Lifecycle of Code Reasoning in LLMs

arXiv:2606.17648v3 Announce Type: replace Abstract: Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle

GDBA-Net: A Gated Dual-Branch Adaptation of SAM2 for Robust Cross-Domain Nuclei Segmentation

arXiv:2610.20073v1 Announce Type: new Abstract: Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries,

Genesis Steering: Better Activation Steering Through Training Data

arXiv:2610.19114v1 Announce Type: new Abstract: Activation steering controls large language models at inference time, but its effectiveness depends on the choice of reference data. We introduce genesi

HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution

arXiv:2610.19524v1 Announce Type: new Abstract: Accurate path loss (PL) estimation is essential for wireless network planning and optimization. This paper proposes HankelKANet, a lightweight physics-i

Agents

HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems

arXiv:2610.15221v1 Announce Type: new Abstract: AI agents can evolve by using environment feedback to revise their harnesses, the software managing model inputs, tool use, memory, and execution, while

agentsarxiv-cs-ai

Learning to Select Features with Manifold Deep Unfolding PCA

arXiv:2610.19436v1 Announce Type: new Abstract: Unsupervised feature selection (UFS) is of great significance in image processing and pattern recognition. Nevertheless, existing model-based methods of

LED-BENCH: A Benchmark for Large-format Engineering Drawing Understanding

arXiv:2610.19571v1 Announce Type: new Abstract: Real-world engineering drawings are often large-format and information-dense. Answering practical engineering questions therefore requires both fine-gra

LintEval: Benchmarking LLMs on Linter-Guided File Refactoring

arXiv:2610.20027v1 Announce Type: cross Abstract: Linters are widely used in code maintenance and real-time IDE editing to surface lint alerts related to performance, style, error-prone, and other pat

More Than Seeing: Vision as Scalable Anchors in Native Multimodal Pre-Training

arXiv:2610.19443v1 Announce Type: new Abstract: Native multimodal pre-training trains a single model from scratch on text and multimodal data, enabling it to understand both language and visual inputs

researcharxiv-cs-cl

MS-Exam-Gen: An Automatic Benchmark Construction Toolkit for Evaluating LLMs on Multiple Sclerosis-MRI Imaging Knowledge

arXiv:2610.06170v2 Announce Type: replace Abstract: Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple

NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis

arXiv:2610.19190v1 Announce Type: new Abstract: Many sentiment datasets use one annotator for each item, so the test labels show the conventions of that person. We build a Nepali benchmark from two an

Safety

On the Limits of Representation-Based Test-Time Adaptation for Multimodal LLMs

arXiv:2610.19892v1 Announce Type: cross Abstract: Vision-language models suffer significant accuracy drops under common corruptions. While test-time adaptation (TTA) successfully mitigates these shift

safetyarxiv-cs-cv

Local Ai

Overfitting and creativity

I remember great creativity from GPT-2. It was trained on a dataset that is orders smaller than current training datasets. The problem with overfitting a model to the training data is that it becomes

local-air-localllama

Local Ai

Remember, Revisit, Revise: Scaffold Memory for Long-Horizon 3D Geometry

arXiv:2610.19674v1 Announce Type: new Abstract: Geometry foundation models estimate camera poses and 3D scene structure from video sequences, but maintaining consistent reconstructions over long horiz

local-aiarxiv-cs-cv

SAM3RL: Memory Control via Reinforcement Learning for Visual Object Tracking

arXiv:2610.19931v1 Announce Type: new Abstract: Segment Anything Model 3 (SAM 3) is a foundation model for image and video segmentation that sets the state of the art in visual object tracking. Its tr

researcharxiv-cs-cv

Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation

arXiv:2610.20221v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed across languages, yet their safety and reasoning capabilities remain predominantly evaluated in E

Scaling Personality Control in LLMs with Big Five Scaler Prompts

arXiv:2508.06149v2 Announce Type: replace Abstract: We present Big5-Scaler, a prompt-based framework for conditioning large language models (LLMs) with controllable Big Five personality traits. By emb

researcharxiv-cs-cl

Scheduling Parallel Optical Circuit Switches for AI Training

arXiv:2603.07373v2 Announce Type: replace-cross Abstract: The rapid growth of AI training has dramatically increased datacenter traffic demand and energy consumption, which has motivated renewed inter

SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise

arXiv:2610.17093v1 Announce Type: new Abstract: A model diff credits post-training with whatever separates a base checkpoint from one post-trained descendant, presuming a rerun would land close by. On

SpectrumQuant: Spectrum Alignment for LLM Quantization

arXiv:2610.20303v1 Announce Type: new Abstract: Orthogonal transforms have emerged as an effective tool for low-bit quantization of large language models (LLMs), as they reshape activation distributio

STAMP as a topology-aware molecular language for fragment-level learning and editing

arXiv:2610.18438v1 Announce Type: cross Abstract: Molecular fragments provide chemically meaningful units, but existing molecular languages rarely make their locations and connections explicit for edi

Strata might be actually really detrimental for intelligence? or is it Swift and GSQ-RCO? or both?

model-releasesr-localllama

I continued to use Strata but something just feels off for this an large model, it is very fast but intelligence seemed even under 27B 3.8 UD Q4, it had a hard time coding a basic 3d game and a 2d pla

Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent

arXiv:2610.19187v1 Announce Type: cross Abstract: Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, e

researcharxiv-cs-lg

Target-Token Asymmetry Can Masquerade As Depth Specialization

arXiv:2610.19344v1 Announce Type: new Abstract: Layer-ablation studies often perturb early or late layers of a language model, compare the damage to prediction on two corpora, and read a larger early-

researcharxiv-cs-cl

Ternary Bonsai 2 (2B, 4B, and 9B) just released on Hugging Face. The 2B can run entirely locally in the browser at up to 500 tok/s on an M4 Max with custom WebGPU kernels.

model-releasesr-localllama

Models on HF: https://huggingface.co/collections/prism-ml/bonsai-2-small WebGPU demo: https://huggingface.co/spaces/webml-community/ternary-bonsai-2-hermes-webgpu submitted by /u/xenovatech [link] [co

TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering

arXiv:2610.19551v1 Announce Type: new Abstract: Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missi

The Agent Knew: Internal States Forecast Unauthorized Actions and Stop Them, and the Lies That Follow

arXiv:2610.17301v1 Announce Type: new Abstract: Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact de

Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules

arXiv:2610.19210v1 Announce Type: new Abstract: Transformers are efficient to pre-train at scale because attention permits parallel computation across sequence positions, but long-context inference re

Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers

arXiv:2610.19608v1 Announce Type: cross Abstract: Counts of which brands a large language model names are useful only if they hold long enough to act on. We test this with a pre-registered replication

Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation

arXiv:2610.15227v1 Announce Type: new Abstract: Unified vision-language models (VLMs) perform both text-to-image (T2I) and image-to-text (I2T) generation with one shared Transformer backbone, yet how

researcharxiv-cs-ai

Safety

Verifier-Grounded Agentic RL for Provably Correct Code

arXiv:2610.19298v1 Announce Type: new Abstract: Reinforcement learning for code usually rewards passing tests, which a model can do while remaining wrong on the inputs the tests do not cover. A progra

safetyarxiv-cs-lg

VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?

arXiv:2605.06068v2 Announce Type: replace Abstract: For years, we have built LLM serving systems like any other critical infrastructure, with a single general-purpose stack hand-tuned over many engine

Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells

arXiv:2610.18949v1 Announce Type: cross Abstract: Existing machine-learning models for perovskite solar cells struggle to predict devices made from new materials. Most treat device layers as arbitrary

VisualWorldBench: A Fine-Grained Multi-Task Benchmark for Evaluating Visual World Knowledge in MLLMs

arXiv:2610.20083v1 Announce Type: new Abstract: Visual world knowledge is a core capability of multimodal large language models (MLLMs), underpinning knowledge-intensive visual question answering and

What You Sign Is Not What You Run: The Verifiability Gap in Quantized LLM Supply Chains

arXiv:2610.19498v1 Announce Type: cross Abstract: Model signing authenticates the full-precision weights W an LLM is released with, yet users run a quantized artifact Q whose producing transformation

When Should Enterprise Policy Decisions Be Learned, and When Should They Be Reasoned?

arXiv:2610.17643v1 Announce Type: new Abstract: An enterprise agent that answers under a written policy must choose an action before it says anything: answer, ask for a missing fact, ask for confirmat

Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions

arXiv:2610.14334v1 Announce Type: new Abstract: In Model Context Protocol (MCP) systems, LLMs select tools from metadata rather than from their implementations. Names and descriptions summarize what a