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Newest papers, page 232
An Empirical Study of VLM Pipelines for Long-Document QA
Vision-Language Models (VLMs) are increasingly used for long-document processing, where the inputs combine text with charts, tables, figures, and complex layouts.Augur: A Synthetic Decision Lab for Rehearsing Reactions to Product and Policy Changes
Before a product or policy change ships, the question that matters is how people will react to it.VietPrism: A large-scale Vietnamese speech and deepfake corpus with diverse dialects and code-switching
Vietnamese speech research is constrained by resources that isolate automatic speech recognition from speaker, dialect, code-switching, and deepfake analysis.Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation
Financial institutions operate under dense, frequently amended rulebooks, and answering a compliance question correctly requires not only fluency but verifiable grounding in the authoritative text.Low-Cost Assays for Measuring Model Behavior Across Vendors and Releases
Language models advise people, keep them company, and write software while they sleep.Artificial Societies Benchmark: A Validation Framework for Synthetic Research
A synthetic survey can reproduce the average answer while misrepresenting how people differ, how their answers relate to one another, or how they respond to changes in conditions.Style, Not Self: Surface Cues Explain Zero-Shot Code Attribution by Large Language Models
If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude.Self-Play Pretraining with Zero Data
Advances in language modeling have been driven by scaling pretraining on ever more data.Scoring Both Directions: LLMs realize the MRS they cannot reliably parse
The English Resource Grammar (ERG) is a hand-written computational grammar of English.How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure
Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table.A Native-Reference Phone-Class Geometry for Second-Language Pronunciation Analysis
Automatic speaking assessment systems can provide holistic proficiency scores, but often lack interpretable measures that characterize pronunciation quality.Return or Revise? Learning When Revision Helps Retrieval-Augmented QA
We consider the decision of whether to return an existing draft answer or revise it using retrieved evidence, as in answer-revision systems.PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
Large language models increasingly operate as persistent assistants in user-facing, shared-session, and tool-augmented settings.R-DEIM Net: An Efficient Rationale-Augmented Dual-Expert Interaction Model for Paraphrase Detection
Recent advances in paraphrase detection reveal a fundamental trade-off: large language models achieve high accuracy but require high computation, while efficient Siamese-BERT variants offer…What, When, and How: Audio Description as Constrained Global Optimization
Audio Description (AD) makes movies accessible to blind and visually impaired audiences by narrating visual information in gaps between dialogue.Multimodal Thinking with Renderable Programs
Current vision-language models (VLMs) excel at visual content understanding and text-based reasoning, yet their structure limits the advancement of incorporating images into the reasoning chain.Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale
Customer experience (CX) agents use tools and large language models to address customer requests and guide conversational interactions with an organization's products.GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI
Large Language Models (LLMs) typically exhibit a performance profile where reliability degrades as task complexity increases.Does a model's stated reason for rejecting a candidate do any work?
Asked to choose between candidates and explain the choice, a language model often rejects a rival by naming a fact its profile lacks: no director, no date of death.A Training Criterion with Token-Level Tolerance to Transcription Ambiguity for Automatic Speech Recognition
Automatic speech recognition is typically trained assuming that the reference transcript is the only valid labeling of an utterance, yet even nominally verbatim transcripts contain localized…