Search: “models”

BrokenML: can agents fix broken ML jobs?

Write-up: https://zhebrak.io/posts/brokenml/ Repo: https://github.com/zhebrak/brokenml I built a benchmark to determine the access level agents need to successfully fix broken ML workloads (PyTorch ov

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BT-Agent: Deploying Onboard Robot Agents with Behavior Trees and Multi-Fidelity Evaluation

DGX agent

arXiv:2610.13862v1 Announce Type: new Abstract: Deploying language-model agents onboard mobile robots requires task reasoning and responsive execution within limited compute and memory. We propose BT-

model-releasesarxiv-cs-ro

12 Oct 2026

CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

arXiv:2603.05911v2 Announce Type: replace-cross Abstract: Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although

Golden Anchor for Video Generation

arXiv:2610.13217v1 Announce Type: cross Abstract: Video generation supports digital content creation, with applications in virtual avatars, AI-driven filmmaking, interactive game engines, and world mo

researcharxiv-cs-lg

Improve my vibe-slopped pipeline

tl;dr: Are there other models I should try? Is there something better than Kimi K3 for a code review? Is GLM-5.3 better at finding/patching security vulnerabilities? Are skills like https://github.com

Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability

arXiv:2610.12737v1 Announce Type: new Abstract: Gradient-based attribution provides input-specific information about model parameters, but computing it via backpropagation for many samples is expensiv

ManeuverFlow: Maneuver Hypothesis Generation with Future Evidence for Trajectory Evaluation and Refinement in End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2610.13409v1 Announce Type: new Abstract: End-to-end planning in complex scenes must model diverse driving intentions and select the most appropriate trajectory using scene context and future ri

MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph

arXiv:2610.12961v1 Announce Type: cross Abstract: We introduce MVGraph, a benchmark for evaluating large vision-language models (LVLMs) on multi-graph reasoning where the evidence required by a query

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

QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code

arXiv:2610.13811v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong results in classical code generation and are increasingly used to write the unit tests that safeguard t

Agents

Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting

arXiv:2610.12601v1 Announce Type: new Abstract: Prompt optimization is an attractive alternative to reinforcement learning for adapting language-model agents, since it is cheap and produces human-read

agentsarxiv-cs-cl

Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing

arXiv:2610.13823v1 Announce Type: cross Abstract: Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executi

researcharxiv-cs-ai

Safety

The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs

arXiv:2510.13154v3 Announce Type: replace Abstract: What happens inside a language model when alignment training conflicts with a cultural value it encodes? Across 16 MENA countries, 26 models, and 1.

safetyarxiv-cs-cl

Tutorials

When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors

arXiv:2610.13765v1 Announce Type: cross Abstract: Discrete graph diffusion models corrupt graph structure through independent edge updates with a Markovian noise model. Combined with the exchangeabili

tutorialsarxiv-cs-ai

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

11 Oct 2026

'Strata' for GLM5.3 Flash is here for some! Project Maya

I stumbled across this as I was currently having glm5.3 flash run only around 10tok/s basically unusable. With Maya i am running 30 tok/s now. GLM5.3 feels even with this low quant like a much more en

11 Oct 2026

vllm-ascend updates (GLM5.3-flash) on dual 310p Ascend cards

Alright, I learend a few valuable lessons since I posted about a week ago, and I've made more strides with the DDR4x Ascend 96GB 310P cards I purchased, so here we go, meet the new friendly and less v

11 Oct 2026

48Gb VRAM speed AND quality ! (Qwen 3.8 27B Swift 1.5 W8A16)

Hi, Because sometimes you need both speed AND quality, I made my own Qwen 3.8 27B Swift 1.5 quant. It's an 8 bits AWQ + GPTQ quant. Made with love and precision (read the model card) on my 4 RTX 3090,

10 Oct 2026

Mreaia v1.0.0: Universal Zero-Token Engine & Hardware-Locked Context Distillation for LLMs (>99.9% Reduction)

Hey everyone! 👋 Feeding multi-gigabyte files (SQL dumps, videos, audio, PDFs, and spreadsheets) directly into LLMs often causes context window overflow, GPU VRAM bottlenecks, massive token bills, and

model-releasesr-ollama

10 Oct 2026

Agentic-TTT: Training test-time policy for test-time training

arXiv:2610.12002v1 Announce Type: cross Abstract: Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settin

Amortized Off-Policy Evaluation for LLMs

arXiv:2610.10848v1 Announce Type: new Abstract: Accurate evaluation is central to selecting which LLM to deploy, yet testing a candidate on live traffic exposes real users to an unvetted model. Teams

Local Ai

Boosting the Local Invariance for Better Adversarial Transferability

arXiv:2503.06140v3 Announce Type: replace Abstract: Transfer-based attacks pose a significant threat to real-world applications by directly targeting victim models with adversarial examples generated

local-aiarxiv-cs-cv

CAFE: Causal Black-Box Testing of Machine Unlearning

arXiv:2509.16525v2 Announce Type: replace-cross Abstract: Machine learning models are increasingly deployed as software components that must evolve as requirements change. When specific training recor

researcharxiv-cs-ai

Applications

Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness

arXiv:2610.12361v1 Announce Type: new Abstract: Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision fo

applicationsarxiv-cs-ai

Local Ai

Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning

arXiv:2405.15662v2 Announce Type: replace Abstract: Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This

local-aiarxiv-cs-lg

Closed-loop evaluation of LLM agents for embedded software development

arXiv:2610.11447v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as coding agents that edit files, run builds and tests, inspect execution results, and repair s

