Search: “models”

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

model-releasesarxiv-cs-cv

arXiv:2606.19651v2 Announce Type: replace-cross Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented

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DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English

DGX agent

arXiv:2601.22888v4 Announce Type: replace Abstract: More than 80% of the 1.6B English speakers do not use Standard American English (SAE), yet LLMs often fail to correctly identify non-SAE dialects an

model-releasesarxiv-cs-cl

30 Jul 2026

Evaluating Prompt Scope and Demonstration Similarity in Local LLM Machine Translation

arXiv:2607.26286v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as general-purpose translation systems, but their behavior is usually evaluated under a single prompt

FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing

model-releasesarxiv-cs-cv

arXiv:2607.26432v1 Announce Type: new Abstract: Face anti-spoofing (FAS) is increasingly expected to provide not only bona fide/spoof decisions, but also attack semantics and image-grounded evidence f

From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

arXiv:2607.26571v1 Announce Type: new Abstract: The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprin

Hearsay: Vision-Language Medical Diagnoses Without an Image

arXiv:2607.26886v1 Announce Type: cross Abstract: When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show

InferScale: GPU-Native KV Injection for Personalized LLM Serving

arXiv:2607.27090v1 Announce Type: cross Abstract: Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histori

Investigating three real-world incidents in our cybersecurity evaluations

model-releasessimon-willison

Investigating three real-world incidents in our cybersecurity evaluations It happened again! This is turning into something of a pattern. Last week OpenAI accidentally exploited Hugging Face when one

Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification

arXiv:2607.26397v1 Announce Type: new Abstract: Enzyme function prediction is a hierarchical, knowledge-intensive form of protein function classification. Existing benchmarks expose an anomaly: genera

Local Ai

Making a synthetic dataset for fine-tuning

I've been thinking about building a pipeline to generate reasoning training data for LLMs, but I want to avoid the common failure mode of synthetic data where you just generate the same template with

local-air-localllama

Mechanistic interpretability streamlined for everyday users like us😎 🧠

model-releasesr-localllama

Context: I want to give the community an Open Research (well open under Apache 2.0 clause) - tool that allows everyday users like us to look deeper into the local models we use consistently. Mechanist

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment

arXiv:2607.27081v1 Announce Type: cross Abstract: Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers

safetyarxiv-cs-cl

ReCo: Reweighting GRPO Against Distributional Concentration

arXiv:2607.26862v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has become a standard reinforcement learning method for post-training language models. Recent work shows that

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

arXiv:2607.26643v1 Announce Type: cross Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applica

SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response

arXiv:2607.26791v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces

sure we lose money on every inference but we make it up in volume

model-releasesgary-marcus--x

sure we lose money on every inference but we make it up in volume We are committed to pushing the model frontier across cost efficiency, capability, and speed. Starting today, we are reducing prices f

Symphony of Bias: Exploring Gender Associations with Musical Instruments in Multimodal LLMs

arXiv:2607.26355v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly embedded in everyday life and widely used for information seeking, raising concerns about their potential

This is absolutely wild... Anthropic reviewed their logs and found out that their own supposedly-sandboxed cyber evals had hacked three sepa…

model-releasessimon-willison--x

This is absolutely wild... Anthropic reviewed their logs and found out that their own supposedly-sandboxed cyber evals had hacked three separate companies back in April without them noticing! In a rev

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations

arXiv:2607.25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making. However, the c

Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI

arXiv:2602.15021v2 Announce Type: replace-cross Abstract: Cross-survey generalization is a critical challenge in stellar spectral analysis, particularly in cases such as transferring from low- to mode

Harm is not Universal: Community-Specific Toxicity Detection is Urgently Needed

arXiv:2607.24898v1 Announce Type: cross Abstract: State-of-the-art toxicity detectors for text-to-image generation adopt a one-size-fits-all approach: a single universal model applying fixed safety gu

Learned, Relied Upon, or Necessary? Separating Checkpoint Dependence from Task-Level Value in Sheaf GNNs

arXiv:2607.25387v1 Announce Type: new Abstract: Learned restriction maps in sheaf graph neural networks are often treated as proof that the model has discovered useful edge geometry. That conclusion d

MedJudgeRAG: Option-Wise Evidence Judgment with Dynamic Knowledge Graphs for Medical MCQA

arXiv:2607.24838v1 Announce Type: cross Abstract: In medical multiple-choice question answering (MCQA), Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of language models (LMs

MemSFT: Mitigating Alignment Tax with an External Parametric Memory

arXiv:2607.25614v1 Announce Type: cross Abstract: Adapting Large Language Models (LLMs) to specialized domains often incurs an alignment tax, as fine-tuning on domain-specific tasks can cause catastro

Penelope: Localized Latent Recurrence for Efficient Structured Reasoning

arXiv:2607.25915v1 Announce Type: new Abstract: Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or b

