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Newest papers, page 122
Into the Unknown: Benchmarking AI agents on open scientific research problems
Can AI agents carry out research on open scientific problems?From Qualitative Text to Integrated Evidence: The Parallel-Then-Integrated (PTI) Procedure
Mixed methods research offers established strategies for integrating qualitative and quantitative evidence, but less guidance for integrating two computational representations of the same…ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models
Many real-world questions demand not a single fact but an organized collection of them, yet existing factual knowledge benchmarks almost exclusively target single answer retrieval.Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation
Meta-evaluation of text generation metrics and LLM-judges is primarily conducted using metric-human correlations.HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation
Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as text-to-SQL and text-to-Python.How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking
Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them.NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis
Many sentiment datasets use one annotator for each item, so the test labels show the conventions of that person.Weighted Ensemble Sampling for Rare-Event Estimation in Language Models
Rare but severe failures of large language models can become practically significant at deployment scale.DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression
While work on LLM-based regression has largely focused on scalar prediction, multi-output text regression remains less explored.Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules
Transformers are efficient to pre-train at scale because attention permits parallel computation across sequence positions, but long-context inference requires increasingly large KV caches.Do Syntax Heads Influence Model Behavior?
Transformer language models develop attention heads that specialize in syntactic relations, for example, attending from verbs to their subjects.Copy-Paste Language Models
Transformers are intuitively considered to be efficient at copying from their inputs.MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis
Online memes are often carriers of unsafe content such as disinformation and propaganda.Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources
Benchmarks shape how model capabilities are measured and interpreted, yet their scores can obscure whose conventions, contexts, and judgments define successful performance.From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring
Compute cost in automated essay scoring (AES) is usually treated as a technical efficiency issue rather than a measurement design concern.Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance
In so-called "Industry 4.0," integrated sensors, analytics tools, and other digital technologies supplement industrial workflows, potentiating enhanced visibility and automation.Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text
This work concerns our submission for the Multilingual track of the 2026 BabyLM Challenge.PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information?
Large language model (LLM) evaluators are increasingly used to assess personalized assistants, yet whether they can reliably judge personalization has rarely been tested.Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows
Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switching providers may require processing accumulated context again.PAIR: Memory-Efficient and Fast Inference with Reading--Reasoning Fusion
For context-based question answering, using one large model for both evidence extraction and answer reasoning requires substantial memory and computation.