Local Ai

Dynamic Free-Rider Detection in Cross-Silo Federated Learning via Simulated Attack Patterns

arXiv:2604.04611v3 Announce Type: replace Abstract: Federated learning (FL) enables multiple clients to collaboratively train a global model by aggregating local updates without sharing private data.

local-aiarxiv-cs-lg

Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology

arXiv:2610.12022v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in sociotechnical systems where social attribution, the reasoning process attributing external

FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams?

arXiv:2610.12427v1 Announce Type: cross Abstract: Streaming Video Large Language Models (VLMs) enable continuous video understanding, yet existing benchmarks focus on low-dynamic scenarios. Under boun

How Narrative Wrapping Affects LLM Refusal: A Cross-Language Benchmark and Defense

arXiv:2610.11005v1 Announce Type: new Abstract: Safety-aligned language models often refuse a harmful request stated directly but answer the same request inside a role-play or narrative wrapper. We me

Invariant-Measure Reasoners: Stable Representations for Latent Reasoning

arXiv:2610.10996v1 Announce Type: new Abstract: Latent reasoning models repeatedly update a latent state using the same recurrent block. As the recurrent depth increases, the sequence of latent states

researcharxiv-cs-lg

My 'Anthropic/ChatGPT in a box' is... now open source and publicly hosted on Github.

It's been a long time coming, and I should have done this in the first place. But LumaBrowser is now open source. Since I first released it a year ago, it's had several thousand installs and an absolu

Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment System

arXiv:2610.11768v1 Announce Type: cross Abstract: Large language model (LLM) agents are beginning to operate industrial energy equipment, and what they get right depends on what they are told about th

Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning

arXiv:2610.10655v1 Announce Type: cross Abstract: Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning

Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora

arXiv:2601.14994v2 Announce Type: replace-cross Abstract: Data contamination can invalidate benchmark evaluation when a model benefits from memorized evaluation content rather than genuine generalizat

ProtoSemImage: Image-Valued Prototypes with Deformable Row Alignment for Interpretable Document Classification

arXiv:2610.11460v1 Announce Type: cross Abstract: Prototypes in classification models are almost always vectors, and a vector has no readable form. This paper asks what happens when a prototype is an

RAG-Stress: Probing the Limits of Evidence Reliance in Retrieval-Augmented Generation

arXiv:2610.11183v1 Announce Type: cross Abstract: Following retrieved evidence does not guarantee factual correctness: misleading evidence can induce a model to replace an answer it previously gave co

researcharxiv-cs-ai

SignRAG: Unified Retrieval-Augmented Gloss-Free Sign Language Translation

arXiv:2610.11371v1 Announce Type: new Abstract: Contemporary decoder-only large language models (LLMs) have demonstrated strong capabilities across a wide range of domains. However, existing pretraini

TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution

arXiv:2610.10619v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly reshaping software engineering, accompanied by an explosion of new code benchmarks. Yet nearly all exist

ThinkingBox: Solving an agent task once vs. solving it 20/20: 507 stateful workflows graded on terminal database state [R]

model-releasesr-machinelearning

Figure 1b from our paper Disclosure: I'm one of the authors (Microsoft). The paper, code, dataset are public and ThinkingBox is on Hugging Face OpenEnv as well. Raw evaluation trajectories are not rel

Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting

arXiv:2610.12232v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth. But th

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

Adjoint-Based Calibration and Optimal Control of Stochastic Multiscale Bioprocess Digital Twins

arXiv:2610.09505v1 Announce Type: cross Abstract: We develop a bias-aware digital-twin calibration and control framework for multiscale bioprocess models within a biological systems-of-systems (Bio-So

AutoAdapt: Automatic Domain Discovery Enables Low-Cost Extensibility

arXiv:2610.10349v1 Announce Type: new Abstract: Instruction-tuned models are deployed into environments where domains are heterogeneous and evolve, yet adding new domains or data typically requires co

b11512

model-releasesllama-cpp-releases

model : fix DFlash output head sharing (#30111) llama : fix DFlash output head sharing Assisted-by: Codex dflash : read tied output weights from GGUF metadata Assisted-by: Codex llama : share tied wor

Best current R9700 inference engine?

There are way too many forks to keep track of, so I've gotten lost. As far as I can tell, Radiance VLLM is best for models that fit in GPUs while some form of llama.cpp is probably best for MOE RAM-sp

Child ASR Adaptation with Adult Retention: An Empirical Study

arXiv:2610.08827v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems often underperform for children and non-native speakers, while adapting adult ASR models to child speech can

researcharxiv-cs-cl

Hardware

HE-OFT: Privacy-Preserving One-Shot Federated Fine-Tuning under Homomorphic Encryption

arXiv:2610.08255v1 Announce Type: cross Abstract: Many organizations adapt large pretrained models to their own tasks by fine-tuning on private data. Several of these parties often hold data for the s

hardwarearxiv-cs-lg