arXiv AI
Aug 7
MameLoshnLM: Yiddish Language Model and Evaluation Benchmark
arXiv:2608. 05850v1 Announce Type: cross Abstract: We present MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Aug 7
The em-dash em-beds in Congress: A population-level rise in em-dash frequency in U.S. congressional press releases at the dawn of the large-language-model era, 2021-2025
arXiv:2608. 05889v1 Announce Type: cross Abstract: Large language models (LLMs) can leave small stylistic traces in text written with their help.
arXiv AI
Aug 7
CodeGrep: An RL-Trained Retrieval Agent for LLM Coding Agents
arXiv:2608. 05886v1 Announce Type: cross Abstract: Modern LLM coding agents such as Claude Code and OpenHands share a common inefficiency: they spend much of their token budget finding the file to patch, rather than patching it.
arXiv AI
Aug 7
Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models
arXiv:2608. 05960v1 Announce Type: cross Abstract: Routine CT interpretation is inherently comprehensive, capturing incidental findings across the entire scan volume.
arXiv AI
Aug 7
FormBharo: Designing and Evaluating a Voice Agent for Conversational Form Filling in Rural India
arXiv:2608. 06027v1 Announce Type: cross Abstract: In India, almost every social benefit starts with a form, yet the people who need these benefits most are often unable to read or write.
arXiv AI
Aug 7
Domain-Grounded Candidate Selection for Agentic Image Editing: A Shadow Removal Case
arXiv:2608. 06075v1 Announce Type: cross Abstract: Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems.
arXiv AI
Aug 7
Learning Globally Reusable Skills for Coding Agents
arXiv:2608. 06153v1 Announce Type: cross Abstract: Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining.
arXiv AI
Aug 7
Visual Grounding in Zero-Shot Vision-Language Control
arXiv:2608. 06154v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as zero-shot controllers, but successful trajectories do not necessarily show that decisions are grounded in visual input: simulator dynamics and conservative action priors can produce favourable scores without meaningful perception.
arXiv AI
Aug 7
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.
arXiv AI
Aug 7
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.
arXiv AI
Aug 7
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
arXiv:2608. 06362v1 Announce Type: cross Abstract: Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time.
arXiv AI
Aug 7
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
arXiv:2607. 28609v2 Announce Type: replace Abstract: Computer-using agents (CUAs) are advancing rapidly across the digital world.
arXiv AI
Aug 7
Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising
arXiv:2510. 05589v3 Announce Type: replace-cross Abstract: Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices.
arXiv AI
Aug 7
One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs
arXiv:2512. 14751v3 Announce Type: replace-cross Abstract: Finetuning pretrained large language models (LLMs) has become the standard paradigm for developing downstream applications.
arXiv AI
Aug 7
MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs
arXiv:2603. 05997v2 Announce Type: replace-cross Abstract: Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables.
arXiv AI
Aug 7
Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering
arXiv:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.
arXiv AI
Aug 7
Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning
arXiv:2604. 01170v2 Announce Type: replace-cross Abstract: While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute costs.
arXiv AI
Aug 7
Plausible Patients, Impossible Populations: Auditing Epidemiological Fidelity in Large Language Model Mental Health Simulations
arXiv:2604. 17359v2 Announce Type: replace-cross Abstract: Language models asked to simulate psychiatric patients produce cases that survive inspection one at a time and populations that match no real one.
arXiv Machine Learning
Aug 7
PPDL: LLM-Based Flows as Probabilistic Programs
arXiv:2608. 05234v1 Announce Type: new Abstract: Building reliable applications that leverage large language models (LLMs) remains a significant challenge.
arXiv Machine Learning
Aug 7
Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language
arXiv:2608. 05238v1 Announce Type: new Abstract: Training multimodal models to align time series with language runs into a self-supervision trap.