Rashomon Alignment

arXiv:2607.25680v1 Announce Type: cross Abstract: We propose Rashomon Alignment (RA), a new measure to assess functional similarity between two models. Existing functional similarity measures are dist

safetyarxiv-cs-ai

RoboProcessBench: Benchmarking Process-Aware Understanding in Vision-Language Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.13040v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly explored as visual critics, reward generators, and failure detectors in robotic manipulation. These r

Shieldstral

arXiv:2607.25857v1 Announce Type: new Abstract: We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7imes its size on text s

TabRank: Chain-of-Thought Distillation for Table Re-Rankers

arXiv:2607.25182v1 Announce Type: cross Abstract: The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval. Multi-stage retrieval systems rely

TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking

arXiv:2607.24750v1 Announce Type: new Abstract: Large Language Models (LLMs) are temporally overexposed: trained on vast contemporary corpora, they encode present-day concepts that make them unreliabl

Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm

arXiv:2607.25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), ho

Research

A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning

arXiv:2601.09624v2 Announce Type: replace-cross Abstract: Machine unlearning is becoming essential for building trustworthy and compliant language models. Yet unlearning success varies considerably ac

researcharxiv-cs-ai

Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series

arXiv:2607.23165v1 Announce Type: cross Abstract: We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The centra

Research

Adversarial Test-Hardening for AI-Written Code: An Instrument Autopsy and a Pre-Registered Causal Estimate of the Critic Loop

arXiv:2607.23002v1 Announce Type: cross Abstract: Large language models increasingly write both code and the tests meant to check it; coverage records what ran, not what was verified. We study an adve

researcharxiv-cs-ai

AlloBench: Measuring Online Tool Allocation Capability in LLM Agents

arXiv:2607.23332v1 Announce Type: new Abstract: Creating a reusable tool is an investment: an agent pays a fixed cost now in exchange for the potential of future reuse. Therefore, a user should prefer

AptAvatar: Fast and Vivid Long-Form Audio-Driven Video Generation for Production-Ready Avatars

model-releasesarxiv-cs-cv

arXiv:2607.24013v1 Announce Type: new Abstract: Production-ready audio-driven avatar generation requires efficient inference without sacrificing fidelity or motion expressiveness. However, existing ac

AssumptionMiner: Extracting, Tracing, and Revising Implicit Assumptions in LLM Code Generation

arXiv:2607.22898v1 Announce Type: cross Abstract: Large language models (LLMs) generate code from natural-language prompts, yet real-world prompts rarely provide complete specifications. When prompts

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS

arXiv:2607.22657v1 Announce Type: cross Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing

Compressing LLMs with MoP: Mixture of Pruners

arXiv:2602.06127v2 Announce Type: replace Abstract: The high computational demands of Large Language Models (LLMs) motivate methods that reduce parameter count and accelerate inference. In response, m

Local Ai

Context-Aware Concept Distillation for Trustworthy Flood Prediction

arXiv:2607.23237v1 Announce Type: cross Abstract: Effective flood risk management relies on accurate forecasting, yet the 'black box' nature of stateof-the-art Deep Learning models creates a barrier t

local-aiarxiv-cs-ai

Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discovery

arXiv:2607.23896v1 Announce Type: new Abstract: Autonomous laboratories automate experimental execution, but a campaign must also decide which recovery pathway merits optimization. We formulate this a

Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities

arXiv:2607.23018v1 Announce Type: cross Abstract: Nonstationary Gaussian process (GP) models are powerful tools for capturing input-dependent variability by adapting to observed data. However, with li

safetyarxiv-cs-lg

Data Pyramid for Embodied Manipulation

arXiv:2607.24744v1 Announce Type: cross Abstract: Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require d

safetyarxiv-cs-cv

Do LLMs Know Their Vulnerable Scenarios?

arXiv:2607.23496v1 Announce Type: new Abstract: Safety-aligned large language models are trained to refuse harmful requests, yet embedding the same requests in particular scenarios can bypass their sa

DocHRL: A Hierarchical Reinforcement Learning Framework for Cost-Optimised Document Classification

arXiv:2607.22644v1 Announce Type: new Abstract: Real-world document classification pipelines typically apply the same sequence of models to every incoming document, regardless of its complexity or typ

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

arXiv:2607.23822v1 Announce Type: new Abstract: Driving style captures stable, driver-specific patterns in how a vehicle is driven. In naturalistic data, however, this signal is hard to isolate becaus

FilmBench: A Film-Grade Benchmark for Cinematic Video Generation

arXiv:2607.24241v1 Announce Type: cross Abstract: Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw p

I built a tool to actually test which weights matter before quantizing, instead of guessing (Qwen3.6-27B, 3 builds: Bedrock/Tightrope/Gambit)

model-releasesr-localllama

Most quantization works like this: pick a bit depth, apply it everywhere, maybe let imatrix take a rough guess at what matters, ship it. Most don't check which specific weight groups can take a hit an